Dynamic Meeting Visual Guide Using Tiered NLP Analysis
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Solution Overview
Problem
Conventional video conferencing systems lack the ability to dynamically analyze content covered during a meeting and identify key remaining items, relying on static information and failing to leverage machine learning-based approaches for real-time adjustments.
Innovation Solution
A computer-implemented method using natural language processing (NLP) and speech-to-text technology to categorize meeting content into tier levels, dynamically adjust the architecture based on conversation analysis, and provide visual indicators to presenters to focus on core information within the remaining meeting time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional video conferencing systems are used, then basic communication functionality is provided, but the system lacks the ability to dynamically analyze meeting content and identify key remaining items
Solution Approach 1:
The patent implements a tiered architecture where multiple levels of analysis are nested within each other. The first tier performs basic content analysis, the second tier performs conversation analysis, and the third tier performs dynamic adjustment, with each tier building upon and integrating with the previous levels. This nested structure enables comprehensive meeting content analysis while organizing system complexity into manageable hierarchical layers.
Solution Approach 2:
The system segments the meeting analysis process into distinct functional modules: content analysis module, conversation analysis module, and dynamic adjustment module. Each module handles specific aspects of the analysis independently, then integrates results through the tiered architecture. This segmentation allows the system to process complex meeting data through specialized components rather than a monolithic structure.
2Adaptability or versatility
If static information is used for meeting guidance, then simplicity is maintained, but the system fails to leverage machine learning-based approaches for real-time adjustments
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models offline before the actual meeting occurs. The tiered models are trained on historical meeting data in advance, so that during the live meeting, the system can perform rapid real-time analysis without the overhead of training. This separates the time-consuming training phase from the time-sensitive inference phase, enabling both adaptability and efficiency.
Solution Approach 2:
The system transitions from static meeting agendas to dynamic, adaptive guidance by implementing real-time conversation analysis and model updating. The tiered architecture allows the system to dynamically adjust its analysis depth and focus based on ongoing meeting dynamics, speaker interactions, and time remaining, making the guidance adaptive rather than fixed.
3Measurement precision
If comprehensive meeting analysis is performed, then accuracy of content coverage identification is improved, but the complexity of processing and computing resources increases
Solution Approach 1:
The patent applies local quality by assigning different analysis depths and computational resources to different tiers and meeting contexts. The first tier provides high-level content coverage analysis, the second tier provides detailed conversation analysis for specific segments, and the third tier provides focused dynamic adjustments. Each tier processes data at the appropriate level of detail for its specific function, optimizing computational efficiency while maintaining overall accuracy.
Data Source
AI summary
A method, system, and computer program product that is configured to: receive a meeting agenda, a meeting time, a speaker, and an audience of a meeting; analyze the meeting agenda and the meeting time for an outcome of the meeting; categorize the meeting agenda and the meeting time into a tier level architecture using natural language processing (NLP); create a knowledge corpus using the categorized tier level architecture and the outcome of the meeting; dynamically adjust the categorized tier level architecture based on a conversation between the speaker and the audience during the meeting; train a tier level model using the created knowledge corpus; and dynamically adjust the categorized tier level architecture based on the trained tier level model.