system

Abstract

The system according to this embodiment aims to quantify and visualize the emotions of the other party during an online meeting. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a visualization unit, and a feedback unit. The collection unit collects video images and audio data. The analysis unit analyzes the data collected by the collection unit and determines the emotions of the other party. The visualization unit quantifies and visualizes the emotions determined by the analysis unit. The feedback unit provides feedback based on the results visualized by the visualization unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult to accurately grasp the emotions of the other party in an online meeting, and there is room for improvement.

[0005] The system according to the embodiment aims to quantify and visualize the emotions of the other party in an online meeting.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a visualization unit, and a feedback unit. The collection unit collects video and audio data. The analysis unit analyzes the data collected by the collection unit and determines the emotions of the other party. The visualization unit quantifies and visualizes the emotions determined by the analysis unit. The feedback unit provides feedback based on the results visualized by the visualization unit. [Effects of the Invention]

[0007] The system according to this embodiment can quantify and visualize the emotions of the other party during an online meeting. [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, etc. The communication I / F manages 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 ३2 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus ३4. 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) An online meeting evaluation system according to an embodiment of the present invention is a system that evaluates the quality of a presentation using an online meeting tool. This online meeting evaluation system collects video and audio data of the other party when a user conducts a meeting using an online meeting tool. Next, a generating AI analyzes the collected video and audio data and determines the emotions based on the other party's facial expressions and tone of voice. For example, it analyzes facial data such as eyebrow movements, eye opening, and mouth movements, and audio data such as tone and pitch of voice. Based on the analysis results, the generating AI quantifies and visualizes the emotions. For example, it represents emotions such as joy, sadness, anger, and surprise numerically and displays them as graphs or charts. This allows the user to intuitively grasp the other party's emotions. Furthermore, the online meeting evaluation system provides feedback based on the analysis results. For example, it specifically indicates which parts of the presentation pleased the other party and which parts saddened them, and suggests areas for improvement in the next meeting. This allows the user to improve their presentation skills. This online meeting evaluation system is extremely useful for company employees working from home and business people conducting online business negotiations. In online meetings, it's difficult to directly observe the other party's reactions. This system allows you to understand their emotions and achieve more effective communication. For example, the online meeting evaluation system analyzes the audience's facial expressions and tone of voice in real time during a user's presentation, instantly grasping changes in their emotions. Furthermore, based on the analysis results, the system can suggest specific areas for improvement to the user. For instance, it provides feedback on various elements such as presentation content, delivery style, and slide design. This allows the user to deliver a better presentation in the next meeting. Additionally, the online meeting evaluation system can accumulate analysis results and analyze long-term trends. For example, it can track the improvement of the user's presentation skills over time, allowing for a clear understanding of their progress. This allows the user to feel a sense of growth and increase their motivation.This allows the online meeting evaluation system to assess the quality of a user's presentation and provide specific feedback.

[0029] The online meeting evaluation system according to this embodiment comprises a collection unit, an analysis unit, a visualization unit, and a feedback unit. The collection unit collects video and audio data. The collection unit collects video and audio data of the other party in real time, for example, through an online meeting tool. The collection unit can also collect video and audio data in high resolution. For example, the collection unit collects video at a resolution of 1080p and audio data at a sampling rate of 44.1kHz. Furthermore, the collection unit can collect video and audio data synchronously. For example, the collection unit collects video and audio data simultaneously and maintains synchronization by adding a timestamp. The analysis unit analyzes the data collected by the collection unit and determines the emotions of the other party. The analysis unit analyzes video and audio data, for example, using a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The analysis unit analyzes facial expression data such as eyebrow movements, eye opening, and mouth movements. For example, the analysis unit analyzes the angle of eyebrow rise, the frequency of eye opening and closing, and changes in the shape of the mouth. The analysis unit also analyzes audio data such as voice tone and pitch. For example, the analysis unit analyzes voice pitch, volume, and sound fluctuation patterns. The visualization unit quantifies and visualizes the emotions determined by the analysis unit. For example, the visualization unit represents emotions such as joy, sadness, anger, and surprise numerically and displays them as graphs and charts. For example, the visualization unit quantifies emotions based on their intensity and frequency of occurrence. The visualization unit can also display changes in emotions over time. For example, the visualization unit shows in a graph how the audience's emotions changed from the beginning to the end of a presentation. The feedback unit provides feedback based on the results visualized by the visualization unit. For example, the feedback unit specifically indicates which parts of the presentation made the audience happy and which parts made them sad. For example, the feedback unit shows changes in the audience's emotions based on slide numbers and time divisions. The feedback unit also suggests areas for improvement in the next meeting. For example, the feedback department provides specific advice on the content of the presentation, delivery style, slide design, and so on.This allows the online meeting evaluation system according to the embodiment to evaluate the quality of the user's presentation and provide specific feedback.

[0030] The data collection unit collects video and audio data. For example, it collects video and audio data from participants in real time via online meeting tools. Specifically, the unit acquires data directly from the camera and microphone of meeting participants and transmits it to a central server. Video is collected in high resolution (1080p), and audio data is collected at a sampling rate of 44.1kHz. This allows the data collection unit to acquire very high-quality data. Furthermore, the data collection unit can collect video and audio data synchronously. For example, it can collect video and audio data simultaneously, adding timestamps to maintain synchronization. This allows the subsequent analysis unit to accurately analyze the data. The data collection unit can also compress data and adjust resolution depending on network conditions. For example, if the network is unstable, the data collection unit automatically lowers the video resolution or adjusts the audio data sampling rate to prevent data interruptions. This ensures that the data collection unit always collects data in optimal condition and provides it to the analysis unit. Additionally, the data collection unit can collect data from multiple participants simultaneously and manage each data set individually. This allows for a comprehensive understanding of the overall situation of the meeting.

[0031] The analysis unit analyzes the data collected by the collection unit to determine the other party's emotions. The analysis unit analyzes video footage and audio data, for example, using a generative AI. Generative AIs include, for example, text generation AI (e.g., LLM) and multimodal generation AI. Specifically, the analysis unit analyzes facial expression data from video footage, such as eyebrow movement, eye opening, and mouth movement. For example, it determines the other party's emotions by analyzing the angle of eyebrow rise, the frequency of eye opening and closing, and changes in mouth shape. The analysis unit also analyzes voice tone and pitch from audio data. For example, it determines the other party's emotions by analyzing voice pitch, volume, and sound fluctuation patterns. The generative AI integrates this data to determine the other party's emotions with high accuracy. Furthermore, the analysis unit can learn patterns of emotional change by utilizing past data and statistical information to make more accurate judgments. For example, it can learn emotional changes in response to specific situations and statements based on past meeting data and reflect this in real-time analysis. Furthermore, the analysis unit can use an anomaly detection algorithm to detect unusual emotional changes early and issue warnings. This allows the analysis unit to not only determine emotions in real time but also to detect long-term emotional changes and abnormal emotions, thereby improving the reliability and accuracy of the entire system.

