Virtual Meeting Transcript AI for Automatic Content Access
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Solution Overview
Problem
Existing virtual meeting platforms require participants to manually perform actions such as sharing content or scheduling meetings, which disrupts the meeting flow and can lead to missed actions or incorrect execution.
Innovation Solution
Implementing an AI model that analyzes participant interest based on meeting transcripts to automatically perform actions outside the virtual meeting interface, allowing participants to continue engaging without interruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If participants manually perform actions such as sharing content or scheduling meetings during virtual meetings, then these actions can be completed, but the meeting flow is disrupted and participants may miss actions or execute them incorrectly
Solution Approach 1:
The system performs actions in advance by monitoring meeting transcripts and automatically executing tasks (content sharing, meeting scheduling) before participants need to manually intervene. This preliminary automated detection and execution ensures actions are completed accurately without disrupting the natural meeting flow, as the system acts proactively based on contextual understanding of meeting content.
2Productivity
If participants focus on discussions without interruption, then meeting efficiency improves, but actions like content sharing may not be completed
Solution Approach 1:
The system introduces an intermediary AI layer that acts as a bridge between meeting discussions and action execution. This intermediary monitors transcripts, identifies action requirements, and automatically performs tasks without requiring participants to break their focus. The intermediary ensures both continuous discussion flow and reliable action completion by handling tasks in the background.
3Ease of operation
If AI model automatically performs actions based on transcript analysis, then computing resource usage increases, but manual participant intervention is reduced
Solution Approach 1:
The system applies partial action by selectively analyzing only relevant portions of meeting transcripts that indicate action requirements, rather than processing entire transcripts continuously. This approach reduces unnecessary computing resource consumption while still effectively detecting when participants need content sharing or meeting scheduling actions, balancing automation benefits with resource efficiency.
Data Source
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AI summary
A method includes causing a virtual meeting UI to be presented during a virtual meeting between one or more participants. The virtual meeting UI may include one or more first regions each corresponding to a participant of the one or more participants. The virtual meeting UI may include a second region corresponding to a presentation of content by a first participant. The method includes determining, using an AI model and using at least a first portion of a transcript of the virtual meeting as input to the AI model, that a second participant is interested in accessing the content outside of the virtual meeting UI. The method includes causing the content to be accessible to the second participant outside of the virtual meeting UI.