System

Abstract

An object of a system according to an embodiment is to estimate an interest or deep psychology of a partner during an online meeting.SOLUTION: A system includes an analysis unit, a digitizing unit, a graphing unit, and an estimation unit. The analysis part analyzes the video projected by the WEB camera in real time. The digitizing unit digitizes each facial expression based on the data analyzed by the analysis unit. The graphing unit graphs the data digitized by the digitizing unit. The estimation part analyzes the data graphed by the graphing part and estimates the interest and deep psychology of the other party.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to accurately grasp the interests and subconscious of the other person during an online meeting, and there is room for improvement.

[0005] The system according to the embodiment aims to estimate the interests and subconscious mind of the other person during an online meeting. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a quantification unit, a graphing unit, and an estimation unit. The analysis unit analyzes the video captured by the web camera in real time. The quantification unit quantifies each facial expression based on the data analyzed by the analysis unit. The graphing unit graphs the data quantified by the quantification unit. The estimation unit analyzes the data graphed by the graphing unit and estimates the interests and subconscious of the other party. [Effects of the Invention]

[0007] The system according to the embodiment can estimate the interests and subconscious mind of the other person during an online meeting. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses AI to analyze video captured by a webcam during an online meeting and quantify facial expressions such as "happiness," "sadness," and "surprise." The system graphs the changes in the numerical values ​​during and after the meeting, and uses the changes in facial expressions to explore the other party's interests and subconscious. This allows the effectiveness of the meeting to be verified and makes it easier to develop a strategy for the next meeting. This allows the system to verify the effectiveness of the online meeting and provide useful information for formulating a strategy for the next meeting. For example, the system analyzes video captured by a webcam in real time and quantifies each facial expression. Next, the changes in the numerical values ​​are graphed to visualize changes in facial expressions during the meeting. Furthermore, the graph is analyzed after the meeting to estimate the other party's interests and subconscious. This provides useful information for formulating a strategy for the next meeting.

[0029] The analysis system according to the embodiment includes an analysis unit, a quantification unit, a graphing unit, and an estimation unit. The analysis unit analyzes video captured by a webcam in real time. The analysis unit analyzes the video using, for example, an image analysis algorithm. The analysis unit can also improve the accuracy of the video analysis using AI. The analysis unit can analyze the video in real time and minimize delays. The quantification unit quantifies each facial expression based on data analyzed by the analysis unit. The quantification unit, for example, sets a quantification scale and quantifies each facial expression. The quantification unit can also improve the accuracy of the quantification using AI. The quantification unit can set a quantification standard and consistently quantify each facial expression. The graphing unit graphs the data quantified by the quantification unit. The graphing unit visualizes the data using graphs such as bar graphs and line graphs. The graphing unit can also improve the accuracy of the graphing using AI. The graphing unit can adjust the type of graph and the display method to make the data easier to understand visually. The estimation unit analyzes the data graphed by the graphing unit and estimates the interests and subconscious of the other party. The estimation unit analyzes the data using, for example, a psychological scale. The estimation unit can also improve the accuracy of the estimation using AI. Furthermore, the estimation unit can estimate the interests and subconscious of the other party in detail based on the data. As a result, the analysis system according to the embodiment can verify the effectiveness of the online meeting and provide useful information for formulating a strategy for the next meeting.

[0030] When analyzing video, the analysis unit can improve the accuracy of the analysis by referring to the user's past facial expression data. The analysis unit, for example, analyzes the current facial expression more accurately based on the user's past facial expression data. The analysis unit can also refer to the user's past facial expression data to identify specific facial expression patterns. The analysis unit can also use the user's past facial expression data to optimize the analysis algorithm. In this way, by referring to the past facial expression data, the accuracy of the current analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without using AI.

[0031] When analyzing the video, the analysis unit can adjust the analysis algorithm based on the user's facial angle and lighting conditions. The analysis unit, for example, detects the user's facial angle and applies the optimal analysis algorithm. The analysis unit can also detect lighting conditions in real time and adjust the analysis algorithm. The analysis unit can also improve analysis accuracy by combining the user's facial angle and lighting conditions. This improves analysis accuracy by taking the facial angle and lighting conditions into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without AI.

[0032] When analyzing the video, the analysis unit can improve the analysis accuracy by filtering background information of the user. For example, the analysis unit filters the background information of the user to improve the accuracy of face detection. The analysis unit can also remove background movement to improve the accuracy of facial expression analysis. The analysis unit can also adjust the brightness of the background to improve the accuracy of face recognition. In this way, filtering the background information improves the analysis accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0033] When analyzing the video, the analysis unit can correct the analysis results based on the user's geographical location information. The analysis unit corrects the analysis results based on, for example, the user's geographical location information. The analysis unit can also identify facial expression patterns in a specific region by taking the geographical location information into account. The analysis unit can also optimize the analysis algorithm using the geographical location information. This improves the accuracy of the analysis results by taking the geographical location information into account. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without using AI.

