Semantic Content Clustering for Low-Burden Moderation
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Solution Overview
Problem
Existing AI networks face a high processing burden when identifying similar content in large-scale content corpora, especially with user-generated content that lacks consistent formatting or metadata, requiring direct access and comparison of content portions.
Innovation Solution
The Topic Engine reduces processing burden by clustering semantically related content identifiers based on captured sequences, using dimensionality reduction and embedding techniques, and identifies topics through keyword extraction and centroid representations, while ensuring privacy and compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct access and comparison of content portions is performed to identify similar content, then measurement precision of content similarity is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The system performs preliminary actions by generating content embeddings and organizing them into hierarchical clusters before actual similarity detection is needed. This pre-processing creates a structured framework that enables rapid similarity queries without requiring full content re-analysis, thus reducing processing time while maintaining detection accuracy.
Solution Approach 2:
The patent introduces content embeddings as an intermediary representation between raw content and similarity comparison. Instead of directly comparing content portions, the system compares their embedding vectors in a reduced-dimensional semantic space. This intermediary layer enables efficient similarity detection with significantly lower computational requirements.
2Measurement precision
If direct access and comparison of content portions is performed to identify similar content, then measurement precision of content similarity is improved, but computational resources and processing power increase significantly
Solution Approach 1:
The patent introduces content embeddings as an intermediary representation between raw content and similarity comparison. Instead of directly comparing content portions, the system compares their embedding vectors in a reduced-dimensional semantic space. This intermediary layer enables efficient similarity detection with significantly lower computational requirements.
Solution Approach 2:
The system transforms content from its original high-dimensional representation to a lower-dimensional embedding space that preserves semantic relationships. This dimensionality reduction maintains the ability to detect content similarity while dramatically reducing the computational resources required for comparison operations.
3Productivity
If content identifiers are clustered based on sequences of accessed content, then productivity of content analysis is improved, but device complexity increases due to embedding and dimensionality reduction processes
Solution Approach 1:
The patent segments the content analysis process into distinct modular components: embedding generation, dimensionality reduction, clustering, and topic identification. Each module performs a specific function and can be independently optimized or replaced, managing system complexity while maintaining high productivity in content analysis.
Data Source
AI summary
Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to a Topic Engine. The Topic Engine captures a plurality of content identifier sequences. Each respective sequence represents an order at which a corresponding user account accessed content. The Topic Engine generates a plurality of clusters. Each cluster is associated with respective content identifiers appearing within a proximity to each other across the plurality of content identifier sequences of different user accounts. The Topic Engine obtains one or more sample content identifiers from at least one cluster via sampling the cluster. The Topic Engine extracts keywords from content represented by the one or more sampled content identifiers. The Topic Engine identifies a topic for the cluster based on the one or more extracted keywords.