Geolocation Clustering for Delivery Point Accuracy
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Solution Overview
Problem
Current global positioning satellite systems and maps of delivery points often provide imprecise coordinates, with the correct delivery point sometimes being different from the location identified, such as the center of a house versus the mailbox or driveway.
Innovation Solution
A system and method that uses mobile devices to record GPS coordinates of delivery locations, groups them into clusters, calculates a confidence value for each cluster, and updates delivery point coordinates in maps or databases if the confidence value is high enough, employing methodologies like DBSCAN for accurate geolocation determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS coordinates are recorded at delivery locations, then delivery point accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the delivery point determination process into distinct modules: a recording module that collects GPS coordinates at delivery locations, a cluster identification module that groups coordinates into clusters, and a delivery point determination module that selects representative points. This segmentation reduces processing complexity by handling data in organized stages rather than as a monolithic task.
Solution Approach 2:
The patent introduces an intermediary processing layer that collects multiple GPS coordinates and processes them through cluster analysis before determining final delivery points. This intermediary step filters and organizes raw GPS data, reducing the complexity burden on the final determination system while maintaining high accuracy through aggregated data analysis.
2Measurement precision
If multiple GPS coordinates are collected and clustered, then delivery point precision is improved, but system complexity increases
Solution Approach 1:
The system segments the coordinate processing task into distinct functional modules: coordinate collection, cluster identification using DBSCAN methodology, and delivery point determination. Each module handles a specific aspect of the problem, reducing overall system complexity while achieving high precision through coordinated operation of specialized components.
Solution Approach 2:
The cluster identification module autonomously processes multiple GPS coordinates and automatically identifies delivery points without requiring manual intervention. The system self-organizes the data into clusters and selects representative points, reducing operational complexity while maintaining high precision through algorithmic self-service.
3Measurement precision
If accurate delivery points are determined, then delivery window accuracy is improved, but data collection requirements increase
Solution Approach 1:
The patent merges multiple GPS coordinate measurements from different deliveries into unified clusters, combining data from numerous sources to determine accurate delivery points. This merging approach achieves high delivery window accuracy by aggregating data across multiple deliveries rather than relying on single-point measurements.
Solution Approach 2:
The system continuously collects GPS coordinates from ongoing deliveries and progressively refines delivery point accuracy through accumulating data. This continuous data collection approach improves delivery window accuracy over time without requiring intensive upfront data gathering, as the system learns and refines delivery points through sustained operational data.
Data Source
AI summary
A system and method for determining delivery points associated with particular addresses. In some embodiments, mobile devices can record the geo-location that an item carrier is at when making a delivery of an item. An address association server can receive these locations, organize the recorded geo-locations into clusters, and then calculate accurate delivery points based upon those clusters for a specific address.