system

Abstract

The system according to this embodiment aims to analyze individual student data in real time and provide optimal instruction. [Solution] The system according to the embodiment comprises a recognition unit, a display unit, an analysis unit, an advice unit, a recording unit, and a progress analysis unit. The recognition unit recognizes the participant. The display unit displays the participant's personal data recognized by the recognition unit in real time. The analysis unit analyzes the participant's posture based on the personal data displayed by the display unit. The advice unit provides appropriate advice based on the posture analyzed by the analysis unit. The recording unit records the progress status based on the advice provided by the advice unit. The progress analysis unit analyzes the progress status recorded by the recording unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to analyze the individual data of the learners in real time and provide optimal guidance.

[0005] The system according to the embodiment aims to analyze the individual data of the learners in real time and provide optimal guidance.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recognition unit, a display unit, an analysis unit, an advice unit, a recording unit, and a progress analysis unit. The recognition unit recognizes the participant. The display unit displays the participant's personal data recognized by the recognition unit in real time. The analysis unit analyzes the participant's posture based on the personal data displayed by the display unit. The advice unit provides appropriate advice based on the posture analyzed by the analysis unit. The recording unit records the progress status based on the advice provided by the advice unit. The progress analysis unit analyzes the progress status recorded by the recording unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze individual student data in real time and provide optimal instruction. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The customer management system according to an embodiment of the present invention is a system that realizes individually optimized instruction by recognizing students using an AI camera and performing automatic data analysis. This customer management system simultaneously achieves improved service quality and operational efficiency. Specifically, it consists of the following steps. First, the AI ​​camera automatically recognizes the student and displays personal data (health condition, goals, limitations, etc.) in real time. Next, the AI ​​analyzes the student's posture and provides accurate advice. Furthermore, by automatically recording and analyzing progress, the system supports instructors in helping students achieve their goals. This system streamlines student management for online yoga and fitness lessons and facilitates individualized support. It also allows for continuous monitoring of students' health and goals, enabling accurate instruction even through a screen. For example, the AI ​​camera automatically recognizes the student. At this time, the AI ​​analyzes the student's facial and physical characteristics to identify the individual. For example, in an online yoga lesson, when a student stands in front of the camera, the AI ​​recognizes the student and displays personal data such as name, age, improvement goals, goal deadlines, points to watch out for, and past lesson history in real time. This allows instructors to instantly grasp the student's information. Next, the AI ​​analyzes the student's posture and provides precise advice. For example, when a student takes a yoga pose, the AI ​​analyzes their posture and presents the instructor with the correct form and areas for improvement. This allows the instructor to give the student accurate advice. For instance, for a student with lower back pain, the AI ​​can advise them on how to perform poses in a way that doesn't strain their back. Furthermore, the system automatically records and analyzes progress. The AI ​​records the student's lesson history and progress, and analyzes the data to support the student in achieving their goals. For example, if a student aims to improve their backbend form, the AI ​​records their progress and provides advice to help them achieve their goal. This allows students to understand their progress and maintain their motivation towards achieving their goals. This system solves challenges such as the increased complexity of student management due to the surge in demand for online lessons and the difficulty of providing individualized support when many students are taking lessons simultaneously.Furthermore, it becomes possible to continuously monitor the physical condition and goals of students, enabling accurate instruction even through a screen. In addition, it is expected that the efficiency of instructors' work will be improved, leading to increased customer satisfaction. For example, instructors can centrally manage student information and easily provide individualized support, allowing them to conduct lessons efficiently. In this way, by utilizing AI cameras and automated data analysis, it is possible to realize individually optimized instruction and simultaneously achieve improved service quality and operational efficiency. This will enable differentiation from competing services and the provision of new value in the rapidly growing online lesson market. For example, it is expected that continuous improvement of instruction quality based on data analysis and the establishment of a new teaching style that combines AI and human touch will be possible. As a result, the customer management system will be able to recognize students, display personal data, analyze posture, provide advice, and record and analyze progress.

[0029] The customer management system according to this embodiment comprises a recognition unit, a display unit, an analysis unit, an advice unit, a recording unit, and a progress analysis unit. The recognition unit recognizes the learner. The recognition unit identifies the individual by analyzing the features of the learner's face and body using, for example, an AI camera. For example, when a learner stands in front of the camera, the recognition unit can identify the individual by analyzing the contours of their face, the position of their eyes, their posture, etc. The recognition unit can also analyze the learner's past recognition history and select the optimal recognition algorithm. For example, the recognition unit selects the most suitable algorithm based on previously recognized data. The display unit displays the learner's personal data recognized by the recognition unit in real time. The display unit can display personal data such as the learner's name, age, reinforcement goals, deadlines, points to watch out for, and past learning history. For example, when a learner stands in front of the camera, the display unit immediately displays the personal data. The display unit can also estimate the learner's emotions and adjust the display content based on the estimated emotions of the learner. For example, if the learner is nervous, the display unit provides display content in calm colors. The analysis unit analyzes the participant's posture based on the personal data displayed by the display unit. For example, when a participant performs a yoga pose, the analysis unit can analyze the posture and present the instructor with the correct form and areas for improvement. For example, the analysis unit can analyze the angle and range of motion of the participant's posture and present the correct form. The analysis unit can also estimate the participant's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the analysis unit is tense, it will apply analysis criteria to help the participant relax. The advice unit provides appropriate advice based on the posture analyzed by the analysis unit. For example, the advice unit can advise a participant with lower back pain on how to perform poses without straining their lower back. For example, it will advise on the position of the hips and how to distribute weight. The advice unit can also estimate the participant's emotions and adjust the way the advice is expressed based on the estimated emotions. For example, if the advice unit is tense, it will provide advice in a calm manner. The recording unit records the progress based on the advice provided by the advice unit.The recording unit can, for example, record the student's lesson history and progress. For example, the recording unit records the date and time of the lesson, the content of the lesson, and the instructor's comments. The recording unit can also estimate the student's emotions and adjust the recorded content based on the estimated emotions. For example, if the student is nervous, the recording unit will provide concise and to-the-point recorded content. The progress analysis unit analyzes the progress recorded by the recording unit. For example, based on the recorded progress, the progress analysis unit can provide advice to help the student achieve their goals. For example, if the student has a goal of improving their backbend form, the progress analysis unit will analyze their progress and provide advice to help them achieve that goal. The progress analysis unit can also estimate the student's emotions and adjust the progress analysis criteria based on the estimated emotions. For example, if the student is nervous, the progress analysis unit will apply progress analysis criteria to help them relax. Thus, the customer management system according to this embodiment can recognize students, display personal data, analyze posture, provide advice, and record and analyze progress.

[0030] The recognition unit recognizes the learner. For example, the recognition unit uses an AI camera to analyze the facial and physical features of the learner and identify them as an individual. Specifically, the AI ​​camera acquires high-resolution video and uses a facial recognition algorithm to analyze features such as the contour of the learner's face, the position of their eyes, the shape of their nose, and the position of their mouth. Furthermore, it also analyzes their posture and movements and uses this as additional information to identify the individual. For example, when a learner stands in front of the camera, the AI ​​camera instantly captures their facial features and identifies them by comparing them with a past database. The recognition unit can also analyze the learner's past recognition history and select the optimal recognition algorithm. For example, based on previously recognized data, it can select the most accurate algorithm for a particular learner, improving recognition accuracy. In addition, the recognition unit can estimate emotions from the learner's facial expressions and movements, and understand their state of tension or relaxation. This allows the recognition unit to accurately recognize the learner's personal information and improve the overall accuracy and reliability of the system.

[0031] The display unit shows the learner's personal data in real time, as recognized by the recognition unit. The display unit can show personal data such as the learner's name, age, reinforcement goals, deadlines, points to watch out for, and past learning history. Specifically, when a learner stands in front of the camera, the display unit immediately displays the personal data on the screen for the instructor and the learner to review. The display unit can also estimate the learner's emotions and adjust the displayed content based on those emotions. For example, if the learner is nervous, the display unit will use a calming background and font to create a relaxed atmosphere. Furthermore, the display unit visually displays the learner's progress and goal achievement using graphs and charts, allowing the learner to grasp their progress at a glance. This allows the display unit to effectively display the learner's personal data and increase their motivation.

[0032] The analysis unit analyzes the participant's posture based on personal data displayed by the display unit. For example, when a participant performs a yoga pose, the analysis unit can analyze their posture and present the instructor with the correct form and areas for improvement. Specifically, it uses AI to analyze the angle and range of motion of the participant's posture and present the correct form. For example, when a participant performs a yoga pose, the AI ​​analyzes the position and angle of each part of the participant's body and identifies areas for improvement by comparing them to the correct posture. The analysis unit can also estimate the participant's emotions and adjust the analysis criteria based on the estimated emotions. For example, if a participant is tense, it applies analysis criteria to help them relax, allowing them to perform the pose comfortably. Furthermore, the analysis unit can continuously monitor the participant's growth and areas for improvement based on past data and develop long-term instruction plans. This allows the analysis unit to accurately analyze the participant's posture and support effective instruction.

