Voice Conference Phase Detection for Meeting Start Delay
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Voice conferencing systems face significant delays in commencing meetings due to technical obstacles and inefficiencies, particularly in larger groups, leading to lost time and decreased user experience, which affects business success.
Innovation Solution
A method and system for automatically determining the commencement time of a voice conference by analyzing logging data, pattern matching, and heuristics to track the transition from an unproductive preparatory phase to a productive phase, allowing for adjustments in the conferencing system settings to reduce delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voice conferencing technology is used to enable remote meetings, then accessibility and convenience are improved, but meeting commencement delay increases due to technical setup requirements
Solution Approach 1:
The system performs preliminary actions by automatically determining the commencement time of voice conferences and identifying phases before the meeting actually starts. This allows proactive detection of delays and enables preliminary adjustments to be made to reduce commencement delays.
Solution Approach 2:
The system implements feedback by automatically monitoring and determining the phase of voice conferences in real-time, then using this information to adjust system settings dynamically. This closed-loop feedback mechanism enables the system to respond to actual meeting conditions and reduce delays through automated adjustments.
2Productivity
If automated phase determination is implemented, then meeting efficiency measurement is improved, but system complexity increases due to additional processing requirements
Solution Approach 1:
The system applies self-service by automatically determining conference phases and commencement times without requiring manual intervention or complex external processing. The system serves itself by using its own recorded data and analysis capabilities to perform phase determination, reducing the need for additional complex infrastructure.
Solution Approach 2:
The system uses parameter changes by analyzing variations in recorded data parameters (such as audio activity patterns, participant joining times, and speech characteristics) to automatically determine conference phases. This approach transforms complex qualitative assessment into quantitative parameter analysis, improving efficiency measurement while managing system complexity through standardized parameter processing.
3Productivity
If system settings are adjusted based on commencement time estimates, then meeting promptness is improved, but measurement precision requirements increase to accurately identify productive phase transition
Solution Approach 1:
The system applies partial action by determining conference phases at key transition points rather than continuously monitoring every moment. This allows the system to identify the commencement time with sufficient precision for adjusting settings, without requiring excessive measurement precision across the entire meeting duration. The system focuses measurement efforts on the critical transition from unproductive to productive phases.
Data Source
AI summary
A method (400) for determining that an audio conference is in a first phase of a plurality of phases. The method comprises determining (401) a plurality of sequences (301) of events (305) for a plurality of terminals (120, 170); wherein an event from the sequences (301) of events (305) indicates that a talker activity at a terminal (120, 170) has been detected; determining (403) a sequence (350) of feature vectors (330) based on the plurality of sequences (301) of events (305); wherein a feature vector (330) of the sequence (350) of feature vectors (330) is indicative of the talker activity (332) of at least one of the plurality of terminals (120, 170) relative to the talker activity at least another one of the plurality of terminals (120, 170); and determining (406) that the audio conference is in the first phase based on the sequence (350) of feature vectors (330). DETAILS: audio-conference phases identified as: I) introductory phase (comprising a waiting phase, an uncoordinated activity or chatting phase, and a participant introductions / attendance check phase); II) cooperative phase (comprising productive discussions between participants); III) sign-off phase. In addition to participant join/ leave/mute events, voice-activity detection algorithms are used for measuring a ratio of voice-activity of each participant (RTT, Relative Talking Time) and other features (CTT, Concurrent Talking Time, NT, number of Turns, NAE, Number of Active Endpoints) and for inferring the current phase of the conference; conference logs or recordings may be used instead of real-time measurements; the time difference (delay) between scheduled time or first call and the reach of phase II is used as a measure of efficiency of the conference.