Keyword Filtering System Using Multi-Metric Scoring

The patent segments the keyword filtering process into distinct modules: a data collection module that gathers multiple metrics (search volume, competition level, conversion rate), a scoring module that calculates weighted scores for each keyword, and a filtering module that selects keywords based on predefined criteria. This segmentation allows each component to handle specific aspects of the complex evaluation independently, improving overall accuracy while maintaining manageable complexity through modular architecture.

The patent applies parameter changes by allowing dynamic adjustment of metric weights and filtering thresholds. Users can modify the importance weights assigned to different metrics (e.g., prioritizing conversion rate over search volume) and adjust minimum score thresholds based on campaign objectives. This flexibility enables the system to adapt to different marketing scenarios, maintaining high accuracy across diverse keyword selection requirements while simplifying the user interaction through intuitive parameter customization.

The patent implements preliminary action by pre-calculating and storing metric values for keywords in a database before the actual filtering process. Historical data on search volume, competition levels, and conversion rates are collected and stored in advance, allowing the system to retrieve ready-to-use data during keyword evaluation rather than performing complex calculations in real-time. This pre-processing approach significantly reduces processing time while maintaining high data quality through comprehensive metric collection.

The patent replaces complex real-time mechanical processing with optimized data retrieval and pre-computed scoring mechanisms. Instead of performing exhaustive calculations for each keyword during the filtering process, the system uses pre-calculated scores and stored metric data that can be quickly retrieved and applied to current keyword evaluation. This substitution of real-time computation with pre-processed data significantly reduces processing time while preserving the quality of multi-metric analysis.

The patent applies dynamics by enabling flexible adjustment of filtering criteria and score thresholds based on campaign objectives and available resources. Users can dynamically modify the minimum score requirements, weightings of different metrics, and inclusion/exclusion rules to balance between keyword relevance and quantity. This dynamic configuration allows the same system to serve different marketing scenarios - from highly selective campaigns requiring top-tier keywords to broader campaigns needing larger keyword sets - thereby maintaining relevance while preserving adequate keyword volume.

The patent implements partial action by allowing users to apply filtering criteria selectively rather than uniformly across all keywords. The system can identify and apply stricter filtering to specific keyword categories or campaigns while maintaining more inclusive criteria for others. This partial application of filtering logic enables the system to deliver high-relevance keywords for critical campaigns while preserving broader keyword availability for less demanding campaigns, effectively balancing relevance and quantity through differentiated filtering strategies.