System

Abstract

An object of a system according to an embodiment is to analyze biological data of participants and automatically organize an effective breakout room.SOLUTION: A system includes a biological data collection section, an analysis section, a classification section, and an organization section. The biological data collection unit collects biological data such as facial expressions and voice quality from a camera or a microphone of a terminal of a participant. The analysis section analyzes the biological data collected by the biological data collection section. The classification component classifies the participants by character on the basis of the data analyzed by the analysis component. The organization unit automatically organizes a breakout room based on the characters classified by the classification unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to properly classify participants in web conferences and organize effective breakout rooms.

[0005] The system according to the embodiment aims to analyze the biological data of participants and automatically organize effective breakout rooms. [Means for solving the problem]

[0006] The system according to the embodiment includes a biological data collection unit, an analysis unit, a classification unit, and an organization unit. The biological data collection unit collects biological data such as facial expressions and voice quality from the cameras and microphones of the participants' devices. The analysis unit analyzes the biological data collected by the biological data collection unit. The classification unit classifies the participants by character based on the data analyzed by the analysis unit. The organization unit automatically organizes breakout rooms based on the characters classified by the classification unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the biological data of participants and automatically organize effective breakout rooms. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 web conferencing system according to an embodiment of the present invention uses artificial intelligence (AI) to analyze biometric data such as facial expressions and voice quality obtained from the cameras and microphones on participants' devices, classifying participants by character, and automatically organizing breakout rooms to promote lively discussions. This allows the web conferencing system to maximize the characteristics of participants and organize breakout rooms to promote lively discussions.

[0029] A web conferencing system according to an embodiment includes a biometric data collection unit, an analysis unit, a classification unit, and an organization unit. The biometric data collection unit collects biometric data, such as facial expressions and voice quality, from the cameras and microphones of participants' devices. For example, the camera captures participants' facial expressions in real time, and the microphone records their vocal tone and tempo. The biometric data collection unit can also collect ambient sounds and background changes. The analysis unit analyzes the biometric data collected by the biometric data collection unit. For example, the analysis unit determines whether a participant is in a good mood based on the frequency of smiles and the tone of voice. The analysis unit can also predict the performance and behavioral patterns of each participant by referencing the participants' past meeting data. The classification unit classifies participants by character based on the data analyzed by the analysis unit. For example, the classification may include a leader type, a supporter type, an ideation type, etc. The organization unit automatically organizes breakout rooms based on the characters classified by the classification unit. For example, a balanced allocation of leader-type and supporter-type participants can ensure smooth discussions. As a result, the web conferencing system according to the embodiment can make the most of the characteristics of the participants and organize breakout rooms to promote lively discussions.

[0030] The biometric data collection unit can capture participants' facial expressions in real time through a camera and record their voice tone and tempo through a microphone. For example, the biometric data collection unit can use a camera and microphone installed on the participants' devices to collect ambient sounds and background changes in real time. For example, it can analyze the brightness and volume of the background to evaluate the atmosphere and concentration level of the meeting. This allows participants' facial expressions, voice tone, and tempo to be collected in real time.

[0031] The analysis unit can determine whether participants are in a happy mood based on the frequency of smiles and tone of voice. For example, the analysis unit collects past meeting data of participants, and the AI ​​analyzes that data to predict the performance and behavioral patterns of each participant. For example, it predicts speaking tendencies in the next meeting based on the frequency and content of past comments. This makes it possible to determine whether participants are in a happy mood.

[0032] The programming unit can ensure that discussions proceed smoothly by arranging a balance between leader-type participants and supporter-type participants. The programming unit, for example, uses an emotion estimation function to monitor changes in participants' emotions in real time and collect emotional waves as data. For example, the programming unit analyzes participants' facial expressions and voice quality and uses the emotion estimation function to monitor changes in emotions in real time. This allows discussions to proceed smoothly.

[0033] The scheduling department can monitor biological data in real time during the meeting and adjust the formation of breakout rooms as needed. For example, the scheduling department collects biometric information such as heart rate and electrodermal activity from wearable devices worn by participants, and analyzes the data using AI. For example, it can evaluate participants' levels of tension or relaxation based on changes in heart rate. This allows the formation of breakout rooms to be adjusted in real time.