[0032] The visualization unit quantifies and visualizes the emotions determined by the analysis unit. For example, the visualization unit represents emotions such as joy, sadness, anger, and surprise numerically and displays them as graphs and charts. Specifically, the visualization unit quantifies emotions based on their intensity and frequency of occurrence. For example, it can represent the intensity of an emotion on a scale from 0 to 100 and display the frequency of occurrence as a percentage. The visualization unit can also display emotional changes over time. For example, it can show a graph of how the audience's emotions changed from the beginning to the end of a presentation. This allows the user to quickly understand what emotions the audience felt at different points in the presentation. Furthermore, the visualization unit can display multiple emotions simultaneously. For example, it can display changes in multiple emotions such as joy and sadness, or anger and surprise, allowing for a detailed understanding of the complex emotional shifts of the audience. The visualization unit can also update emotional changes in real time, always displaying the latest situation. This allows the user to understand the audience's emotional changes in real time during a meeting and take appropriate action. Furthermore, the visualization unit can also perform sentiment trend analysis based on past data. For example, it can analyze changes in sentiment towards specific themes or statements based on past meeting data, and use this information to improve future meetings.

[0033] The feedback unit provides feedback based on the results visualized by the visualization unit. For example, the feedback unit specifically indicates which parts of the presentation pleased the audience and which parts saddened them. Specifically, the feedback unit indicates changes in the audience's emotions based on slide numbers and time divisions. For example, it provides specific feedback such as, "The audience felt pleased at slide 5, and sad at slide 10." The feedback unit also suggests areas for improvement in the next meeting. For example, it provides specific advice on the presentation content, delivery style, and slide design. This allows the user to gain concrete guidance for delivering a better presentation in the next meeting. Furthermore, the feedback unit can also provide personalized feedback based on the user's past performance. For example, it analyzes the user's strengths and weaknesses based on past meeting data and suggests individual areas for improvement. The feedback unit can also provide real-time feedback. For example, it can analyze changes in the audience's emotions in real time during the meeting and provide immediate feedback. This allows the user to take appropriate action during the meeting. Additionally, the feedback unit can customize the content of the feedback according to the user's preferences. For example, users who want detailed feedback will be provided with specific advice and suggestions, while users who prefer concise feedback will receive summarized feedback. This allows the feedback department to provide flexible feedback tailored to user needs, thereby improving user satisfaction.

[0034] The analysis unit can analyze facial expression data such as eyebrow movement, eye opening, and mouth movement. For example, the analysis unit can analyze the angle of eyebrow movement. For instance, a greater angle of eyebrow movement indicates stronger feelings of surprise or joy. The analysis unit can also analyze the frequency of eye opening and closing. For example, more frequent eye opening and closing indicates stronger feelings of tension or anxiety. The analysis unit can also analyze changes in mouth shape. For example, more upward movement of the corners of the mouth indicates stronger feelings of joy. By analyzing facial expression data, it becomes possible to more accurately determine the emotions of the other person. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input video footage into the generation AI and have the generation AI perform the analysis of facial expression data such as eyebrow movement, eye opening, and mouth movement.

[0035] The analysis unit can analyze audio data such as voice tone and pitch. For example, the analysis unit can analyze the pitch of the voice. For instance, it can determine that the higher the pitch, the stronger the emotion of excitement or joy. The analysis unit can also analyze the volume. For example, it can determine that the louder the volume, the stronger the emotion of anger or surprise. Furthermore, the analysis unit can analyze the fluctuation patterns of sound. For example, if the tone of the voice changes rapidly, it can determine that the emotion of tension or anxiety is strong. In this way, by analyzing the audio data, it is possible to determine the emotions of the other person more accurately. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input audio data into the generation AI and have the generation AI perform the analysis of audio data such as voice tone and pitch.

[0036] The visualization unit can quantify emotions such as joy, sadness, anger, and surprise, and display them as graphs and charts. For example, the visualization unit can quantify the intensity of an emotion. For instance, a higher score is assigned to a stronger emotion like joy. The visualization unit can also quantify the frequency of an emotion's appearance. For example, a higher score is assigned to an emotion that appears more frequently. Furthermore, the visualization unit can display changes in emotions over time. For example, it can show a graph how the audience's emotions changed from the beginning to the end of a presentation. This allows for an intuitive understanding of the audience's emotions by quantifying them and displaying them as graphs and charts. Some or all of the above processing in the visualization unit is performed using a generative AI. For example, the visualization unit can input the analysis results into the generative AI, which can then perform the quantification and visualization of emotions.

[0037] The feedback function can specifically indicate which parts of the presentation pleased the audience and which parts disappointed them, and suggest areas for improvement in the next meeting. For example, the feedback function might indicate changes in the audience's emotions based on slide numbers or time divisions. For instance, it might show that the audience was pleased with slide 5 and disappointed with slide 10. The feedback function can also provide specific advice on the presentation content, delivery, and slide design. For example, it might suggest adding more intonation next time because the delivery was monotonous. This allows for a concrete understanding of areas for improvement in the next meeting through the feedback. Some or all of the above processing in the feedback function may be performed using AI or not. For example, the feedback function could input analysis results into AI and have the AI ​​generate the feedback.

[0038] The data collection unit can analyze the user's past meeting history and select the optimal collection method when collecting video and audio data. For example, the data collection unit can select a similar collection method based on data from successful meetings the user has held in the past. Alternatively, the data collection unit can analyze data from unsuccessful meetings the user has held in the past and select a different collection method. Furthermore, the data collection unit can identify specific patterns from the user's past meeting history and select a collection method based on those patterns. In this way, the optimal collection method can be selected by analyzing past meeting history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past meeting data into AI and have the AI ​​select the optimal collection method.

