Regression Analysis for Personal Data Prediction Accuracy
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Solution Overview
Problem
Current methods for predicting individual behavior from personal data records are not sufficiently effective, as they lack improved techniques to accurately identify individuals likely to perform specific actions or engage in certain behaviors.
Innovation Solution
A method and system utilizing regression analysis on personal data records to train a prediction function, which outputs an outcome score based on specific categories, allowing for the identification of individuals likely to perform similar actions by processing and matching data records across multiple sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional behavior prediction methods are used on personal data records, then the prediction process is simple, but the prediction accuracy is insufficient
Solution Approach 1:
The patent transforms the prediction approach by changing from traditional statistical methods to regression analysis with optimized parameter selection. The system identifies and selects specific categories (parameters) from personal data records that have the strongest correlation with target behaviors, then applies regression models to predict outcomes. This parameter change enables more accurate predictions while managing complexity through focused variable selection.
Solution Approach 2:
The patent replaces traditional mechanical/statistical prediction methods with a regression analysis-based system that uses mathematical modeling. Instead of relying on simple correlation or rule-based systems, the invention employs regression equations that can capture non-linear relationships and interactions between multiple data categories, thereby improving prediction accuracy.
2Loss of information
If all personal data categories are used for prediction, then more information is available, but processing time and computational resources increase
Solution Approach 1:
The patent extracts and selects only the most relevant categories from the complete set of personal data record fields. Through regression analysis, the system identifies which categories contribute most significantly to predicting the target behavior and extracts only those for inclusion in the final prediction model. This extraction process reduces the data volume requiring processing while maintaining or improving prediction accuracy by eliminating noise from irrelevant categories.
Solution Approach 2:
The patent segments the comprehensive personal data record into distinct categories and evaluates each category's contribution to prediction accuracy. By dividing the data structure into manageable segments (categories) and selectively applying regression analysis to the most predictive segments, the system processes information more efficiently without losing critical predictive signals.
3Measurement precision
If regression analysis with multiple categories is applied, then prediction accuracy improves, but the complexity of determining the prediction function increases
Solution Approach 1:
The patent applies partial action by selecting a subset of categories rather than using all available data fields in the regression model. The system identifies the minimum necessary set of categories that provide sufficient predictive power, avoiding the complexity that would arise from incorporating excessive variables. This selective approach maintains prediction accuracy while simplifying the prediction function.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for performing a regression analysis on lawfully collected personal data records. The analysis enables discovery of individuals likely to perform certain actions based on their personal data records and the personal data records and actions of others. The disclosed system, method, and computer program product may process vast quantities of data, including personal data records with thousands of categories and lawfully stored databases with millions of personal data records. Through the regression analysis, the disclosed system, method, and computer program product learn the most relevant categories for predicting an individual's actions based on input data provided by a user. The analysis then analyzes the categories of personal data records stored in a lawfully stored database to predict actions of individuals associated with those records and outputs results to the user.