Conversational Context Modeling for Real-Time Conference Coaching
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Solution Overview
Problem
Existing audio and video conferencing systems lack effective methods to monitor and analyze conversational data for context, sentiment, and behavior, making it difficult for managers to assess representative performance and adherence to best practices.
Innovation Solution
An automated conference monitoring system using AI/ML techniques attributes context to conversational data, providing context-aware speech-to-text transcription, generating summarized reports, and enabling automated actions and chatbot interactions to improve conference outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If managers manually review and rate each conversation, then performance assessment accuracy is improved, but time consumption and workload increase significantly
Solution Approach 1:
The system enables automated self-assessment of representative performance through AI-driven analysis of conversational data. The system automatically transcribes conversations, identifies key events, analyzes sentiment, and generates performance ratings without requiring manual manager intervention, thus maintaining assessment accuracy while eliminating time consumption.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated AI/ML-based system. The system uses machine learning models to analyze conversational data, extract contextual information, and generate performance assessments automatically, substituting human manual effort with computational processing.
2Speed
If keyword search is used to isolate relevant parts of conversation, then search speed is improved, but context accuracy deteriorates due to lack of sentiment and behavioral information
Solution Approach 1:
The system adds multiple new dimensions to the search capability beyond simple keyword matching. It incorporates sentiment analysis, behavioral pattern recognition, contextual event identification, and tone detection as additional search dimensions, allowing users to search for conversations based on emotional state, behavioral patterns, or contextual themes rather than just specific words.
Solution Approach 2:
The patent creates a composite search approach that combines multiple types of data analysis: keyword matching, sentiment analysis, behavioral pattern recognition, and contextual event detection. This composite methodology integrates different analytical layers to provide both speed and accuracy simultaneously.
3Productivity
If traditional transcription services are used, then text generation is improved, but contextual understanding deteriorates due to omission of sentiment, emotions, and behavior
Solution Approach 1:
The system merges multiple analytical functions into a single integrated transcription and analysis platform. It combines speech-to-text conversion, sentiment analysis, behavioral pattern recognition, contextual event detection, and performance assessment into one unified system that processes conversational data comprehensively, maintaining efficiency while preventing information loss.
Data Source
AI summary
Disclosed is a conference monitoring system that classifies conversations and performs automated actions based on different context detected within the conversations. The system receives conversations that result in an unsuccessful engagement, classifies different segments of the conversations with contextual trackers that identify different context within each segment, and determines a recurring pattern of a common set of contextual trackers in different segments of the conversations that contribute to the unsuccessful engagement. The system monitors a particular conversation, tags one or more segments of the particular conversation with the common set of contextual trackers, and performs an automated action that contributes to a successful engagement in response to tagging the one or more segments with the common set of contextual trackers and the common set of contextual trackers contributing to the unsuccessful engagement.