Personalized Graph Summarizer Using Pattern Detection
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Solution Overview
Problem
Current systems for summarizing data visualizations face challenges in providing meaningful, user-tailored summaries without requiring annotations or underlying data files, struggling with variability in data visualization types and user preferences, leading to poor performance and limited capabilities.
Innovation Solution
A personalized graph summarizer that analyzes data visualizations to detect predefined patterns, generates textual summaries based on these patterns, and adapts to user preferences by learning from interactions, extracting context, and tailoring summaries without relying on annotations or data files.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If current summarization systems are used without annotations or data files, then the system simplicity is improved, but the summarization quality deteriorates
Solution Approach 1:
The patent introduces an intermediary component (annotation generator or data file creator) that bridges the gap between simple input (graph image) and high-quality output (summary). This intermediary either generates synthetic annotations from the graph image using OCR and pattern recognition, or creates synthetic data files from the visual information, thereby enabling quality summarization without requiring pre-existing annotations or data files.
Solution Approach 2:
The system performs self-service by automatically generating its own annotations or data files from the graph image using embedded OCR, pattern recognition, and graph type detection capabilities. This eliminates the need for external annotations or data files while maintaining summarization quality, as the system creates the necessary intermediate representations from the visual input itself.
2Adaptability or versatility
If the system supports multiple graph types and user preferences, then the adaptability is improved, but the device complexity worsens
Solution Approach 1:
The patent segments the complex task of supporting multiple graph types and user preferences into distinct modular components: graph type detection module, pattern recognition module, annotation generation module, and summary generation module. Each component handles a specific aspect, allowing the system to support diverse graph types and user preferences through composition of specialized modules rather than monolithic complexity.
Solution Approach 2:
The system implements universal components that can handle multiple graph types and user preferences through a common interface. The graph type detection module, for example, can identify various graph types (line graphs, bar charts, scatter plots) using a single unified approach, and the summary generator can adapt to different user preferences through parameter configuration rather than requiring separate systems for each case.
3Loss of information
If pattern detection and customization are implemented, then the summarization relevance is improved, but the processing time worsens
Solution Approach 1:
The patent applies preliminary action by pre-defining a library of common graph patterns and summary templates before processing the actual graph image. The system pre-processes the graph image to detect graph type and extract basic features (axes, data points, labels) before pattern matching, thereby reducing the computational burden during the actual summarization process and enabling relevant pattern detection without excessive processing time.
Solution Approach 2:
The system implements partial action by focusing pattern detection on the most relevant and common graph patterns rather than attempting to detect all possible patterns. The summary generation applies customization to the extent necessary for relevance (using detected patterns and user preferences) without over-customizing, thereby achieving adequate summarization relevance while controlling processing time through selective application of computational resources.
Data Source
AI summary
Various embodiments are generally directed to systems for summarizing data visualizations (i.e., images of data visualizations), such as a graph image, for instance. Some embodiments are particularly directed to a personalized graph summarizer that analyzes a data visualization, or image, to detect pre-defined patterns within the data visualization, and produces a textual summary of the data visualization based on the pre-defined patterns detected within the data visualization. In various embodiments, the personalized graph summarizer may include features to adapt to the preferences of a user for generating an automated, personalized computer-generated narrative. For instance, additional pre-defined patterns may be created for detection and/or the textual summary may be tailored based on user preferences. In some such instances, one or more of the user preferences may be automatically determined by the personalized graph summarizer without requiring the user to explicitly indicate them. Embodiments may integrate machine learning and computer vision concepts.