Predictive Income Modeling via Hierarchical Segmentation
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Solution Overview
Problem
Current methods fail to accurately predict average disposable income across geographic regions, which is crucial for targeted marketing and institutional lending, as they rely on simplistic models and do not account for varying regional characteristics.
Innovation Solution
A computer-implemented method using machine learning to aggregate and analyze data on income and geographic location, constructing predictive models that convert predicted average income values into indices, allowing for the ranking of regions based on their disposable income potential.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a static snapshot model of high rent areas is used to predict discretionary income, then the model is simple and easy to implement, but the prediction accuracy is low and does not account for regional characteristics
Solution Approach 1:
The patent segments the geographic regions into multiple hierarchical levels (e.g., countries, states, counties, census tracts, zip codes) and applies different predictive models to each segment. This allows the system to maintain simplicity at the national level while achieving high precision at local levels by accounting for regional characteristics specific to each segment.
Solution Approach 2:
The patent transitions from a single-dimension static snapshot model to a multi-dimensional dynamic model that incorporates multiple geographic hierarchies, multiple income factors, and temporal variations. This dimensional expansion enables the model to capture complex regional characteristics while maintaining computational feasibility through systematic decomposition.
2Measurement precision
If machine learning iterative analysis is performed on aggregated sample data to construct predictive models, then the prediction accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent divides the large-scale machine learning problem into smaller, manageable segments by processing different geographic regions and income factors separately. This segmentation reduces the computational burden on any single processing unit while maintaining overall model accuracy through systematic aggregation of results across all segments.
Solution Approach 2:
The patent performs preliminary data aggregation and preprocessing of sample data before initiating the iterative machine learning analysis. By organizing and preparing the data in advance according to the hierarchical geographic structure, the system reduces the complexity of the subsequent iterative analysis and accelerates the model construction process.
3Adaptability or versatility
If predictive models are constructed using multiple factors associated with income and geographic location, then the model captures regional characteristics better, but the data aggregation and processing requirements increase
Solution Approach 1:
The patent segments the multiple income factors and geographic location data into hierarchical categories (e.g., demographic factors, economic factors, geographic factors at different levels). This segmentation allows the system to process and manage large volumes of diverse data systematically, reducing the overwhelming complexity of handling all factors simultaneously while maintaining comprehensive regional characteristic capture.
Solution Approach 2:
The patent creates a universal data aggregation framework that handles multiple types of income and geographic data through a common hierarchical structure. This multi-functional approach allows the same processing methodology to be applied across different data types and geographic levels, reducing the overall processing requirements compared to handling each factor separately.
Data Source
AI summary
A method, computer system, and computer program product that aggregates sample data regarding a plurality of factors associated with income and geographic location; performs iterative analysis on the sample data using machine learning to construct a predictive model; populates, using the predictive model, a database with predicted values of average income for a selected set of predefined geographic regions; converts the predicted values of average income in the database into percentages of observed values of average income for geographic regions within the selected set over a specified time period to create indices of average income; and rank orders the regions within the selected set according to their indices of average income.