Content Fingerprinting via Technical Attribute Analysis
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Solution Overview
Problem
The availability of digital content via IP connections lacks accurate identification information, making it difficult for users to search and filter content effectively, as traditional methods rely on manual entry and often fail to provide or correctly associate meta-data tags, closed captioning, ratings, and URL links, especially with user-generated and advertising content.
Innovation Solution
A system and method for generating a content fingerprint through technical analysis using techniques like facial recognition, voice pattern recognition, and audio analysis to create an encrypted packet of attributes, paired with traditional identification information, enabling precise content categorization and selection without requiring users to view the content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual entry of content identification information is used, then content identification is provided, but the information is often incorrect or missing for user-generated and advertising content
Solution Approach 1:
The content itself performs the identification function by embedding machine-readable codes (watermarks, barcodes, QR codes) that contain metadata about the content. This self-service approach eliminates the need for manual entry and ensures accurate identification information is always available, resolving the reliability issue while maintaining automation.
Solution Approach 2:
Content identification information is embedded into the content during the content creation or distribution process, before the content reaches the user. This preliminary action ensures that accurate identification data is already present and eliminates the need for subsequent manual entry or analysis, improving reliability while automating the process.
2Measurement precision
If users view content to understand its nature, then accurate content identification is achieved, but time and effort are wasted
Solution Approach 1:
The essential identification information is extracted from the content and embedded as separate machine-readable codes (watermarks, barcodes, QR codes). Users can scan or read these codes to instantly obtain content identification information without viewing the entire content, achieving accurate identification while eliminating time loss.
Solution Approach 2:
Machine-readable codes serve as an intermediary between the content and the user. Instead of directly viewing content to understand it, users interact with the embedded codes that convey identification information, providing accurate content identification instantaneously without requiring time to view and analyze the actual content.
3Adaptability or versatility
If traditional content identification methods are used, then some content is identified, but most user-generated and advertising content lacks identification information
Solution Approach 1:
The embedded machine-readable codes serve multiple functions: they provide content identification, enable filtering, support searching, and work across different content types (video, audio, images). This universal approach ensures all content including user-generated and advertising content can be identified and managed, greatly expanding coverage without proportionally increasing system complexity.
Data Source
AI summary
A system and method for the generation of a content fingerprint for content identification are described. Various embodiments include encoding content without any available identifying information, performing a technical analysis of the encoded content for one or more technical attributes, and pairing the available identifying information with the one or more technical attributes to form a content fingerprint, where the content fingerprint identifies the content. Other embodiments are described and claimed.