AI Narrative Vector Matching for Secure Legacy Content Discovery
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Solution Overview
Problem
Existing keyword-based searching methods are inefficient in discovering suitable legacy narrative content due to their inability to capture the attributes and nuances of narratives, and there are concerns about protecting copyrighted material, leading to underutilization of content libraries.
Innovation Solution
A processor-based system employing a trained neural network, specifically an autoencoder, generates vector representations that capture the attributes and nuances of narratives, allowing for robust content discovery while maintaining security by sharing vector representations outside network security walls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-based searching is used to discover legacy narrative content, then the search process is simple and fast, but the ability to capture attributes and nuances of narratives is limited
Solution Approach 1:
The patent replaces traditional keyword-based mechanical searching with an AI-based semantic analysis system that uses natural language processing and vector representations to understand and match narrative attributes, thereby improving measurement precision while managing complexity through automated processing
Solution Approach 2:
The system transforms narrative content from simple keyword tags into multi-dimensional vector representations that capture semantic attributes and nuances, changing the parameter space from discrete keywords to continuous semantic vectors for more precise matching
2Ease of operation
If source narrative content is loaded to a centralized server for analysis, then content discovery can be performed, but security concerns arise regarding protection of copyrighted material
Solution Approach 1:
The patent extracts only the essential vector representations and metadata from the source narrative content, leaving the actual copyrighted material securely stored in its original location. This extraction allows content discovery operations to be performed on the extracted representations without exposing the full source content
Solution Approach 2:
The system introduces vector representations as an intermediary layer between the secure source content and the discovery system. These vectors serve as mediators that enable semantic searching and matching without requiring direct access to or transfer of the copyrighted source material
3Productivity
If keyword-based searching is used, then the system is easy to operate, but the efficiency of discovering suitable legacy narrative content is very low
Solution Approach 1:
The patent performs preliminary processing of narrative content into vector representations and structured metadata before discovery operations are needed. This preprocessing creates ready-to-use semantic indexes that enable efficient querying without requiring complex real-time analysis during the discovery process
Solution Approach 2:
The system creates simplified vector representations as copies of the essential semantic content, allowing efficient searching and matching operations to be performed on these lightweight copies rather than on the full, complex source narratives
Data Source
AI summary
Processor-based systems and/or methods of operation may generate queries and suggest legacy narrative content (e.g., video content, script content) for a narrative under development. An artificial neural network (ANN, e.g., autoencoder) is trained on pairs of video and text vectors to capture attributes or nuances beyond those typical of keyword searching. Query vector representations generated using an instance of the ANN may be matched against candidate vector representations, for instance generated using an instance of the ANN from legacy narratives. Such may query for missing video and/or text for a narrative under development. Matches may be returned, including scores or ranks. Feature vectors may be shared without jeopardizing source narrative content. Legacy source narrative content may remain secure behind a controlling entity's network security wall.