AI Question Prioritization in Teleconference Chat
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Solution Overview
Problem
Managing questions in live chat during online meetings with multiple participants becomes challenging, leading to overlooked important questions and increased meeting duration due to inadequate question/answer traffic management.
Innovation Solution
A method that uses AI and NLP to extract, group, and prioritize questions in real-time, predicting answerability and sequencing them for presentation, with a dashboard that captures all questions and links answers from content or chat transcripts, improving communication efficiency and engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual chat monitoring is used in teleconferences, then all questions can be captured, but the system complexity and time required to manage questions increases significantly
Solution Approach 1:
The system automatically monitors chat, extracts questions using NLP, groups duplicates, prioritizes them, and presents to presenters without requiring manual moderation. The AI engine self-manages the entire question workflow from capture to presentation.
Solution Approach 2:
Manual chat monitoring and question management is replaced with an automated AI-powered NLP system that performs extraction, grouping, prioritization, and presentation of questions, eliminating the need for human moderators to manually process chat traffic.
2Adaptability or versatility
If all questions are answered in sequence, then participant engagement is maintained, but meeting duration increases significantly
Solution Approach 1:
The system extracts and separates trivial or duplicate questions from the main question flow, filtering them out or grouping them for later handling. This allows the presenter to focus on answering only the most important unique questions during the meeting.
Solution Approach 2:
Different questions receive different treatments based on their characteristics: high-priority unique questions are answered immediately, while duplicate or low-priority questions are filtered, grouped, or deferred, creating localized quality variations in question handling.
3Productivity
If important questions receive priority attention, then meeting efficiency improves, but some less important questions may be overlooked
Solution Approach 1:
Duplicate questions are merged into single grouped items that are still tracked and can be answered. The system combines multiple instances of the same question into one prioritized item, ensuring coverage without redundant handling.
Solution Approach 2:
The system provides feedback to presenters about question priorities, duplicates, and coverage status. Presenters can see which questions have been answered, which are pending, and which are duplicates, allowing them to ensure comprehensive coverage while maintaining efficiency.
Data Source
AI summary
Method, computer program product, and computer system are provided. Questions are extracted from a chat in real-time during an online meeting and are aggregated into groups of duplicate questions. The groups are presented to a subset of attendees whose question is in the group. Feedback is received and applied to the group from the subset of attendees. Whether a question is answerable is predicted. For answerable questions an amount of time to answer the question is predicted. The answerable questions are sequenced, filtered, prioritized, and presented to an attendee interface and a presenter interface.