Meeting Attendance Probability Prediction System
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Solution Overview
Problem
Scheduling large meetings is challenging due to multiple meeting conflicts, especially across different time zones, as existing technologies only provide availability status without indicating the likelihood of attendees prioritizing new meetings over existing ones, leading to inefficient scheduling processes.
Innovation Solution
Systems and methods that analyze past attendance patterns and behavior of invitees to provide a likelihood score for attending a prospective meeting, using AI to determine factors such as absence of conflicting meetings, interest in the topic, role in existing meetings, and external influences, to select a timeslot with the highest probability of attendance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the organizer relies on basic availability status (busy/free) of invitees, then the scheduling process is simple, but the accuracy of predicting actual attendance is poor
Solution Approach 1:
The system performs preliminary analysis of invitee behavior patterns, calendar data, and meeting history before the scheduling decision is made. By pre-calculating attendance probabilities based on historical data, the system prepares predictive information in advance that guides the scheduling process without requiring complex real-time interactions.
Solution Approach 2:
The system introduces an intermediary layer of attendance probability prediction between the basic availability status and the final scheduling decision. This intermediary component translates simple busy/free indicators into nuanced probability scores, enabling more accurate attendance prediction while maintaining operational simplicity.
2Measurement precision
If the organizer sends multiple queries and rescheduling messages to maximize attendance, then the attendance accuracy improves, but the time required for scheduling increases significantly
Solution Approach 1:
The system performs preliminary computation of attendance probabilities by analyzing historical meeting data, invitee behavior patterns, and calendar configurations before the actual scheduling occurs. This pre-computation eliminates the need for multiple iterative queries and rescheduling messages, significantly reducing the time required to reach accurate attendance predictions.
Solution Approach 2:
The system enables self-service scheduling by automatically generating attendance probability scores based on pre-established algorithms that analyze invitee history and preferences. This eliminates the need for manual back-and-forth communication with invitees, allowing the system to autonomously determine optimal scheduling decisions.
3Productivity
If the organizer accepts the first available timeslot without analyzing conflict priority, then the scheduling speed is fast, but the quality of meeting attendance is compromised
Solution Approach 1:
The system performs preliminary analysis of conflict priority and attendance probabilities before finalizing the timeslot selection. By pre-evaluating factors such as invitee behavior patterns, meeting importance, and historical attendance data, the system ensures that the selected timeslot maximizes attendance quality without requiring slow, iterative decision-making processes.
Data Source
AI summary
Scheduling meetings should be a simple task but rarely is, especially for a large number of attendees. Meetings may be scheduled at a timeslot that affords the largest number of invitees the opportunity to attend. However, some of those invitees may have a conflict at that particular timeslot. By determining if an invitee will or will not attended the conflict, more opportunities to conduct the meeting are presented and a meeting may be scheduled at a time when the greatest number of invitees will attend.