ML-Based Calendar Rescheduling for Out-of-Office Conflicts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computer-based systems lack efficient automated solutions for rescheduling meetings when attendees are out of office, leading to inefficiencies in resource allocation and meeting management.
Innovation Solution
A method utilizing a meeting scheduling machine learning model to predict optimal rescheduled meeting parameters, including location and time, based on attendee availability, meeting history, and location information, which dynamically secures the rescheduled meeting upon user selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated machine learning models are implemented for meeting rescheduling, then meeting management efficiency is improved, but system complexity increases
Solution Approach 1:
The system enables automated self-service meeting rescheduling through machine learning models that autonomously analyze attendee availability, predict optimal rescheduling options, and secure new meeting times without requiring manual intervention from meeting organizers or participants
Solution Approach 2:
The patent replaces manual mechanical processes of meeting coordination with automated electronic systems using machine learning algorithms to analyze calendar data, predict attendee availability, and automatically secure rescheduled meeting times
2Measurement precision
If comprehensive schedule information and meeting history data are analyzed, then prediction accuracy is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary actions by continuously analyzing and storing schedule information and meeting history data in advance, building trained machine learning models that can quickly generate predictions when rescheduling is needed, rather than processing all data from scratch at the moment of rescheduling
Solution Approach 2:
The machine learning models transform raw schedule and history data into meaningful predictive parameters, changing the state of the data from unprocessed information to optimized prediction inputs that balance comprehensiveness with processing efficiency
Data Source
AI summary
In order to facilitate automatic electronic calendar rescheduling in response to out-of-office statuses, systems and methods are described including receiving, by processors, an out-of-office notification associated with meeting attendees. The processors identify a need-to-reschedule meeting data item of respective need-to-reschedule meetings. The processors utilize a meeting scheduling machine learning model to predict a plurality of parameters of a meeting room object representing respective candidate rescheduled meetings based at least in part on schedule information and location information associated with the at least one need-to-reschedule meeting data items. The processors cause to display an indication of the respective candidate rescheduled meetings in response to the out-of-office notification on a screen of a computing device associated with the respective attendees. The processors receive a selection of the at least one respective candidate rescheduled meeting from the at least one respective attendee and dynamically secure the respective candidate rescheduled meetings.