Approximate Index Tree Build for Content Search
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Solution Overview
Problem
Conventional search facilities face challenges in efficiently indexing and searching large collections of content due to high computational resource requirements and high-dimensional vector spaces, leading to increased search times and potential accuracy losses.
Innovation Solution
The implementation of an approximate index tree build method, where an initial clustering is performed on a subset of content descriptors, and sub-trees are built and merged to create the index tree, along with iterative refinement, to reduce computational load and improve search efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional hierarchical k-means clustering is used to build the index tree, then the search accuracy is maintained, but the computational resource requirements and build time become prohibitively high
Solution Approach 1:
The patent divides the content descriptors into multiple subsets and builds multiple separate index trees (sub-trees) for each subset. This segmentation allows parallel processing of different subsets, significantly reducing the overall build time while maintaining search accuracy by ensuring each sub-tree is built with sufficient computational resources.
Solution Approach 2:
The patent performs clustering on a subset of content descriptors rather than the entire collection. By selecting representative subsets for initial clustering, the build process completes faster while the iterative refinement process gradually incorporates remaining descriptors, balancing speed and accuracy.
2Measurement precision
If high-dimensional vector spaces are used to characterize content descriptors, then the search accuracy and fine discernment are improved, but the rate of false positive matches increases
Solution Approach 1:
The patent segments the high-dimensional vector space into multiple lower-dimensional sub-spaces by dividing content descriptors into separate subsets. Each subset is clustered independently, creating multiple specialized index trees that reduce false positives while preserving the ability to discern fine features through the combined coverage of all sub-trees.
Solution Approach 2:
Different regions of the high-dimensional space are handled differently by creating specialized sub-trees for different subsets of descriptors. Each sub-tree is optimized for its specific subset, providing locally optimized quality that reduces false positives while maintaining overall high discernment across the entire feature space.
3Quantity of substance
If the size of content collections is increased, then the comprehensiveness of search results is improved, but the search time and computational resources required increase
Solution Approach 1:
The patent segments large content collections into multiple manageable subsets, building separate index trees for each subset. This allows the system to handle larger overall collections by distributing the computational load across multiple smaller, parallelizable tasks, reducing search time while maintaining comprehensive coverage of all content.
Solution Approach 2:
The patent introduces a new dimension of organization by creating multiple index trees arranged in a forest structure. This multi-dimensional indexing approach allows efficient navigation through large collections by providing alternative pathways for search, reducing the time required to traverse extensive content while maintaining comprehensive search capability.
Data Source
AI summary
Multiple paths of an index tree may be traversed to discover a set of content descriptors that are match candidates for a set of query descriptors. A size of the set of candidate content descriptors may be optimized, for example, to reduce false positive matching errors, query latencies and/or index tree traversal times, at least in part by determining a number of child nodes to traverse based at least in part on current traverse level and/or traverse neighborhood thresholds. Index trees for large content descriptor sets may be built in resource constrained environments with approximation and/or refining build techniques.