Digital Content Identification Segmentation for Fewer False Positives
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Solution Overview
Problem
Existing digital content identification methods suffer from significant false positives and resource-intensive searches, particularly in scenarios where time is limited.
Innovation Solution
A method involving the comparison of digital content elements with a combination of two sets of data to identify known content, mitigate false positives, and update these sets based on population data to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital content elements are compared with a single large database of known content, then identification capability is improved, but false positives increase and resource consumption increases
Solution Approach 1:
The patent divides the identification system into two separate data sets: a first data set containing common or less distinctive digital content elements, and a second data set containing more distinctive or confirmatory elements. By segmenting the search process into two stages - first checking the first data set, then checking the second data set only for elements not found in the first - the system maintains high identification capability while reducing false positives from common elements.
Solution Approach 2:
The first data set acts as an intermediary filter between the search query and the second data set. Common digital content elements are filtered out by the first data set before reaching the second data set, preventing them from causing false positives in the more distinctive second data set while still allowing the system to identify known content efficiently.
2Reliability
If digital content elements are compared with a single large database of known content, then identification capability is improved, but resource consumption increases
Solution Approach 1:
The patent segments the database into two separate data sets with different characteristics. The first data set handles common elements that can be quickly identified and filtered, reducing the burden on the second data set. This segmentation allows the system to maintain comprehensive identification capability while distributing resource consumption across two smaller, more efficient data structures.
Solution Approach 2:
The system performs a partial search in the first data set before proceeding to the second data set. By checking the first data set first and only searching the second data set for elements not found in the first, the system performs less total work than searching a single comprehensive database, reducing resource consumption while maintaining identification capability.
3Reliability
If digital content elements are compared with a single large database of known content, then identification capability is improved, but search time increases
Solution Approach 1:
The patent divides the search process into two sequential stages using two separate data sets. The first data set is optimized for quickly filtering common elements, while the second data set handles more distinctive elements. This segmentation reduces the average search time by eliminating the need to search through a single large database for every query, as common elements are resolved in the first, faster stage.
Solution Approach 2:
The system performs a partial search in the first data set before proceeding to the second data set only when necessary. By checking whether an element exists in the first data set before searching the second data set, the system avoids unnecessary searches in the second data set, significantly reducing average search time while maintaining comprehensive identification capability.
Data Source
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AI summary
Many areas of investigation require searching through data that may be of interest. In a first method step, a digital content element is provided. The digital content element may have any suitable format or data structure of interest to a searching entity.The digital content element may be a particular data file that is of interest to a searching entity. In a second step, the digital content element is compared with a first set of data provided by a combination of a second set of data and a third set of data. The first set of data is a collection of known digital content elements that are of interest to a searching entity, for example contraband digital content elements or digital content elements owned by or represented by the searching entity. In a third method step, the digital content element is identified as known if the digital content element is detected within the first set of data.