[0039] The data collection unit can filter video and audio data based on the user's current projects and areas of interest. For example, the data collection unit can collect only data related to the user's current project. The data collection unit can also prioritize the collection of highly relevant data based on the user's areas of interest. Furthermore, the data collection unit can filter and collect necessary data according to the progress of the user's project. This allows for the collection of highly relevant data by filtering data based on the current project and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input project and area of ​​interest information into the AI ​​and have the AI ​​perform the data filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting video and audio data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. Furthermore, if the user is on the move, the data collection unit can also collect the most relevant data based on their current location. Additionally, if the user is in a specific location, the data collection unit can prioritize the collection of data related to that location. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0041] The data collection unit can analyze the user's social media activity and collect relevant data when collecting video and audio data. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also analyze the activity of the user's social media followers and friends and collect relevant data. Furthermore, the data collection unit can analyze the content of the user's social media posts and collect relevant data. In this way, relevant data can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media data into AI and have the AI ​​perform the collection of relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the video and audio data during the analysis. For example, the analysis unit performs a detailed analysis on important video data. It can also perform a simplified analysis on less important audio data. Furthermore, the analysis unit can apply multiple analysis algorithms to high-importance data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input video and audio data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of video and audio data during analysis. For example, the analysis unit can apply a face recognition algorithm to facial expression data. It can also apply a speech recognition algorithm to audio data. Furthermore, it can apply a motion analysis algorithm to motion data. By applying analysis algorithms according to the data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input video and audio data into the generation AI and have the generation AI execute the application of analysis algorithms.

[0044] The analysis unit can determine the priority of analysis based on the timing of video and audio data collection. For example, the analysis unit may prioritize the analysis of the most recent video data. It can also prioritize the analysis of audio data from important meetings. Furthermore, the analysis unit can prioritize the analysis of current data while referring to past data. This enables efficient analysis by determining the priority of analysis based on the timing of data collection. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input video and audio data into the generative AI and have the generative AI determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relationship between video footage and audio data during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant video footage data. It can also postpone the analysis of less relevant audio data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input video footage and audio data into the generative AI and have the generative AI perform the adjustment of the analysis order.

[0046] The visualization unit can adjust the level of detail of the visualization based on the importance of the analysis results during visualization. For example, the visualization unit can display detailed graphs and charts for important analysis results. It can also display simplified graphs and charts for less important analysis results. Furthermore, the visualization unit can apply multiple visualization methods to high-importance data. This allows for efficient visualization by adjusting the level of detail of the visualization based on the importance of the analysis results. Some or all of the above processing in the visualization unit is performed using a generation AI. For example, the visualization unit can input analysis results into the generation AI and have the generation AI perform the adjustment of the level of detail of the visualization.

[0047] The visualization unit can apply different visualization algorithms depending on the category of the analysis results during visualization. For example, for facial expression data, the visualization unit can display a graph showing facial movement. It can also display a chart showing voice tone for audio data. Furthermore, for motion data, the visualization unit can display a graph showing changes in motion. This allows for more accurate visualization by applying visualization algorithms according to the category of the analysis results. Some or all of the above processing in the visualization unit is performed using a generation AI. For example, the visualization unit can input analysis results into the generation AI and have the generation AI execute the application of visualization algorithms.

[0048] The visualization unit can determine the visualization priority based on the timing of analysis result collection during visualization. For example, the visualization unit can prioritize the visualization of the most recent analysis results. It can also prioritize the visualization of analysis results from important meetings. Furthermore, the visualization unit can prioritize the visualization of current data while referring to past data. This enables efficient visualization by determining the visualization priority based on the timing of analysis result collection. Some or all of the above processing in the visualization unit is performed using a generation AI. For example, the visualization unit can input analysis results into the generation AI and have the generation AI determine the visualization priority.

[0049] The visualization unit can adjust the visualization order based on the relevance of the analysis results during visualization. For example, the visualization unit prioritizes the visualization of highly relevant analysis results. It can also postpone the visualization of less relevant analysis results. Furthermore, the visualization unit can dynamically adjust the visualization order based on highly relevant data. This allows for efficient visualization by adjusting the visualization order based on the relevance of the analysis results. Some or all of the above processing in the visualization unit is performed using a generation AI. For example, the visualization unit can input analysis results into the generation AI and have the generation AI perform the adjustment of the visualization order.

[0050] The feedback unit can adjust the level of detail of the feedback based on the importance of the analysis results. For example, the feedback unit provides detailed feedback for important analysis results. It can also provide simplified feedback for less important analysis results. Furthermore, the feedback unit can apply multiple feedback methods to highly important data. This allows for efficient feedback by adjusting the level of detail of the feedback based on the importance of the analysis results. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the analysis results into AI and have the AI ​​adjust the level of detail of the feedback.

[0051] The feedback unit can apply different feedback algorithms depending on the category of the analysis result during the feedback process. For example, for facial expression data, the feedback unit can provide feedback indicating facial movement. It can also provide feedback indicating voice tone for audio data. Furthermore, it can provide feedback indicating changes in motion for motion data. This allows for more accurate feedback by applying feedback algorithms according to the category of the analysis result. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the analysis results into AI and have the AI ​​apply the feedback algorithm.

[0052] The feedback unit can determine the priority of feedback based on when the analysis results were collected. For example, the feedback unit can prioritize the most recent analysis results. It can also prioritize the analysis results of important meetings. Furthermore, the feedback unit can prioritize current data while referring to past data. This enables efficient feedback by determining the priority of feedback based on when the analysis results were collected. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the analysis results into AI and have the AI ​​determine the priority of feedback.

[0053] The feedback unit can adjust the order of feedback based on the relevance of the analysis results during the feedback process. For example, the feedback unit can prioritize providing feedback on highly relevant analysis results. It can also postpone providing feedback on less relevant analysis results. Furthermore, the feedback unit can dynamically adjust the order of feedback based on highly relevant data. This allows for efficient feedback by adjusting the order of feedback based on the relevance of the analysis results. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the analysis results into an AI and have the AI ​​adjust the order of feedback.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The online meeting evaluation system can also include a speech analysis unit that analyzes the content of user statements. The speech analysis unit collects user statements as text data and analyzes them using natural language processing technology. For example, if a particular keyword frequently appears in a user's statements, the system can estimate the emotions associated with that keyword. Furthermore, by analyzing the tone and context of the statements, the system can more accurately grasp the user's intentions and emotions. This allows for the provision of more appropriate feedback based on the user's statements. In addition, the speech analysis unit can track user statements over time and analyze long-term trends. For example, it can understand how user statements have changed over time and use this information to improve presentations.

[0056] The online meeting evaluation system can also be equipped with a gesture analysis unit that analyzes the user's gestures. The gesture analysis unit analyzes the user's hand and body movements from video footage and determines the type and frequency of gestures. For example, if there are many gestures involving large hand movements, it can be determined that the user is speaking with confidence. Also, if there are many movements of swaying the body back and forth, it can be determined that the user is nervous. This allows for specific suggestions for improvement in the presentation based on the user's gestures. Furthermore, the gesture analysis unit can also track changes in the user's gestures over time and analyze long-term trends. For example, it can be used to understand how the user's gestures have changed over time and to improve presentation skills.

