Semantic Vector Policy Compliance Verification
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Solution Overview
Problem
Conventional systems for verifying content compliance in online systems are inefficient and costly due to the need for human reviewers to manually assess hundreds of thousands of content items, with existing automation methods failing to identify complex policy violations and requiring significant processing time and storage resources.
Innovation Solution
The system performs semantic analysis by extracting semantic vectors from content items and comparing them to labeled vectors using a nearest neighbor search, employing a weighed voting scheme to determine compliance with policies, thereby reducing computational expense and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human reviewers manually review content items to determine policy compliance, then measurement precision is improved, but productivity deteriorates due to the large volume of content items requiring review
Solution Approach 1:
The patent introduces semantic vectors as an intermediary representation between raw content items and policy compliance determination. These vectors capture the semantic meaning of content in a compressed numerical form, enabling automated systems to perform nuanced policy violation detection that approaches human-level accuracy while processing large volumes of content efficiently
Solution Approach 2:
The patent replaces the mechanical system of manual human review with an automated computational system that uses semantic vector analysis, nearest neighbor search, and weighted voting schemes. This substitution maintains high measurement precision for policy violation detection while dramatically improving productivity by eliminating the bottleneck of manual review
2Productivity
If automated keyword searching is used to identify policy violations, then productivity is improved, but measurement precision deteriorates due to inability to identify complex violations
Solution Approach 1:
The patent transforms the parameter representation of content from raw text or simple keywords to semantic vectors that capture nuanced meaning. This parameter change enables the system to detect complex policy violations that keyword searching would miss, while maintaining high productivity through efficient vector-based comparison and nearest neighbor search algorithms
3Productivity
If conventional automated review systems process large volumes of content, then productivity is improved, but use of energy deteriorates due to large processing time and storage space requirements
Solution Approach 1:
The patent extracts only the essential semantic features of content items into compact vector representations, discarding redundant information. This extraction enables efficient storage and processing of large volumes of content with reduced computational resource consumption, while maintaining the ability to perform accurate policy compliance verification through nearest neighbor search in the semantic vector space
Data Source
AI summary
An online system determines whether a test content item violates a policy of the online system. The online system extracts a semantic from the test content item and determines a distance between the extracted semantic vector and the stored semantic vectors for content items that have been labeled to indicate whether they violate a policy. Using a nearest neighbor search, the online system selects a set of the stored semantic vectors and assigns a weight to the selected semantic vectors that is inversely related to the distances. The online system then determines whether the test content item violates a policy using a weighed voting scheme, where the labels of the stored semantic vectors are aggregated based on their associated weights. The online system may first attempt to match the test content with known bad content and terminate the more complex nearest neighbor search if such a match is found.