Dotplot Pattern Analysis for Sequential Data
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Solution Overview
Problem
Current techniques for analyzing sequential data, such as eye tracking or web browsing patterns, lack effective methods to identify and visualize patterns within sequences, making it difficult to derive insights or improve designs based on user behavior.
Innovation Solution
The method involves generating a dotplot representing matches between sequences, filtering points against a high-pass threshold, fitting a linear regression line, and recomputing it based on variance criteria to identify linear relationships, allowing for the identification and aggregation of patterns within sequential data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If traditional sequence analysis methods are used, then data processing is simple, but pattern identification capability is insufficient
Solution Approach 1:
The patent introduces a dotplot as an intermediary visual representation between raw sequence data and pattern identification. The dotplot transforms complex sequence comparison into a visual format where patterns become detectable through graphical analysis, serving as a mediator that simplifies the detection process while maintaining analytical depth
Solution Approach 2:
The patent employs visual differentiation through dotplot rendering where matching tokens are represented by dots at specific coordinate positions. This visual encoding transforms abstract sequence matches into visible patterns, enabling human operators to easily identify relationships that would be difficult to detect in raw data
2Loss of information
If comprehensive pattern analysis is performed, then insight quality improves, but processing time increases
Solution Approach 1:
The patent segments the sequence analysis process into distinct stages: generating the dotplot representation, filtering points based on criteria, fitting linear regression lines, and identifying patterns. This segmentation allows each stage to be optimized independently and enables progressive disclosure of results, reducing overall processing time while maintaining comprehensive analysis
Solution Approach 2:
The patent applies partial action by first generating the complete dotplot with all possible token matches, then selectively analyzing only the regions that meet predetermined criteria (such as minimum dot density or specific coordinate patterns). This approach ensures comprehensive pattern detection while avoiding unnecessary processing of clearly non-pattern regions
3Measurement precision
If detailed sequence comparison is conducted, then pattern accuracy improves, but computational complexity increases
Solution Approach 1:
The patent creates a simplified copy of the sequence comparison problem in the form of a dotplot. Instead of directly analyzing complex sequence alignments, the system plots matched tokens as dots in a二维 space, creating a simplified representation that preserves pattern information while reducing computational complexity for visual pattern recognition
Data Source
AI summary
Embodiments of the invention provide systems and methods for analyzing sequential data. The sequential data can comprise a sequence of data points arranged in a particular order and thus representing a sequence. A number of such sequences can be analyzed, for example, to identify patterns or commonalities within the sequences or portions of sequences represented by the data. According to one embodiment, a method of identifying patterns in sequences of data points can comprise reading a set of sequential data. The sequential data can comprises a plurality of sequences and each of the plurality of sequences can represent an ordered sequence of tokens. A dotplot representing matches between each sequence of the plurality sequences can be generated. One or more patterns within the sequential data can then be identified based on the dotplot.