[0033] The Advice Department provides precise advice based on the posture analyzed by the Analysis Department. For example, the Advice Department can advise a participant with lower back pain on how to assume poses that do not strain the lower back. Specifically, it analyzes the participant's posture and movements and advises on the position of the hips and how to distribute weight. The Advice Department can also estimate the participant's emotions and adjust the way the advice is expressed based on those emotions. For example, if the participant is nervous, the Advice Department will provide advice in a calm manner to help the participant relax. Furthermore, the Advice Department can provide individually customized advice, taking into account the participant's progress and degree of goal achievement. For example, if a participant is working towards a specific goal, the Advice Department will provide specific advice tailored to that goal to increase the participant's motivation. In this way, the Advice Department can provide accurate and effective advice to participants and support their growth.

[0034] The recording unit records progress based on advice provided by the advice unit. For example, the recording unit can record a student's lesson history and progress. Specifically, it records details such as the date and time of lessons, lesson content, and instructor comments to track the student's growth. The recording unit can also estimate the student's emotions and adjust the recorded content based on those emotions. For example, if a student is nervous, it will provide concise and to-the-point records to make them easier for the student to understand. Furthermore, the recording unit can analyze the student's growth and areas for improvement based on past data, providing information for developing long-term instruction plans. In this way, the recording unit can accurately record the student's progress and improve the overall effectiveness of the system.

[0035] The Progress Analysis Department analyzes the progress recorded by the Recording Department. For example, based on the recorded progress, the Progress Analysis Department can provide advice to help trainees achieve their goals. Specifically, if a trainee has a goal of improving their backbend form, the Department will analyze their progress and provide advice to help them achieve that goal. The Progress Analysis Department can also estimate the trainee's emotions and adjust the progress analysis criteria based on those emotions. For example, if a trainee is feeling anxious, the Department can apply progress analysis criteria to help them relax, allowing them to progress towards their goal without undue stress. Furthermore, the Progress Analysis Department can continuously monitor trainee growth and areas for improvement based on past data and develop long-term instruction plans. This allows the Progress Analysis Department to accurately analyze trainees' progress and support effective instruction.

[0036] The recognition unit can identify individuals by analyzing the facial and physical characteristics of participants. For example, the recognition unit can identify individuals by analyzing the facial contours, eye positions, and body posture of participants. For example, when a participant stands in front of a camera, the recognition unit can analyze facial features and identify the individual. The recognition unit can also identify individuals by analyzing the physical characteristics of participants. For example, the recognition unit can identify individuals by analyzing the posture and movements of participants. This improves the accuracy of individual identification by analyzing the facial and physical characteristics of participants. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the facial and physical characteristics of participants into AI and have the AI ​​perform individual identification.

[0037] The display unit can show personal data of participants in real time, such as their name, age, improvement goals, deadlines, points to watch out for, and past participation history. For example, when a participant stands in front of the camera, the display unit instantly displays the personal data. For example, the display unit displays the participant's name, age, improvement goals, deadlines, points to watch out for, and past participation history. The display unit can also estimate the participant's emotions and adjust the displayed content based on the estimated emotions. For example, if the display unit is nervous, it will provide content in calmer colors. This allows instructors to grasp information immediately by displaying the participant's personal data in real time. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input the participant's personal data into AI and have the AI ​​adjust the displayed content.

[0038] The analysis unit can analyze a participant's posture and present the instructor with the correct form and areas for improvement. For example, when a participant performs a yoga pose, the analysis unit can analyze the posture and present the instructor with the correct form and areas for improvement. For example, the analysis unit can analyze the angle and range of motion of the participant's posture and present the correct form. The analysis unit can also estimate the participant's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the analysis unit is tense, it can apply analysis criteria to help the participant relax. This allows the analysis unit to present the instructor with the correct form and areas for improvement by analyzing the participant's posture. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the participant's posture data into a generating AI and have the generating AI present the correct form and areas for improvement.

[0039] The advice unit can advise participants with lower back pain on how to assume poses that do not strain their lower back. For example, the advice unit can advise participants with lower back pain on how to assume poses that do not strain their lower back. For example, the advice unit can advise on the position of the lower back and how to distribute weight. The advice unit can also estimate the participant's emotions and adjust the way the advice is expressed based on the estimated emotions. For example, if the advice unit is nervous, it will provide advice in a calm manner. This allows the advice unit to advise participants with lower back pain on how to assume poses that do not strain their lower back. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the participant's posture data into a generating AI and have the generating AI generate advice on poses that do not strain the lower back.

[0040] The recording unit can record the student's lesson history and progress. For example, the recording unit records the date and time of the lesson, the content of the lesson, and the instructor's comments. The recording unit can also estimate the student's emotions and adjust the recorded content based on the estimated emotions. For example, if the student is nervous, the recording unit will provide concise and to-the-point recorded content. This allows for tracking the student's progress by recording their lesson history and progress. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input the student's lesson data into a generating AI and have the generating AI adjust the recorded content.

[0041] The progress analysis unit can analyze recorded progress and provide advice to help learners achieve their goals. For example, the progress analysis unit can provide advice to help learners achieve their goals based on recorded progress. For example, if a learner has a goal of improving their backbend form, the progress analysis unit will analyze their progress and provide advice to help them achieve that goal. The progress analysis unit can also estimate the learner's emotions and adjust the progress analysis criteria based on the estimated emotions. For example, if a learner is nervous, the progress analysis unit will apply progress analysis criteria to help them relax. This allows the unit to provide advice to help learners achieve their goals by analyzing recorded progress. Some or all of the above processes in the progress analysis unit may be performed using AI, for example, or not. For example, the progress analysis unit can input learner progress data into a generating AI and have the generating AI adjust the progress analysis criteria.

[0042] The recognition unit can analyze the learner's past recognition history and select the optimal recognition algorithm. For example, the recognition unit can select the most suitable algorithm based on data previously recognized by the learner. For example, the recognition unit can extract specific patterns from the learner's past recognition history and optimize the algorithm. The recognition unit can also analyze the learner's recognition history and select an algorithm to reduce misrecognition. In this way, the optimal recognition algorithm can be selected by analyzing past recognition history. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the learner's past recognition data into a generating AI and have the generating AI perform the selection of the optimal recognition algorithm.

[0043] The recognition unit can improve recognition accuracy by considering the learner's movement patterns during recognition. For example, the recognition unit can analyze the learner's movement patterns in real time to improve recognition accuracy. For example, the recognition unit can learn the learner's movement patterns in advance and apply them during recognition. The recognition unit can also improve recognition accuracy for specific movements by considering the learner's movement patterns. In this way, recognition accuracy is improved by considering the learner's movement patterns. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the learner's movement data into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0044] The recognition unit can improve recognition accuracy by considering the learner's geographical location information during recognition. For example, the recognition unit adjusts recognition accuracy based on the learner's geographical location information. For example, the recognition unit applies a recognition algorithm suitable for a specific environment, taking into account the learner's geographical location information. The recognition unit can also acquire the learner's geographical location information in real time and improve recognition accuracy. This improves recognition accuracy by considering the learner's geographical location information. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the learner's geographical location data into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0045] The recognition unit can analyze the participant's social media activity during recognition and reflect relevant information in the recognition. For example, the recognition unit can analyze the participant's social media activity and improve recognition accuracy based on their interests. For example, the recognition unit can extract specific patterns from the participant's social media activity and reflect them in the recognition. The recognition unit can also analyze the participant's social media activity in real time and reflect it in the recognition results. This improves recognition accuracy by analyzing the participant's social media activity. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the participant's social media data into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0046] The display unit can adjust the level of detail displayed based on the importance of the learner. For example, the display unit can display detailed information to important learners, or basic information to general learners. The display unit can also dynamically adjust the level of detail of the displayed content according to the importance of the learner. This improves the efficiency of the display by adjusting the level of detail based on the importance of the learner. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input learner importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the display.

[0047] The display unit can apply different display algorithms depending on the learner's category during display. For example, the display unit can apply a simple display algorithm to beginner learners, and a more detailed display algorithm to advanced learners. The display unit can also dynamically change the display algorithm depending on the learner's category. This improves the accuracy of the display by applying the appropriate display algorithm according to the learner's category. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input learner category data into a generating AI and have the generating AI execute the application of the display algorithm.

[0048] The display unit can determine the display priority based on the student's submission timing. For example, if a student submits early, the display unit will prioritize displaying that student's work. For example, if a student submits late, the display unit will postpone displaying that work. The display unit can also dynamically adjust the display priority based on the student's submission timing. This improves the efficiency of the display by determining the display priority based on the student's submission timing. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input student submission timing data into a generating AI and have the generating AI determine the display priority.

[0049] The display unit can adjust the display order based on the relevance of the learners during display. For example, the display unit will prioritize displaying learners with high relevance, or postpone displaying learners with low relevance. The display unit can also dynamically adjust the display order based on the relevance of the learners. This improves the efficiency of the display by adjusting the display order based on the relevance of the learners. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input learner relevance data into a generating AI and have the generating AI perform the adjustment of the display order.

[0050] The analysis unit can improve the accuracy of its analysis by considering the relationships between participants. For example, the analysis unit can perform group analysis while considering the relationships between participants. For example, the analysis unit can improve the accuracy of individual analyses based on the relationships between participants. The analysis unit can also analyze the relationships between participants in real time and reflect this in the analysis results. This improves the accuracy of the analysis by considering the relationships between participants. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input participant relationship data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0051] The analysis unit can perform analysis while considering the attribute information of the participants. For example, the analysis unit can perform analysis based on attribute information such as the participant's age and gender. For example, the analysis unit can perform analysis while considering attribute information such as the participant's health status and goals. The analysis unit can also acquire participant attribute information in real time and reflect it in the analysis results. This improves the accuracy of the analysis by considering the participant's attribute information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input participant attribute information into a generating AI and have the generating AI perform the analysis.

