Virtual Meeting Assistant Debrief Mode for Post-Meeting Task Generation
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Solution Overview
Problem
Conventional interactive virtual assistants face challenges in integrating with disparate meeting platforms and services, and providing useful features in a meeting environment due to the lack of standardization and the complexity of natural language and audio processing with multiple vocally active participants.
Innovation Solution
An interactive virtual meeting assistant system that includes a data receiving engine, communications engine, scheduling engine, in-meeting engine, and meeting post-processing engine, which parses meeting invitations, connects to meetings, performs natural language processing, and generates metrics and tasks, also offering a debrief mode for capturing meeting information and providing a graphical user interface for participants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional virtual assistants are integrated into meeting environments, then meeting assistance capabilities are improved, but integration complexity with disparate platforms increases
Solution Approach 1:
The patent introduces a meeting assistant system that acts as an intermediary between users and disparate meeting platforms. The system includes a platform adapter layer that mediates between the virtual assistant core and various meeting platforms (Zoom, Teams, Webex, etc.), translating platform-specific protocols into standardized internal operations. This intermediary architecture enables broad platform compatibility without requiring complex direct integrations for each platform.
Solution Approach 2:
The meeting assistant system is designed with universal multi-functionality to handle diverse meeting platforms and tasks through a unified interface. The system can perform multiple functions including real-time transcription, sentiment analysis, action item extraction, and debrief generation across different meeting platforms. This universal design reduces integration complexity by providing a single solution that works across all platforms rather than requiring separate integrations for each.
2Measurement precision
If natural language processing is performed with multiple vocally active participants, then meeting transcription accuracy is improved, but audio processing complexity increases
Solution Approach 1:
The audio processing system segments the complex task of multi-speaker transcription into distinct components: audio source separation, speaker diarization, individual speech recognition, and result integration. The system first separates audio signals from multiple participants, then processes each speaker's audio independently through speech-to-text conversion, and finally integrates the results with speaker attribution. This segmentation reduces overall processing complexity by breaking down the challenging multi-speaker problem into manageable sequential steps.
Solution Approach 2:
The system introduces intermediary processing layers between raw audio input and final transcription output. These intermediaries include audio preprocessing modules that enhance speech signals, speaker identification modules that attribute speech to specific participants, and post-processing modules that resolve ambiguities. These intermediary components simplify the core speech recognition task by preparing cleaner, more structured input data.
3Loss of information
If comprehensive meeting analysis is provided, then information completeness is improved, but processing time increases
Solution Approach 1:
The system performs preliminary processing actions during the meeting itself rather than waiting until after the meeting concludes. Real-time transcription, sentiment analysis, and key topic identification are executed concurrently with the meeting, allowing the system to prepare processed data in advance. This preliminary action during the meeting reduces the time required for post-meeting analysis while maintaining comprehensive information coverage.
Solution Approach 2:
The system implements a tiered analysis approach that provides partial processing during the meeting and completes full analysis afterward. During the meeting, the system delivers essential outputs such as real-time captions and basic sentiment indicators. After the meeting concludes, the system performs more time-intensive comprehensive analysis including detailed action item extraction, full sentiment trends, and debrief generation. This partial action during and excessive action after the meeting balances information completeness with time efficiency.
Data Source
AI summary
In one embodiment, the interactive virtual meeting assistant implements a meeting debrief post-processing operation. For a given meeting that the interactive virtual meeting assistant participated in, a meeting post-processing engine enables one or more participants of the meeting to associate a debrief with the meeting. The debrief may be an audio recording, a video recording, text, or any file. The meeting post-processing engine stores the debrief in data stores and provides access to the debrief via the meeting GUI associated with the meeting. The meeting post-processing engine also processes the debrief to generate tasks to be assigned to participants or other entities and/or schedule reminders to be provided to the participants or other entities. The debrief may be private, such that only the participant who provided the debrief may subsequently access the debrief.