Geolocation Rescheduling System for Meeting Location Optimization
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Solution Overview
Problem
Current scheduling systems fail to optimize meeting locations based on the real-time locations of attendees, often resulting in attendees traveling long distances when a more convenient location could be available.
Innovation Solution
A computer-implemented method that determines the current locations of attendees and compares them to the initial meeting location to suggest alternative locations, considering factors like geographic proximity and transit time, and allows for rescheduling to a more convenient location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If meeting location is determined in advance without considering attendee locations, then scheduling is simple and quick, but attendees may need to travel long distances
Solution Approach 1:
The system determines attendee locations and transportation modes in advance of the meeting time, allowing alternative location suggestions to be prepared beforehand. This enables the system to have location optimization ready before the meeting occurs, reducing last-minute travel issues while maintaining scheduling efficiency
Solution Approach 2:
The system continuously monitors attendee locations and provides feedback about alternative meeting locations that reduce travel distance. This feedback loop allows the system to suggest optimized locations based on real-time attendee positioning, thereby reducing travel time while maintaining scheduling effectiveness
2Loss of time
If alternative meeting locations are suggested based on real-time attendee locations, then travel distance is reduced, but system complexity increases
Solution Approach 1:
The system automatically determines attendee locations, analyzes transportation modes, and generates alternative location suggestions without requiring manual input from attendees. This self-service approach handles the complexity internally while presenting a simple interface to users, reducing travel distance without increasing perceived system complexity
Solution Approach 2:
The system dynamically changes scheduling parameters based on attendee location data, such as adjusting meeting location coordinates and timing. By automatically modifying these parameters based on real-time data, the system reduces travel distance while managing complexity through automated parameter optimization rather than complex manual coordination
3Productivity
If attendee locations are tracked in real-time, then meeting location can be optimized, but privacy concerns increase
Solution Approach 1:
The system uses location data as an intermediary to achieve meeting optimization without directly exposing personal attendee information. Location coordinates serve as a mediator that enables location-based suggestions while maintaining privacy, as the system processes spatial data without revealing sensitive personal details about attendees
Solution Approach 2:
The system processes location data locally and generates suggestions based on spatial relationships rather than personal information. By focusing on the geometric and spatial qualities of locations rather than personal attributes, the system optimizes meeting efficiency while minimizing privacy intrusion, treating location as an abstract spatial parameter rather than personal data
Data Source
AI summary
A method, computer program product, and computing system for determining an initial meeting location for a meeting previously-scheduled to be attended by a plurality of attendees at a defined meeting time. A current attendee location is determined for each of the plurality of attendees proximate the defined meeting time, thus defining a plurality of attendee locations. The initial meeting location and one or more of the plurality of attendee locations are compared to determine if the initial meeting location could be changed. If the initial meeting location could be changed, at least one alternative meeting location is suggested.