[0052] The analysis unit can perform analyses while considering the geographical distribution of participants. For example, the analysis unit can perform region-specific analyses based on the geographical distribution of participants. For example, the analysis unit can perform analyses suitable for specific regions, taking into account the geographical distribution of participants. The analysis unit can also acquire the geographical distribution of participants in real time and reflect it in the analysis results. This improves the accuracy of the analysis by considering the geographical distribution of participants. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of participants into a generating AI and have the generating AI perform the analysis.

[0053] The analysis unit can improve the accuracy of its analysis by referring to the learner's relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on the learner's relevant literature. For example, the analysis unit can refer to the learner's relevant literature to extract specific patterns and reflect them in the analysis. The analysis unit can also acquire the learner's relevant literature in real time and reflect it in the analysis results. This improves the accuracy of the analysis by referring to the learner's relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the learner's relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0054] The advice unit can adjust the level of detail of advice based on the importance of the student. For example, the advice unit provides detailed advice to important students, and basic advice to general students. The advice unit can also dynamically adjust the level of detail of advice according to the importance of the student. This improves the efficiency of advice by adjusting the level of detail based on the importance of the student. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input student importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0055] The advice unit can apply different advice algorithms depending on the student's category when providing advice. For example, the advice unit might apply a simple advice algorithm to beginner students, or a more detailed one to advanced students. The advice unit can also dynamically change the advice algorithm depending on the student's category. This improves the accuracy of the advice by applying the appropriate algorithm based on the student's category. Some or all of the above processing in the advice unit may be performed using AI, or not. For example, the advice unit can input student category data into a generating AI and have the generating AI apply the advice algorithm.

[0056] The advice unit can determine the priority of advice based on the student's submission timing. For example, if a student submits early, the advice unit will provide priority advice. For example, if a student submits late, the advice unit will provide advice later. The advice unit can also dynamically adjust the priority of advice based on the student's submission timing. This improves the efficiency of advice by determining the priority of advice based on the student's submission timing. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input student submission timing data into a generating AI and have the generating AI determine the priority of advice.

[0057] The advice unit can adjust the order of advice based on the relevance of the learner. For example, the advice unit will prioritize advice for learners with high relevance, and postpone advice for learners with low relevance. The advice unit can also dynamically adjust the order of advice based on the relevance of the learners. This improves the efficiency of advice by adjusting the order of advice based on the relevance of the learners. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input learner relevance data into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0058] The recording unit can adjust the level of detail in the recording based on the importance of the learner. For example, the recording unit can provide detailed records to important learners, and basic records to general learners. The recording unit can also dynamically adjust the level of detail in the recording content according to the importance of the learner. This improves the efficiency of recording by adjusting the level of detail based on the importance of the learner. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input learner importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the recording.

[0059] The recording unit can apply different recording algorithms depending on the learner's category during recording. For example, the recording unit can apply a simple recording algorithm to beginner learners, and a more detailed recording algorithm to advanced learners. The recording unit can also dynamically change the recording algorithm depending on the learner's category. This improves the accuracy of the recording by applying the appropriate recording algorithm according to the learner's category. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input learner category data into a generating AI and have the generating AI execute the application of the recording algorithm.

[0060] The recording unit can determine the priority of recordings based on the student's submission timing. For example, if a student submits early, the recording unit will prioritize providing the recording. For example, if a student submits late, the recording unit will postpone providing the recording. The recording unit can also dynamically adjust the priority of recordings based on the student's submission timing. This improves the efficiency of recording by determining the priority of recordings based on the student's submission timing. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input student submission timing data into a generating AI and have the generating AI perform the determination of the recording priority.

[0061] The recording unit can adjust the order of recordings based on the relevance of the learners during recording. For example, the recording unit will prioritize providing recordings to learners with high relevance, or postpone providing recordings to learners with low relevance. The recording unit can also dynamically adjust the order of recordings based on the relevance of the learners. This improves the efficiency of recording by adjusting the order of recordings based on the relevance of the learners. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input learner relevance data into a generating AI and have the generating AI perform the adjustment of the order of recordings.

[0062] The progress analysis unit can improve the accuracy of progress analysis by considering the relationships between participants. For example, the progress analysis unit can perform group progress analysis by considering the relationships between participants. For example, the progress analysis unit can improve the accuracy of individual progress analysis based on the relationships between participants. The progress analysis unit can also analyze the relationships between participants in real time and reflect them in the progress analysis results. This provides information on the relationships between participants. For example, the progress analysis unit analyzes the relationships between participants in real time and reflects them in the progress analysis results. This improves the accuracy of progress analysis by considering the relationships between participants. Some or all of the above processes in the progress analysis unit may be performed using AI, for example, or without AI. For example, the progress analysis unit can input participant relationship data into a generating AI and have the generating AI perform the improvement of the accuracy of the progress analysis.

[0063] The progress analysis unit can perform progress analysis while considering the attribute information of the participants. For example, the progress analysis unit can perform progress analysis based on attribute information such as the participant's age and gender. For example, the progress analysis unit can perform progress analysis while considering attribute information such as the participant's health status and goals. Furthermore, the progress analysis unit can acquire participant attribute information in real time and reflect it in the progress analysis results. This improves the accuracy of the progress analysis by considering the participant's attribute information. Some or all of the above processing in the progress analysis unit may be performed using AI, for example, or without using AI. For example, the progress analysis unit can input participant attribute information into a generating AI and have the generating AI perform the progress analysis.

[0064] The progress analysis unit can perform progress analysis while considering the geographical distribution of participants. For example, the progress analysis unit can perform progress analysis for each region based on the geographical distribution of participants. For example, the progress analysis unit can perform progress analysis suitable for a specific region, taking into account the geographical distribution of participants. Furthermore, the progress analysis unit can acquire the geographical distribution of participants in real time and reflect it in the progress analysis results. This improves the accuracy of the progress analysis by considering the geographical distribution of participants. Some or all of the above processing in the progress analysis unit may be performed using AI, for example, or without using AI. For example, the progress analysis unit can input the geographical distribution data of participants into a generating AI and have the generating AI perform the progress analysis.

[0065] The progress analysis unit can improve the accuracy of its progress analysis by referring to the learner's relevant literature during the analysis process. For example, the progress analysis unit can improve the accuracy of its progress analysis based on the learner's relevant literature. For example, the progress analysis unit can refer to the learner's relevant literature, extract specific patterns, and reflect them in the progress analysis. The progress analysis unit can also acquire the learner's relevant literature in real time and reflect them in the progress analysis results. This improves the accuracy of the progress analysis by referring to the learner's relevant literature. Some or all of the above processes in the progress analysis unit may be performed using AI, for example, or without AI. For example, the progress analysis unit can input the learner's relevant literature data into a generating AI and have the generating AI perform the improvement of the accuracy of the progress analysis.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The customer management system can also include a feedback section. This section can collect feedback from students and use it to improve the system. For example, the feedback section can allow students to input their impressions and suggestions for improvement through a questionnaire after a lesson. It can also analyze student feedback and suggest improvements to instructors. Furthermore, based on student feedback, the feedback section can adjust the system's algorithms to provide more personalized instruction. This improves student satisfaction and continuously enhances the quality of the system.

[0068] The customer management system can also include a rewards section. This section can provide rewards based on the learner's achievement of goals. For example, it can award badges or points when a learner achieves their set goals. It can also offer perks or discount coupons when a learner accumulates a certain number of points. Furthermore, the rewards section can provide tiered rewards based on progress to maintain learner motivation. This can boost learner motivation and promote continuous learning.

[0069] A customer management system can also include a communications section. This section can facilitate communication between students and between students and instructors. For example, it can allow students to exchange questions and opinions using a chat function during lessons. It can also enable students to interact after lessons through forums and group chats. Furthermore, the communications section can provide a platform for instructors to provide individual feedback and advice to students. This can improve the students' learning experience and promote community building.

[0070] The customer management system can also include a customization section. This customization section can tailor lesson content to the individual needs of each student. For example, it can adjust the difficulty and content of lessons based on student feedback and progress. It can also recommend specific exercises or poses based on the student's goals and limitations. Furthermore, it can change the music and background of the lessons according to the student's preferences. This can increase student satisfaction and provide more effective instruction.