[0034] When analyzing the video, the analysis unit can analyze the user's social media activity and acquire related facial expression data. For example, the analysis unit analyzes the user's social media activity and acquires related facial expression data. The analysis unit can also improve the accuracy of facial expression analysis based on the content of social media posts. The analysis unit can also acquire facial expression data by referring to the activity of friends on social media. In this way, by analyzing social media activity, related facial expression data can be acquired and the analysis accuracy can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0035] When analyzing a video, the analysis unit can customize the analysis method by reflecting the user's past feedback. The analysis unit customizes the analysis method, for example, based on the user's past feedback. The analysis unit can also reflect the feedback and optimize the analysis algorithm. The analysis unit can also improve the analysis accuracy by utilizing the user's feedback. In this way, by reflecting the past feedback, the analysis method is customized and the analysis accuracy is improved. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0036] The quantification unit can adjust the level of detail of the quantification based on the importance of the facial expression when digitizing. For example, the quantification unit performs detailed quantification for important facial expressions. The quantification unit can also perform simplified quantification for less important facial expressions. The quantification unit can also adjust the accuracy of the quantification according to the importance of the facial expression. In this way, adjusting the level of detail of the quantification based on the importance of the facial expression improves the accuracy of the quantification. Some or all of the above-mentioned processing in the quantification unit may be performed using, for example, AI, or may be performed without using AI.

[0037] The quantification unit can apply different quantification algorithms depending on the category of the facial expression when quantifying the facial expression. For example, the quantification unit applies a dedicated quantification algorithm to a facial expression of joy. The quantification unit can also apply a dedicated quantification algorithm to a facial expression of sadness. The quantification unit can also apply a dedicated quantification algorithm to a facial expression of surprise. In this way, by applying different quantification algorithms depending on the category of the facial expression, the accuracy of the quantification is improved. Some or all of the above-mentioned processing in the quantification unit may be performed using, for example, AI, or may be performed without using AI.

[0038] The quantification unit can improve the accuracy of quantification by referring to the user's past quantification results during quantification. The quantification unit can improve the current accuracy of quantification, for example, based on the user's past quantification results. The quantification unit can also identify specific facial expression patterns by referring to the past quantification results. The quantification unit can also optimize the quantification algorithm by using the user's past quantification results. This improves the accuracy of quantification by referring to the past quantification results. Some or all of the above-mentioned processing in the quantification unit may be performed, for example, using AI or without AI.

[0039] The quantification unit can determine the priority of quantification based on the time when facial expressions occur during digitization. For example, the quantification unit prioritizes digitization of facial expressions that occur at important times. The quantification unit can also determine the order of quantification based on the time when facial expressions occur. The quantification unit can also adjust the accuracy of quantification by taking into account the time when facial expressions occur. In this way, by determining the priority of quantification based on the time when facial expressions occur, the accuracy of quantification is improved. Some or all of the above-mentioned processing in the quantification unit may be performed using, for example, AI, or may be performed without using AI.

[0040] The digitizing unit can adjust the order of digitization based on the relevance of facial expressions when digitizing. For example, the digitizing unit prioritizes digitization of highly relevant facial expressions. The digitizing unit can also adjust the order of digitization based on the relevance of facial expressions. The digitizing unit can also improve the accuracy of digitization by taking the relevance of facial expressions into consideration. Thus, by adjusting the order of digitization based on the relevance of facial expressions, the accuracy of digitization is improved. Some or all of the above-mentioned processing in the digitizing unit may be performed using, for example, AI, or may be performed without using AI.

[0041] The digitizing unit can adjust the use of technical terminology in the digitization according to the user's level of expertise during digitization. For example, the digitizing unit performs digitization using a lot of technical terminology for a user with high level of expertise. The digitizing unit can also perform digitization using simpler terminology for a user with low level of expertise. The digitizing unit can also adjust the way in which the digitization is expressed according to the user's level of expertise. This improves the user's understanding of the digitization by adjusting the use of technical terminology in the digitization according to the user's level of expertise. Some or all of the above-mentioned processing in the digitizing unit may be performed using, for example, AI, or may be performed without using AI.

[0042] The graphing unit can adjust the level of detail of the graph based on the importance of the numerical data when graphing. For example, the graphing unit displays a detailed graph for important numerical data. The graphing unit can also display a simplified graph for less important numerical data. The graphing unit can also adjust the precision of the graph according to the importance of the numerical data. This improves visual understanding by adjusting the level of detail of the graph based on the importance of the numerical data. Some or all of the above-described processing in the graphing unit may be performed, for example, using AI or without using AI.

[0043] The graphing unit can apply different graphing algorithms depending on the category of the numerical data when graphing. For example, the graphing unit applies a dedicated graphing algorithm to the numerical data of joy. The graphing unit can also apply a dedicated graphing algorithm to the numerical data of sadness. The graphing unit can also apply a dedicated graphing algorithm to the numerical data of surprise. In this way, by applying different graphing algorithms depending on the category of the numerical data, visual understanding is improved. Some or all of the above-mentioned processing in the graphing unit may be performed, for example, using AI or without using AI.

[0044] When graphing, the graphing unit can improve the accuracy of the graph by referring to the user's past graphing results. The graphing unit can improve the accuracy of the current graph, for example, based on the user's past graphing results. The graphing unit can also refer to the past graphing results and identify specific patterns. The graphing unit can also optimize the graphing algorithm by using the user's past graphing results. In this way, the accuracy of the graph can be improved by referring to the past graphing results. Some or all of the above-mentioned processing in the graphing unit can be performed, for example, using AI, or can be performed without using AI.

[0045] The graphing unit can determine the priority of graphs based on the time of occurrence of the numerical data when graphing. For example, the graphing unit prioritizes graphing of numerical data that occurred at important times. The graphing unit can also determine the order of graphing based on the time of occurrence of the numerical data. The graphing unit can also adjust the accuracy of graphing by taking into account the time of occurrence of the numerical data. In this way, determining the priority of graphs based on the time of occurrence of the numerical data improves visual understanding. Some or all of the above-mentioned processing in the graphing unit may be performed using, for example, AI, or may be performed without using AI.

