Automated Action Item Detection in Meeting Transcripts
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Solution Overview
Problem
Conventional computer-implemented event technologies fail to automatically determine and present action items from meetings, relying on manual input which is time-consuming and often inaccurate, and inefficient in terms of computing resource consumption.
Innovation Solution
The system automatically determines action items by analyzing factors such as speaker's language style, user role, historical communication patterns, event purpose, and participant names, using models to identify, extract, and attribute action items, reducing the need for manual processing and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual note taking or manual computer user input is used for meetings, then users can record event information, but the process is time consuming and important action items are often missed
Solution Approach 1:
The system automatically analyzes meeting transcripts and identifies action items without requiring manual user input. The computer system performs self-service by autonomously detecting action items, extracting relevant information, and presenting results, thereby eliminating the time-consuming manual note-taking process while maintaining high detection accuracy
Solution Approach 2:
The patent replaces the mechanical manual process of note-taking with an automated computer-based system that uses natural language processing and machine learning algorithms to detect and extract action items from meeting transcripts, substituting human manual labor with intelligent automated processing
2Productivity
If existing technologies are used to process meeting data, then basic functionality is provided, but computing resource consumption is high
Solution Approach 1:
The system extracts only the essential features and key information from meeting transcripts that are necessary for action item detection, rather than processing entire transcripts or all meeting data. This selective extraction approach maintains high productivity in automated detection while significantly reducing computing resource consumption by focusing only on relevant data portions
Solution Approach 2:
The patent applies partial action by implementing a multi-stage filtering process where only promising segments of meeting transcripts are subjected to full analysis. The system performs preliminary filtering to identify potential action items, then applies more computationally intensive analysis only to these candidates, achieving high productivity with optimized resource usage
Data Source
AI summary
Computerized systems are provided for automatically determining action items of an event, such as a meeting. The determined action items may be personalized to a particular user, such as a meeting attendee, and may include contextual information enabling the user to understand the action item. In particular, a personalized action item may be determined based in part from determining and utilizing particular factors in combination with an event dialog, such as an event speaker's language style; user role in an organization; historical patterns in communication; event purpose, name, or location; event participants, or other contextual information. Particular statements are evaluated to determine whether the statement likely is or is not an action item. Contextual information may be determined for action items, which then may be provided to the particular user during or following the event.