Filter Selection for Search Result Reduction
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Solution Overview
Problem
Existing search engine algorithms face challenges in efficiently reducing large datasets to meaningful subsets due to sub-linear complexity and hierarchical organization issues, which hinder performance as the amount of electronic content grows.
Innovation Solution
The method involves identifying filter reduction ratios for a set of filters, selecting a subset based on a requested reduction ratio, and applying these filters to media assets to achieve the desired reduction, using techniques like artificial term creation and Boolean operations to combine filters effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If hierarchical clustering or hierarchical organization is used to organize electronic content items, then the search result organization is improved, but the structure becomes difficult to distribute over multiple machines and requires rebuilding when unbalanced
Solution Approach 1:
The patent divides the search result set into multiple segments or batches, applying filters incrementally to different portions of the data. This segmentation allows parallel processing across multiple machines without requiring a monolithic hierarchical structure, thereby improving distributability while maintaining organization.
Solution Approach 2:
The patent employs dynamic filter selection where the filtering process adapts based on the distribution of data across machines and the specific query requirements. This dynamic approach eliminates the need for static hierarchical structures that require rebuilding, allowing the system to flexibly organize results across distributed machines.
2Productivity
If sub-linear complexity algorithms are used to process search results, then the processing speed is improved, but the algorithms do not adequately account for the expanding amount of available electronic content
Solution Approach 1:
The patent implements a dynamic filtering system that adapts to varying data volumes by adjusting the number and specificity of filters applied. As content volume expands, the system dynamically selects from an expanded set of filters to maintain sub-linear processing complexity while effectively handling larger datasets.
Solution Approach 2:
The patent changes key parameters of the filtering process, including the number of filters applied, the specificity of filter criteria, and the order of filter application, based on the volume of available content. This allows the system to maintain efficient sub-linear processing while adapting to expanding electronic content volumes.
3Measurement precision
If more filters are applied to reduce the result set size, then the search accuracy is improved, but the search time increases
Solution Approach 1:
The patent applies a selective subset of filters rather than all available filters, choosing only those most relevant to the specific query and data distribution. This partial application of filtering achieves sufficient accuracy for most search scenarios while avoiding the time cost of applying every possible filter.
Solution Approach 2:
The patent performs preliminary analysis of the data distribution and query requirements to pre-select the most effective filters before executing the full search. This preliminary action identifies which filters will provide the greatest accuracy improvement with minimal time cost, allowing the system to apply only those filters in the main processing phase.
Data Source
AI summary
Techniques for utilizing sets of filters to reduce a large number of searchable assets to a meaningful or reduced number of searchable assets. Feature information may be extracted from a particular asset of a set of training assets to create an artificial term. A reduction ratio may then be calculated by utilizing the created artificial term to filter the set of training assets. The reduction ratio may represent the ratio of training assets that contain a particular artificial term. A plurality of filters and their associated reduction ratios may be created this way by utilizing the set of training assets. This process can also involve receiving a requested reduction ratio associated with a set of searchable assets. A combination of filters may then be selected which, when applied to the set of searchable assets, results in a reduced number of searchable assets according to the requested reduction ratio.