Personalized Asset Library Retrieval via Automated Embeddings
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Solution Overview
Problem
Current content retrieval systems rely on large, generic content libraries that require extensive human intervention for content addition and labeling, leading to inefficiencies and irrelevant search results.
Innovation Solution
A data processing system that uses a trained query representation ML model to encode search queries and compare them to automatically generated asset representations in personalized content libraries, allowing for efficient and relevant content retrieval without human labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If new content is added to the library with manual annotation, then the content library becomes more comprehensive and useful, but the process becomes time-consuming and expensive
Solution Approach 1:
The system enables self-service content indexing by automatically generating embeddings for new content items. The embedding generation process is automated through trained ML models that process content without requiring manual human intervention for annotation or indexing, thus reducing time and cost while maintaining library comprehensiveness
Solution Approach 2:
The patent replaces the mechanical process of manual content annotation with an automated ML-based embedding generation system. The trained query representation model and content embedding model automatically process and index new content, substituting human labor with computational processes that are faster and more scalable
2Measurement precision
If large ML models are used for asset indexing and retrieval, then search accuracy improves, but memory space requirements and operational complexity increase significantly
Solution Approach 1:
The patent segments the ML model functionality into separate components: a query representation model for encoding search queries and a content embedding model for indexing assets. This segmentation allows each model to be optimized independently and deployed separately, reducing operational complexity while maintaining search accuracy through specialized processing
Solution Approach 2:
The system changes the parameter representation by using embedding vectors to represent both queries and content items in a shared vector space. This parameter transformation enables efficient similarity computation through vector operations, reducing computational complexity compared to traditional large-scale model approaches while preserving search accuracy
3Quantity of substance
If generalized content libraries are used, then memory space requirements are reduced, but search result relevance to specific user needs deteriorates
Solution Approach 1:
The patent implements local quality by maintaining different content collections for different users or organizations, each with their own personalized embeddings. The system can switch between generalized and personalized content based on user needs, providing locally optimized search results that are relevant to specific user contexts while using efficient embedding representations to manage memory space
Data Source
AI summary
A system and method and for retrieving assets from a personalized asset library includes receiving a search query for searching for assets in one or more asset libraries, the one or more asset libraries including a personalized asset library; encoding the search query into embedding representations via a trained query representation machine-learning (ML) model; comparing, via a matching unit, the query embedding representations to a plurality of asset representations, each of the plurality of asset representations being a representation of one of the plurality of candidate assets; identifying, based on the comparison, at least one of the plurality of the candidate assets as a search result for the search query; and providing the identified plurality of candidate assets for display as the search result. The plurality of asset representations for the one or more assets in the personalized content library are generated automatically without human labeling.