Meeting Content Suggestions via Speech Context
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Solution Overview
Problem
Existing technologies fail to intelligently recommend relevant content items during meetings based on real-time natural language utterances, and are deficient in computer information security, user privacy, and resource consumption.
Innovation Solution
The system automatically recommends relevant content items by detecting natural language utterances, determining context, and using a weak supervision machine learning model to rank content items, while enhancing security and reducing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing technologies are used for content recommendation during meetings, then manual user requests are required, but user experience and productivity deteriorate due to lack of automation
Solution Approach 1:
The system automatically detects natural language utterances, determines meeting and attendee context, ranks content items using machine learning models, and presents recommendations without requiring manual user requests. The system serves itself by autonomously completing the content recommendation workflow from detection to presentation.
Solution Approach 2:
The system performs preliminary actions by pre-processing audio data through speech-to-text conversion, pre-computing context information about meetings and attendees, and pre-ranking content items before they are needed during the meeting, enabling rapid automatic recommendation when utterances occur.
2Reliability
If traditional content recommendation systems are implemented, then relevant content can be suggested, but computer information security and user privacy deteriorate due to human annotator involvement
Solution Approach 1:
The system replaces the mechanical process of human annotators reviewing and labeling content with an automated machine learning pipeline. Speech-to-text services convert audio to text, machine learning models analyze context and rank content items, eliminating the need for human annotators to handle sensitive meeting data and improving security and privacy.
Solution Approach 2:
The system introduces machine learning models as intermediaries between the raw meeting data and the content recommendation output. These models process and analyze data without exposing sensitive information to human annotators, acting as a secure intermediary layer that maintains reliability while protecting privacy.
3Measurement precision
If comprehensive content analysis is performed during meetings, then accurate content recommendations are achieved, but resource consumption deteriorates due to high computational requirements
Solution Approach 1:
The system performs partial analysis by focusing computational resources on the most relevant aspects of content recommendation. It analyzes natural language utterances and associated context information selectively, ranking content items based on their relevance to the detected utterances and meeting context, rather than performing exhaustive analysis of all available content.
Solution Approach 2:
The system applies different levels of analysis to different content items based on their local relevance. Content items that are more closely related to the detected natural language utterances and meeting context receive more detailed analysis and higher ranking, while less relevant items receive minimal processing, optimizing the balance between accuracy and resource consumption.
Data Source
AI summary
Various embodiments discussed herein are directed to improving existing technologies by causing presentation, to one or more user devices associated with one or more meeting attendees, of one or more indications of one or more content items during or before a meeting based at least in part on one or more natural language utterances associated with the meeting, a context of the meeting, and/or a context associated with one or more meeting attendees. In other words, particular embodiments automatically recommend relevant content items responsive to the real-time natural language utterances in the meeting, and/or other context.