POI Location Selection Using Consensus Metrics and Gradient Boosting
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Solution Overview
Problem
Existing systems face challenges in accurately determining the location of points of interest (POIs) due to inconsistent and conflicting data from multiple sources, leading to difficulties in providing reliable navigation routing.
Innovation Solution
A system that combines a metric-based scoring system and a machine learning model to normalize and evaluate candidate locations, using metrics like across-the-road consensus, building footprint consensus, and distance from the nearest road segment, along with a gradient boosted decision tree algorithm to select the authoritative POI location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple data sources are used to provide POI location information, then data coverage and source diversity are improved, but data consistency and location accuracy deteriorate due to conflicting information
Solution Approach 1:
The patent segments the evaluation of candidate locations into multiple independent metric components (consensus metrics, geographic constraints, road proximity, building footprint analysis). Each metric evaluates a specific aspect of location validity separately, then combines results to determine overall authority. This segmentation allows comprehensive evaluation while maintaining clarity in handling conflicting data from multiple sources.
Solution Approach 2:
The patent changes the evaluation parameters by introducing a multi-dimensional scoring system that transforms raw location data into standardized metric scores. Different parameters (consensus level, geographic validity, road proximity) are weighted and combined to produce an authoritative ranking, allowing systematic resolution of conflicts between diverse data sources through parameter-based comparison.
2Device complexity
If simple outlier removal is used to handle conflicting location data, then processing complexity is reduced, but location selection accuracy deteriorates
Solution Approach 1:
Instead of simple distance-based outlier removal, the patent transforms the evaluation into a multi-parameter scoring system. Each candidate location is assessed on multiple dimensions (consensus metrics, geographic constraints, road proximity, building footprint) and ranked by composite score. This parameter-based approach maintains higher accuracy while keeping processing manageable through systematic evaluation.
Solution Approach 2:
The patent replaces the mechanical approach of simple geometric outlier removal with a computational evaluation system using multiple metrics and scoring. Instead of purely spatial filtering, the system uses algorithmic assessment of consensus, geographic validity, and contextual factors to determine authoritative locations, achieving higher precision through computational methods.
Data Source
AI summary
An authoritative candidate is selected for determining a location of a point of interest (POI). Source data including name, address, and location for POIs is received from multiple data sources. The received data is normalized for ease of comparison, and coordinates for each candidate are compared to coordinates of other candidates to determine which candidate if any is an authoritative location for the POI. The candidate locations are compared using two models a metric-based scoring system and a machine learning model that may utilize a gradient boosted decision tree. The authoritative candidate can be used to render digital maps that include the POI. In addition, the authoritative candidate's location can be used to provide vehicle route guidance to the POI.