In-Meeting Content Suggestions from Real-Time Utterance Detection
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Solution Overview
Problem
Existing technologies fail to intelligently recommend content items during meetings based on real-time natural language utterances, negatively impacting user experience, resource consumption, and compromising security and privacy.
Innovation Solution
Implement a system that uses weak supervision machine learning models to automatically recommend relevant content items during meetings based on natural language utterances, meeting context, and attendee patterns, while reducing resource consumption and enhancing security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual content item recommendation systems are used during meetings, then users can access relevant content, but users must manually query and search for content items, increasing time consumption and reducing productivity
Solution Approach 1:
The system automatically detects natural language utterances, identifies relevant content items, and presents recommendations without requiring manual user queries. The system serves itself by autonomously monitoring meeting transcripts, analyzing context, and surfacing relevant content items, thereby eliminating the need for users to manually search and significantly improving content access efficiency while reducing time loss
Solution Approach 2:
The system performs preliminary analysis of meeting contexts, attendee patterns, and content repositories before content is needed. By pre-processing and indexing content items based on historical meeting data and attendee preferences, the system prepares recommendation candidates in advance, enabling rapid content delivery when users need it without manual search delays
2Productivity
If traditional content recommendation systems are implemented, then content can be suggested, but computer resources such as disk I/O, network bandwidth, and processing power are heavily consumed
Solution Approach 1:
The system applies local quality by analyzing and processing only the specific portions of data that are relevant to the current meeting context. Instead of globally scanning all content repositories, it focuses computational resources on analyzing utterances, identifying relevant topics, and retrieving only matching content items, thereby reducing overall computer resource consumption while maintaining recommendation effectiveness
Solution Approach 2:
The system performs partial action by generating a ranked list of content recommendations rather than exhaustively analyzing all possible content items. It processes a subset of most promising candidates based on initial context analysis and stops when sufficient recommendations are generated, avoiding excessive computation while still providing high-quality content suggestions
3Ease of operation
If content items are automatically recommended during meetings, then user experience is improved, but system complexity increases due to natural language processing and machine learning requirements
Solution Approach 1:
The system introduces intermediary components that bridge natural language processing and content recommendation functions. The meeting transcript analyzer serves as an intermediary that converts spoken utterances into structured data, which then feeds into the content matching engine. This intermediary layer simplifies the overall system architecture by breaking down complex tasks into manageable modules with clear interfaces
Solution Approach 2:
The system achieves universality by designing a multi-functional platform that handles multiple tasks: natural language transcription, utterance detection, context analysis, content retrieval, and recommendation generation. This unified system reduces overall complexity compared to having separate specialized systems for each function, as it shares common infrastructure and data structures across all operations
4Measurement precision
If comprehensive content analysis is performed based on natural language utterances and meeting context, then recommendation accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary indexing and categorization of content items based on historical meeting data, attendee profiles, and content metadata before recommendations are needed. This pre-processing creates ready-to-match data structures that enable rapid accurate matching during actual meetings, improving recommendation accuracy without adding processing delays during content delivery
Solution Approach 2:
The system applies local quality by intensively analyzing only the specific utterances and context elements that are most relevant to content matching. Instead of uniformly processing all meeting data, it focuses computational effort on key indicators such as explicit content mentions, topic keywords, and attendee-specific preferences, achieving high accuracy while minimizing overall processing time
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.