[0034] The analysis unit can record the content of discussions after the meeting and analyze which group was the most active in the discussion. For example, the analysis unit collects participants' chat history and browsing history, and the AI ​​analyzes that data to evaluate their level of interest and concentration during the meeting. For example, if participants are browsing other websites during the meeting, it will determine that their concentration level is low. This allows the content of discussions to be recorded and analyzed after the meeting has ended.

[0035] The analysis unit can analyze which character combination was most effective. For example, the analysis unit uses an emotion estimation function to analyze the facial expressions and voice quality of participants and evaluate stress levels during the meeting in real time. For example, it detects facial expressions of tension or anxiety and calculates stress levels. This allows the effectiveness of character combinations to be analyzed.

[0036] The biological data collection unit can collect changes in the surrounding environmental sounds and background in real time using a camera and a microphone. The biological data collection unit collects changes in the surrounding environmental sounds and background in real time using, for example, a camera and a microphone mounted on the participant's device. For example, the biological data collection unit analyzes the brightness and volume of the background to evaluate the atmosphere and concentration level of the meeting. This allows changes in the surrounding environmental sounds and background to be collected in real time.

[0037] The biological data collection unit can refer to past meeting data and predict the performance and behavioral patterns of individual participants. For example, the biological data collection unit collects past meeting data of participants, and the AI ​​analyzes that data to predict the performance and behavioral patterns of individual participants. For example, it predicts speaking tendencies in the next meeting based on the frequency and content of past comments. This makes it possible to predict performance and behavioral patterns based on participants' past meeting data.

[0038] The biological data collection unit can collect biometric information from wearable devices in addition to the participants' biological data, and perform more detailed analysis. The biological data collection unit collects biometric information, such as heart rate and electrodermal activity, from the wearable devices worn by the participants, and analyzes the data using AI. For example, the change in heart rate can be used to evaluate the participant's level of tension or relaxation. This allows for more detailed analysis of the collected biometric information from the wearable devices.

[0039] The biometric data collection unit can collect participants' digital activities and analyze their levels of interest and concentration during the meeting. For example, the biometric data collection unit collects participants' chat history and browsing history, and the AI ​​analyzes that data to evaluate their levels of interest and concentration during the meeting. For example, if participants are browsing other websites during the meeting, it can determine that their level of concentration is low. This allows the digital activities of participants to be collected and their levels of interest and concentration during the meeting to be analyzed.

[0040] The analysis unit analyzes gesture and posture data in addition to participants' facial expressions and voice quality, enabling more accurate character classification. For example, the analysis unit collects gesture and posture data in addition to participants' facial expressions and voice quality, and AI analyzes that data to perform character classification. For example, it analyzes hand movements and posture changes to evaluate participants' personalities and emotional states. This allows for more accurate character classification by analyzing gesture and posture data.

[0041] The analysis unit can combine psychological profiling techniques with the character classification of the participants to perform a more detailed personality analysis. For example, the analysis unit can combine psychological profiling techniques with the character classification of the participants, and the AI ​​can analyze the data to perform a more detailed personality analysis. For example, the personality of the participants can be evaluated based on the results of a psychological test. This allows for a more detailed personality analysis to be performed by combining psychological profiling techniques.

[0042] The analysis unit can share the character classification results of the participants with other conference participants and provide feedback to deepen mutual understanding. The analysis unit, for example, builds a system that shares the character classification results of the participants with other conference participants and provides feedback to deepen mutual understanding. For example, the compatibility between participants is evaluated based on the character classification results. This allows the character classification results to be shared and feedback to deepen mutual understanding to be provided.

[0043] When forming breakout rooms, the formation unit can form optimal groups by taking into account participants' past cooperative relationships and compatibility data. For example, the formation unit collects participants' past cooperative relationships and compatibility data, and the AI ​​analyzes that data to form optimal groups. For example, participants who have had good cooperative relationships in the past can be placed in the same group. This allows optimal groups to be formed by taking into account past cooperative relationships and compatibility data.

[0044] After the breakout rooms are organized, the organization department can monitor the performance of the participants in real time and reallocate members as necessary. For example, the organization department builds a system in which, after the breakout rooms are organized, AI monitors the performance of the participants in real time and reallocates members as necessary. For example, if the discussion is stagnating, members will be reallocated. This makes it possible to monitor performance in real time and reallocate members as necessary.

