Location-Based Predictive Analysis with Cohort Machine Learning
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Solution Overview
Problem
Existing predictive data analysis solutions face efficiency challenges due to the large number of location data objects that need to be processed, leading to high computational load and resource requirements.
Innovation Solution
Utilizing locality cohorts and machine learning models to reduce the number of cross-temporal predictive data analysis operations, improving computational efficiency and reducing storage resources by generating locality cohorts and inferred cross-temporal growth predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing predictive data analysis solutions process all location data objects, then prediction accuracy is maintained, but computational load and resource requirements increase significantly
Solution Approach 1:
The patent segments location data objects into cohorts based on shared characteristics (demographics, geography, behavior patterns). Instead of processing all location data objects individually, the system processes cohort-level aggregated data, reducing computational complexity while maintaining prediction accuracy through representative sampling of cohort members.
Solution Approach 2:
The patent creates synthetic cohort representations that capture the essential characteristics of location data objects without processing each individual object. These cohort proxies serve as representative copies that enable predictive analysis at a reduced computational scale while preserving the predictive power of the original detailed data.
2Reliability
If the system processes all location data objects for predictive analysis, then comprehensive predictions are achieved, but storage resources and computational load increase
Solution Approach 1:
The patent merges multiple location data objects into unified cohort entities by aggregating their shared characteristics. This consolidation reduces the total quantity of data objects from millions of individual location records to a manageable number of cohort representations, while maintaining prediction reliability through the aggregated statistical properties of the cohorts.
Solution Approach 2:
The patent extracts and retains only the essential characteristics needed for predictive analysis (demographic profiles, geographic attributes, behavior patterns) while discarding redundant detailed information about individual location objects. This extraction reduces data volume to the minimum necessary for reliable predictions.
3Measurement precision
If detailed individual location data is processed, then granular predictions are achieved, but system complexity and processing time increase
Solution Approach 1:
The patent performs preliminary cohort formation and characteristic aggregation before conducting predictive analysis. By pre-computing and storing cohort-level statistics and prototypes, the system eliminates the need to process individual location objects during prediction time, significantly reducing processing time while maintaining granular prediction capability through the pre-aggregated cohort data.
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing temporally dynamic location-based predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform temporally dynamic location-based predictive data analysis by using at least one of cohort generation machine learning models and cohort-based growth forecast machine learning models.