ML Engine Automated Teleconference Transcript and Summary Generation
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Solution Overview
Problem
Large enterprise organizations face difficulties in optimizing, maintaining, and utilizing complex computer systems and services due to growing technical complexity, especially when multiple entities need to collaborate effectively.
Innovation Solution
A data processing system equipped with a machine learning engine provides automated collaboration assistance functions by generating real-time transcripts, summarizing teleconferences, identifying subject matter experts, and executing automated tasks, using organization-specific, team-specific, and individual-specific data to enhance collaboration and task management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated collaboration assistance functions are implemented using machine learning, then collaboration efficiency is improved, but device complexity increases
Solution Approach 1:
The system segments complex collaboration assistance into multiple specialized modules: a machine learning engine for transcript generation, a summary generation module, an action item detection module, and a task execution module. Each module handles specific functions independently, improving collaboration efficiency while managing system complexity through functional decomposition.
Solution Approach 2:
The machine learning engine acts as an intermediary between teleconference content streams and collaboration assistance functions. It processes audio, video, and chat data to generate transcripts, which then serve as input for summary generation and action item detection, mediating the complex transformation of raw meeting data into structured collaboration outputs.
2Loss of information
If real-time transcript generation is performed during teleconferences, then information completeness is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary action by generating transcripts during the teleconference itself rather than after. The machine learning engine processes audio, video, and chat data in real-time, creating transcripts concurrently with the meeting. This preliminary transcription enables immediate summary generation and action item detection without requiring post-meeting processing time.
Solution Approach 2:
The transcript generation maintains continuity of useful action by operating throughout the entire teleconference duration. The machine learning engine continuously processes incoming content streams, ensuring that transcript information is accumulated without interruption. This continuous processing allows the system to maintain complete transcripts while minimizing delays between the meeting and available analysis.
3Adaptability or versatility
If multiple entities collaborate on complex computer systems, then system functionality is improved, but ease of operation deteriorates
Solution Approach 1:
The system implements self-service by automatically generating transcripts, summaries, and action items without requiring manual intervention from collaborating entities. The machine learning engine autonomously processes meeting data and produces structured outputs, while the task execution module automatically assigns and tracks action items. This automation eliminates the need for participants to manually document meeting outcomes, significantly improving ease of operation while maintaining multi-entity collaboration capabilities.
Data Source
AI summary
Aspects of the disclosure relate to implementing and using a data processing system with a machine learning engine to provide automated collaboration assistance functions. A computing platform may receive, from a teleconference hosting computer system, a content stream associated with a teleconference. Responsive to receiving the content stream associated with the teleconference, the computing platform may generate, based on a machine learning dataset, real-time transcript data comprising a real-time textual transcript of the teleconference. The computing platform may receive a request for a summary of the teleconference. Responsive to receiving the request for the summary of the teleconference, the computing platform may generate, based on the real-time transcript data comprising the real-time textual transcript of the teleconference, a summary report of the teleconference. Based on generating the summary report of the teleconference, the computing platform may send the summary report of the teleconference to one or more recipient devices.