Empty Space Matrix Condensation for Discernible Cluster Displays
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Solution Overview
Problem
Existing methods of compressing representations of clusters of points in a constrained viewing space result in individual points becoming difficult to discern due to excessive scaling, while maintaining relative positioning within clusters is challenging.
Innovation Solution
A computer-implemented method that maps objects to a grid with a defined cell size, applies a grid compression threshold to remove empty cells, and remaps objects to reduce space between clusters while preserving their relative positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the representation of points is scaled down to fit all clusters in the constrained viewing space, then all clusters can be observed within the viewing space, but the points within clusters become compacted and difficult to discern
Solution Approach 1:
The patent segments the viewing space into multiple clusters, where each cluster is represented by a set of grid cells. This segmentation allows different scaling factors to be applied to different clusters, enabling points within each cluster to remain discernible while all clusters fit within the overall viewing space.
Solution Approach 2:
The patent applies local quality by allowing each cluster to have its own scaling factor based on the density of points within that cluster. Clusters with higher point density use smaller scaling factors, while clusters with lower density use larger scaling factors, ensuring optimal point discernibility in each local region.
2Measurement precision
If the scaling is increased to maintain point discernibility within clusters, then points within clusters remain easy to discern, but the distance between clusters increases making it difficult to represent all clusters in the constrained viewing space
Solution Approach 1:
The patent implements dynamic scaling where the scaling factor for each cluster is adjusted based on the cluster's point density and the available viewing space. This dynamic adjustment allows the system to optimize both point discernibility and viewing space utilization by allocating more space to denser clusters and less space to sparser clusters.
3Area of stationary object
If existing compression methods are applied to reduce space between clusters, then the representation becomes more compact, but individual points become difficult to discern due to excessive scaling
Solution Approach 1:
The patent segments the space between clusters into removable grid cells. By identifying and removing empty or sparsely populated grid cells between clusters, the patent achieves compression without uniformly scaling down all points, thus maintaining point discernibility while reducing overall space consumption.
Solution Approach 2:
The patent extracts and removes unnecessary grid cells that represent empty space between clusters. This extraction process reduces the overall representation size while preserving the integrity and discernibility of points within each cluster by not applying uniform scaling compression.
Data Source
AI summary
A computer implemented method includes obtaining a grid cell size for multiple clusters of objects in a display space. The objects are mapped to cells in the grid having the grid cell size. A grid compression threshold value expressed as a number of cells is used to condense the grid by removing cells based on the grid compression threshold value. The objects are remapped in the display space such that space between clusters of objects is reduced while maintaining relative positioning of objects within the clusters.