Meeting Content Derivation Using Phase-Aware File Updates
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Solution Overview
Problem
Large-scale programs face challenges in efficiently tracking and updating meeting content due to the large number of participants, unclear participant roles, and misalignment of personnel schedules, leading to interrupted updates and missed event tracking.
Innovation Solution
A computer-implemented method and system for adaptive derivation of meeting content using a phase context profile model to determine the current phase, detect speakers, and update files based on meeting content, ensuring participants do not lose track of the meeting content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tracking of meeting content is used in large-scale programs, then participant roles and meeting progress can be monitored, but the system becomes complex and updates become interrupted due to the large number of participants and misaligned schedules
Solution Approach 1:
The system automatically derives meeting content from audio signals without requiring manual intervention. The automatic speaker identification and content extraction mechanisms enable the system to self-manage meeting tracking, eliminating the need for complex manual coordination and schedule alignment among participants.
Solution Approach 2:
Manual tracking mechanisms are replaced with automated audio analysis and speech recognition systems. The mechanical process of manual note-taking and content tracking is substituted with electronic signal processing and automated content derivation, significantly reducing system complexity while maintaining reliability.
2Productivity
If automated content extraction is implemented, then meeting tracking efficiency improves, but the system requires complex processing of audio signals and speaker identification
Solution Approach 1:
The system performs preliminary speaker identification and audio signal classification before full content extraction. By pre-identifying speakers and their roles early in the processing pipeline, the system simplifies subsequent content derivation tasks and improves overall efficiency without requiring equally complex processing throughout the entire system.
Solution Approach 2:
The meeting content derivation process is divided into distinct segments: speaker detection, role identification, content extraction, and file updating. This segmentation allows each component to be optimized independently, improving overall productivity while managing complexity through modular processing stages.
3Loss of information
If continuous meeting monitoring is performed, then participants do not lose track of meeting progress, but the system consumes excessive computational resources and time
Solution Approach 1:
Instead of continuous monitoring, the system performs periodic content derivation at key meeting phases and transition points. This periodic approach prevents information loss by capturing essential content at regular intervals while significantly reducing computational resource consumption compared to continuous analysis.
Solution Approach 2:
The system applies partial monitoring by focusing computational resources on identifying and extracting only the most critical meeting content and speaker transitions. Rather than analyzing every utterance in detail, the system performs sufficient analysis to capture essential progress information while minimizing processing time and resource usage.
Data Source
AI summary
A computer-implemented method, a system and a computer program product for adaptive derivation of meeting content is provided. In the method, for a meeting held for a program, a current phase can be determined from a plurality of phases of the program. A speaker can be detected in the meeting from a plurality of participants of the program. Meeting content associated with the meeting can be obtained based on analyzing input data from the speaker. A file can be activated based on the current phase, the meeting content and a phase context profile model which is trained with history data associated with past meetings held for the program in terms of file inputs. Then, the file can be updated based on the meeting content.