[0046] The graphing unit can adjust the order of graphs based on the relevance of the numerical data when graphing. For example, the graphing unit prioritizes graphing highly relevant numerical data. The graphing unit can also adjust the order of graphing based on the relevance of the numerical data. The graphing unit can also improve the accuracy of graphing by taking the relevance of the numerical data into consideration. Thus, adjusting the order of graphs based on the relevance of the numerical data improves visual understanding. Some or all of the above-described processing in the graphing unit may be performed, for example, using AI, or may be performed without using AI.

[0047] When graphing, the graphing unit can adjust the use of technical terms in the graph according to the user's level of expertise. For example, the graphing unit displays a graph that uses a lot of technical terms for a user with high level of expertise. The graphing unit can also display a graph that uses simpler terms for a user with low level of expertise. The graphing unit can also adjust the way the graph is expressed according to the user's level of expertise. This improves visual understanding by adjusting the use of technical terms in the graph according to the user's level of expertise. Some or all of the above-mentioned processing in the graphing unit may be performed using, for example, AI, or may be performed without using AI.

[0048] The estimation unit can adjust the level of detail of the estimation based on the importance of the numerical data during estimation. For example, the estimation unit performs a detailed estimation for important numerical data. The estimation unit can also perform a simplified estimation for less important numerical data. The estimation unit can also adjust the accuracy of the estimation according to the importance of the numerical data. In this way, adjusting the level of detail of the estimation based on the importance of the numerical data improves the accuracy of the estimation. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI.

[0049] The estimation unit can apply different estimation algorithms depending on the category of the numerical data during estimation. For example, the estimation unit applies a dedicated estimation algorithm to numerical data of joy. The estimation unit can also apply a dedicated estimation algorithm to numerical data of sadness. The estimation unit can also apply a dedicated estimation algorithm to numerical data of surprise. In this way, by applying different estimation algorithms depending on the category of the numerical data, the accuracy of estimation is improved. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI.

[0050] The estimation unit can improve the accuracy of estimation by referring to the user's past estimation results during estimation. The estimation unit can improve the current estimation accuracy, for example, based on the user's past estimation results. The estimation unit can also refer to the past estimation results and identify specific patterns. The estimation unit can also optimize the estimation algorithm by using the user's past estimation results. In this way, the accuracy of estimation is improved by referring to the past estimation results. Some or all of the above-described processing in the estimation unit may be performed, for example, using AI or without using AI.

[0051] During estimation, the estimation unit can determine the priority of estimation based on the time of occurrence of the numerical data. For example, the estimation unit prioritizes estimation of numerical data that occurred at important times. The estimation unit can also determine the order of estimation based on the time of occurrence of the numerical data. The estimation unit can also adjust the accuracy of estimation by taking into account the time of occurrence of the numerical data. In this way, determining the priority of estimation based on the time of occurrence of the numerical data improves the accuracy of estimation. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI.

[0052] The estimation unit can adjust the order of estimation based on the relevance of the numerical data during estimation. For example, the estimation unit prioritizes estimation of highly relevant numerical data. The estimation unit can also adjust the order of estimation based on the relevance of the numerical data. The estimation unit can also improve the accuracy of estimation by taking the relevance of the numerical data into consideration. Thus, by adjusting the order of estimation based on the relevance of the numerical data, the accuracy of estimation is improved. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI.

[0053] During estimation, the estimation unit can adjust the use of technical terms in the estimation according to the user's level of expertise. For example, the estimation unit can make an estimation using a lot of technical terms for a user with high level of expertise. The estimation unit can also make an estimation using simpler terms for a user with low level of expertise. The estimation unit can also adjust the way the estimation is expressed according to the user's level of expertise. This improves the understanding of the estimation by adjusting the use of technical terms in the estimation according to the user's level of expertise. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI.

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

[0055] The analysis unit can refer to the user's past meeting data to optimize the progress of the current meeting. For example, it can predict reactions in the current meeting based on facial expression data and comments from past meetings. It can also analyze successful patterns from past meetings and apply them to the current meeting. Furthermore, by referring to past meeting data, it is possible to understand the user's preferences and interests and use this information to help with the progress of the meeting. In this way, by referring to past meeting data, it is possible to improve the effectiveness of the current meeting.

[0056] The analysis unit can obtain information about the user's occupation and industry and customize the analysis results. For example, if the user works in the medical industry, emphasis can be placed on medical-related facial expressions and reactions. Also, if the user works in the IT industry, emphasis can be placed on facial expressions and reactions that show technical interest. Furthermore, by analyzing information about occupation and industry, the user's expertise and interests can be understood and used to help progress in meetings. Thus, by analyzing information about occupation and industry, the analysis results can be more appropriately customized.

[0057] The analysis unit can correct the analysis results by taking into account the user's cultural background. For example, the same facial expression may indicate different emotions in different cultures, so taking cultural background into account can improve the accuracy of the analysis. It can also interpret the meaning of specific gestures and facial expressions based on cultural background. Furthermore, taking cultural background into account can understand the user's communication style and values, which can be used to progress the meeting. In this way, taking cultural background into account can improve the accuracy of the analysis results.

