Content Similarity Detection Using Cross-Platform Content IDs
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Solution Overview
Problem
Enterprise organizations face challenges in managing the exponential growth of content created with Generative AI, leading to content data standard issues, duplicative assets, inconsistent metadata, and difficulty in locating and reusing assets across disparate systems, exacerbated by non-standard naming conventions and manual effort.
Innovation Solution
A system generates unique Content IDs for digital assets based on perceptual and non-perceptual aspects, enabling content similarity detection and automated, scalable management through a Content Similarity Detection (CSD) service, which assigns consistent IDs across multiple platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Generative AI is adopted to create multiple permutations of campaign assets, then content creation efficiency and personalization capability are improved, but content volume and management complexity increase exponentially
Solution Approach 1:
The system segments content identification into perceptual aspects (visual similarity) and non-perceptual aspects (metadata, naming conventions), allowing independent processing and management of different content attributes through separate indexing mechanisms
Solution Approach 2:
The patent introduces Content IDs as intermediary identifiers that bridge disparate content systems and platforms. These standardized IDs enable automated tracking and management across DAMs, CMSs, and marketing tools without requiring direct integration between each system
2Adaptability or versatility
If multiple content storage options are maintained across different platforms, then content accessibility and system flexibility are improved, but data consistency and asset location difficulty increase
Solution Approach 1:
The Content ID system serves multiple functions simultaneously: it identifies content across different platforms, tracks asset performance, enables discovery and reuse, and maintains metadata consistency. This universal identifier works across DAMs, CMSs, MAPs, and cloud storage without requiring platform-specific modifications
Solution Approach 2:
The system implements feedback loops where Content IDs track asset performance across platforms and feed this information back to improve content discovery and reuse. Performance data from disparate systems is aggregated and used to enhance the overall content management ecosystem
3Reliability
If manual content management methods are used to handle large volumes of assets, then content organization capability is maintained, but time consumption and human error increase significantly
Solution Approach 1:
The system enables automated self-service content identification and organization. Content IDs are automatically generated and assigned based on perceptual analysis of content characteristics, eliminating the need for manual classification and reducing human intervention in routine organization tasks
Solution Approach 2:
Manual mechanical processes of content classification and tracking are replaced with automated computational systems that use perceptual algorithms to generate Content IDs and organize assets, dramatically reducing time consumption while maintaining or improving organization accuracy
Data Source
AI summary
In this approach, a system and workflow are provided for generating unique content identifiers (“Content IDs”) for digital assets. A Content ID is uniquely associated with each piece of content within a customer's asset space The Content ID need not be based on the contents of the asset (such as pixel values of an image), and both perceptual and non-perceptual aspects of identification of the asset may be evaluated to enable different permutations of the same digital asset to have different Content IDs. The system provides for content similarity detection by identifying a content match and surfacing this match within a customer data set, providing automated, scalable, and consistent Content IDs for multiple use cases associated with a digital asset management solution.