Virtual Conference Video Editing Using ML Stream Selection

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Participants in virtual conferences often desire edited recordings but lack access to necessary editing software or knowledge, and providers face time constraints in manually editing large volumes of unstructured video data.

Innovation Solution

Implementing machine learning models by video conference providers to automatically select, segment, and edit media streams based on content analysis, applying filters, and generating edited videos with minimal human intervention.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If manual editing is performed by providers, then video quality can be ensured, but time consumption increases significantly

Engineering Contradiction:

Improvevideo editing qualityVSAvoidediting time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system enables automated self-editing of conference recordings through AI models that automatically analyze, segment, and assemble video content without human intervention, resolving the contradiction between quality and time consumption

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual editing operations are replaced by machine learning models including speech-to-text transcription, topic identification, and automated clip assembly, substituting human mechanical editing with intelligent automated systems

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If automated editing is implemented, then productivity increases, but editing precision may deteriorate

Engineering Contradiction:

Improvevideo production speedVSAvoidediting accuracy

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The patent employs advanced machine learning models including speech-to-text transcription and topic identification algorithms to replace manual editing, achieving both high productivity and maintained precision through intelligent automation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system incorporates feedback mechanisms where AI models analyze conference content, identify relevant segments, and automatically assemble edited videos while maintaining quality through iterative refinement and quality control processes

Inventive Principle:

Principle #23Feedback

3Adaptability or versatility

If participants use editing software themselves, then customization increases, but ease of operation decreases

Engineering Contradiction:

Improveediting customizationVSAvoidsoftware usability

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system provides automated self-editing services that eliminate the need for participants to operate complex editing software, achieving both customization through AI-driven topic identification and ease of operation through fully automated processing

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12567444B2Automated video editing for a virtual conference

Publication Date: 2026.03.03 ZOOM COMMUNICATIONS INC

AI summary

In some aspects, techniques may include receiving media streams from one or more client devices. The media streams can be received by a virtual conference provider. Also, the techniques may include selecting a subset of the media streams based on one or more characteristics of the media streams. The streams may be selected using a machine learning (ML) model. In addition, the techniques may include identifying one or more segments of the subset of media streams satisfying an inclusion criteria. Moreover, the techniques may include generating a recording of the virtual conference including the one or more identified segments.