[0058] The analysis unit can obtain real-time feedback from users and instantly adjust the analysis results. For example, if a user provides feedback during a meeting, the analysis unit can adjust the analysis algorithm based on that feedback. The progress of the meeting can also be optimized based on real-time feedback. Furthermore, by reflecting user feedback, the reliability of the analysis results can be improved. As a result, by obtaining real-time feedback, the analysis results can be instantly adjusted and the effectiveness of the meeting can be improved.

[0059] The analysis unit can analyze the user's device usage and optimize the progress of the meeting. For example, if a user frequently uses a smartphone, the progress of the meeting can be adjusted taking into account the usage of that device. Also, if a user uses multiple devices, data obtained from each device can be integrated and analyzed. Furthermore, analyzing device usage can identify the user's interests and distractions and use this information to help progress the meeting. As a result, analyzing device usage can optimize the progress of the meeting and improve its effectiveness.

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

[0061] Step 1: The analysis unit analyzes the video captured by the webcam in real time. The analysis unit uses image analysis algorithms and AI to improve the accuracy of the video analysis and minimize delays. Step 2: The quantification unit quantifies each facial expression based on the data analyzed by the analysis unit. The quantification unit sets the scale and standards for quantification and can use AI to improve the accuracy of the quantification. Step 3: The graphing unit graphs the data quantified by the quantification unit. The graphing unit visualizes the data using graphs such as bar graphs and line graphs, and can use AI to improve the accuracy of the graphing. Step 4: The estimation unit analyzes the data graphed by the graphing unit and estimates the other party's interests and subconscious mind. The estimation unit can improve the accuracy of its estimation by using psychological scales and AI.

[0062] (Example 2) A system according to an embodiment of the present invention uses AI to analyze video captured by a webcam during an online meeting and quantify facial expressions such as "happiness," "sadness," and "surprise." The system graphs the changes in the numerical values ​​during and after the meeting, and uses the changes in facial expressions to explore the other party's interests and subconscious. This allows the effectiveness of the meeting to be verified and makes it easier to develop a strategy for the next meeting. This allows the system to verify the effectiveness of the online meeting and provide useful information for formulating a strategy for the next meeting. For example, the system analyzes video captured by a webcam in real time and quantifies each facial expression. Next, the changes in the numerical values ​​are graphed to visualize changes in facial expressions during the meeting. Furthermore, the graph is analyzed after the meeting to estimate the other party's interests and subconscious. This provides useful information for formulating a strategy for the next meeting.

[0063] The analysis system according to the embodiment includes an analysis unit, a quantification unit, a graphing unit, and an estimation unit. The analysis unit analyzes video captured by a webcam in real time. The analysis unit analyzes the video using, for example, an image analysis algorithm. The analysis unit can also improve the accuracy of the video analysis using AI. The analysis unit can analyze the video in real time and minimize delays. The quantification unit quantifies each facial expression based on data analyzed by the analysis unit. The quantification unit, for example, sets a quantification scale and quantifies each facial expression. The quantification unit can also improve the accuracy of the quantification using AI. The quantification unit can set a quantification standard and consistently quantify each facial expression. The graphing unit graphs the data quantified by the quantification unit. The graphing unit visualizes the data using graphs such as bar graphs and line graphs. The graphing unit can also improve the accuracy of the graphing using AI. The graphing unit can adjust the type of graph and the display method to make the data easier to understand visually. The estimation unit analyzes the data graphed by the graphing unit and estimates the interests and subconscious of the other party. The estimation unit analyzes the data using, for example, a psychological scale. The estimation unit can also improve the accuracy of the estimation using AI. Furthermore, the estimation unit can estimate the interests and subconscious of the other party in detail based on the data. As a result, the analysis system according to the embodiment can verify the effectiveness of the online meeting and provide useful information for formulating a strategy for the next meeting.

[0064] The analysis unit can estimate the user's emotions and adjust the timing of analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit can reduce the frequency of analysis and perform analysis when the user is relaxed. Furthermore, if the user is relaxed, the analysis unit can increase the frequency of analysis and acquire more detailed data. Furthermore, if the user is in a hurry, the analysis unit can focus on analyzing important moments. This improves the accuracy of analysis by adjusting the timing of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI.

[0065] When analyzing video, the analysis unit can improve the accuracy of the analysis by referring to the user's past facial expression data. The analysis unit, for example, analyzes the current facial expression more accurately based on the user's past facial expression data. The analysis unit can also refer to the user's past facial expression data to identify specific facial expression patterns. The analysis unit can also use the user's past facial expression data to optimize the analysis algorithm. In this way, by referring to the past facial expression data, the accuracy of the current analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without using AI.

[0066] When analyzing the video, the analysis unit can adjust the analysis algorithm based on the user's facial angle and lighting conditions. The analysis unit, for example, detects the user's facial angle and applies the optimal analysis algorithm. The analysis unit can also detect lighting conditions in real time and adjust the analysis algorithm. The analysis unit can also improve analysis accuracy by combining the user's facial angle and lighting conditions. This improves analysis accuracy by taking the facial angle and lighting conditions into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without AI.

[0067] When analyzing the video, the analysis unit can improve the analysis accuracy by filtering background information of the user. For example, the analysis unit filters the background information of the user to improve the accuracy of face detection. The analysis unit can also remove background movement to improve the accuracy of facial expression analysis. The analysis unit can also adjust the brightness of the background to improve the accuracy of face recognition. In this way, filtering the background information improves the analysis accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0068] The analysis unit can estimate the user's emotion and determine the priority of facial expressions to be analyzed based on the estimated user's emotion. For example, if the user is happy, the analysis unit can prioritize analyzing a happy facial expression. Also, if the user is sad, the analysis unit can prioritize analyzing a sad facial expression. Also, if the user is surprised, the analysis unit can prioritize analyzing a surprised facial expression. This improves the efficiency of analysis by determining the priority of facial expressions based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI.