[0071] The customer management system can also include a health management department. This department can monitor the health status of participants and provide appropriate advice. For example, it can measure participants' heart rate and calorie expenditure in real time to understand their health status. Furthermore, based on participants' health data, the health management department can recommend appropriate exercise levels and rest periods. In addition, it can provide advice on diet and nutrition according to participants' health goals. This allows for comprehensive support of participants' health and more effective instruction.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The recognition unit recognizes the participant. The recognition unit identifies the individual by analyzing the participant's facial and body features, for example, using an AI camera. When a participant stands in front of the camera, it can identify the individual by analyzing their facial contours, eye position, body posture, etc. The recognition unit can also analyze the participant's past recognition history and select the optimal recognition algorithm. Step 2: The display unit displays the learner's personal data recognized by the recognition unit in real time. The display unit can display personal data such as the learner's name, age, improvement goals, deadlines, points to watch out for, and past learning history. When the learner stands in front of the camera, the personal data is displayed immediately. The display unit can also estimate the learner's emotions and adjust the displayed content based on the estimated emotions of the learner. Step 3: The analysis unit analyzes the participant's posture based on the personal data displayed by the display unit. For example, when a participant performs a yoga pose, the analysis unit can analyze the posture and present the correct form and areas for improvement to the instructor. It analyzes the angle and range of motion of the participant's posture and presents the correct form. The analysis unit can also estimate the participant's emotions and adjust the analysis criteria based on the estimated emotions of the participant. Step 4: The advice department provides precise advice based on the posture analyzed by the analysis department. For example, the advice department can advise a participant with lower back pain on how to assume poses that do not strain the lower back. They can advise on the position of the hips and how to distribute weight. The advice department can also estimate the participant's emotions and adjust the way the advice is expressed based on those estimated emotions. Step 5: The recording unit records progress based on the advice provided by the advice unit. The recording unit can, for example, record the student's lesson history and progress. This includes recording the date and time of the lesson, the content of the lesson, and the instructor's comments. The recording unit can also estimate the student's emotions and adjust the recorded content based on the estimated emotions. Step 6: The progress analysis unit analyzes the progress recorded by the recording unit. For example, based on the recorded progress, the progress analysis unit can provide advice to help the trainee achieve their goals. If a trainee has a goal of improving their backbend form, the unit will analyze their progress and provide advice to help them achieve that goal. The progress analysis unit can also estimate the trainee's emotions and adjust the criteria for progress analysis based on the estimated emotions of the trainee.

[0074] (Example of form 2) The customer management system according to an embodiment of the present invention is a system that realizes individually optimized instruction by recognizing students using an AI camera and performing automatic data analysis. This customer management system simultaneously achieves improved service quality and operational efficiency. Specifically, it consists of the following steps. First, the AI ​​camera automatically recognizes the student and displays personal data (health condition, goals, limitations, etc.) in real time. Next, the AI ​​analyzes the student's posture and provides accurate advice. Furthermore, by automatically recording and analyzing progress, the system supports instructors in helping students achieve their goals. This system streamlines student management for online yoga and fitness lessons and facilitates individualized support. It also allows for continuous monitoring of students' health and goals, enabling accurate instruction even through a screen. For example, the AI ​​camera automatically recognizes the student. At this time, the AI ​​analyzes the student's facial and physical characteristics to identify the individual. For example, in an online yoga lesson, when a student stands in front of the camera, the AI ​​recognizes the student and displays personal data such as name, age, improvement goals, goal deadlines, points to watch out for, and past lesson history in real time. This allows instructors to instantly grasp the student's information. Next, the AI ​​analyzes the student's posture and provides precise advice. For example, when a student takes a yoga pose, the AI ​​analyzes their posture and presents the instructor with the correct form and areas for improvement. This allows the instructor to give the student accurate advice. For instance, for a student with lower back pain, the AI ​​can advise them on how to perform poses in a way that doesn't strain their back. Furthermore, the system automatically records and analyzes progress. The AI ​​records the student's lesson history and progress, and analyzes the data to support the student in achieving their goals. For example, if a student aims to improve their backbend form, the AI ​​records their progress and provides advice to help them achieve their goal. This allows students to understand their progress and maintain their motivation towards achieving their goals. This system solves challenges such as the increased complexity of student management due to the surge in demand for online lessons and the difficulty of providing individualized support when many students are taking lessons simultaneously.Furthermore, it becomes possible to continuously monitor the physical condition and goals of students, enabling accurate instruction even through a screen. In addition, it is expected that the efficiency of instructors' work will be improved, leading to increased customer satisfaction. For example, instructors can centrally manage student information and easily provide individualized support, allowing them to conduct lessons efficiently. In this way, by utilizing AI cameras and automated data analysis, it is possible to realize individually optimized instruction and simultaneously achieve improved service quality and operational efficiency. This will enable differentiation from competing services and the provision of new value in the rapidly growing online lesson market. For example, it is expected that continuous improvement of instruction quality based on data analysis and the establishment of a new teaching style that combines AI and human touch will be possible. As a result, the customer management system will be able to recognize students, display personal data, analyze posture, provide advice, and record and analyze progress.

[0075] The customer management system according to this embodiment comprises a recognition unit, a display unit, an analysis unit, an advice unit, a recording unit, and a progress analysis unit. The recognition unit recognizes the learner. The recognition unit identifies the individual by analyzing the features of the learner's face and body using, for example, an AI camera. For example, when a learner stands in front of the camera, the recognition unit can identify the individual by analyzing the contours of their face, the position of their eyes, their posture, etc. The recognition unit can also analyze the learner's past recognition history and select the optimal recognition algorithm. For example, the recognition unit selects the most suitable algorithm based on previously recognized data. The display unit displays the learner's personal data recognized by the recognition unit in real time. The display unit can display personal data such as the learner's name, age, reinforcement goals, deadlines, points to watch out for, and past learning history. For example, when a learner stands in front of the camera, the display unit immediately displays the personal data. The display unit can also estimate the learner's emotions and adjust the display content based on the estimated emotions of the learner. For example, if the learner is nervous, the display unit provides display content in calm colors. The analysis unit analyzes the participant's posture based on the personal data displayed by the display unit. For example, when a participant performs a yoga pose, the analysis unit can analyze the posture and present the instructor with the correct form and areas for improvement. For example, the analysis unit can analyze the angle and range of motion of the participant's posture and present the correct form. The analysis unit can also estimate the participant's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the analysis unit is tense, it will apply analysis criteria to help the participant relax. The advice unit provides appropriate advice based on the posture analyzed by the analysis unit. For example, the advice unit can advise a participant with lower back pain on how to perform poses without straining their lower back. For example, it will advise on the position of the hips and how to distribute weight. The advice unit can also estimate the participant's emotions and adjust the way the advice is expressed based on the estimated emotions. For example, if the advice unit is tense, it will provide advice in a calm manner. The recording unit records the progress based on the advice provided by the advice unit.The recording unit can, for example, record the student's lesson history and progress. For example, the recording unit records the date and time of the lesson, the content of the lesson, and the instructor's comments. The recording unit can also estimate the student's emotions and adjust the recorded content based on the estimated emotions. For example, if the student is nervous, the recording unit will provide concise and to-the-point recorded content. The progress analysis unit analyzes the progress recorded by the recording unit. For example, based on the recorded progress, the progress analysis unit can provide advice to help the student achieve their goals. For example, if the student has a goal of improving their backbend form, the progress analysis unit will analyze their progress and provide advice to help them achieve that goal. The progress analysis unit can also estimate the student's emotions and adjust the progress analysis criteria based on the estimated emotions. For example, if the student is nervous, the progress analysis unit will apply progress analysis criteria to help them relax. Thus, the customer management system according to this embodiment can recognize students, display personal data, analyze posture, provide advice, and record and analyze progress.

[0076] The recognition unit recognizes the learner. For example, the recognition unit uses an AI camera to analyze the facial and physical features of the learner and identify them as an individual. Specifically, the AI ​​camera acquires high-resolution video and uses a facial recognition algorithm to analyze features such as the contour of the learner's face, the position of their eyes, the shape of their nose, and the position of their mouth. Furthermore, it also analyzes their posture and movements and uses this as additional information to identify the individual. For example, when a learner stands in front of the camera, the AI ​​camera instantly captures their facial features and identifies them by comparing them with a past database. The recognition unit can also analyze the learner's past recognition history and select the optimal recognition algorithm. For example, based on previously recognized data, it can select the most accurate algorithm for a particular learner, improving recognition accuracy. In addition, the recognition unit can estimate emotions from the learner's facial expressions and movements, and understand their state of tension or relaxation. This allows the recognition unit to accurately recognize the learner's personal information and improve the overall accuracy and reliability of the system.

[0077] The display unit shows the learner's personal data in real time, as recognized by the recognition unit. The display unit can show personal data such as the learner's name, age, reinforcement goals, deadlines, points to watch out for, and past learning history. Specifically, when a learner stands in front of the camera, the display unit immediately displays the personal data on the screen for the instructor and the learner to review. The display unit can also estimate the learner's emotions and adjust the displayed content based on those emotions. For example, if the learner is nervous, the display unit will use a calming background and font to create a relaxed atmosphere. Furthermore, the display unit visually displays the learner's progress and goal achievement using graphs and charts, allowing the learner to grasp their progress at a glance. This allows the display unit to effectively display the learner's personal data and increase their motivation.

[0078] The analysis unit analyzes the participant's posture based on personal data displayed by the display unit. For example, when a participant performs a yoga pose, the analysis unit can analyze their posture and present the instructor with the correct form and areas for improvement. Specifically, it uses AI to analyze the angle and range of motion of the participant's posture and present the correct form. For example, when a participant performs a yoga pose, the AI ​​analyzes the position and angle of each part of the participant's body and identifies areas for improvement by comparing them to the correct posture. The analysis unit can also estimate the participant's emotions and adjust the analysis criteria based on the estimated emotions. For example, if a participant is tense, it applies analysis criteria to help them relax, allowing them to perform the pose comfortably. Furthermore, the analysis unit can continuously monitor the participant's growth and areas for improvement based on past data and develop long-term instruction plans. This allows the analysis unit to accurately analyze the participant's posture and support effective instruction.

