Document Cluster Visualization via Spatial Similarity Mapping
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Solution Overview
Problem
Current technologies are limited in effectively visualizing similarities among clusters of electronic documents, making it difficult for users to identify relevant clusters efficiently.
Innovation Solution
A computer-implemented method and system that generate data visualizations by representing clusters of electronic documents with sizes indicative of their content, displaying them in a user interface with distances representing similarities between clusters, allowing users to efficiently assess and modify visualizations to identify relevant clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If documents are organized into clusters with same or similar attributes, then document searchability and organization are improved, but it becomes difficult to ascertain which clusters are relevant to a particular query and to identify similarities among multiple clusters
Solution Approach 1:
The patent transforms the abstract concept of cluster similarity into a visual spatial dimension. Clusters are displayed as graphical representations where physical distance on the screen corresponds to similarity magnitude - closer clusters are more similar, farther clusters are less similar. This dimensional transformation allows users to intuitively assess relationships that would otherwise require complex analysis of multiple document attributes.
Solution Approach 2:
The patent employs color coding to represent different clusters and their relationships. Each cluster can be displayed with distinct colors or shading patterns, allowing users to quickly identify and differentiate between clusters. Color variations can also indicate similarity levels or selection states, enhancing the visual perception of cluster relationships without requiring users to analyze textual content.
2Measurement precision
If visualizations depict similarities among multiple clusters, then user ability to identify relevant clusters is improved, but current technologies are limited in their abilities to effectively and accurately generate such visualizations
Solution Approach 1:
The patent changes the parameter representation from abstract similarity scores to concrete visual parameters including spatial distance, graphical size, and color intensity. The system calculates similarity metrics between clusters and transforms these into visual properties - for example, clusters with higher similarity are positioned closer together or displayed with larger sizes. This parameter transformation makes abstract similarity measurements visually perceivable and intuitively understandable.
Solution Approach 2:
The patent segments the large set of document clusters into manageable visual groups displayed in a spatial arrangement. Rather than presenting all clusters simultaneously in a flat list, the system organizes them in a two-dimensional space where related clusters are grouped together. This segmentation allows users to focus on specific regions of interest while maintaining awareness of the overall cluster landscape.
Data Source
AI summary
Systems and methods for generating visualizations of a set of processed electronic documents are disclosed. According to certain aspects, a set of clusters may be generated to reflect similarities among content of a set of electronic documents. An electronic device may generate a visualization of the set of clusters, where the visualization may include a set of representations corresponding to the set of clusters. A user interface may display the visualization, where the representations may be positioned to reflect similarities and differences between a set of documents included in a target cluster and additional sets of documents included in additional clusters.