Meeting Speech Biasing and Document Generation
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Solution Overview
Problem
Current video meeting technologies face challenges in accurately transcribing discussions due to varied subject matters and unique terminology, leading to incomplete or inaccurate action items.
Innovation Solution
Implementing techniques to bias automated speech recognition (ASR) using relevant meeting documents and content, generating meeting summaries, action items, and reminders based on transcribed speech, notes, and visual cues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated speech recognition is used to transcribe meeting content, then transcription speed and automation are improved, but transcription accuracy deteriorates when unique or recently adopted terminology is involved
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing documents related to the meeting topic before the transcription process begins. This pre-processing establishes a customized vocabulary and context that improves ASR accuracy for domain-specific terminology without sacrificing automation efficiency
Solution Approach 2:
The patent introduces an intermediary component that acts as a bridge between the generic ASR system and the meeting-specific terminology. This intermediary layer processes and adapts the transcription engine to understand organization-specific jargon, acronyms, and recently adopted terms, thereby resolving the accuracy-automation contradiction
2Speed
If meeting transcripts are generated without contextual biasing, then processing speed is improved, but the ability to identify important talking points deteriorates
Solution Approach 1:
The system applies local quality by differentiating the processing of different segments of meeting content. Rather than uniformly processing all speech, the system identifies and prioritizes specific talking points based on their importance, applying enhanced analysis only where needed to preserve critical information while maintaining overall processing efficiency
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously evaluates transcribed content against meeting context and objectives. This feedback loop allows the system to identify and flag important talking points, adjusting the transcription priority dynamically to ensure critical information is captured without significantly slowing down the overall process
3Manufacturing precision
If participants manually take notes during meetings, then attention to detail is improved, but time for actual meeting participation deteriorates
Solution Approach 1:
The system enables self-service by automatically generating accurate meeting notes and action items without requiring participant intervention. The AI-driven transcription and analysis system serves itself to capture meeting content, releasing participants from note-taking duties while maintaining high accuracy through contextual understanding and domain-specific vocabulary
Data Source
AI summary
Implementations relate to an application that can bias automatic speech recognition for meetings using data that may be associated with the meeting and/or meeting participants. A transcription of inputs provided during a meeting can additionally and/or alternatively be processed to determine whether the inputs should be incorporated into a meeting document, which can provide a summary for the meeting. In some instances, entries into a meeting document can be designated as action items, and those action items can optionally have conditions for reminding meeting participants about the action items and/or for determining whether an action item has been fulfilled. In this way, various tasks that may typically be manually performed by meeting participants, such as creating a meeting summary, can be automated in a more accurate manner. This can preserve resources that may otherwise be wasted during video conferences, in-person meetings, and/or other gatherings.