[0045] When organizing breakout rooms, the Planning Department can combine participants from different industries and specialties to draw out new perspectives and ideas. For example, the Planning Department can build a system that combines participants from different industries and specialties to draw out new perspectives and ideas. For example, technical and design participants can be placed in the same group. This allows participants from different industries and specialties to be combined to draw out new perspectives and ideas.

[0046] After the breakout rooms are organized, the organization department can have AI automatically propose agendas and issues to each group, supporting the direction of the discussion. For example, the organization department will build a system in which, after the breakout rooms are organized, AI automatically proposes agendas and issues to each group. For example, it will propose agendas based on the characteristics of the group. This allows AI to automatically propose agendas and issues to each group, supporting the direction of the discussion.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] Web conferencing systems can also collect participants' digital activities and analyze their level of interest and concentration during the meeting. For example, participants' chat history and browsing history can be collected, and AI can analyze that data to evaluate their level of interest and concentration during the meeting. If participants browse other websites during the meeting, it can be determined that they are not concentrating. Also, if participants chat a lot about a particular topic, it can be determined that they are highly interested in that topic. This allows for more effective management of the meeting's progress.

[0049] Web conferencing systems can also collect and analyze participants' gesture and posture data. For example, participants' hand movements and changes in posture can be captured by a camera, and AI can analyze the data to evaluate their personalities and emotional states. If participants' hand movements are active, it can be determined that they are actively participating in the discussion. If their posture is leaning forward, it can be determined that they are concentrating. This allows for more accurate character classification.

[0050] The web conferencing system can also take into account participants' past cooperative relationships and compatibility data to form optimal groups. For example, by placing participants who have had good cooperative relationships in the past in the same group, discussions can proceed smoothly. It can also combine participants who have good compatibility based on performance data from past meetings. This allows optimal groups to be formed taking into account past cooperative relationships and compatibility data.

[0051] Web conferencing systems can also bring together participants from different industries and specialties to draw out new perspectives and ideas. For example, placing technical and design participants in the same group can encourage the exchange of opinions from different perspectives. Similarly, combining marketing and engineering experts can lead to new ideas for product development. This allows participants from different industries and specialties to come together and draw out new perspectives and ideas.

[0052] Web conferencing systems can also record the content of discussions after a meeting and analyze which group had the most active discussions. For example, the frequency and content of participants' comments can be recorded, and AI can analyze the data to evaluate the liveliness of the discussion. Groups that speak frequently can be determined to have had lively discussions. The quality of the discussion can also be evaluated by evaluating the diversity and depth of the comments. This makes it possible to record and analyze the content of discussions after a meeting has ended.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The biometric data collection unit collects biometric data such as facial expressions and voice quality from the participants' devices' cameras and microphones. For example, the camera captures participants' facial expressions in real time, and the microphone records their vocal tone and tempo. The biometric data collection unit can also collect ambient sounds and background changes. Step 2: The analysis unit analyzes the biological data collected by the biological data collection unit. For example, it determines whether participants are in a good mood based on the frequency of smiles and tone of voice. The analysis unit can also refer to past meeting data of participants to predict the performance and behavioral patterns of individual participants. Step 3: The classification unit classifies the participants into characters based on the data analyzed by the analysis unit, such as leader type, supporter type, idea man type, etc. Step 4: The organization unit automatically organizes breakout rooms based on the characters classified by the classification unit. For example, by balancing leader-type participants with supporter-type participants, the discussion can proceed smoothly.

[0055] (Example 2) A web conferencing system according to an embodiment of the present invention uses artificial intelligence (AI) to analyze biometric data such as facial expressions and voice quality obtained from the cameras and microphones on participants' devices, classifying participants by character, and automatically organizing breakout rooms to promote lively discussions. This allows the web conferencing system to maximize the characteristics of participants and organize breakout rooms to promote lively discussions.