[0069] When analyzing the video, the analysis unit can correct the analysis results based on the user's geographical location information. The analysis unit corrects the analysis results based on, for example, the user's geographical location information. The analysis unit can also identify facial expression patterns in a specific region by taking the geographical location information into account. The analysis unit can also optimize the analysis algorithm using the geographical location information. This improves the accuracy of the analysis results by taking the geographical location information into account. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without using AI.

[0070] When analyzing the video, the analysis unit can analyze the user's social media activity and acquire related facial expression data. For example, the analysis unit analyzes the user's social media activity and acquires related facial expression data. The analysis unit can also improve the accuracy of facial expression analysis based on the content of social media posts. The analysis unit can also acquire facial expression data by referring to the activity of friends on social media. In this way, by analyzing social media activity, related facial expression data can be acquired and the analysis accuracy can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0071] When analyzing a video, the analysis unit can customize the analysis method by reflecting the user's past feedback. The analysis unit customizes the analysis method, for example, based on the user's past feedback. The analysis unit can also reflect the feedback and optimize the analysis algorithm. The analysis unit can also improve the analysis accuracy by utilizing the user's feedback. In this way, by reflecting the past feedback, the analysis method is customized and the analysis accuracy is improved. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0072] The quantification unit can estimate the user's emotion and adjust the method of expressing the quantification based on the estimated user's emotion. For example, if the user is happy, the quantification unit uses a positive quantification expression. Also, if the user is sad, the quantification unit can use a negative quantification expression. Also, if the user is surprised, the quantification unit can quantify the degree of surprise. By adjusting the method of expressing the quantification based on the user's emotion, the accuracy of the quantification is improved. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the quantification unit may be performed, for example, using AI or without AI.

[0073] The quantification unit can adjust the level of detail of the quantification based on the importance of the facial expression when digitizing. For example, the quantification unit performs detailed quantification for important facial expressions. The quantification unit can also perform simplified quantification for less important facial expressions. The quantification unit can also adjust the accuracy of the quantification according to the importance of the facial expression. In this way, adjusting the level of detail of the quantification based on the importance of the facial expression improves the accuracy of the quantification. Some or all of the above-mentioned processing in the quantification unit may be performed using, for example, AI, or may be performed without using AI.

[0074] The quantification unit can apply different quantification algorithms depending on the category of the facial expression when quantifying the facial expression. For example, the quantification unit applies a dedicated quantification algorithm to a facial expression of joy. The quantification unit can also apply a dedicated quantification algorithm to a facial expression of sadness. The quantification unit can also apply a dedicated quantification algorithm to a facial expression of surprise. In this way, by applying different quantification algorithms depending on the category of the facial expression, the accuracy of the quantification is improved. Some or all of the above-mentioned processing in the quantification unit may be performed using, for example, AI, or may be performed without using AI.

[0075] The quantification unit can improve the accuracy of quantification by referring to the user's past quantification results during quantification. The quantification unit can improve the current accuracy of quantification, for example, based on the user's past quantification results. The quantification unit can also identify specific facial expression patterns by referring to the past quantification results. The quantification unit can also optimize the quantification algorithm by using the user's past quantification results. This improves the accuracy of quantification by referring to the past quantification results. Some or all of the above-mentioned processing in the quantification unit may be performed, for example, using AI or without AI.

[0076] The quantification unit can estimate the user's emotion and adjust the length of the quantification based on the estimated user's emotion. For example, if the user is happy, the quantification unit can lengthen the quantification. Also, if the user is sad, the quantification unit can shorten the quantification. Also, if the user is surprised, the quantification unit can adjust the length of the quantification according to the degree of surprise. In this way, by adjusting the length of the quantification based on the user's emotion, the accuracy of the quantification is improved. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the quantification unit can be performed, for example, using AI, or can be performed without using AI.

[0077] The quantification unit can determine the priority of quantification based on the time when facial expressions occur during digitization. For example, the quantification unit prioritizes digitization of facial expressions that occur at important times. The quantification unit can also determine the order of quantification based on the time when facial expressions occur. The quantification unit can also adjust the accuracy of quantification by taking into account the time when facial expressions occur. In this way, by determining the priority of quantification based on the time when facial expressions occur, the accuracy of quantification is improved. Some or all of the above-mentioned processing in the quantification unit may be performed using, for example, AI, or may be performed without using AI.

[0078] The digitizing unit can adjust the order of digitization based on the relevance of facial expressions when digitizing. For example, the digitizing unit prioritizes digitization of highly relevant facial expressions. The digitizing unit can also adjust the order of digitization based on the relevance of facial expressions. The digitizing unit can also improve the accuracy of digitization by taking the relevance of facial expressions into consideration. Thus, by adjusting the order of digitization based on the relevance of facial expressions, the accuracy of digitization is improved. Some or all of the above-mentioned processing in the digitizing unit may be performed using, for example, AI, or may be performed without using AI.

