Content Overlap Detection Using Usage Heatmaps
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Solution Overview
Problem
User-generated content (UGC) often lacks standard identification metadata, leading to duplication and misidentification across platforms, resulting in inefficient distribution, excessive storage usage, and potential unauthorized distribution, with existing comparison techniques being processor-intensive and impractical for high volumes.
Innovation Solution
Generating and analyzing 'heatmaps' from user usage data to identify patterns, which are then used to determine matching content, optimizing storage and playback by associating heatmaps with UGC files and comparing them for similarity using machine learning and statistical analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If processor-intensive comparisons between different content are used to identify similarities, then content identification accuracy is improved, but processing speed and practicality deteriorate
Solution Approach 1:
The patent segments content identification into multiple levels: first using lightweight metadata extraction and hashing to generate content fingerprints, then using these fingerprints for rapid comparison and matching. Only when initial filtering suggests potential matches does the system proceed to more intensive analysis, thereby dividing the identification process into coarse-grained and fine-grained stages that balance speed and accuracy.
Solution Approach 2:
The patent introduces intermediate representations such as content fingerprints, metadata profiles, and usage pattern signatures that serve as mediators between the raw content and the identification system. These intermediaries enable rapid comparison and matching without requiring direct processor-intensive analysis of the actual content data, thus improving processing speed while maintaining identification capability.
2Reliability
If metadata standards are implemented for UGC identification, then content verification and duplication avoidance are improved, but implementation complexity and cost increase
Solution Approach 1:
The patent implements self-service mechanisms where content automatically generates its own identification metadata through hashing algorithms and fingerprint generation during upload or processing. The system autonomously creates metadata profiles, usage pattern signatures, and identification tags without requiring manual annotation or complex external verification processes, thereby improving reliability while minimizing implementation complexity.
Solution Approach 2:
The patent develops a universal metadata framework that serves multiple functions simultaneously: identification, verification, duplication detection, and usage pattern analysis. This multi-functional metadata system eliminates the need for separate specialized systems for each function, reducing overall implementation complexity while enhancing content verification capability.
3Productivity
If UGC is redistributed across multiple platforms, then content distribution reach is improved, but duplication and misidentification increase
Solution Approach 1:
The patent applies preliminary action by establishing robust content identification, fingerprinting, and metadata association during the initial upload or first distribution phase. These identification markers are embedded and preserved as the content is redistributed across multiple platforms, enabling consistent tracking and recognition regardless of the platform or transmission path, thus preventing duplication and misidentification while maintaining broad distribution reach.
Solution Approach 2:
The patent implements feedback mechanisms where usage data from distributed content is continuously collected, analyzed, and used to refine identification accuracy. The system monitors content performance, detection accuracy, and usage patterns across platforms, then adjusts metadata generation and matching algorithms accordingly, improving identification reliability while supporting multi-platform distribution.
Data Source
AI summary
A computer implemented method that includes receiving first user usage data and second user usage data indicative of user usage during consumption of a first content and of a second content, respectively. The method includes determining a first user usage pattern for the first content and a second user usage pattern for the second content based on the first user usage data and on the second user usage data, respectively. The method determines potential content overlap between the first content and the second content based on a comparison of the first usage pattern and the second usage pattern and performs an optimization operation based on the potential content overlap.