Real-Time Video Conference Analytics via Transformed Snippets
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Solution Overview
Problem
Current video conferencing tools lack real-time analytics capabilities similar to human perception during physical face-to-face meetings, requiring manual and labor-intensive data recording and analysis post-meeting, which is time-consuming and resource-intensive.
Innovation Solution
A system and method for real-time video conference analytics that captures and analyzes participant data and metadata during the conference, generating insights and displaying them on a dashboard, including participant activity, participation rates, and system/network conditions, using transformed video snippets sent to a central processing server for immediate analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual recording and analysis of participant data is performed, then comprehensive data insights can be obtained, but the process becomes labor intensive and time consuming
Solution Approach 1:
The patent replaces manual mechanical processes of recording and analyzing participant data with automated electronic systems. The video conferencing system automatically captures participant data points, processes them through analytics engines, and generates insights without human intervention, thereby eliminating the labor-intensive and time-consuming nature of manual analysis while maintaining comprehensive data collection.
Solution Approach 2:
The system performs self-service by automatically collecting, processing, and analyzing participant data without requiring external manual intervention. The analytics engine continuously processes data streams from multiple participants, generates insights in real-time, and presents them through dashboards, enabling the system to serve its own data analysis needs autonomously.
2Measurement precision
If video conference recordings are processed through data analytics software, then post-meeting analytics can be achieved, but large storage capacity and resources are required
Solution Approach 1:
The patent extracts only the essential data points and metadata from video conference streams rather than processing entire video recordings. The system identifies and extracts relevant participant attributes, activity metrics, and interaction data, eliminating the need to store and process large volumes of video footage while maintaining comprehensive analytics capability.
Solution Approach 2:
Instead of retaining large video recording files for extended periods, the system processes transient data streams in real-time and discards the original video content after extraction. This approach uses temporary, disposable data representations that require minimal storage capacity compared to permanent video recording archives.
3Speed
If real-time analytics are implemented during video conference, then immediate insights are provided, but system complexity increases
Solution Approach 1:
The patent segments the analytics system into distinct functional modules: data collection components that capture participant information, processing components that analyze specific data points, and presentation components that display insights. This modular segmentation enables real-time processing while managing system complexity through organized, independent functional units that can be developed and maintained separately.
Solution Approach 2:
The system employs universal analytics engines and processing algorithms that can handle multiple types of participant data and conferencing scenarios through a single integrated platform. This multi-functionality reduces overall system complexity by avoiding the need for separate specialized systems for different analytics tasks.
Data Source
AI summary
A system, platform, computer program product, and/or method to analyze a computer-implemented video conference includes: a plurality of participant devices, and a central processing server. Each participant device is configured to form a video snippet for a time interval of the video conference having audio data and video data; generate a transformed video snippet by embedding extracted participant data and/or metadata into the video snippet; and send each transformed video snippet to the central processing server. The central processing server receives each transformed video snippet; performs analytics on each transformed video snippet; and transmits to at least one of the participant devices, results of the performed analytics. Participant devices can display one or more results of the performed analytics.