[0079] The digitizing unit can adjust the use of technical terminology in the digitization according to the user's level of expertise during digitization. For example, the digitizing unit performs digitization using a lot of technical terminology for a user with high level of expertise. The digitizing unit can also perform digitization using simpler terminology for a user with low level of expertise. The digitizing unit can also adjust the way in which the digitization is expressed according to the user's level of expertise. This improves the user's understanding of the digitization by adjusting the use of technical terminology in the digitization according to the user's level of expertise. Some or all of the above-mentioned processing in the digitizing unit may be performed using, for example, AI, or may be performed without using AI.

[0080] The graphing unit can estimate the user's emotions and adjust the graph display method based on the estimated user emotions. For example, if the user is happy, the graphing unit can display a graph in bright colors. If the user is sad, the graphing unit can also display a graph in subdued colors. If the user is surprised, the graphing unit can also display a visually stimulating graph. This improves visual understanding by adjusting the graph display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the graphing unit can be performed using, for example, AI, or without AI.

[0081] The graphing unit can adjust the level of detail of the graph based on the importance of the numerical data when graphing. For example, the graphing unit displays a detailed graph for important numerical data. The graphing unit can also display a simplified graph for less important numerical data. The graphing unit can also adjust the precision of the graph according to the importance of the numerical data. This improves visual understanding by adjusting the level of detail of the graph based on the importance of the numerical data. Some or all of the above-described processing in the graphing unit may be performed, for example, using AI or without using AI.

[0082] The graphing unit can apply different graphing algorithms depending on the category of the numerical data when graphing. For example, the graphing unit applies a dedicated graphing algorithm to the numerical data of joy. The graphing unit can also apply a dedicated graphing algorithm to the numerical data of sadness. The graphing unit can also apply a dedicated graphing algorithm to the numerical data of surprise. In this way, by applying different graphing algorithms depending on the category of the numerical data, visual understanding is improved. Some or all of the above-mentioned processing in the graphing unit may be performed, for example, using AI or without using AI.

[0083] When graphing, the graphing unit can improve the accuracy of the graph by referring to the user's past graphing results. The graphing unit can improve the accuracy of the current graph, for example, based on the user's past graphing results. The graphing unit can also refer to the past graphing results and identify specific patterns. The graphing unit can also optimize the graphing algorithm by using the user's past graphing results. In this way, the accuracy of the graph can be improved by referring to the past graphing results. Some or all of the above-mentioned processing in the graphing unit can be performed, for example, using AI, or can be performed without using AI.

[0084] The graphing unit can estimate the user's emotion and adjust the length of the graph based on the estimated user's emotion. For example, if the user is happy, the graphing unit can display a longer graph. Also, if the user is sad, the graphing unit can display a shorter graph. Also, if the user is surprised, the graphing unit can adjust the length of the graph according to the degree of surprise. In this way, adjusting the length of the graph based on the user's emotion improves visual understanding. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the graphing unit can be performed, for example, using AI, or can be performed without using AI.

[0085] The graphing unit can determine the priority of graphs based on the time of occurrence of the numerical data when graphing. For example, the graphing unit prioritizes graphing of numerical data that occurred at important times. The graphing unit can also determine the order of graphing based on the time of occurrence of the numerical data. The graphing unit can also adjust the accuracy of graphing by taking into account the time of occurrence of the numerical data. In this way, determining the priority of graphs based on the time of occurrence of the numerical data improves visual understanding. Some or all of the above-mentioned processing in the graphing unit may be performed using, for example, AI, or may be performed without using AI.

[0086] The graphing unit can adjust the order of graphs based on the relevance of the numerical data when graphing. For example, the graphing unit prioritizes graphing highly relevant numerical data. The graphing unit can also adjust the order of graphing based on the relevance of the numerical data. The graphing unit can also improve the accuracy of graphing by taking the relevance of the numerical data into consideration. Thus, adjusting the order of graphs based on the relevance of the numerical data improves visual understanding. Some or all of the above-described processing in the graphing unit may be performed, for example, using AI, or may be performed without using AI.

[0087] When graphing, the graphing unit can adjust the use of technical terms in the graph according to the user's level of expertise. For example, the graphing unit displays a graph that uses a lot of technical terms for a user with high level of expertise. The graphing unit can also display a graph that uses simpler terms for a user with low level of expertise. The graphing unit can also adjust the way the graph is expressed according to the user's level of expertise. This improves visual understanding by adjusting the use of technical terms in the graph according to the user's level of expertise. Some or all of the above-mentioned processing in the graphing unit may be performed using, for example, AI, or may be performed without using AI.

[0088] The estimation unit can estimate the user's emotion and adjust the estimation method based on the estimated user's emotion. For example, if the user is happy, the estimation unit can use a positive estimation method. Also, if the user is sad, the estimation unit can use a negative estimation method. Also, if the user is surprised, the estimation unit can use an estimation method that takes the degree of surprise into consideration. By adjusting the estimation method based on the user's emotion, the accuracy of estimation is improved. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the estimation unit can be performed, for example, using AI or without AI.

[0089] The estimation unit can adjust the level of detail of the estimation based on the importance of the numerical data during estimation. For example, the estimation unit performs a detailed estimation for important numerical data. The estimation unit can also perform a simplified estimation for less important numerical data. The estimation unit can also adjust the accuracy of the estimation according to the importance of the numerical data. In this way, adjusting the level of detail of the estimation based on the importance of the numerical data improves the accuracy of the estimation. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI.

[0090] The estimation unit can apply different estimation algorithms depending on the category of the numerical data during estimation. For example, the estimation unit applies a dedicated estimation algorithm to numerical data of joy. The estimation unit can also apply a dedicated estimation algorithm to numerical data of sadness. The estimation unit can also apply a dedicated estimation algorithm to numerical data of surprise. In this way, by applying different estimation algorithms depending on the category of the numerical data, the accuracy of estimation is improved. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI.