[0057] Online meeting evaluation systems can also be equipped with an eye-tracking unit that tracks the user's gaze. The eye-tracking unit analyzes the user's eye movements from video footage to determine the direction of their gaze and the points of fixation. For example, if a user is fixated on a specific part of a slide for a long time, it can be determined that that part is important. Also, if a user is frequently shifting their gaze, it can be determined that their concentration is decreasing. This allows for specific suggestions for improving the presentation based on the user's gaze. Furthermore, the eye-tracking unit can also track changes in the user's gaze over time and analyze long-term trends. For example, understanding how the user's gaze changes over time can be used to improve presentation skills.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The collection unit collects video and audio data. For example, it collects the other party's video and audio data in real time through an online meeting tool. The collection unit can also collect video and audio data in high resolution. For example, it can collect video at 1080p resolution and audio data at a sampling rate of 44.1kHz. Furthermore, the collection unit can collect video and audio data synchronously. For example, it can collect video and audio data simultaneously and maintain synchronization by adding a timestamp. Step 2: The analysis unit analyzes the data collected by the collection unit to determine the other person's emotions. For example, it analyzes video footage and audio data using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. The analysis unit analyzes facial expression data such as eyebrow movement, eye opening, and mouth movement. For example, it analyzes the angle of eyebrow rise, the frequency of eye opening and closing, and changes in mouth shape. It also analyzes audio data such as voice tone and pitch. For example, it analyzes voice pitch, volume, and sound variation patterns. Step 3: The visualization unit quantifies and visualizes the emotions determined by the analysis unit. For example, emotions such as joy, sadness, anger, and surprise are represented numerically and displayed as graphs or charts. Emotions can also be quantified based on their intensity and frequency of occurrence, and changes in emotions can be displayed over time. For example, a graph can show how the audience's emotions changed from the beginning to the end of a presentation. Step 4: The Feedback team provides feedback based on the results visualized by the Visualization team. For example, they specifically indicate which parts of the presentation pleased the audience and which parts saddened them. They show changes in the audience's emotions based on slide numbers and time divisions. They also suggest areas for improvement in the next meeting. For example, they provide specific advice on the content of the presentation, delivery style, slide design, etc.

[0060] (Example of form 2) An online meeting evaluation system according to an embodiment of the present invention is a system that evaluates the quality of a presentation using an online meeting tool. This online meeting evaluation system collects video and audio data of the other party when a user conducts a meeting using an online meeting tool. Next, a generating AI analyzes the collected video and audio data and determines the emotions based on the other party's facial expressions and tone of voice. For example, it analyzes facial data such as eyebrow movements, eye opening, and mouth movements, and audio data such as tone and pitch of voice. Based on the analysis results, the generating AI quantifies and visualizes the emotions. For example, it represents emotions such as joy, sadness, anger, and surprise numerically and displays them as graphs or charts. This allows the user to intuitively grasp the other party's emotions. Furthermore, the online meeting evaluation system provides feedback based on the analysis results. For example, it specifically indicates which parts of the presentation pleased the other party and which parts saddened them, and suggests areas for improvement in the next meeting. This allows the user to improve their presentation skills. This online meeting evaluation system is extremely useful for company employees working from home and business people conducting online business negotiations. In online meetings, it's difficult to directly observe the other party's reactions. This system allows you to understand their emotions and achieve more effective communication. For example, the online meeting evaluation system analyzes the audience's facial expressions and tone of voice in real time during a user's presentation, instantly grasping changes in their emotions. Furthermore, based on the analysis results, the system can suggest specific areas for improvement to the user. For instance, it provides feedback on various elements such as presentation content, delivery style, and slide design. This allows the user to deliver a better presentation in the next meeting. Additionally, the online meeting evaluation system can accumulate analysis results and analyze long-term trends. For example, it can track the improvement of the user's presentation skills over time, allowing for a clear understanding of their progress. This allows the user to feel a sense of growth and increase their motivation.This allows the online meeting evaluation system to assess the quality of a user's presentation and provide specific feedback.

[0061] The online meeting evaluation system according to this embodiment comprises a collection unit, an analysis unit, a visualization unit, and a feedback unit. The collection unit collects video and audio data. The collection unit collects video and audio data of the other party in real time, for example, through an online meeting tool. The collection unit can also collect video and audio data in high resolution. For example, the collection unit collects video at a resolution of 1080p and audio data at a sampling rate of 44.1kHz. Furthermore, the collection unit can collect video and audio data synchronously. For example, the collection unit collects video and audio data simultaneously and maintains synchronization by adding a timestamp. The analysis unit analyzes the data collected by the collection unit and determines the emotions of the other party. The analysis unit analyzes video and audio data, for example, using a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The analysis unit analyzes facial expression data such as eyebrow movements, eye opening, and mouth movements. For example, the analysis unit analyzes the angle of eyebrow rise, the frequency of eye opening and closing, and changes in the shape of the mouth. The analysis unit also analyzes audio data such as voice tone and pitch. For example, the analysis unit analyzes voice pitch, volume, and sound fluctuation patterns. The visualization unit quantifies and visualizes the emotions determined by the analysis unit. For example, the visualization unit represents emotions such as joy, sadness, anger, and surprise numerically and displays them as graphs and charts. For example, the visualization unit quantifies emotions based on their intensity and frequency of occurrence. The visualization unit can also display changes in emotions over time. For example, the visualization unit shows in a graph how the audience's emotions changed from the beginning to the end of a presentation. The feedback unit provides feedback based on the results visualized by the visualization unit. For example, the feedback unit specifically indicates which parts of the presentation made the audience happy and which parts made them sad. For example, the feedback unit shows changes in the audience's emotions based on slide numbers and time divisions. The feedback unit also suggests areas for improvement in the next meeting. For example, the feedback department provides specific advice on the content of the presentation, delivery style, slide design, and so on.This allows the online meeting evaluation system according to the embodiment to evaluate the quality of the user's presentation and provide specific feedback.

[0062] The data collection unit collects video and audio data. For example, it collects video and audio data from participants in real time via online meeting tools. Specifically, the unit acquires data directly from the camera and microphone of meeting participants and transmits it to a central server. Video is collected in high resolution (1080p), and audio data is collected at a sampling rate of 44.1kHz. This allows the data collection unit to acquire very high-quality data. Furthermore, the data collection unit can collect video and audio data synchronously. For example, it can collect video and audio data simultaneously, adding timestamps to maintain synchronization. This allows the subsequent analysis unit to accurately analyze the data. The data collection unit can also compress data and adjust resolution depending on network conditions. For example, if the network is unstable, the data collection unit automatically lowers the video resolution or adjusts the audio data sampling rate to prevent data interruptions. This ensures that the data collection unit always collects data in optimal condition and provides it to the analysis unit. Additionally, the data collection unit can collect data from multiple participants simultaneously and manage each data set individually. This allows for a comprehensive understanding of the overall situation of the meeting.