[0056] A web conferencing system according to an embodiment includes a biometric data collection unit, an analysis unit, a classification unit, and an organization unit. The biometric data collection unit collects biometric data, such as facial expressions and voice quality, from the cameras and microphones of participants' devices. For example, the camera captures participants' facial expressions in real time, and the microphone records their vocal tone and tempo. The biometric data collection unit can also collect ambient sounds and background changes. The analysis unit analyzes the biometric data collected by the biometric data collection unit. For example, the analysis unit determines whether a participant is in a good mood based on the frequency of smiles and the tone of voice. The analysis unit can also predict the performance and behavioral patterns of each participant by referencing the participants' past meeting data. The classification unit classifies participants by character based on the data analyzed by the analysis unit. For example, the classification may include a leader type, a supporter type, an ideation type, etc. The organization unit automatically organizes breakout rooms based on the characters classified by the classification unit. For example, a balanced allocation of leader-type and supporter-type participants can ensure smooth discussions. As a result, the web conferencing system according to the embodiment can make the most of the characteristics of the participants and organize breakout rooms to promote lively discussions.

[0057] The biometric data collection unit can capture participants' facial expressions in real time through a camera and record their voice tone and tempo through a microphone. For example, the biometric data collection unit can use a camera and microphone installed on the participants' devices to collect ambient sounds and background changes in real time. For example, it can analyze the brightness and volume of the background to evaluate the atmosphere and concentration level of the meeting. This allows participants' facial expressions, voice tone, and tempo to be collected in real time.

[0058] The analysis unit can determine whether participants are in a happy mood based on the frequency of smiles and tone of voice. For example, the analysis unit collects past meeting data of participants, and the AI ​​analyzes that data to predict the performance and behavioral patterns of each participant. For example, it predicts speaking tendencies in the next meeting based on the frequency and content of past comments. This makes it possible to determine whether participants are in a happy mood.

[0059] The programming unit can ensure that discussions proceed smoothly by arranging a balance between leader-type participants and supporter-type participants. The programming unit, for example, uses an emotion estimation function to monitor changes in participants' emotions in real time and collect emotional waves as data. For example, the programming unit analyzes participants' facial expressions and voice quality and uses the emotion estimation function to monitor changes in emotions in real time. This allows discussions to proceed smoothly.

[0060] The scheduling department can monitor biological data in real time during the meeting and adjust the formation of breakout rooms as needed. For example, the scheduling department collects biometric information such as heart rate and electrodermal activity from wearable devices worn by participants, and analyzes the data using AI. For example, it can evaluate participants' levels of tension or relaxation based on changes in heart rate. This allows the formation of breakout rooms to be adjusted in real time.

[0061] The analysis unit can record the content of discussions after the meeting and analyze which group was the most active in the discussion. For example, the analysis unit collects participants' chat history and browsing history, and the AI ​​analyzes that data to evaluate their level of interest and concentration during the meeting. For example, if participants are browsing other websites during the meeting, it will determine that their concentration level is low. This allows the content of discussions to be recorded and analyzed after the meeting has ended.

[0062] The analysis unit can analyze which character combination was most effective. For example, the analysis unit uses an emotion estimation function to analyze the facial expressions and voice quality of participants and evaluate stress levels during the meeting in real time. For example, it detects facial expressions of tension or anxiety and calculates stress levels. This allows the effectiveness of character combinations to be analyzed.

[0063] The biological data collection unit can collect changes in the surrounding environmental sounds and background in real time using a camera and a microphone. The biological data collection unit collects changes in the surrounding environmental sounds and background in real time using, for example, a camera and a microphone mounted on the participant's device. For example, the biological data collection unit analyzes the brightness and volume of the background to evaluate the atmosphere and concentration level of the meeting. This allows changes in the surrounding environmental sounds and background to be collected in real time.

[0064] The biological data collection unit can refer to past meeting data and predict the performance and behavioral patterns of individual participants. For example, the biological data collection unit collects past meeting data of participants, and the AI ​​analyzes that data to predict the performance and behavioral patterns of individual participants. For example, it predicts speaking tendencies in the next meeting based on the frequency and content of past comments. This makes it possible to predict performance and behavioral patterns based on participants' past meeting data.

