Meeting Scheduling ML Model Predicts Room Parameters
Solution Overview
Problem
Current computer-based systems for automated electronic calendar management lack efficiency in scheduling meetings by failing to accurately predict meeting room needs and optimize meeting times and locations based on attendee availability, meeting history, and location data.
Innovation Solution
A method utilizing a meeting scheduling machine learning model that predicts meeting room parameters, including location and time, by analyzing attendee availability, meeting history, and location information, and dynamically secures candidate meeting rooms for scheduling and rescheduling requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated electronic calendar management systems are used to schedule meetings, then scheduling speed is improved, but accuracy in predicting meeting room needs and optimizing meeting times deteriorates
Solution Approach 1:
The system implements feedback mechanisms by analyzing meeting history data, cancellation data, and rescheduling data to continuously improve prediction accuracy. The machine learning model uses feedback from actual meeting outcomes to refine its predictions of meeting room needs, attendee availability patterns, and optimal scheduling decisions, thereby resolving the contradiction between automated scheduling speed and prediction accuracy.
Solution Approach 2:
The system performs preliminary actions by predicting meeting room parameters, attendee availability, and optimal meeting times in advance before final scheduling decisions are made. The machine learning model pre-analyzes multiple potential scheduling scenarios and identifies the most suitable options, allowing the automated system to make accurate predictions while maintaining high scheduling speed.
2Productivity
If machine learning models are used to predict meeting parameters, then scheduling efficiency is improved, but system complexity deteriorates
Solution Approach 1:
The machine learning model acts as an intermediary between the complex data analysis requirements and the simple scheduling output needs. It absorbs the complexity of analyzing meeting history, cancellation patterns, and attendee behavior, while providing simplified predictions about optimal meeting times and room requirements to the scheduling system, thereby maintaining scheduling efficiency without exposing system complexity.
Solution Approach 2:
The machine learning model performs self-service by automatically training on historical data and improving its own prediction capabilities without requiring manual intervention. The system autonomously processes meeting history data, cancellation data, and rescheduling data to refine its algorithms, reducing the operational complexity burden on users while maintaining high scheduling efficiency.
3Measurement precision
If real-time data analysis is performed for meeting predictions, then prediction accuracy is improved, but processing time deteriorates
Solution Approach 1:
The system performs preliminary analysis of attendee availability patterns, meeting history trends, and room requirements in advance to pre-compute prediction models. By preparing these analyses beforehand, the system can provide accurate real-time predictions during actual scheduling events without experiencing significant processing delays, thus resolving the contradiction between prediction accuracy and processing time.
Solution Approach 2:
The system uses periodic action by analyzing data at strategically determined intervals rather than continuously processing all data in real-time. The machine learning model periodically updates its predictions based on accumulated data patterns, maintaining high prediction accuracy while avoiding the processing time penalties of continuous real-time analysis.
Data Source
AI summary
In order to facilitate electronic meeting scheduling and coordination, systems and methods are disclosed including receiving, by a processor, a plurality of electronic meeting requests to schedule a meeting. The processor determines, for each electronic meeting request, meeting room needs. A meeting scheduling machine learning model is utilized to predict parameters of meeting room objects representing the candidate meeting rooms based at least in part on the meeting room needs, schedule information associated with a respective electronic meeting request and location information associated with the respective electronic meeting request. The processor causes an indication of the candidate meeting rooms to display in response to the electronic meeting request on a screen of computing devices associated with the respective attendees based at least in part on the predicted parameters. The processor receives a selection of the respective candidate meeting rooms from the respective attendees, and dynamically secures each candidate meeting room.