[0063] The analysis unit analyzes the data collected by the collection unit to determine the other party's emotions. The analysis unit analyzes video footage and audio data, for example, using a generative AI. Generative AIs include, for example, text generation AI (e.g., LLM) and multimodal generation AI. Specifically, the analysis unit analyzes facial expression data from video footage, such as eyebrow movement, eye opening, and mouth movement. For example, it determines the other party's emotions by analyzing the angle of eyebrow rise, the frequency of eye opening and closing, and changes in mouth shape. The analysis unit also analyzes voice tone and pitch from audio data. For example, it determines the other party's emotions by analyzing voice pitch, volume, and sound fluctuation patterns. The generative AI integrates this data to determine the other party's emotions with high accuracy. Furthermore, the analysis unit can learn patterns of emotional change by utilizing past data and statistical information to make more accurate judgments. For example, it can learn emotional changes in response to specific situations and statements based on past meeting data and reflect this in real-time analysis. Furthermore, the analysis unit can use an anomaly detection algorithm to detect unusual emotional changes early and issue warnings. This allows the analysis unit to not only determine emotions in real time but also to detect long-term emotional changes and abnormal emotions, thereby improving the reliability and accuracy of the entire system.

[0064] The visualization unit quantifies and visualizes the emotions determined by the analysis unit. For example, the visualization unit represents emotions such as joy, sadness, anger, and surprise numerically and displays them as graphs and charts. Specifically, the visualization unit quantifies emotions based on their intensity and frequency of occurrence. For example, it can represent the intensity of an emotion on a scale from 0 to 100 and display the frequency of occurrence as a percentage. The visualization unit can also display emotional changes over time. For example, it can show a graph of how the audience's emotions changed from the beginning to the end of a presentation. This allows the user to quickly understand what emotions the audience felt at different points in the presentation. Furthermore, the visualization unit can display multiple emotions simultaneously. For example, it can display changes in multiple emotions such as joy and sadness, or anger and surprise, allowing for a detailed understanding of the complex emotional shifts of the audience. The visualization unit can also update emotional changes in real time, always displaying the latest situation. This allows the user to understand the audience's emotional changes in real time during a meeting and take appropriate action. Furthermore, the visualization unit can also perform sentiment trend analysis based on past data. For example, it can analyze changes in sentiment towards specific themes or statements based on past meeting data, and use this information to improve future meetings.

[0065] The feedback unit provides feedback based on the results visualized by the visualization unit. For example, the feedback unit specifically indicates which parts of the presentation pleased the audience and which parts saddened them. Specifically, the feedback unit indicates changes in the audience's emotions based on slide numbers and time divisions. For example, it provides specific feedback such as, "The audience felt pleased at slide 5, and sad at slide 10." The feedback unit also suggests areas for improvement in the next meeting. For example, it provides specific advice on the presentation content, delivery style, and slide design. This allows the user to gain concrete guidance for delivering a better presentation in the next meeting. Furthermore, the feedback unit can also provide personalized feedback based on the user's past performance. For example, it analyzes the user's strengths and weaknesses based on past meeting data and suggests individual areas for improvement. The feedback unit can also provide real-time feedback. For example, it can analyze changes in the audience's emotions in real time during the meeting and provide immediate feedback. This allows the user to take appropriate action during the meeting. Additionally, the feedback unit can customize the content of the feedback according to the user's preferences. For example, users who want detailed feedback will be provided with specific advice and suggestions, while users who prefer concise feedback will receive summarized feedback. This allows the feedback department to provide flexible feedback tailored to user needs, thereby improving user satisfaction.

[0066] The analysis unit can analyze facial expression data such as eyebrow movement, eye opening, and mouth movement. For example, the analysis unit can analyze the angle of eyebrow movement. For instance, a greater angle of eyebrow movement indicates stronger feelings of surprise or joy. The analysis unit can also analyze the frequency of eye opening and closing. For example, more frequent eye opening and closing indicates stronger feelings of tension or anxiety. The analysis unit can also analyze changes in mouth shape. For example, more upward movement of the corners of the mouth indicates stronger feelings of joy. By analyzing facial expression data, it becomes possible to more accurately determine the emotions of the other person. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input video footage into the generation AI and have the generation AI perform the analysis of facial expression data such as eyebrow movement, eye opening, and mouth movement.

[0067] The analysis unit can analyze audio data such as voice tone and pitch. For example, the analysis unit can analyze the pitch of the voice. For instance, it can determine that the higher the pitch, the stronger the emotion of excitement or joy. The analysis unit can also analyze the volume. For example, it can determine that the louder the volume, the stronger the emotion of anger or surprise. Furthermore, the analysis unit can analyze the fluctuation patterns of sound. For example, if the tone of the voice changes rapidly, it can determine that the emotion of tension or anxiety is strong. In this way, by analyzing the audio data, it is possible to determine the emotions of the other person more accurately. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input audio data into the generation AI and have the generation AI perform the analysis of audio data such as voice tone and pitch.

[0068] The visualization unit can quantify emotions such as joy, sadness, anger, and surprise, and display them as graphs and charts. For example, the visualization unit can quantify the intensity of an emotion. For instance, a higher score is assigned to a stronger emotion like joy. The visualization unit can also quantify the frequency of an emotion's appearance. For example, a higher score is assigned to an emotion that appears more frequently. Furthermore, the visualization unit can display changes in emotions over time. For example, it can show a graph how the audience's emotions changed from the beginning to the end of a presentation. This allows for an intuitive understanding of the audience's emotions by quantifying them and displaying them as graphs and charts. Some or all of the above processing in the visualization unit is performed using a generative AI. For example, the visualization unit can input the analysis results into the generative AI, which can then perform the quantification and visualization of emotions.

[0069] The feedback function can specifically indicate which parts of the presentation pleased the audience and which parts disappointed them, and suggest areas for improvement in the next meeting. For example, the feedback function might indicate changes in the audience's emotions based on slide numbers or time divisions. For instance, it might show that the audience was pleased with slide 5 and disappointed with slide 10. The feedback function can also provide specific advice on the presentation content, delivery, and slide design. For example, it might suggest adding more intonation next time because the delivery was monotonous. This allows for a concrete understanding of areas for improvement in the next meeting through the feedback. Some or all of the above processing in the feedback function may be performed using AI or not. For example, the feedback function could input analysis results into AI and have the AI ​​generate the feedback.

[0070] The data collection unit can estimate the user's emotions and adjust the timing of video and audio data collection based on the estimated emotions. For example, if the user is nervous, the data collection unit will collect video and audio data more frequently to obtain detailed data. If the user is relaxed, the data collection unit will collect video and audio data at intervals to obtain only the minimum necessary data. Furthermore, if the user is excited, the data collection unit can collect video and audio data in real time to obtain immediate data. This allows for the collection of more appropriate data by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's emotion data into the generative AI and have the generative AI adjust the collection timing.

