Automated Identifier Classification via Third-Party Search
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Solution Overview
Problem
Big data often contains incomplete or nonsensical information, leading to inaccurate analysis and prediction due to improperly classified or unidentified identifiers.
Innovation Solution
A method that determines unidentified identifiers within aggregated data by transmitting them to a third-party service provider's search interface, parsing results for common patterns, and associating classifications based on these patterns, with user validation and scoring to ensure accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is collected from a variety of different sources, then the quantity and diversity of data increases, but the data becomes incomplete or nonsensical leading to inaccurate analysis
Solution Approach 1:
The patent introduces an intermediary classification system that acts as a mediator between raw aggregated data and analysis processes. Unknown identifiers are routed through a classification interface where they are matched against known identifiers using similarity metrics. This intermediary process filters and validates data before it reaches analysis systems, resolving the contradiction by maintaining data quantity while improving reliability through automated classification and verification mechanisms.
2Productivity
If automated classification is performed on unidentified identifiers, then data processing efficiency increases, but classification accuracy may decrease without proper verification
Solution Approach 1:
The patent implements a feedback mechanism where classification results are verified against multiple criteria including similarity thresholds, user input, and cross-referencing with known identifier databases. The system continuously refines its classification accuracy by learning from verification outcomes and adjusting its matching algorithms. This feedback loop maintains high processing efficiency while ensuring classification accuracy through iterative validation.
Solution Approach 2:
The system performs preliminary classification actions on unidentified identifiers before final analysis occurs. By pre-classifying identifiers using automated matching against known databases and establishing confidence scores in advance, the system prepares data for subsequent verification steps. This preliminary action enables efficient batch processing while maintaining accuracy through pre-validation mechanisms.
3Measurement precision
If manual verification of identifiers is performed, then classification accuracy improves, but the time and resources required increase significantly
Solution Approach 1:
The patent applies partial verification action by performing automated classification on all identifiers first, then applying manual or enhanced verification only to cases that fall below a confidence threshold or exhibit ambiguous characteristics. This selective approach achieves high overall accuracy by focusing verification resources on problematic cases while leaving clear cases to be processed automatically, thereby reducing total verification time while maintaining precision.
Data Source
AI summary
Apparatuses, systems, methods, and computer program products are disclosed for data supplementation and verification. A method includes determining that an identifier within aggregated data is not a previously classified known identifier. A method includes transmitting an identifier to a search interface of a server of a third party service provider. A method includes receiving results associated with an identifier from a third party service provider. A method includes parsing results to determine whether a plurality of results have a common pattern associated with a classification. A method includes, in response to determining that a plurality of results have a common pattern associated with a classification, associating the classification with an identifier based on the common pattern.