Bearing-Angle Clustering for N-Dimensional Regular Polygon Vertices
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Solution Overview
Problem
Current methods fail to efficiently cluster store locations arranged as an n-dimensional regular polygon, leading to inefficiencies in assigning field agents due to uniform distances, which hinder data collection efficiency.
Innovation Solution
Utilize bearing angle calculations to cluster store locations, forming groups based on the smallest bearing angles rather than Euclidean distances, optimizing the assignment of field agents to familiar stores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If coordinate clustering is performed based on proximity of coordinates using Euclidean distances, then clustering can be performed for irregular coordinate arrangements, but clustering fails to work efficiently for n-dimensional regular polygons where all distances are uniform
Solution Approach 1:
The patent changes the clustering parameter from Euclidean distance to bearing angle. By detecting whether coordinates form a regular polygon and switching to bearing angle calculations, the system adapts to uniform distance distributions while maintaining clustering efficiency. This parameter change resolves the contradiction by enabling efficient clustering in previously problematic regular polygon configurations.
2Ease of manufacture
If standard Euclidean distance-based clustering is used, then the method is simple to implement, but it cannot distinguish between different vertices in n-dimensional regular polygons leading to arbitrary and inefficient assignments
Solution Approach 1:
The patent applies local quality by calculating bearing angles relative to each vertex's local coordinate system rather than using global Euclidean distances. This allows the system to distinguish between vertices based on their angular relationships with the centroid, creating meaningful local differences that enable rational assignments while maintaining implementation simplicity through standardized bearing angle calculations.
3Extent of automation
If field agents are assigned to stores based on uniform distance metrics, then assignment can be automated, but it increases travel distances and learning curves reducing data collection efficiency
Solution Approach 1:
The patent implements feedback by using bearing angle calculations that reflect the actual spatial relationships between stores and centroids. This feedback mechanism allows the system to automatically assign stores to field agents in a way that minimizes travel and maximizes efficiency, maintaining automation while improving data collection productivity through intelligent assignment algorithms.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed for text extraction from a receipt image. An example apparatus for clustering vertices, the apparatus comprises machine-readable memory, instructions, and processor circuitry to execute the machine-readable instructions to calculate a centroid corresponding to coordinates, calculate distances for respective ones of the coordinates relative to the centroid, calculate differences between the distances, determine whether ones of the differences satisfy a set of thresholds, in response to determining that ones of the differences satisfy the set of thresholds, calculate bearing angles for ones of the coordinates, determine an efficiency metric associated with respective ones of the bearing angles, sort each of the bearing angles based on the associated efficiency metric, and form coordinate clusters based on the sorted bearing angles.