[0065] The biological data collection unit can use the emotion estimation function to monitor changes in the emotions of the participants in real time and collect emotional waves as data. The biological data collection unit, for example, analyzes the facial expressions and voice quality of the participants and monitors changes in emotions in real time using the emotion estimation function. For example, it detects smiling and angry facial expressions and collects emotional waves as data. This makes it possible to monitor changes in the emotions of the participants in real time and collect emotional waves as data.

[0066] The biological data collection unit can collect biometric information from wearable devices in addition to the participants' biological data, and perform more detailed analysis. The biological data collection unit collects biometric information, such as heart rate and electrodermal activity, from the wearable devices worn by the participants, and analyzes the data using AI. For example, the change in heart rate can be used to evaluate the participant's level of tension or relaxation. This allows for more detailed analysis of the collected biometric information from the wearable devices.

[0067] The biometric data collection unit can collect participants' digital activities and analyze their levels of interest and concentration during the meeting. For example, the biometric data collection unit collects participants' chat history and browsing history, and the AI ​​analyzes that data to evaluate their levels of interest and concentration during the meeting. For example, if participants are browsing other websites during the meeting, it can determine that their level of concentration is low. This allows the digital activities of participants to be collected and their levels of interest and concentration during the meeting to be analyzed.

[0068] The biological data collection unit uses the emotion estimation function to analyze the stress levels felt by participants during a meeting in real time and make suggestions for stress reduction. The biological data collection unit uses the emotion estimation function, for example, to analyze the facial expressions and voice quality of participants and evaluate the stress levels during a meeting in real time. For example, it detects facial expressions of tension or anxiety and calculates the stress levels. This makes it possible to analyze the stress levels of participants in real time and make suggestions for stress reduction.

[0069] The analysis unit analyzes gesture and posture data in addition to participants' facial expressions and voice quality, enabling more accurate character classification. For example, the analysis unit collects gesture and posture data in addition to participants' facial expressions and voice quality, and AI analyzes that data to perform character classification. For example, it analyzes hand movements and posture changes to evaluate participants' personalities and emotional states. This allows for more accurate character classification by analyzing gesture and posture data.

[0070] When analyzing participants' emotional states, the analysis unit can compare them with past emotional data to understand long-term emotional trends. For example, the analysis unit collects participants' past emotional data and uses AI to analyze that data to understand long-term emotional trends. For example, the analysis unit evaluates current emotional states based on emotional states in past meetings. This makes it possible to compare with past emotional data and understand long-term emotional trends.

[0071] The analysis unit can use the emotion estimation function to analyze changes in participants' emotions in real time and dynamically adjust character classification based on the emotional waves. The analysis unit, for example, uses the emotion estimation function to build a system that analyzes changes in participants' emotions in real time and dynamically adjusts character classification based on the emotional waves. For example, the character classification is updated according to changes in emotions. This makes it possible to analyze changes in emotions in real time and dynamically adjust character classification.

[0072] The analysis unit can combine psychological profiling techniques with the character classification of the participants to perform a more detailed personality analysis. For example, the analysis unit can combine psychological profiling techniques with the character classification of the participants, and the AI ​​can analyze the data to perform a more detailed personality analysis. For example, the personality of the participants can be evaluated based on the results of a psychological test. This allows for a more detailed personality analysis to be performed by combining psychological profiling techniques.

[0073] The analysis unit can share the character classification results of the participants with other conference participants and provide feedback to deepen mutual understanding. The analysis unit, for example, builds a system that shares the character classification results of the participants with other conference participants and provides feedback to deepen mutual understanding. For example, the compatibility between participants is evaluated based on the character classification results. This allows the character classification results to be shared and feedback to deepen mutual understanding to be provided.

[0074] The analysis unit can use the emotion estimation function to make suggestions regarding the progress of the meeting and the selection of agenda items based on the emotional states of the participants. For example, the analysis unit uses the emotion estimation function to build a system that makes suggestions regarding the progress of the meeting and the selection of agenda items based on the emotional states of the participants. For example, if the participants are relaxed, a creative agenda item can be suggested. This makes it possible to make suggestions regarding the progress of the meeting and the selection of agenda items based on the emotional states.

