Predictive Segments From Sampled Data
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Solution Overview
Problem
Existing predictive modeling techniques require well-defined input-output pairs, which are not readily available in cases where the data consists of samples with and without the event of interest, leading to inefficiencies and inaccuracies in modeling and recommendation systems.
Innovation Solution
A method and system that creates predictive segments by comparing distributions of sample data with and without the event of interest, allowing for the synthesis of functional input-output pairs and enabling recommendations based on demographic, geographic, and behavioral characteristics, even when traditional input-output pairs are not defined.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If clustering techniques are used to generate input-output pairs, then predictive segments can be created, but the computational cost increases significantly due to large number of iterative calculations
Solution Approach 1:
The patent segments the input variable space into distinct clusters representing different user behaviors or characteristics. By dividing the data into meaningful segments rather than treating it as a continuous space, the system can identify predictive patterns more efficiently without requiring exhaustive iterative calculations across the entire dataset.
Solution Approach 2:
The patent performs preliminary segmentation of the input space before applying regression modeling. This pre-processing step creates discrete clusters that serve as ready-made input-output pairs, eliminating the need for iterative optimization during the modeling phase and significantly reducing computational time.
2Adaptability or versatility
If clustering techniques are used to determine groupings, then input-output pairs can be generated, but determining the number of clusters becomes difficult and requires trial and error
Solution Approach 1:
The patent incorporates feedback mechanisms that evaluate the quality of clusters based on their predictive power. The system iteratively adjusts the number and composition of clusters while monitoring performance metrics, automatically selecting the optimal number of clusters without requiring manual trial and error. This feedback-driven approach balances model flexibility with computational efficiency.
3Measurement precision
If traditional regression techniques are used, then functional relationships can be modeled, but they require well-defined input-output pairs that are not readily available in sampled data
Solution Approach 1:
The patent performs preliminary clustering of the input space to automatically generate synthetic input-output pairs from unstructured sampled data. This pre-processing step transforms raw samples into the structured format required by regression techniques, eliminating the need for manual data pairing while maintaining modeling accuracy.
Solution Approach 2:
The patent introduces clustering as an intermediary process between raw sampled data and regression modeling. This intermediate step creates the necessary input-output structure by grouping similar samples and identifying representative output values, bridging the gap between unstructured data and traditional regression requirements.
Data Source
AI summary
A system and method is disclosed which predicts the relative occurrence or presence of an event or item based on sample data consisting of samples which contain and samples which do not contain the event or item. The samples also consist of any number of descriptive attributes, which may be continuous variables, binary variables, or categorical variables. Given the sampled data, the system automatically creates statistically optimal segments from which a functional input/output relationship can be derived. These segments can either be used directly in the form of a lookup table or in some cases as input data to a secondary modeling system such as a linear regression module, a neural network, or other predictive system.