Circular Media Visualization for Complex Storyline Analysis
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Solution Overview
Problem
Conventional storyline visualization techniques are limited to analyzing a single feature of media content and are unsuitable for comparing multiple items of media content with complex storylines, making them inefficient for comprehensive evaluation and management.
Innovation Solution
A system and method for visualizing media content that generates a circular representation based on representative features and metadata, allowing for the classification and visualization of the overall emotional tone, and enabling interaction to explore and compare media content from different perspectives, such as temporal or spatial perspectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional storyline visualization techniques are used to analyze a single feature of media content, then the analysis depth of that single feature is improved, but the ability to compare multiple items of media content with complex storylines deteriorates
Solution Approach 1:
The patent merges multiple storyline features (characters, settings, events) into a single integrated circular visualization. The circular representation consolidates multiple data dimensions into one cohesive visual structure, allowing simultaneous analysis of multiple features while enabling direct comparison across different media content items through standardized visual patterns.
Solution Approach 2:
The patent transitions from linear or tabular representations to a circular dimensional structure. This dimensional change allows multiple storyline features to be arranged radially around a central point, creating a new visual dimension that simultaneously displays multiple features and their interrelationships, thereby enhancing both analysis depth and comparison capability.
2Loss of information
If conventional techniques are applied iteratively to analyze multiple features within a single storyline, then the comprehensive analysis of that storyline is improved, but the time consumption and operational efficiency deteriorate
Solution Approach 1:
The patent combines multiple iterative analysis steps into a single unified circular visualization process. Instead of sequentially analyzing each feature separately, the circular representation displays all storyline features simultaneously in one visual structure, eliminating the need for repeated analysis operations and significantly reducing time consumption while maintaining comprehensive coverage.
Solution Approach 2:
The patent segments the circular visualization into distinct radial sections, each representing a different storyline feature (characters, settings, events). This segmentation allows comprehensive analysis of all features to occur simultaneously within a single visual framework, rather than requiring iterative separate analyses, thereby reducing time loss while preserving information completeness.
3Measurement precision
If detailed text-based storyline analysis is performed, then the accuracy of storyline understanding is improved, but the ease of operation and visual recognition deteriorate
Solution Approach 1:
The patent employs color coding to represent different storyline features and their attributes within the circular visualization. Each radial section or segment can be assigned distinct colors or color intensities to indicate different characters, settings, or emotional tones, enabling rapid visual recognition and accurate storyline understanding without requiring detailed text analysis.
Solution Approach 2:
The patent transforms text-based storyline information into a spatial circular dimension. By mapping storyline features onto radial positions and angular arrangements, the system converts abstract textual relationships into intuitive spatial patterns that are easily visually recognized while preserving the accuracy of storyline understanding through maintained structural relationships.
Data Source
AI summary
According to one implementation, a system for visualizing media content includes a computing platform including a hardware processor and a system memory, storing a content visualization software code. The hardware processor is configured to execute the content visualization software code to receive a media file, parse the media file to identify a primary content and metadata describing the primary content, and analyze the metadata to determine representative features of the primary content. The hardware processor further executes the content visualization software code to generate a circular visual representation of the primary content based on the metadata and the representative features, the circular visual representation having a non-linear correspondence to at least one of the representative features. The circular visual representation includes a central circle having a central radius, and multiple, at least semicircular segments, each having a respective radius greater than the central radius.