Search Query Popularity Scoring with False Positive Correction
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Solution Overview
Problem
Existing methods fail to accurately determine the popularity of entities based on Internet search queries and do not effectively track changes in popularity over time, leading to inaccurate rankings and trends.
Innovation Solution
A system that calculates search scores for entities by counting occurrences of entity descriptors in search terms, adjusts for false positives using correction factors, and assigns movement scores based on changes in popularity rank over time, using a combination of computer-executable instructions and a computing system architecture that includes a query log, bot filter, entity rank, disambiguation, and trend analysis components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used to determine entity popularity, then the system is simple to operate, but the measurement precision of entity popularity is insufficient
Solution Approach 1:
The system segments the popularity measurement process into multiple independent components: query log analysis, entity descriptor matching, false positive filtering, correction factor calculation, and movement score computation. Each component handles a specific aspect of the measurement task, allowing for improved precision while maintaining modular simplicity.
Solution Approach 2:
The patent introduces intermediary elements such as entity descriptors as bridges between search queries and entities, correction factors as mediators to adjust for false positives, and movement scores as intermediate metrics to track popularity changes over time. These intermediaries enable more precise measurement without requiring direct complex analysis of all search data.
2Reliability
If basic search query counting is used, then the device complexity is low, but the reliability of popularity determination is insufficient
Solution Approach 1:
The system incorporates feedback mechanisms through correction factors that are calculated based on the analysis of false positive matches. The movement score calculation provides feedback about changes in popularity over time, allowing the system to adjust and refine its determinations continuously, thereby improving reliability.
Solution Approach 2:
The patent replaces simple mechanical counting of search queries with a more sophisticated information processing system that analyzes entity descriptors, applies correction factors, and calculates movement scores. This substitution of basic counting with multi-step analysis improves reliability while managing complexity through structured processing.
3Difficulty of detecting and measuring
If static popularity ranking is used, then the ease of operation is high, but the ability to detect changes over time is limited
Solution Approach 1:
The system transitions from static popularity ranking to dynamic ranking by introducing movement scores that capture changes in entity popularity over time. The system continuously updates rankings and calculates movement scores based on new search data, enabling detection of popularity changes while maintaining operational simplicity through automated processing.
Data Source
AI summary
Systems, methods, and computer-readable media for determining the Internet search popularity of an entity are provided. Embodiments of the present invention include receiving a group of Internet search records and assigning a popularity ranking based on the number of times an entity descriptor associated with an entity occurs within the group of Internet search records created over a designated time period. An entity descriptor is one or more terms commonly used to identify an entity. The trend in an entity's popularity rank may also be calculated. An entity's popularity rank and trend in popularity rank may be presented in a graph or in a list.