Teleconference Summaries Using Non-Verbal Context Signals
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Solution Overview
Problem
Existing voice-to-text transcription methods fail to robustly contextualize audio and video data due to the absence of consideration for non-verbal signals, making it difficult to efficiently summarize and extract important parts of conversations in group-based communication platforms.
Innovation Solution
A machine-learning model is employed to transcribe and summarize multimedia collaboration sessions by incorporating non-verbal signals, such as contextual cues, user interactions, and ambient data, to generate actionable summary documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing voice-to-text transcription methods are used, then transcription can be performed, but the ability to contextualize audio and video data with non-verbal signals is lost
Solution Approach 1:
The patent combines multiple data streams (audio, video, non-verbal signals) into a unified transcription system. The neural network processes both verbal audio and non-verbal visual signals simultaneously, merging them into a single contextualized transcription output that preserves both transcription accuracy and contextual information.
Solution Approach 2:
The system transitions from traditional one-dimensional audio-only transcription to a multi-dimensional approach that incorporates visual dimensions (video frames, gestures, facial expressions). This dimensional expansion enables the system to capture non-verbal signals while maintaining accurate verbal transcription.
2Reliability
If users review conversations manually to extract important parts, then comprehensive understanding is achieved, but large amounts of time are required
Solution Approach 1:
The system performs preliminary processing by automatically transcribing and contextualizing entire conversations before users need to review them. The neural network pre-processes audio and video data, extracting key information and generating structured outputs that reduce the time users spend manually reviewing conversations while maintaining comprehensive understanding.
Solution Approach 2:
The system provides feedback by generating transcriptions and summaries that users can review and correct. This feedback loop allows users to verify accuracy and extract important parts more efficiently, as the system has already performed initial processing and organization of the conversation data.
3Ease of operation
If search functions and key terms are used to access relevant portions, then specific information can be found, but the time required to extract important parts remains large
Solution Approach 1:
The system performs preliminary extraction and organization of important conversation parts during the transcription process itself. The neural network identifies and structures key information as it processes the audio and video data, creating pre-organized outputs that reduce the time users need to spend searching and extracting relevant portions manually.
Data Source
AI summary
Techniques for transcribing and/or summarizing multimedia collaboration sessions are discussed herein. For example, users can communicate within a teleconferencing meeting associated with a channel. In some examples, a first machine learning model may be configured to receive audio-visual data and user interaction data (e.g., selected emojis, detected gestures, messages or text input by a user, a thread of messages, etc.) and output a teleconferencing meeting summary. In some examples, the teleconferencing meeting summary can include one or more highlights, action items, and/or artificial intelligence (AI) notes generated using a machine learning model(s).