Media File Identifier Generation for Cross-Type Similarity Analysis
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Solution Overview
Problem
Existing methods struggle to compare and determine the similarity between different types of media files, such as video and images, to identify if digital content has been copied or derived from an original work, especially when the files are not of the same type, posing challenges for copyright protection.
Innovation Solution
A system and method that extracts and normalizes various types of data and metadata from media files, using multiple extraction engines to generate media file identifiers, which are then compared to determine a similarity ratio, enabling the identification of derivative works or copied content across different media types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional comparison methods are used for same-type media files, then comparison accuracy is high, but the method cannot handle different-type media files
Solution Approach 1:
The patent segments the comparison process into distinct stages: extracting metadata from different media file types, normalizing the extracted data into a common format, and then comparing the normalized data. This segmentation allows the system to handle diverse file types while maintaining comparison accuracy through specialized processing at each stage.
Solution Approach 2:
The patent introduces a normalization layer as an intermediary between metadata extraction and comparison. This normalization component converts metadata from various media file types into a standardized format, enabling accurate comparison across different file types without losing the unique characteristics of each format.
2Adaptability or versatility
If multiple extraction engines are used to handle different media types, then adaptability improves, but system complexity increases
Solution Approach 1:
The patent designs the extraction engines with universal capabilities to handle multiple media file types. Rather than having separate specialized engines for each file type, the system uses multi-functional engines that can extract metadata from various formats, reducing the total number of engines needed while maintaining broad adaptability.
Solution Approach 2:
The system manages complexity by dynamically adjusting extraction parameters based on the input file type. The extraction engines modify their behavior and parameters according to the detected media format, allowing a single engine to adapt to multiple types without requiring separate specialized engines for each format.
3Reliability
If comprehensive metadata is extracted from all media files, then comparison completeness improves, but processing time increases
Solution Approach 1:
The patent applies partial action by extracting and comparing only the most relevant metadata fields necessary for determining similarity, rather than processing all possible metadata. This selective approach maintains reliable similarity determination while significantly reducing processing time by focusing on critical comparison elements.
Solution Approach 2:
The system performs preliminary filtering and prioritization of metadata fields before full comparison. By pre-identifying and ranking the most important metadata attributes for similarity assessment, the system prepares the data in advance to enable faster, more efficient comparison while maintaining comprehensive evaluation of key factors.
Data Source
AI summary
A method and system for determining the likelihood or similarity ratio that a selected media file of interest is related to one or more predetermined media files is provided that utilizes, combines, analyzes, and evaluates different categories of data and metadata extracted from each media file to generate a media file identifier for each media file that can then be used as a basis to compare any two media files to each other.