[0071] The data collection unit can analyze the user's past meeting history and select the optimal collection method when collecting video and audio data. For example, the data collection unit can select a similar collection method based on data from successful meetings the user has held in the past. Alternatively, the data collection unit can analyze data from unsuccessful meetings the user has held in the past and select a different collection method. Furthermore, the data collection unit can identify specific patterns from the user's past meeting history and select a collection method based on those patterns. In this way, the optimal collection method can be selected by analyzing past meeting history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past meeting data into AI and have the AI ​​select the optimal collection method.

[0072] The data collection unit can filter video and audio data based on the user's current projects and areas of interest. For example, the data collection unit can collect only data related to the user's current project. The data collection unit can also prioritize the collection of highly relevant data based on the user's areas of interest. Furthermore, the data collection unit can filter and collect necessary data according to the progress of the user's project. This allows for the collection of highly relevant data by filtering data based on the current project and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input project and area of ​​interest information into the AI ​​and have the AI ​​perform the data filtering.

[0073] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is tense, the data collection unit may prioritize collecting facial expression data. It can also prioritize collecting audio data if the user is relaxed. Furthermore, if the user is excited, the data collection unit may collect both video and audio data equally. This allows for the priority collection of important data by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the data priority.

[0074] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting video and audio data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. Furthermore, if the user is on the move, the data collection unit can also collect the most relevant data based on their current location. Additionally, if the user is in a specific location, the data collection unit can prioritize the collection of data related to that location. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0075] The data collection unit can analyze the user's social media activity and collect relevant data when collecting video and audio data. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also analyze the activity of the user's social media followers and friends and collect relevant data. Furthermore, the data collection unit can analyze the content of the user's social media posts and collect relevant data. In this way, relevant data can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media data into AI and have the AI ​​perform the collection of relevant data.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can display the analysis results in detail. If the user is tense, the analysis unit can also display the analysis results concisely. Furthermore, if the user is excited, the analysis unit can visually highlight the analysis results. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit is performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the video and audio data during the analysis. For example, the analysis unit performs a detailed analysis on important video data. It can also perform a simplified analysis on less important audio data. Furthermore, the analysis unit can apply multiple analysis algorithms to high-importance data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input video and audio data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the category of video and audio data during analysis. For example, the analysis unit can apply a face recognition algorithm to facial expression data. It can also apply a speech recognition algorithm to audio data. Furthermore, it can apply a motion analysis algorithm to motion data. By applying analysis algorithms according to the data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input video and audio data into the generation AI and have the generation AI execute the application of analysis algorithms.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide analysis results in a short time. It can also provide detailed analysis results if the user is relaxed. Furthermore, if the user is excited, the analysis unit can provide visually emphasized analysis results. This allows for more appropriate analysis results by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit are performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0080] The analysis unit can determine the priority of analysis based on the timing of video and audio data collection. For example, the analysis unit may prioritize the analysis of the most recent video data. It can also prioritize the analysis of audio data from important meetings. Furthermore, the analysis unit can prioritize the analysis of current data while referring to past data. This enables efficient analysis by determining the priority of analysis based on the timing of data collection. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input video and audio data into the generative AI and have the generative AI determine the priority of analysis.

[0081] The analysis unit can adjust the order of analysis based on the relationship between video footage and audio data during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant video footage data. It can also postpone the analysis of less relevant audio data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input video footage and audio data into the generative AI and have the generative AI perform the adjustment of the analysis order.

[0082] The visualization unit can estimate the user's emotions and adjust the display method of the visualization based on the estimated user emotions. For example, if the user is relaxed, the visualization unit can display detailed graphs or charts. If the user is tense, the visualization unit can also display concise graphs or charts. Furthermore, if the user is excited, the visualization unit can display visually emphasized graphs or charts. In this way, by adjusting the display method of the visualization based on the user's emotions, more appropriate visualization results can be provided. 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 visualization unit is performed using generative AI. For example, the visualization unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the visualization.

[0083] The visualization unit can adjust the level of detail of the visualization based on the importance of the analysis results during visualization. For example, the visualization unit can display detailed graphs and charts for important analysis results. It can also display simplified graphs and charts for less important analysis results. Furthermore, the visualization unit can apply multiple visualization methods to high-importance data. This allows for efficient visualization by adjusting the level of detail of the visualization based on the importance of the analysis results. Some or all of the above processing in the visualization unit is performed using a generation AI. For example, the visualization unit can input analysis results into the generation AI and have the generation AI perform the adjustment of the level of detail of the visualization.

[0084] The visualization unit can apply different visualization algorithms depending on the category of the analysis results during visualization. For example, for facial expression data, the visualization unit can display a graph showing facial movement. It can also display a chart showing voice tone for audio data. Furthermore, for motion data, the visualization unit can display a graph showing changes in motion. This allows for more accurate visualization by applying visualization algorithms according to the category of the analysis results. Some or all of the above processing in the visualization unit is performed using a generation AI. For example, the visualization unit can input analysis results into the generation AI and have the generation AI execute the application of visualization algorithms.

[0085] The visualization unit can estimate the user's emotions and adjust the length of the visualization based on the estimated emotions. For example, if the user is in a hurry, the visualization unit can provide a visualization that can be understood in a short time. It can also provide a detailed visualization if the user is relaxed. Furthermore, if the user is excited, the visualization unit can provide a visually emphasized visualization. This allows for more appropriate visualization results by adjusting the length of the visualization based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the visualization unit is performed using generative AI. For example, the visualization unit can input user emotion data into the generative AI and have the generative AI adjust the length of the visualization.

[0086] The visualization unit can determine the visualization priority based on the timing of analysis result collection during visualization. For example, the visualization unit can prioritize the visualization of the most recent analysis results. It can also prioritize the visualization of analysis results from important meetings. Furthermore, the visualization unit can prioritize the visualization of current data while referring to past data. This enables efficient visualization by determining the visualization priority based on the timing of analysis result collection. Some or all of the above processing in the visualization unit is performed using a generation AI. For example, the visualization unit can input analysis results into the generation AI and have the generation AI determine the visualization priority.

[0087] The visualization unit can adjust the visualization order based on the relevance of the analysis results during visualization. For example, the visualization unit prioritizes the visualization of highly relevant analysis results. It can also postpone the visualization of less relevant analysis results. Furthermore, the visualization unit can dynamically adjust the visualization order based on highly relevant data. This allows for efficient visualization by adjusting the visualization order based on the relevance of the analysis results. Some or all of the above processing in the visualization unit is performed using a generation AI. For example, the visualization unit can input analysis results into the generation AI and have the generation AI perform the adjustment of the visualization order.

