Online Meeting Metadata Analysis for Real-Time Group Dynamics
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Solution Overview
Problem
Existing methods for analyzing online meeting data fail to provide insights into group dynamics, group behavior, and meeting efficiency, particularly in substituting for the deficiency of non-verbal communication in virtual environments.
Innovation Solution
A system and method for dynamically generating and analyzing metadata from online meetings using machine learning algorithms to extract participant speaking patterns, audio characteristics, and location data, calculating key performance indicators, and generating real-time visualizations to enhance meeting effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If online meetings are used to facilitate communication among geographically dispersed participants, then accessibility and convenience are improved, but the ability to experience and participate in non-verbal communication deteriorates
Solution Approach 1:
The patent introduces an AI-based analysis system as an intermediary that processes audio and video streams to extract and analyze non-verbal communication cues. This intermediary compensates for the loss of non-verbal information by automatically detecting speech patterns, tone, pauses, and other paralinguistic features, then presenting this analyzed information to participants to restore the lost communicative dimension.
2Loss of information
If existing methods analyze online meeting data, then basic meeting records are obtained, but insights into group dynamics, group behavior, and meeting efficiency are not provided
Solution Approach 1:
The patent segments the complex analysis task into distinct functional modules: audio stream processing, video stream processing, metadata extraction, diarization, speech pattern analysis, and KPI calculation. Each module handles a specific aspect of the analysis, making the overall complex system manageable and scalable while providing comprehensive insights into group dynamics and meeting efficiency.
Solution Approach 2:
The patent introduces an AI-based analysis system as an intermediary that processes audio and video streams to extract and analyze non-verbal communication cues. This intermediary compensates for the loss of non-verbal information by automatically detecting speech patterns, tone, pauses, and other paralinguistic features, then presenting this analyzed information to participants to restore the lost communicative dimension.
3Loss of time
If metadata is extracted and analyzed in real-time, then actionable insights are provided during the meeting, but processing time and computational resources increase
Solution Approach 1:
The patent implements partial action by prioritizing the extraction and analysis of the most critical metadata and KPIs in real-time, while less critical analyses can be performed post-meeting. The system focuses computational resources on delivering immediate actionable insights about group dynamics and meeting efficiency, accepting that not all possible analyses can be completed with full depth during the live meeting.
Solution Approach 2:
The system performs preliminary extraction of metadata and basic diarization during the meeting to enable real-time feedback, while more complex analytical computations are prepared in advance or completed after the meeting concludes. This approach reduces the real-time computational burden while still providing timely insights.
Data Source
AI summary
A system for dynamically generating and analyzing metadata for online meetings is provided. The system is programmed to: a) receive at least one stream of at least one of audio and video of an online meeting, wherein the at least one stream includes one or more participants participating in the online meeting; b) extract a plurality of metadata from the at least one stream; c) perform diarization on the at least one stream and the plurality of metadata the at least one stream to generate diarization information, wherein the diarization information includes information about participation for the one or more participants in the online meeting; d) analyze the diarization information to calculate one or more key performance indicators; and e) generate visualization of the key performance indicators to be displayed to one or more participants in the online meeting.