Meeting Support System Using Semantic Modeling for Agenda Recognition
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Solution Overview
Problem
Current computer systems face challenges in understanding complex human language and intentions, particularly in scenarios like meetings, due to the variable and complex nature of human communication.
Innovation Solution
A system and method utilizing machine-learning models for semantic and phonetic computer modeling to identify agenda items and recognize directives during meetings, integrating with sensory data streams from audio and visual inputs to provide automated support, including agenda creation, action-item classification, and notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If semantic computer modeling is applied to digital data streams to identify agenda items, then meeting preparation efficiency is improved, but system complexity increases
Solution Approach 1:
The system performs semantic computer modeling on digital data streams (emails, documents) before the meeting to pre-identify agenda items and topics. This preliminary processing automates the meeting preparation phase, extracting relevant information from various digital sources and organizing it into structured agenda items, thereby improving efficiency while managing complexity through automated workflows
2Measurement precision
If phonetic and situational computer modeling is applied to audio streams to recognize words and assign them to invitees, then accuracy of speech recognition is improved, but computational resources required increase
Solution Approach 1:
The audio processing system segments the meeting audio stream into individual speaker segments using phonetic modeling to identify speech boundaries and situational modeling to attribute speech to specific invitees. This segmentation approach improves recognition accuracy by processing discrete speech units rather than continuous audio, while managing computational resources through efficient segment-based analysis
Solution Approach 2:
The system introduces intermediary modeling layers (phonetic modeling and situational modeling) between raw audio input and word recognition. These intermediary models act as mediators that bridge the gap between acoustic signals and semantic meaning, improving overall recognition accuracy while distributing computational load across multiple specialized processing stages
3Loss of information
If semantic computer modeling is applied to recognize directives in spoken language, then understanding of user intentions is improved, but processing time increases
Solution Approach 1:
The system performs preliminary semantic analysis during the meeting to identify potential directives and intentions as they are expressed. By continuously analyzing speech patterns and contextual cues in real-time, the system prepares intent interpretations in advance, reducing the processing time required for post-meeting analysis while maintaining comprehensive understanding of user intentions
Data Source
AI summary
A system and method to provide computer support for a meeting of invitees comprises accessing one or more sensory data streams providing digitized sensory data responsive to an activity of one or more of the invitees during the meeting, the one or more sensory data streams including at least one audio stream. The method also comprises subjecting the at least one audio stream to phonetic and situational computer modeling to recognize a sequence of words in the audio stream and to assign each word to an invitee, subjecting the sequence of words to semantic computer modeling to recognize a sequence of directives in the sequence of words, and releasing one or more output data streams based on the sequence of directives, the one or more output data streams including one or more notifications.