[0075] When forming breakout rooms, the formation unit can form optimal groups by taking into account participants' past cooperative relationships and compatibility data. For example, the formation unit collects participants' past cooperative relationships and compatibility data, and the AI ​​analyzes that data to form optimal groups. For example, participants who have had good cooperative relationships in the past can be placed in the same group. This allows optimal groups to be formed by taking into account past cooperative relationships and compatibility data.

[0076] After the breakout rooms are organized, the organization department can monitor the performance of the participants in real time and reallocate members as necessary. For example, the organization department builds a system in which, after the breakout rooms are organized, AI monitors the performance of the participants in real time and reallocates members as necessary. For example, if the discussion is stagnating, members will be reallocated. This makes it possible to monitor performance in real time and reallocate members as necessary.

[0077] The organization unit can use the emotion estimation function to dynamically adjust the organization of breakout rooms based on the emotional states of the participants, thereby maintaining an optimal discussion environment. The organization unit, for example, uses the emotion estimation function to analyze the emotional states of the participants in real time and build a system that dynamically adjusts the organization of breakout rooms. For example, it rearranges members according to changes in their emotions. This allows the organization of breakout rooms to be dynamically adjusted based on the emotional states, thereby maintaining an optimal discussion environment.

[0078] When organizing breakout rooms, the Planning Department can combine participants from different industries and specialties to draw out new perspectives and ideas. For example, the Planning Department can build a system that combines participants from different industries and specialties to draw out new perspectives and ideas. For example, technical and design participants can be placed in the same group. This allows participants from different industries and specialties to be combined to draw out new perspectives and ideas.

[0079] After the breakout rooms are organized, the organization department can have AI automatically propose agendas and issues to each group, supporting the direction of the discussion. For example, the organization department will build a system in which, after the breakout rooms are organized, AI automatically proposes agendas and issues to each group. For example, it will propose agendas based on the characteristics of the group. This allows AI to automatically propose agendas and issues to each group, supporting the direction of the discussion.

[0080] The scheduling unit can use the emotion estimation function to monitor the emotional balance in the breakout rooms in real time and make suggestions to maintain emotional harmony. The scheduling unit, for example, uses the emotion estimation function to build a system that monitors the emotional balance in the breakout rooms in real time and makes suggestions to maintain emotional harmony. For example, the scheduling unit reassigns members when the emotional balance is disrupted. This makes it possible to monitor the emotional balance in real time and make suggestions to maintain emotional harmony.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] Web conferencing systems can also collect participants' digital activities and analyze their level of interest and concentration during the meeting. For example, participants' chat history and browsing history can be collected, and AI can analyze that data to evaluate their level of interest and concentration during the meeting. If participants browse other websites during the meeting, it can be determined that they are not concentrating. Also, if participants chat a lot about a particular topic, it can be determined that they are highly interested in that topic. This allows for more effective management of the meeting's progress.

[0083] Web conferencing systems can also collect and analyze participants' gesture and posture data. For example, participants' hand movements and changes in posture can be captured by a camera, and AI can analyze the data to evaluate their personalities and emotional states. If participants' hand movements are active, it can be determined that they are actively participating in the discussion. If their posture is leaning forward, it can be determined that they are concentrating. This allows for more accurate character classification.

[0084] The web conferencing system can also take into account participants' past cooperative relationships and compatibility data to form optimal groups. For example, by placing participants who have had good cooperative relationships in the past in the same group, discussions can proceed smoothly. It can also combine participants who have good compatibility based on performance data from past meetings. This allows optimal groups to be formed taking into account past cooperative relationships and compatibility data.

[0085] Web conferencing systems can also bring together participants from different industries and specialties to draw out new perspectives and ideas. For example, placing technical and design participants in the same group can encourage the exchange of opinions from different perspectives. Similarly, combining marketing and engineering experts can lead to new ideas for product development. This allows participants from different industries and specialties to come together and draw out new perspectives and ideas.

[0086] Web conferencing systems can also record the content of discussions after a meeting and analyze which group had the most active discussions. For example, the frequency and content of participants' comments can be recorded, and AI can analyze the data to evaluate the liveliness of the discussion. Groups that speak frequently can be determined to have had lively discussions. The quality of the discussion can also be evaluated by evaluating the diversity and depth of the comments. This makes it possible to record and analyze the content of discussions after a meeting has ended.