[0079] The Advice Department provides precise advice based on the posture analyzed by the Analysis Department. For example, the Advice Department can advise a participant with lower back pain on how to assume poses that do not strain the lower back. Specifically, it analyzes the participant's posture and movements and advises on the position of the hips and how to distribute weight. The Advice Department can also estimate the participant's emotions and adjust the way the advice is expressed based on those emotions. For example, if the participant is nervous, the Advice Department will provide advice in a calm manner to help the participant relax. Furthermore, the Advice Department can provide individually customized advice, taking into account the participant's progress and degree of goal achievement. For example, if a participant is working towards a specific goal, the Advice Department will provide specific advice tailored to that goal to increase the participant's motivation. In this way, the Advice Department can provide accurate and effective advice to participants and support their growth.

[0080] The recording unit records progress based on advice provided by the advice unit. For example, the recording unit can record a student's lesson history and progress. Specifically, it records details such as the date and time of lessons, lesson content, and instructor comments to track the student's growth. The recording unit can also estimate the student's emotions and adjust the recorded content based on those emotions. For example, if a student is nervous, it will provide concise and to-the-point records to make them easier for the student to understand. Furthermore, the recording unit can analyze the student's growth and areas for improvement based on past data, providing information for developing long-term instruction plans. In this way, the recording unit can accurately record the student's progress and improve the overall effectiveness of the system.

[0081] The Progress Analysis Department analyzes the progress recorded by the Recording Department. For example, based on the recorded progress, the Progress Analysis Department can provide advice to help trainees achieve their goals. Specifically, if a trainee has a goal of improving their backbend form, the Department will analyze their progress and provide advice to help them achieve that goal. The Progress Analysis Department can also estimate the trainee's emotions and adjust the progress analysis criteria based on those emotions. For example, if a trainee is feeling anxious, the Department can apply progress analysis criteria to help them relax, allowing them to progress towards their goal without undue stress. Furthermore, the Progress Analysis Department can continuously monitor trainee growth and areas for improvement based on past data and develop long-term instruction plans. This allows the Progress Analysis Department to accurately analyze trainees' progress and support effective instruction.

[0082] The recognition unit can identify individuals by analyzing the facial and physical characteristics of participants. For example, the recognition unit can identify individuals by analyzing the facial contours, eye positions, and body posture of participants. For example, when a participant stands in front of a camera, the recognition unit can analyze facial features and identify the individual. The recognition unit can also identify individuals by analyzing the physical characteristics of participants. For example, the recognition unit can identify individuals by analyzing the posture and movements of participants. This improves the accuracy of individual identification by analyzing the facial and physical characteristics of participants. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the facial and physical characteristics of participants into AI and have the AI ​​perform individual identification.

[0083] The display unit can show personal data of participants in real time, such as their name, age, improvement goals, deadlines, points to watch out for, and past participation history. For example, when a participant stands in front of the camera, the display unit instantly displays the personal data. For example, the display unit displays the participant's name, age, improvement goals, deadlines, points to watch out for, and past participation history. The display unit can also estimate the participant's emotions and adjust the displayed content based on the estimated emotions. For example, if the display unit is nervous, it will provide content in calmer colors. This allows instructors to grasp information immediately by displaying the participant's personal data in real time. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input the participant's personal data into AI and have the AI ​​adjust the displayed content.

[0084] The analysis unit can analyze a participant's posture and present the instructor with the correct form and areas for improvement. For example, when a participant performs a yoga pose, the analysis unit can analyze the posture and present the instructor with the correct form and areas for improvement. For example, the analysis unit can analyze the angle and range of motion of the participant's posture and present the correct form. The analysis unit can also estimate the participant's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the analysis unit is tense, it can apply analysis criteria to help the participant relax. This allows the analysis unit to present the instructor with the correct form and areas for improvement by analyzing the participant's posture. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the participant's posture data into a generating AI and have the generating AI present the correct form and areas for improvement.

[0085] The advice unit can advise participants with lower back pain on how to assume poses that do not strain their lower back. For example, the advice unit can advise participants with lower back pain on how to assume poses that do not strain their lower back. For example, the advice unit can advise on the position of the lower back and how to distribute weight. The advice unit can also estimate the participant's emotions and adjust the way the advice is expressed based on the estimated emotions. For example, if the advice unit is nervous, it will provide advice in a calm manner. This allows the advice unit to advise participants with lower back pain on how to assume poses that do not strain their lower back. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the participant's posture data into a generating AI and have the generating AI generate advice on poses that do not strain the lower back.

[0086] The recording unit can record the student's lesson history and progress. For example, the recording unit records the date and time of the lesson, the content of the lesson, and the instructor's comments. The recording unit can also estimate the student's emotions and adjust the recorded content based on the estimated emotions. For example, if the student is nervous, the recording unit will provide concise and to-the-point recorded content. This allows for tracking the student's progress by recording their lesson history and progress. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input the student's lesson data into a generating AI and have the generating AI adjust the recorded content.

[0087] The progress analysis unit can analyze recorded progress and provide advice to help learners achieve their goals. For example, the progress analysis unit can provide advice to help learners achieve their goals based on recorded progress. For example, if a learner has a goal of improving their backbend form, the progress analysis unit will analyze their progress and provide advice to help them achieve that goal. The progress analysis unit can also estimate the learner's emotions and adjust the progress analysis criteria based on the estimated emotions. For example, if a learner is nervous, the progress analysis unit will apply progress analysis criteria to help them relax. This allows the unit to provide advice to help learners achieve their goals by analyzing recorded progress. Some or all of the above processes in the progress analysis unit may be performed using AI, for example, or not. For example, the progress analysis unit can input learner progress data into a generating AI and have the generating AI adjust the progress analysis criteria.

[0088] The recognition unit can estimate the learner's emotions and adjust the recognition accuracy based on the estimated emotions. For example, if the learner is tense, the recognition unit may lower the recognition accuracy to help the AI ​​relax. For example, if the learner is relaxed, the recognition unit may increase the recognition accuracy to allow the AI ​​to capture detailed features. The recognition unit can also adjust the accuracy to allow the AI ​​to recognize even subtle movements if the learner is focused. This improves the accuracy of recognition by adjusting the recognition accuracy based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using an AI, for example, or not using an AI. For example, the recognition unit can input learner emotion data into a generative AI and have the generative AI perform the adjustment of the recognition accuracy.

[0089] The recognition unit can analyze the learner's past recognition history and select the optimal recognition algorithm. For example, the recognition unit can select the most suitable algorithm based on data previously recognized by the learner. For example, the recognition unit can extract specific patterns from the learner's past recognition history and optimize the algorithm. The recognition unit can also analyze the learner's recognition history and select an algorithm to reduce misrecognition. In this way, the optimal recognition algorithm can be selected by analyzing past recognition history. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the learner's past recognition data into a generating AI and have the generating AI perform the selection of the optimal recognition algorithm.

[0090] The recognition unit can improve recognition accuracy by considering the learner's movement patterns during recognition. For example, the recognition unit can analyze the learner's movement patterns in real time to improve recognition accuracy. For example, the recognition unit can learn the learner's movement patterns in advance and apply them during recognition. The recognition unit can also improve recognition accuracy for specific movements by considering the learner's movement patterns. In this way, recognition accuracy is improved by considering the learner's movement patterns. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the learner's movement data into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0091] The recognition unit can estimate the learner's emotions and adjust the display method of the recognition results based on the estimated learner's emotions. For example, if the learner is nervous, the recognition unit provides a simple and highly visible display method. For example, if the learner is relaxed, the recognition unit provides a display method that includes detailed information. The recognition unit can also provide a concise display method if the learner is in a hurry. By adjusting the display method based on the learner's emotions, the visibility of the recognition results is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input learner emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0092] The recognition unit can improve recognition accuracy by considering the learner's geographical location information during recognition. For example, the recognition unit adjusts recognition accuracy based on the learner's geographical location information. For example, the recognition unit applies a recognition algorithm suitable for a specific environment, taking into account the learner's geographical location information. The recognition unit can also acquire the learner's geographical location information in real time and improve recognition accuracy. This improves recognition accuracy by considering the learner's geographical location information. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the learner's geographical location data into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0093] The recognition unit can analyze the participant's social media activity during recognition and reflect relevant information in the recognition. For example, the recognition unit can analyze the participant's social media activity and improve recognition accuracy based on their interests. For example, the recognition unit can extract specific patterns from the participant's social media activity and reflect them in the recognition. The recognition unit can also analyze the participant's social media activity in real time and reflect it in the recognition results. This improves recognition accuracy by analyzing the participant's social media activity. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the participant's social media data into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0094] The display unit can estimate the learner's emotions and adjust the displayed content based on the estimated emotions. For example, if the learner is nervous, the display unit can provide content in calm colors. For example, if the learner is relaxed, the display unit can provide content in bright colors. The display unit can also provide simple and highly visible content if the learner is tired. This improves the visibility of the display by adjusting the displayed content based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input learner emotion data into the generative AI and have the generative AI perform the adjustment of the displayed content.

