Bearing-Angle Clustering of Store Coordinates in Regular Polygons
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Solution Overview
Problem
Existing methods fail to efficiently cluster store coordinates that form an n-dimensional regular polygon, leading to inefficient reassignment of field agents due to uniform distances, which reduces data collection efficiency.
Innovation Solution
Utilize bearing angle calculations to cluster store coordinates, forming clusters based on the smallest bearing angles rather than Euclidean distances, ensuring field agents are reassigned to familiar stores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If Euclidean distance-based clustering is used for store coordinates, then the method is simple and easy to implement, but it fails to produce effective clusters when coordinates form an n-dimensional regular polygon due to uniform distances
Solution Approach 1:
The patent changes the clustering parameter from Euclidean distance to bearing angle. By calculating the bearing angle between each store coordinate and the centroid, and then clustering based on these angles rather than distances, the system resolves the uniform distance problem in regular polygons while maintaining computational simplicity.
Solution Approach 2:
The patent introduces bearing angle as an intermediary parameter between the store coordinates and the clustering result. Instead of directly using coordinate distances, the bearing angle serves as a mediator that captures angular relationships, enabling effective clustering for regular polygon configurations.
2Ease of operation
If field agents are assigned to stores based on uniform distance metrics, then the assignment process is straightforward, but it increases travel distances and learning curve times
Solution Approach 1:
The patent applies local quality by considering the angular position of each store relative to the centroid rather than uniform distance. This creates locally optimized assignments where field agents are assigned to stores with similar bearing angles, resulting in more efficient routes and reduced travel time.
Solution Approach 2:
The patent transitions from two-dimensional Euclidean distance measurement to angular dimension measurement. By using bearing angles, the system adds an angular dimension to the clustering approach, enabling more intelligent assignment that reduces overall travel distance and time.
3Device complexity
If standard distance-based clustering is used, then the computational process is simple, but it cannot handle regular polygon coordinate arrangements effectively
Solution Approach 1:
The patent changes the computational parameter from calculating Euclidean distances to calculating bearing angles. This parameter transformation enables reliable clustering for regular polygon configurations while maintaining similar computational complexity, as both distance and angle calculations involve basic trigonometric operations.
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.