[0088] The feedback unit can estimate the user's emotions and adjust the way it presents feedback based on those emotions. For example, if the user is relaxed, the feedback unit can provide detailed feedback. If the user is tense, it can provide concise feedback. Furthermore, if the user is excited, it can provide visually emphasized feedback. This allows for more appropriate feedback to be provided by adjusting the way it presents feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI adjust the way it presents feedback.

[0089] The feedback unit can adjust the level of detail of the feedback based on the importance of the analysis results. For example, the feedback unit provides detailed feedback for important analysis results. It can also provide simplified feedback for less important analysis results. Furthermore, the feedback unit can apply multiple feedback methods to highly important data. This allows for efficient feedback by adjusting the level of detail of the feedback based on the importance of the analysis results. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the analysis results into AI and have the AI ​​adjust the level of detail of the feedback.

[0090] The feedback unit can apply different feedback algorithms depending on the category of the analysis result during the feedback process. For example, for facial expression data, the feedback unit can provide feedback indicating facial movement. It can also provide feedback indicating voice tone for audio data. Furthermore, it can provide feedback indicating changes in motion for motion data. This allows for more accurate feedback by applying feedback algorithms according to the category of the analysis result. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the analysis results into AI and have the AI ​​apply the feedback algorithm.

[0091] The feedback unit can estimate the user's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the user is in a hurry, the feedback unit can provide quick and easy-to-understand feedback. If the user is relaxed, the feedback unit can also provide detailed feedback. Furthermore, if the user is excited, the feedback unit can provide visually emphasized feedback. By adjusting the length of the feedback based on the user's emotions, more appropriate feedback can be provided. 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 feedback unit may be performed using AI or not. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI adjust the length of the feedback.

[0092] The feedback unit can determine the priority of feedback based on when the analysis results were collected. For example, the feedback unit can prioritize the most recent analysis results. It can also prioritize the analysis results of important meetings. Furthermore, the feedback unit can prioritize current data while referring to past data. This enables efficient feedback by determining the priority of feedback based on when the analysis results were collected. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the analysis results into AI and have the AI ​​determine the priority of feedback.

[0093] The feedback unit can adjust the order of feedback based on the relevance of the analysis results during the feedback process. For example, the feedback unit can prioritize providing feedback on highly relevant analysis results. It can also postpone providing feedback on less relevant analysis results. Furthermore, the feedback unit can dynamically adjust the order of feedback based on highly relevant data. This allows for efficient feedback by adjusting the order of feedback based on the relevance of the analysis results. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the analysis results into an AI and have the AI ​​adjust the order of feedback.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The online meeting evaluation system can also include a stress measurement unit to measure the user's stress level. This unit collects biometric data such as the user's heart rate and skin electrical response to estimate their stress level. For example, an elevated heart rate indicates that the user is stressed. Similarly, a high skin electrical response also indicates stress. This allows for understanding the user's stress level and using that information to improve presentations. Furthermore, the stress measurement unit can adjust feedback based on the user's stress level. For instance, if the user shows a high stress level, the feedback can be concise; if the user is relaxed, more detailed feedback can be provided.

[0096] The online meeting evaluation system can also include a speech analysis unit that analyzes the content of user statements. The speech analysis unit collects user statements as text data and analyzes them using natural language processing technology. For example, if a particular keyword frequently appears in a user's statements, the system can estimate the emotions associated with that keyword. Furthermore, by analyzing the tone and context of the statements, the system can more accurately grasp the user's intentions and emotions. This allows for the provision of more appropriate feedback based on the user's statements. In addition, the speech analysis unit can track user statements over time and analyze long-term trends. For example, it can understand how user statements have changed over time and use this information to improve presentations.

[0097] The online meeting evaluation system can also be equipped with a gesture analysis unit that analyzes the user's gestures. The gesture analysis unit analyzes the user's hand and body movements from video footage and determines the type and frequency of gestures. For example, if there are many gestures involving large hand movements, it can be determined that the user is speaking with confidence. Also, if there are many movements of swaying the body back and forth, it can be determined that the user is nervous. This allows for specific suggestions for improvement in the presentation based on the user's gestures. Furthermore, the gesture analysis unit can also track changes in the user's gestures over time and analyze long-term trends. For example, it can be used to understand how the user's gestures have changed over time and to improve presentation skills.

[0098] Online meeting evaluation systems can also be equipped with an eye-tracking unit that tracks the user's gaze. The eye-tracking unit analyzes the user's eye movements from video footage to determine the direction of their gaze and the points of fixation. For example, if a user is fixated on a specific part of a slide for a long time, it can be determined that that part is important. Also, if a user is frequently shifting their gaze, it can be determined that their concentration is decreasing. This allows for specific suggestions for improving the presentation based on the user's gaze. Furthermore, the eye-tracking unit can also track changes in the user's gaze over time and analyze long-term trends. For example, understanding how the user's gaze changes over time can be used to improve presentation skills.

[0099] The online meeting evaluation system can also include a speech sentiment analysis unit that analyzes the emotions behind user statements. This unit collects user statements as text data and analyzes emotions using natural language processing technology. For example, if a user's statements contain many positive keywords, it can be determined that the user is happy. Conversely, if there are many negative keywords, it can be determined that the user is dissatisfied. This allows for a more accurate understanding of emotions based on the user's statements. Furthermore, the speech sentiment analysis unit can track user statements over time and analyze changes in emotions. For example, understanding how user statements change over time can be used to improve presentations.

[0100] The online meeting evaluation system can further estimate the user's emotions and adjust the presentation content in real time based on those emotions. For example, if the user is nervous, the system can slow down the presentation and add content to help them relax. Conversely, if the user is excited, the system can speed up the presentation and add energetic content. This allows for dynamic adjustment of the presentation content according to the user's emotions, leading to more effective communication. Furthermore, the system can track changes in the user's emotions in real time and optimize the presentation. For example, it can understand how the user's emotions have changed over time and adjust the presentation content accordingly.

[0101] The online meeting evaluation system can further estimate the user's emotions and adjust the presentation slide design based on those emotions. For example, if the user is relaxed, the system can simplify the slide design and present the information clearly. Conversely, if the user is nervous, the system can visually emphasize the slide design to attract attention. This allows for dynamic adjustment of the slide design according to the user's emotions, resulting in a more effective presentation. Furthermore, the system can track changes in the user's emotions in real time and optimize the slide design accordingly. For example, it can understand how the user's emotions have changed over time and adjust the slide design as needed.