[0095] The display unit can adjust the level of detail displayed based on the importance of the learner. For example, the display unit can display detailed information to important learners, or basic information to general learners. The display unit can also dynamically adjust the level of detail of the displayed content according to the importance of the learner. This improves the efficiency of the display by adjusting the level of detail based on the importance of the learner. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input learner importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the display.

[0096] The display unit can apply different display algorithms depending on the learner's category during display. For example, the display unit can apply a simple display algorithm to beginner learners, and a more detailed display algorithm to advanced learners. The display unit can also dynamically change the display algorithm depending on the learner's category. This improves the accuracy of the display by applying the appropriate display algorithm according to the learner's category. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input learner category data into a generating AI and have the generating AI execute the application of the display algorithm.

[0097] The display unit can estimate the learner's emotions and adjust the length of the display based on the estimated emotions. For example, if the learner is in a hurry, the display unit provides a short, concise display. For example, if the learner is relaxed, the display unit provides a longer display that includes detailed explanations. The display unit can also provide a visually stimulating display if the learner is excited. By adjusting the length of the display based on the learner's emotions, the readability of the display is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI or not using AI. For example, the display unit can input learner emotion data into the generative AI and have the generative AI adjust the length of the display.

[0098] The display unit can determine the display priority based on the student's submission timing. For example, if a student submits early, the display unit will prioritize displaying that student's work. For example, if a student submits late, the display unit will postpone displaying that work. The display unit can also dynamically adjust the display priority based on the student's submission timing. This improves the efficiency of the display by determining the display priority based on the student's submission timing. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input student submission timing data into a generating AI and have the generating AI determine the display priority.

[0099] The display unit can adjust the display order based on the relevance of the learners during display. For example, the display unit will prioritize displaying learners with high relevance, or postpone displaying learners with low relevance. The display unit can also dynamically adjust the display order based on the relevance of the learners. This improves the efficiency of the display by adjusting the display order based on the relevance of the learners. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input learner relevance data into a generating AI and have the generating AI perform the adjustment of the display order.

[0100] The analysis unit can estimate the learner's emotions and adjust the analysis criteria based on the estimated learner's emotions. For example, if the learner is tense, the analysis unit applies analysis criteria to promote relaxation. For example, if the learner is relaxed, the analysis unit applies detailed analysis criteria. Furthermore, if the learner is focused, the analysis unit can apply criteria that can analyze even subtle movements. This improves the accuracy of the analysis by adjusting the analysis criteria based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input learner emotion data into a generative AI and have the generative AI perform the adjustment of the analysis criteria.

[0101] The analysis unit can improve the accuracy of its analysis by considering the relationships between participants. For example, the analysis unit can perform group analysis while considering the relationships between participants. For example, the analysis unit can improve the accuracy of individual analyses based on the relationships between participants. The analysis unit can also analyze the relationships between participants in real time and reflect this in the analysis results. This improves the accuracy of the analysis by considering the relationships between participants. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input participant relationship data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0102] The analysis unit can perform analysis while considering the attribute information of the participants. For example, the analysis unit can perform analysis based on attribute information such as the participant's age and gender. For example, the analysis unit can perform analysis while considering attribute information such as the participant's health status and goals. The analysis unit can also acquire participant attribute information in real time and reflect it in the analysis results. This improves the accuracy of the analysis by considering the participant's attribute information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input participant attribute information into a generating AI and have the generating AI perform the analysis.

[0103] The analysis unit can estimate the learner's emotions and adjust the order in which the analysis results are displayed based on the estimated learner's emotions. For example, if the learner is nervous, the analysis unit provides a simple and highly visible display order. For example, if the learner is relaxed, the analysis unit provides a display order that includes detailed information. The analysis unit can also provide a concise display order if the learner is in a hurry. This improves the readability of the analysis results by adjusting the display order based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input learner emotion data into a generative AI and have the generative AI perform the adjustment of the display order.

[0104] The analysis unit can perform analyses while considering the geographical distribution of participants. For example, the analysis unit can perform region-specific analyses based on the geographical distribution of participants. For example, the analysis unit can perform analyses suitable for specific regions, taking into account the geographical distribution of participants. The analysis unit can also acquire the geographical distribution of participants in real time and reflect it in the analysis results. This improves the accuracy of the analysis by considering the geographical distribution of participants. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of participants into a generating AI and have the generating AI perform the analysis.

[0105] The analysis unit can improve the accuracy of its analysis by referring to the learner's relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on the learner's relevant literature. For example, the analysis unit can refer to the learner's relevant literature to extract specific patterns and reflect them in the analysis. The analysis unit can also acquire the learner's relevant literature in real time and reflect it in the analysis results. This improves the accuracy of the analysis by referring to the learner's relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the learner's relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0106] The advice unit can estimate the learner's emotions and adjust the way it expresses advice based on those emotions. For example, if the learner is nervous, the advice unit will provide advice in a calm manner. For example, if the learner is relaxed, the advice unit will provide advice in a cheerful manner. The advice unit can also provide advice in a quick and concise manner if the learner is in a hurry. By adjusting the way advice is expressed based on the learner's emotions, the effectiveness of the advice is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not using AI. For example, the advice unit can input learner emotion data into the generative AI and have the generative AI adjust the way advice is expressed.

[0107] The advice unit can adjust the level of detail of advice based on the importance of the student. For example, the advice unit provides detailed advice to important students, and basic advice to general students. The advice unit can also dynamically adjust the level of detail of advice according to the importance of the student. This improves the efficiency of advice by adjusting the level of detail based on the importance of the student. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input student importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0108] The advice unit can apply different advice algorithms depending on the student's category when providing advice. For example, the advice unit might apply a simple advice algorithm to beginner students, or a more detailed one to advanced students. The advice unit can also dynamically change the advice algorithm depending on the student's category. This improves the accuracy of the advice by applying the appropriate algorithm based on the student's category. Some or all of the above processing in the advice unit may be performed using AI, or not. For example, the advice unit can input student category data into a generating AI and have the generating AI apply the advice algorithm.

[0109] The advice unit can estimate the learner's emotions and adjust the length of the advice based on the estimated emotions. For example, if the learner is in a hurry, the advice unit will provide short, concise advice. For example, if the learner is relaxed, the advice unit will provide longer advice with detailed explanations. The advice unit can also provide visually stimulating advice if the learner is excited. By adjusting the length of the advice based on the learner's emotions, the effectiveness of the advice is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not using AI. For example, the advice unit can input learner emotion data into a generative AI and have the generative AI adjust the length of the advice.

[0110] The advice unit can determine the priority of advice based on the student's submission timing. For example, if a student submits early, the advice unit will provide priority advice. For example, if a student submits late, the advice unit will provide advice later. The advice unit can also dynamically adjust the priority of advice based on the student's submission timing. This improves the efficiency of advice by determining the priority of advice based on the student's submission timing. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input student submission timing data into a generating AI and have the generating AI determine the priority of advice.

[0111] The advice unit can adjust the order of advice based on the relevance of the learner. For example, the advice unit will prioritize advice for learners with high relevance, and postpone advice for learners with low relevance. The advice unit can also dynamically adjust the order of advice based on the relevance of the learners. This improves the efficiency of advice by adjusting the order of advice based on the relevance of the learners. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input learner relevance data into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0112] The recording unit can estimate the learner's emotions and adjust the recording content based on the estimated learner's emotions. For example, if the learner is nervous, the recording unit will provide concise and to-the-point recording content. For example, if the learner is relaxed, the recording unit will provide detailed recording content. The recording unit can also provide recording content that can be quickly reviewed if the learner is in a hurry. This improves the readability of the recording by adjusting the recording content based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not using AI. For example, the recording unit can input learner emotion data into a generative AI and have the generative AI perform the adjustment of the recording content.

[0113] The recording unit can adjust the level of detail in the recording based on the importance of the learner. For example, the recording unit can provide detailed records to important learners, and basic records to general learners. The recording unit can also dynamically adjust the level of detail in the recording content according to the importance of the learner. This improves the efficiency of recording by adjusting the level of detail based on the importance of the learner. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input learner importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the recording.

[0114] The recording unit can apply different recording algorithms depending on the learner's category during recording. For example, the recording unit can apply a simple recording algorithm to beginner learners, and a more detailed recording algorithm to advanced learners. The recording unit can also dynamically change the recording algorithm depending on the learner's category. This improves the accuracy of the recording by applying the appropriate recording algorithm according to the learner's category. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input learner category data into a generating AI and have the generating AI execute the application of the recording algorithm.

[0115] The recording unit can estimate the learner's emotions and adjust the length of the recording based on the estimated emotions. For example, if the learner is in a hurry, the recording unit will provide a short, concise recording. For example, if the learner is relaxed, the recording unit will provide a longer recording with detailed explanations. The recording unit can also provide a visually stimulating recording if the learner is excited. By adjusting the length of the recording based on the learner's emotions, the readability of the recording is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not using AI. For example, the recording unit can input learner emotion data into a generative AI and have the generative AI adjust the length of the recording.