[0091] The estimation unit can improve the accuracy of estimation by referring to the user's past estimation results during estimation. The estimation unit can improve the current estimation accuracy, for example, based on the user's past estimation results. The estimation unit can also refer to the past estimation results and identify specific patterns. The estimation unit can also optimize the estimation algorithm by using the user's past estimation results. In this way, the accuracy of estimation is improved by referring to the past estimation results. Some or all of the above-described processing in the estimation unit may be performed, for example, using AI or without using AI.

[0092] The estimation unit can estimate the user's emotion and adjust the length of the estimation based on the estimated user emotion. For example, if the user is happy, the estimation unit can make a longer estimation. Also, if the user is sad, the estimation unit can make a shorter estimation. Also, if the user is surprised, the estimation unit can adjust the length of the estimation according to the degree of surprise. In this way, by adjusting the length of the estimation based on the user's emotion, the accuracy of the estimation is improved. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the estimation unit can be performed, for example, using AI or without using AI.

[0093] During estimation, the estimation unit can determine the priority of estimation based on the time of occurrence of the numerical data. For example, the estimation unit prioritizes estimation of numerical data that occurred at important times. The estimation unit can also determine the order of estimation based on the time of occurrence of the numerical data. The estimation unit can also adjust the accuracy of estimation by taking into account the time of occurrence of the numerical data. In this way, determining the priority of estimation based on the time of occurrence of the numerical data improves the accuracy of estimation. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI.

[0094] The estimation unit can adjust the order of estimation based on the relevance of the numerical data during estimation. For example, the estimation unit prioritizes estimation of highly relevant numerical data. The estimation unit can also adjust the order of estimation based on the relevance of the numerical data. The estimation unit can also improve the accuracy of estimation by taking the relevance of the numerical data into consideration. Thus, by adjusting the order of estimation based on the relevance of the numerical data, the accuracy of estimation is improved. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI.

[0095] During estimation, the estimation unit can adjust the use of technical terms in the estimation according to the user's level of expertise. For example, the estimation unit can make an estimation using a lot of technical terms for a user with high level of expertise. The estimation unit can also make an estimation using simpler terms for a user with low level of expertise. The estimation unit can also adjust the way the estimation is expressed according to the user's level of expertise. This improves the understanding of the estimation by adjusting the use of technical terms in the estimation according to the user's level of expertise. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned analysis unit, quantification unit, graphing unit, and estimation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit can analyze video captured by a webcam using the camera 42 of the smart device 14 in real time. The analysis unit can also be realized by the specific processing unit 290 of the data processing device 12 and analyzes the video using an image analysis algorithm. The quantification unit quantifies each facial expression based on the analyzed data by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The graphing unit graphs the quantified data by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The estimation unit analyzes the graphed data by the specific processing unit 290 of the data processing device 12 and estimates the other party's interests and subconscious mind. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described analysis unit, quantification unit, graphing unit, and estimation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit can analyze video captured by a webcam in real time using the camera 42 of the smart glasses 214. The analysis unit can also be realized by the specific processing unit 290 of the data processing device 12, and analyzes the video using an image analysis algorithm. The quantification unit quantifies each facial expression based on the analyzed data by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The graphing unit graphs the quantified data by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The estimation unit analyzes the graphed data by the specific processing unit 290 of the data processing device 12 and estimates the other party's interests and subconscious mind. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, quantification unit, graphing unit, and estimation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the analysis unit can analyze video captured by a webcam using the camera 42 of the headset-type terminal 314 in real time. The analysis unit is also realized by the specific processing unit 290 of the data processing device 12 and analyzes the video using an image analysis algorithm. The quantification unit quantifies each facial expression based on the analyzed data by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. The graphing unit graphs the quantified data by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. The estimation unit analyzes the graphed data by the specific processing unit 290 of the data processing device 12 and estimates the other party's interests and subconscious mind. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned analysis unit, quantification unit, graphing unit, and estimation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit can analyze video captured by a webcam using the camera 42 of the robot 414 in real time. The analysis unit can also be realized by the specific processing unit 290 of the data processing device 12 and analyzes the video using an image analysis algorithm. The quantification unit quantifies each facial expression based on the analyzed data by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The graphing unit graphs the quantified data by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The estimation unit analyzes the graphed data by the specific processing unit 290 of the data processing device 12 and estimates the other party's interests and subconscious mind.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The analysis unit can also analyze the user's voice data and infer emotions from the tone and speed of the voice. For example, if the user's voice is high-pitched, it may indicate joy or excitement. On the other hand, if the voice is low and slow, it may indicate sadness or fatigue. Furthermore, by analyzing the voice data, it is possible to estimate the user's stress level and use this information to help progress the MTG. This allows for a more detailed understanding of the user's emotions and state by analyzing the voice data.

[0098] The analysis unit can also acquire the user's biometric information and analyze data such as heart rate and skin galvanic response. For example, an elevated heart rate can indicate tension or excitement. A high skin galvanic response can indicate stress or anxiety. Furthermore, analyzing the biometric information can estimate the user's health condition and use this information to help progress through the MTG. This allows for a more detailed understanding of the user's emotions and condition.