[0102] The online meeting evaluation system can further estimate the user's emotions and adjust the presentation's voice tone based on those estimates. For example, if the user is relaxed, the system can soften the voice tone to enhance the relaxing effect. Conversely, if the user is tense, the system can brighten the voice tone to enhance the tension-relieving effect. This allows for dynamic adjustment of the voice tone according to the user's emotions, resulting in a more effective presentation. Furthermore, the system can track changes in the user's emotions in real time and optimize the voice tone accordingly. For example, it can understand how the user's emotions have changed over time and adjust the voice tone as needed.

[0103] The online meeting evaluation system can further estimate the user's emotions and customize the presentation content based on those emotions. For example, if the user is relaxed, the system can provide detailed information and add content that will interest them. Conversely, if the user is nervous, the system can provide concise information and make it easier for them to understand. This allows for dynamic customization of the presentation content according to the user's emotions, leading to more effective communication. Furthermore, the system can track changes in the user's emotions in real time and optimize the presentation content. For example, it can understand how the user's emotions have changed over time and adjust the presentation content accordingly.

[0104] The online meeting evaluation system can further estimate the user's emotions and adjust the presentation pace based on those emotions. For example, if the user is relaxed, the system can slow down the presentation pace and provide more detailed explanations. Conversely, if the user is nervous, the system can speed up the presentation pace and provide more concise explanations. This allows for dynamic adjustment of the presentation pace according to the user's emotions, leading to more effective communication. Furthermore, the system can track changes in the user's emotions in real time and optimize the presentation pace. For example, it can understand how the user's emotions change over time and adjust the presentation pace accordingly.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The collection unit collects video and audio data. For example, it collects the other party's video and audio data in real time through an online meeting tool. The collection unit can also collect video and audio data in high resolution. For example, it can collect video at 1080p resolution and audio data at a sampling rate of 44.1kHz. Furthermore, the collection unit can collect video and audio data synchronously. For example, it can collect video and audio data simultaneously and maintain synchronization by adding a timestamp. Step 2: The analysis unit analyzes the data collected by the collection unit to determine the other person's emotions. For example, it analyzes video footage and audio data using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. The analysis unit analyzes facial expression data such as eyebrow movement, eye opening, and mouth movement. For example, it analyzes the angle of eyebrow rise, the frequency of eye opening and closing, and changes in mouth shape. It also analyzes audio data such as voice tone and pitch. For example, it analyzes voice pitch, volume, and sound variation patterns. Step 3: The visualization unit quantifies and visualizes the emotions determined by the analysis unit. For example, emotions such as joy, sadness, anger, and surprise are represented numerically and displayed as graphs or charts. Emotions can also be quantified based on their intensity and frequency of occurrence, and changes in emotions can be displayed over time. For example, a graph can show how the audience's emotions changed from the beginning to the end of a presentation. Step 4: The Feedback team provides feedback based on the results visualized by the Visualization team. For example, they specifically indicate which parts of the presentation pleased the audience and which parts saddened them. They show changes in the audience's emotions based on slide numbers and time divisions. They also suggest areas for improvement in the next meeting. For example, they provide specific advice on the content of the presentation, delivery style, slide design, etc.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the data collection unit, analysis unit, visualization unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects video images and audio data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using a generation AI. The visualization unit displays the analysis results as graphs or charts on the display 40A of the smart device 14. The feedback unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides specific feedback based on the analysis results. 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.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the data collection unit, analysis unit, visualization unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects video images and audio data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using generating AI. The visualization unit displays the analysis results as graphs or charts on the display of the smart glasses 214. The feedback unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides specific feedback based on the analysis results. 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.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the data collection unit, analysis unit, visualization unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects video images and audio data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using a generation AI. The visualization unit displays the analysis results as graphs or charts on the display 343 of the headset terminal 314. The feedback unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides specific feedback based on the analysis results. 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.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the data collection unit, analysis unit, visualization unit, and feedback unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects video images and audio data using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data using generated AI. The visualization unit displays the analysis results as graphs or charts on the display of the robot 414. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides specific feedback based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) A collection unit that collects video footage and audio data, An analysis unit analyzes the data collected by the aforementioned collection unit and determines the emotions of the other party, A visualization unit that quantifies and visualizes the emotions determined by the analysis unit, The system includes a feedback unit that provides feedback based on the results visualized by the visualization unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, It analyzes facial expression data such as eyebrow movement, eye opening, and mouth movement. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyzes audio data such as voice tone and pitch. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned visualization unit, Emotions such as joy, sadness, anger, and surprise are quantified and displayed as graphs and charts. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is Identify specifically which parts of the presentation pleased and which parts disappointed the audience, and propose areas for improvement in the next meeting. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of video and audio data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting video and audio data, the system analyzes the user's past meeting history to select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting video and audio data, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting video and audio data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting video and audio data, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the video footage and audio data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of video footage and audio data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the analysis priority is determined based on when the video footage and audio data were collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relationship between video footage and audio data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned visualization unit, It estimates the user's emotions and adjusts the display method of the visualization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned visualization unit, During visualization, adjust the level of detail based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned visualization unit, When visualizing the results, different visualization algorithms are applied depending on the category of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned visualization unit, It estimates the user's emotions and adjusts the length of the visualization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned visualization unit, When visualizing the data, prioritize visualizations based on when the analysis results were collected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned visualization unit, During visualization, adjust the order of visualizations based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is When providing feedback, adjust the level of detail in the feedback based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is During feedback, different feedback algorithms are applied depending on the category of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is It estimates the user's emotions and adjusts the length of the feedback based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is When providing feedback, we prioritize feedback based on when the analysis results were collected. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is During feedback, adjust the order of feedback based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 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 collection unit that collects video footage and audio data, An analysis unit analyzes the data collected by the aforementioned collection unit and determines the emotions of the other party, A visualization unit that quantifies and visualizes the emotions determined by the analysis unit, The system includes a feedback unit that provides feedback based on the results visualized by the visualization unit. A system characterized by the following features.

2. The aforementioned analysis unit, It analyzes facial expression data such as eyebrow movement, eye opening, and mouth movement. The system according to feature 1.

3. The aforementioned analysis unit, Analyzes audio data such as voice tone and pitch. The system according to feature 1.

4. The aforementioned visualization unit, Emotions such as joy, sadness, anger, and surprise are quantified and displayed as graphs and charts. The system according to feature 1.

5. The aforementioned feedback unit is Identify specifically which parts of the presentation pleased and which parts disappointed the audience, and propose areas for improvement in the next meeting. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of video and audio data collection based on the estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is When collecting video and audio data, the system analyzes the user's past meeting history to select the optimal collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting video and audio data, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

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