[0116] The recording unit can determine the priority of recordings based on the student's submission timing. For example, if a student submits early, the recording unit will prioritize providing the recording. For example, if a student submits late, the recording unit will postpone providing the recording. The recording unit can also dynamically adjust the priority of recordings based on the student's submission timing. This improves the efficiency of recording by determining the priority of recordings based on the student's submission timing. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input student submission timing data into a generating AI and have the generating AI perform the determination of the recording priority.

[0117] The recording unit can adjust the order of recordings based on the relevance of the learners during recording. For example, the recording unit will prioritize providing recordings to learners with high relevance, or postpone providing recordings to learners with low relevance. The recording unit can also dynamically adjust the order of recordings based on the relevance of the learners. This improves the efficiency of recording by adjusting the order of recordings based on the relevance of the learners. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input learner relevance data into a generating AI and have the generating AI perform the adjustment of the order of recordings.

[0118] The progress analysis unit can estimate the learner's emotions and adjust the progress analysis criteria based on the estimated learner's emotions. For example, if a learner is tense, the progress analysis unit applies progress analysis criteria to help them relax. For example, if a learner is relaxed, the progress analysis unit applies detailed progress analysis criteria. Furthermore, if a learner is focused, the progress analysis unit can also apply criteria that can analyze even subtle movements. This improves the accuracy of the progress analysis by adjusting the progress analysis criteria based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress analysis unit may be performed using AI or not. For example, the progress analysis unit can input learner emotion data into a generative AI and have the generative AI perform the adjustment of the progress analysis criteria.

[0119] The progress analysis unit can improve the accuracy of progress analysis by considering the relationships between participants. For example, the progress analysis unit can perform group progress analysis by considering the relationships between participants. For example, the progress analysis unit can improve the accuracy of individual progress analysis based on the relationships between participants. The progress analysis unit can also analyze the relationships between participants in real time and reflect them in the progress analysis results. This provides information on the relationships between participants. For example, the progress analysis unit analyzes the relationships between participants in real time and reflects them in the progress analysis results. This improves the accuracy of progress analysis by considering the relationships between participants. Some or all of the above processes in the progress analysis unit may be performed using AI, for example, or without AI. For example, the progress analysis unit can input participant relationship data into a generating AI and have the generating AI perform the improvement of the accuracy of the progress analysis.

[0120] The progress analysis unit can perform progress analysis while considering the attribute information of the participants. For example, the progress analysis unit can perform progress analysis based on attribute information such as the participant's age and gender. For example, the progress analysis unit can perform progress analysis while considering attribute information such as the participant's health status and goals. Furthermore, the progress analysis unit can acquire participant attribute information in real time and reflect it in the progress analysis results. This improves the accuracy of the progress analysis by considering the participant's attribute information. Some or all of the above processing in the progress analysis unit may be performed using AI, for example, or without using AI. For example, the progress analysis unit can input participant attribute information into a generating AI and have the generating AI perform the progress analysis.

[0121] The progress analysis unit can estimate the learner's emotions and adjust the order in which the progress analysis results are displayed based on the estimated learner's emotions. For example, if the learner is nervous, the progress analysis unit provides a simple and highly visible display order. For example, if the learner is relaxed, the progress analysis unit provides a display order that includes detailed information. Furthermore, if the learner is in a hurry, the progress analysis unit can also provide a concise display order. This improves the readability of the progress analysis results by adjusting the display order based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress analysis unit may be performed using AI, for example, or not using AI. For example, the progress analysis unit can input learner emotion data into a generative AI and have the generative AI perform the adjustment of the display order.

[0122] The progress analysis unit can perform progress analysis while considering the geographical distribution of participants. For example, the progress analysis unit can perform progress analysis for each region based on the geographical distribution of participants. For example, the progress analysis unit can perform progress analysis suitable for a specific region, taking into account the geographical distribution of participants. Furthermore, the progress analysis unit can acquire the geographical distribution of participants in real time and reflect it in the progress analysis results. This improves the accuracy of the progress analysis by considering the geographical distribution of participants. Some or all of the above processing in the progress analysis unit may be performed using AI, for example, or without using AI. For example, the progress analysis unit can input the geographical distribution data of participants into a generating AI and have the generating AI perform the progress analysis.

[0123] The progress analysis unit can improve the accuracy of its progress analysis by referring to the learner's relevant literature during the analysis process. For example, the progress analysis unit can improve the accuracy of its progress analysis based on the learner's relevant literature. For example, the progress analysis unit can refer to the learner's relevant literature, extract specific patterns, and reflect them in the progress analysis. The progress analysis unit can also acquire the learner's relevant literature in real time and reflect them in the progress analysis results. This improves the accuracy of the progress analysis by referring to the learner's relevant literature. Some or all of the above processes in the progress analysis unit may be performed using AI, for example, or without AI. For example, the progress analysis unit can input the learner's relevant literature data into a generating AI and have the generating AI perform the improvement of the accuracy of the progress analysis.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The customer management system can also include a feedback section. This section can collect feedback from students and use it to improve the system. For example, the feedback section can allow students to input their impressions and suggestions for improvement through a questionnaire after a lesson. It can also analyze student feedback and suggest improvements to instructors. Furthermore, based on student feedback, the feedback section can adjust the system's algorithms to provide more personalized instruction. This improves student satisfaction and continuously enhances the quality of the system.

[0126] The customer management system can also include a rewards section. This section can provide rewards based on the learner's achievement of goals. For example, it can award badges or points when a learner achieves their set goals. It can also offer perks or discount coupons when a learner accumulates a certain number of points. Furthermore, the rewards section can provide tiered rewards based on progress to maintain learner motivation. This can boost learner motivation and promote continuous learning.

[0127] A customer management system can also include a communications section. This section can facilitate communication between students and between students and instructors. For example, it can allow students to exchange questions and opinions using a chat function during lessons. It can also enable students to interact after lessons through forums and group chats. Furthermore, the communications section can provide a platform for instructors to provide individual feedback and advice to students. This can improve the students' learning experience and promote community building.

[0128] The customer management system can also include a customization section. This customization section can tailor lesson content to the individual needs of each student. For example, it can adjust the difficulty and content of lessons based on student feedback and progress. It can also recommend specific exercises or poses based on the student's goals and limitations. Furthermore, it can change the music and background of the lessons according to the student's preferences. This can increase student satisfaction and provide more effective instruction.

[0129] The customer management system can also include a health management department. This department can monitor the health status of participants and provide appropriate advice. For example, it can measure participants' heart rate and calorie expenditure in real time to understand their health status. Furthermore, based on participants' health data, the health management department can recommend appropriate exercise levels and rest periods. In addition, it can provide advice on diet and nutrition according to participants' health goals. This allows for comprehensive support of participants' health and more effective instruction.

[0130] The customer management system can further utilize emotion estimation capabilities to estimate the stress levels of learners and provide appropriate relaxation methods. For example, the system can estimate stress levels from learners' facial expressions and voice and suggest breathing exercises or meditation to help them relax. It can also provide relaxing music or videos if a learner is feeling stressed. Furthermore, the system can adjust the content and pace of lessons according to the learner's stress level. This reduces learner stress and provides a more comfortable learning environment.

[0131] The customer management system can further utilize emotion estimation capabilities to estimate the learner's motivation and provide appropriate encouragement and support messages. For example, the system can estimate the learner's motivation level from their facial expressions and voice and display an encouraging message. It can also share success stories and goal-achievement narratives if the learner is losing motivation. Furthermore, the system can adjust lesson content and goals according to the learner's motivation level. This helps maintain learner motivation and provides support towards achieving their goals.

[0132] The customer management system can further utilize emotion estimation capabilities to estimate the fatigue level of learners and provide appropriate rest and recovery methods. For example, the system can estimate fatigue levels from learners' facial expressions and movements and encourage them to rest. It can also suggest stretching or light exercise if learners are feeling fatigued. Furthermore, the system can adjust the intensity and duration of lessons according to the learners' fatigue levels. This reduces learner fatigue and provides a more effective learning environment.

[0133] The customer management system can further utilize emotion estimation capabilities to estimate the concentration level of learners and provide appropriate methods for improving their focus. For example, the system can estimate concentration levels from learners' facial expressions and movements and provide advice to enhance their concentration. It can also suggest short breaks or ways to refresh if learners are lacking concentration. Furthermore, the system can adjust the content and pace of lessons according to the learners' concentration levels. This helps maintain learners' focus and provides a more effective learning environment.

