Automated Meeting Moderator Assistant for Agenda Deviation Detection
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Solution Overview
Problem
Business meetings often deviate from their agendas due to discussions on peripheral topics, leading to inefficiencies and incomplete capture of key items, unresolved issues, and follow-up actions, posing challenges for moderators in managing time effectively.
Innovation Solution
A computer-implemented method that monitors participant dialog during meetings, identifies agenda items, computes deviations from the agenda topics, and provides notifications to moderators, using natural language processing and image analysis to quantify engagement and alert participants when deviations occur, allowing for real-time adjustments and post-meeting summaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If moderators manually monitor and track meeting discussions, then meeting control and time management are maintained, but the complexity of the moderator's workload increases and time is lost
Solution Approach 1:
An automated dialog monitoring system acts as an intermediary between meeting participants and the moderator. The system includes a dialog monitor that tracks participant discussions, a topic identifier that extracts discussion themes, and a deviation detector that compares actual discussions against the agenda. This intermediary system handles the complex task of monitoring and tracking, freeing the moderator from manual workload while maintaining meeting control and efficiency
Solution Approach 2:
The patent replaces the mechanical/manual system of moderator monitoring with an automated computational system. The dialog monitor captures and analyzes participant dialog using natural language processing, automatically identifying topics and detecting deviations from the agenda without requiring manual human intervention, thus substituting mechanical monitoring with automated digital processing
2Adaptability or versatility
If meeting discussions allow free exploration of peripheral topics, then participant engagement and idea generation improve, but the meeting deviates from agenda items and loses focus
Solution Approach 1:
The system implements continuous feedback by comparing identified dialog topics against agenda items in real-time. The deviation detector analyzes the degree of alignment between actual discussions and planned agenda topics, providing ongoing feedback that enables dynamic adjustment of discussion focus while maintaining necessary flexibility for relevant tangential conversations
Solution Approach 2:
The monitoring system dynamically adjusts its detection sensitivity and alert thresholds based on the meeting context. It allows flexible exploration of topics that are tangentially related to agenda items while automatically identifying and alerting on significant deviations, enabling the meeting to adapt between structured agenda-following and flexible discussion modes as needed
3Loss of information
If comprehensive meeting summaries are created to capture all key items and actions, then documentation completeness improves, but the time required to create accurate summaries increases
Solution Approach 1:
The dialog monitoring system performs preliminary action by continuously capturing and structuring meeting discussions in real-time during the meeting itself. It identifies topics, tracks deviations, and records key discussion points as they occur, creating a structured foundation that significantly reduces the time required for post-meeting summary creation while ensuring comprehensive information capture
Solution Approach 2:
The system creates automated copies of the meeting discussion structure and key content during the meeting. The dialog monitor and topic identifier generate a structured record of discussions, decisions, and action items that serves as a template or draft for the final meeting minutes, eliminating the need for manual transcription and significantly reducing documentation time
Data Source
AI summary
Disclosed embodiments provide a computer-implemented technique for monitoring deviation from a meeting agenda. A meeting moderator and meeting agenda are obtained. Meeting dialog, along with facial expressions and/or body language of attendees is monitored. Natural language processing, using entity detection, disambiguation, and other language processing techniques, determines a level of deviation in the meeting dialog from the meeting agenda. Computer-implemented image analysis techniques ascertain participant engagement from facial expressions and/or gestures of participants. A deviation alert is presented to the moderator and/or meeting participants when a deviation is detected, allowing the moderator to steer the meeting conversation back to the planned agenda.