Heat Map Match Line Identification via Short Line Merging
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Solution Overview
Problem
Existing heat map analysis techniques are insufficient in identifying patterns and correlations between data sets, particularly those with one dimension of input and two dimensions of output, leading to false positives and false negatives due to redundancy and low complexity, which complicates the identification of representative data correlations.
Innovation Solution
The system generates a heat map representing similarities between two data sets, employing a distance metric to calculate similarity values, and uses a series of filters and analyses to identify match lines formed by aligned data points, including region and area identification, short line detection, and continuity evaluation, to accurately determine overlapping portions between data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated heat map analysis techniques are used to identify patterns, then analysis speed is improved, but false positives and false negatives increase due to redundancy and low complexity
Solution Approach 1:
The patent segments the heat map analysis into distinct stages: identifying regions of interest, detecting lines within those regions, evaluating line characteristics (strength, complexity, continuity), and filtering results. This multi-stage segmentation allows each stage to focus on specific aspects, improving overall accuracy while maintaining automation speed.
Solution Approach 2:
The patent applies local quality by evaluating different regions of the heat map with different criteria. Regions of interest are identified based on local characteristics, and line detection parameters are adjusted locally rather than applying uniform thresholds across the entire heat map, thereby improving pattern identification accuracy in diverse data contexts.
2Quantity of substance
If heat maps are used to represent data with one dimension of input and two dimensions of output, then information density is improved, but pattern identification becomes more difficult due to redundancy
Solution Approach 1:
The patent extracts lines from the heat map by identifying continuous sequences of data points that form linear patterns. This extraction process separates the relevant pattern information from the redundant background data, making pattern identification easier while preserving the high information density of the heat map representation.
Solution Approach 2:
The patent performs preliminary actions by first identifying regions of interest before conducting detailed line detection. This preliminary segmentation reduces the search space and prepares the data structure for more efficient pattern identification, addressing the difficulty caused by high information density and redundancy.
3Productivity
If existing automated techniques are used for heat map analysis, then processing efficiency is improved, but sensitivity and granularity of pattern identification deteriorate
Solution Approach 1:
The patent employs dynamic evaluation of line characteristics by calculating strength, complexity, and continuity metrics for each detected line. These metrics are computed dynamically based on the specific characteristics of each line and its surrounding context, allowing the system to maintain high processing efficiency while achieving sensitive and granular pattern identification.
Solution Approach 2:
The patent changes multiple parameters simultaneously: it evaluates line strength based on data point density, assesses complexity based on deviation from linearity, and determines continuity based on spacing between consecutive points. By changing and evaluating multiple parameters, the system achieves both processing efficiency and measurement precision.
Data Source
AI summary
Systems and methods are provided for identifying lines in a heat map representative of overlap between two data sets. In an aspect, a method includes generating a heat map comprising a plurality of data points that represent similarities between a first data set and a second data set. The method further includes identifying areas of the heat map within a region of the heat map comprising a potential match line, identifying short lines formed via subsets of the data points respectively contained within the areas, merging the short lines to form a long line, determining degrees of continuity along the long line, and identifying a portion of the long line associated with a degree of continuity above a continuity threshold.