[0087] The web conferencing system can further use the emotion estimation function to make suggestions regarding the progress of the meeting and the selection of agenda items based on the emotional state of the participants. For example, if the participants are relaxed, a creative agenda item can be suggested. Also, if the participants are nervous, an agenda item that will help them relax can be selected. This makes it possible to make suggestions regarding the progress of the meeting and the selection of agenda items based on the participants' emotional state.

[0088] The web conferencing system can also use emotion estimation functionality to analyze changes in participants' emotions in real time and dynamically adjust character classifications based on the emotional waves. For example, character classifications can be updated according to changes in emotions to prevent discussions from stagnating. Participants' roles can also be dynamically changed based on emotional waves. This allows for real-time analysis of emotional changes and dynamic adjustment of character classifications.

[0089] The web conferencing system can also use emotion estimation functionality to analyze the stress levels felt by participants during the meeting in real time and make suggestions to reduce stress. For example, it can detect facial expressions of tension or anxiety and calculate stress levels. If the stress level is high, it can make suggestions to help participants relax. This allows the system to analyze participants' stress levels in real time and make suggestions to reduce stress.

[0090] The web conferencing system can further use the emotion estimation function to monitor the emotional balance in breakout rooms in real time and make suggestions to maintain emotional harmony. For example, if the emotional balance is disrupted, members can be rearranged. The progress of the discussion can also be adjusted based on the emotional balance. This allows the system to monitor the emotional balance in real time and make suggestions to maintain emotional harmony.

[0091] The web conferencing system can further use the emotion estimation function to dynamically adjust the formation of breakout rooms based on the emotional state of participants, thereby maintaining an optimal discussion environment. For example, members can be rearranged according to changes in emotion. The progress of the discussion can also be adjusted based on the emotional state. This allows the formation of breakout rooms to be dynamically adjusted based on the emotional state, thereby maintaining an optimal discussion environment.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The biometric data collection unit collects biometric data such as facial expressions and voice quality from the participants' devices' cameras and microphones. For example, the camera captures participants' facial expressions in real time, and the microphone records their vocal tone and tempo. The biometric data collection unit can also collect ambient sounds and background changes. Step 2: The analysis unit analyzes the biological data collected by the biological data collection unit. For example, it determines whether participants are in a good mood based on the frequency of smiles and tone of voice. The analysis unit can also refer to past meeting data of participants to predict the performance and behavioral patterns of individual participants. Step 3: The classification unit classifies the participants into characters based on the data analyzed by the analysis unit, such as leader type, supporter type, idea man type, etc. Step 4: The organization unit automatically organizes breakout rooms based on the characters classified by the classification unit. For example, by balancing leader-type participants with supporter-type participants, the discussion can proceed smoothly.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0120] 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.

[0121] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A biological data collection unit collects biological data such as facial expressions and voice quality from the cameras and microphones on participants' devices. an analysis unit that analyzes the biological data collected by the biological data collection unit; a classification unit that classifies participants into characters based on the data analyzed by the analysis unit; and an organizing unit that automatically organizes breakout rooms based on the characters classified by the classification unit. A system characterized by:

2. The biological data collection unit Using emotion estimation functionality, participants' emotional changes are monitored in real time, and these emotional waves are collected as data. The system of claim 1 .

3. The biological data collection unit Using emotion estimation, the system analyzes the stress levels felt by participants during meetings in real time and makes suggestions for reducing stress. The system of claim 1 .

4. The analysis unit Using emotion estimation, participants' emotional changes are analyzed in real time, and character classifications are dynamically adjusted based on these emotional waves. The system of claim 1 .

5. The analysis unit Using emotion estimation, suggestions are made regarding meeting progress and agenda selection based on participants' emotional states. The system of claim 1 .

6. The knitting unit Using emotion estimation functionality, the breakout room configuration is dynamically adjusted based on the emotional state of the participants to maintain an optimal discussion environment. The system of claim 1 .

7. The knitting unit The formation of the breakout rooms will bring together participants from different industries and specialties to draw out new perspectives and ideas. The system of claim 1 .

8. The knitting unit Using emotion estimation, the system monitors the emotional balance within the breakout rooms in real time and makes suggestions to maintain emotional harmony. The system of claim 1 .

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