[0134] The customer management system can further estimate the student's happiness level using emotion estimation capabilities and provide appropriate positive feedback. For example, the system can estimate happiness levels from the student's facial expressions and voice and display positive feedback. Furthermore, if the student is feeling happy, the system can suggest further challenges or goals. In addition, the system can adjust the lesson content and goals according to the student's happiness level. This helps maintain student happiness and provides a more fulfilling learning experience.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The recognition unit recognizes the participant. The recognition unit identifies the individual by analyzing the participant's facial and body features, for example, using an AI camera. When a participant stands in front of the camera, it can identify the individual by analyzing their facial contours, eye position, body posture, etc. The recognition unit can also analyze the participant's past recognition history and select the optimal recognition algorithm. Step 2: The display unit displays the learner's personal data recognized by the recognition unit in real time. The display unit can display personal data such as the learner's name, age, improvement goals, deadlines, points to watch out for, and past learning history. When the learner stands in front of the camera, the personal data is displayed immediately. The display unit can also estimate the learner's emotions and adjust the displayed content based on the estimated emotions of the learner. Step 3: The analysis unit analyzes the participant's posture based on the personal data displayed by the display unit. For example, when a participant performs a yoga pose, the analysis unit can analyze the posture and present the correct form and areas for improvement to the instructor. It analyzes the angle and range of motion of the participant's posture and presents the correct form. The analysis unit can also estimate the participant's emotions and adjust the analysis criteria based on the estimated emotions of the participant. Step 4: The advice department provides precise advice based on the posture analyzed by the analysis department. For example, the advice department can advise a participant with lower back pain on how to assume poses that do not strain the lower back. They can advise on the position of the hips and how to distribute weight. The advice department can also estimate the participant's emotions and adjust the way the advice is expressed based on those estimated emotions. Step 5: The recording unit records progress based on the advice provided by the advice unit. The recording unit can, for example, record the student's lesson history and progress. This includes recording the date and time of the lesson, the content of the lesson, and the instructor's comments. The recording unit can also estimate the student's emotions and adjust the recorded content based on the estimated emotions. Step 6: The progress analysis unit analyzes the progress recorded by the recording unit. For example, based on the recorded progress, the progress analysis unit can provide advice to help the trainee achieve their goals. If a trainee has a goal of improving their backbend form, the unit will analyze their progress and provide advice to help them achieve that goal. The progress analysis unit can also estimate the trainee's emotions and adjust the criteria for progress analysis based on the estimated emotions of the trainee.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0139] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the recognition unit, display unit, analysis unit, advice unit, recording unit, and progress analysis unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the recognition unit uses the AI ​​camera of the smart device 14 to analyze the facial and physical characteristics of the participant and identify the individual. The display unit displays the participant's personal data in real time, for example, on the display 40A of the smart device 14. The analysis unit analyzes the participant's posture, for example, using the identification processing unit 290 of the data processing unit 12. The advice unit provides appropriate advice, for example, using the identification processing unit 290 of the data processing unit 12. The recording unit records the progress status, for example, using the identification processing unit 290 of the data processing unit 12. The progress analysis unit analyzes the progress status, for example, using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the recognition unit, display unit, analysis unit, advice unit, recording unit, and progress analysis unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the recognition unit uses the camera 42 of the smart glasses 214 to analyze the facial and body features of the participant and identify the individual. The display unit displays the participant's personal data in real time using the display of the smart glasses 214. The analysis unit analyzes the participant's posture using the identification processing unit 290 of the data processing unit 12. The advice unit provides appropriate advice using the identification processing unit 290 of the data processing unit 12. The recording unit records the progress using the identification processing unit 290 of the data processing unit 12. The progress analysis unit analyzes the progress using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0165] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements described above, including the recognition unit, display unit, analysis unit, advice unit, recording unit, and progress analysis unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the recognition unit uses the camera 42 of the headset terminal 314 to analyze the facial and body features of the participant and identify the individual. The display unit displays the participant's personal data in real time using the display 343 of the headset terminal 314. The analysis unit analyzes the participant's posture using the identification processing unit 290 of the data processing unit 12. The advice unit provides appropriate advice using the identification processing unit 290 of the data processing unit 12. The recording unit records the progress using the identification processing unit 290 of the data processing unit 12. The progress analysis unit analyzes the progress using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0175] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0176] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0177] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0179] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0180] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0181] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0182] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0183] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0184] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0185] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0186] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0187] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0188] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0189] Each of the multiple elements described above, including the recognition unit, display unit, analysis unit, advice unit, recording unit, and progress analysis unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the recognition unit uses the camera 42 of the robot 414 to analyze the facial and physical characteristics of the participant and identify the individual. The display unit displays the participant's personal data in real time using the display of the robot 414. The analysis unit analyzes the participant's posture using the identification processing unit 290 of the data processing unit 12. The advice unit provides appropriate advice using the identification processing unit 290 of the data processing unit 12. The recording unit records the progress using the identification processing unit 290 of the data processing unit 12. The progress analysis unit analyzes the progress using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0190] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0191] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0192] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0193] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0194] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0195] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0197] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0198] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0199] 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.

[0200] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0201] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0202] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0203] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0204] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0205] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0206] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0207] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0208] (Note 1) A recognition unit that recognizes the participant, A display unit that displays the personal data of the participant recognized by the recognition unit in real time, An analysis unit analyzes the posture of the participant based on the personal data displayed on the aforementioned display unit, An advice unit provides accurate advice based on the posture analyzed by the aforementioned analysis unit, A recording unit that records the progress based on the advice provided by the aforementioned advice unit, The system comprises a progress analysis unit that analyzes the progress status recorded by the recording unit. A system characterized by the following features. (Note 2) The aforementioned recognition unit, The system analyzes the facial and physical characteristics of participants to identify individuals. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is Personal data such as participant's name, age, improvement goals, goal deadline, points to note, and past course history are displayed in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is Analyze the participants' posture and present the correct form and areas for improvement to the instructor. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned advice section, For students suffering from lower back pain, I advise them on how to take poses that do not strain their lower back. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned recording unit is Record the student's lesson history and progress. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned progress analysis unit, We analyze recorded progress and provide advice to help participants achieve their goals. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned recognition unit, The system estimates the emotions of the participants and adjusts the recognition accuracy based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recognition unit, Analyze the participants' past recognition history and select the optimal recognition algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recognition unit, During recognition, the recognition accuracy is improved by considering the participant's movement patterns. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recognition unit, The system estimates the emotions of the participants and adjusts the display method of the recognition results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recognition unit, During recognition, the recognition accuracy is improved by considering the geographical location information of the participant. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned recognition unit, During the recognition process, the social media activity of participants is analyzed, and relevant information is reflected in the recognition. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned display unit is The system estimates the emotions of the participants and adjusts the displayed content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned display unit is When displaying information, adjust the level of detail based on the importance of the student. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned display unit is When displaying information, different display algorithms are applied depending on the participant's category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned display unit is The system estimates the participant's emotions and adjusts the display length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned display unit is When displaying the results, the display priority is determined based on when the participant submitted their work. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is When displaying information, adjust the display order based on the relevance of the participant. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is We estimate the participants' emotions and adjust the analysis criteria based on the estimated participants' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is When conducting analysis, consider the interrelationships of the participants to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is When conducting the analysis, the participant's attribute information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is The system estimates the emotions of the participants and adjusts the order in which the analysis results are displayed based on the estimated emotions of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is During the analysis, the geographical distribution of the participants will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is During the analysis, we refer to relevant literature used by the participants to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned advice section, The system estimates the emotions of the participants and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned advice section, When providing advice, adjust the level of detail based on the importance of the participant. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the participant's category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned advice section, The system estimates the participant's emotions and adjusts the length of the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned advice section, When providing advice, prioritize the advice based on the student's submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned advice section, When giving advice, adjust the order of advice based on the relevance of the participant. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned recording unit is The system estimates the participants' emotions and adjusts the recording content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned recording unit is When recording, adjust the level of detail in the recording based on the importance of the participant. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned recording unit is During recording, different recording algorithms are applied depending on the participant's category. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned recording unit is The system estimates the participants' emotions and adjusts the length of the recording based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned recording unit is When recording, prioritize the recordings based on when the participants submitted them. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned recording unit is When recording, adjust the order of recordings based on the relevance of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned progress analysis unit, We estimate the emotions of the participants and adjust the progress analysis criteria based on the estimated emotions of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned progress analysis unit, When analyzing progress, consider the relationships between participants to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned progress analysis unit, When conducting progress analysis, the participant's attribute information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned progress analysis unit, The system estimates the emotions of the participants and adjusts the order in which progress analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned progress analysis unit, When conducting progress analysis, the geographical distribution of participants should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned progress analysis unit, When analyzing progress, refer to relevant literature used by participants to improve the accuracy of the progress analysis. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A recognition unit that recognizes the participant, A display unit that displays the personal data of the participant recognized by the recognition unit in real time, An analysis unit analyzes the posture of the participant based on the personal data displayed on the aforementioned display unit, An advice unit provides accurate advice based on the posture analyzed by the aforementioned analysis unit, A recording unit that records the progress based on the advice provided by the aforementioned advice unit, The system comprises a progress analysis unit that analyzes the progress status recorded by the recording unit. A system characterized by the following features.

2. The aforementioned recognition unit, The system analyzes the facial and physical characteristics of participants to identify individuals. The system according to feature 1.

3. The aforementioned display unit is Personal data such as participant's name, age, improvement goals, goal deadline, points to note, and past course history are displayed in real time. The system according to feature 1.

4. The aforementioned analysis unit is Analyze the participants' posture and present the correct form and areas for improvement to the instructor. The system according to feature 1.

5. The aforementioned advice section, For students suffering from lower back pain, I advise them on how to take poses that do not strain their lower back. The system according to feature 1.

6. The aforementioned recording unit is Record the student's lesson history and progress. The system according to feature 1.

7. The aforementioned progress analysis unit, We analyze recorded progress and provide advice to help participants achieve their goals. The system according to feature 1.

8. The aforementioned recognition unit, The system estimates the emotions of the participants and adjusts the recognition accuracy based on the estimated emotions. The system according to feature 1.

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