[0099] The analysis unit can also analyze the user's gestures and posture to understand non-verbal communication. For example, if the user frequently moves their hands, it can indicate excitement or tension. If the user maintains a relaxed posture, it can indicate a sense of security or satisfaction. Furthermore, analyzing gestures and posture can infer the user's intentions and emotions, which can be used to advance the meeting. By analyzing non-verbal communication, it is possible to grasp the user's emotions and state in more detail.

[0100] The analysis unit can also acquire user gaze data and estimate user interests and concerns from gaze movements. For example, if a user focuses their gaze on a particular document or slide, it can indicate a strong interest in that content. Furthermore, if the gaze moves frequently, it can indicate distraction or a loss of interest. Furthermore, analyzing gaze data can estimate the user's level of understanding and interest, which can be used to guide the progress of meetings. Thus, analyzing gaze data can provide a more detailed understanding of user interests and concerns.

[0101] The analysis unit can also obtain the user's keyboard and mouse operation data and infer emotions from the frequency and speed of operation. For example, if a user types frequently on the keyboard, it can indicate tension or impatience. On the other hand, if the mouse is operated slowly, it can indicate relaxation. Furthermore, analyzing the operation data can estimate the user's concentration level and stress level, which can be used to help progress the MTG. In this way, analyzing the operation data can provide a more detailed understanding of the user's emotions and state.

[0102] The analysis unit can refer to the user's past meeting data to optimize the progress of the current meeting. For example, it can predict reactions in the current meeting based on facial expression data and comments from past meetings. It can also analyze successful patterns from past meetings and apply them to the current meeting. Furthermore, by referring to past meeting data, it is possible to understand the user's preferences and interests and use this information to help with the progress of the meeting. In this way, by referring to past meeting data, it is possible to improve the effectiveness of the current meeting.

[0103] The analysis unit can obtain information about the user's occupation and industry and customize the analysis results. For example, if the user works in the medical industry, emphasis can be placed on medical-related facial expressions and reactions. Also, if the user works in the IT industry, emphasis can be placed on facial expressions and reactions that show technical interest. Furthermore, by analyzing information about occupation and industry, the user's expertise and interests can be understood and used to help progress in meetings. Thus, by analyzing information about occupation and industry, the analysis results can be more appropriately customized.

[0104] The analysis unit can correct the analysis results by taking into account the user's cultural background. For example, the same facial expression may indicate different emotions in different cultures, so taking cultural background into account can improve the accuracy of the analysis. It can also interpret the meaning of specific gestures and facial expressions based on cultural background. Furthermore, taking cultural background into account can understand the user's communication style and values, which can be used to progress the meeting. In this way, taking cultural background into account can improve the accuracy of the analysis results.

[0105] The analysis unit can obtain real-time feedback from users and instantly adjust the analysis results. For example, if a user provides feedback during a meeting, the analysis unit can adjust the analysis algorithm based on that feedback. The progress of the meeting can also be optimized based on real-time feedback. Furthermore, by reflecting user feedback, the reliability of the analysis results can be improved. As a result, by obtaining real-time feedback, the analysis results can be instantly adjusted and the effectiveness of the meeting can be improved.

[0106] The analysis unit can analyze the user's device usage and optimize the progress of the meeting. For example, if a user frequently uses a smartphone, the progress of the meeting can be adjusted taking into account the usage of that device. Also, if a user uses multiple devices, data obtained from each device can be integrated and analyzed. Furthermore, analyzing device usage can identify the user's interests and distractions and use this information to help progress the meeting. As a result, analyzing device usage can optimize the progress of the meeting and improve its effectiveness.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The analysis unit analyzes the video captured by the webcam in real time. The analysis unit uses image analysis algorithms and AI to improve the accuracy of the video analysis and minimize delays. Step 2: The quantification unit quantifies each facial expression based on the data analyzed by the analysis unit. The quantification unit sets the scale and standards for quantification and can use AI to improve the accuracy of the quantification. Step 3: The graphing unit graphs the data quantified by the quantification unit. The graphing unit visualizes the data using graphs such as bar graphs and line graphs, and can use AI to improve the accuracy of the graphing. Step 4: The estimation unit analyzes the data graphed by the graphing unit and estimates the other party's interests and subconscious mind. The estimation unit can improve the accuracy of its estimation by using psychological scales and AI.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0153] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0154] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] 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.

[0172] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0174] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0180] [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis unit that analyzes the images captured by the web camera in real time, a digitization unit that digitizes each facial expression based on the data analyzed by the analysis unit; a graphing unit that graphs the data digitized by the digitizing unit; a unit for analyzing the data graphed by the graphing unit and estimating the interests and deep psychology of the other party. A system characterized by:

2. The analysis unit Estimate the user's emotions and adjust the timing of analysis based on the estimated user emotions.

2. The system of claim 1.

3. The analysis unit When analyzing video, the accuracy of the analysis is improved by referencing the user's past facial expression data.

2. The system of claim 1.

4. The analysis unit When analyzing video, the analysis algorithm adjusts based on the angle of the user's face and lighting conditions 2. The system of claim 1.

5. The analysis unit When analyzing video, filter out user background information to improve analysis accuracy.

2. The system of claim 1.

6. The analysis unit Estimate the user's emotions and determine the priority of facial expressions to be analyzed based on the estimated user emotions.

2. The system of claim 1.

7. The analysis unit When analyzing video, the analysis results are corrected based on the user's geographical location information.

2. The system of claim 1.

8. The analysis unit When analyzing the video, the system analyzes the user's social media activity and obtains relevant facial expression data.

2. The system of claim 1.

Images