18064720. EFFICIENT GENERATION OF AN EXTRAPOLATED INFERENCE ASSOCIATED WITH A SUBSEQUENT EVENT WITH INCREASED FIDELITY AND REDUCED REDUNDANCY simplified abstract (Truist Bank)
EFFICIENT GENERATION OF AN EXTRAPOLATED INFERENCE ASSOCIATED WITH A SUBSEQUENT EVENT WITH INCREASED FIDELITY AND REDUCED REDUNDANCY
Organization Name
Inventor(s)
Jasmeet Singh Bhatia of Glen Allen VA (US)
Daria Hadalski of Fayetteville GA (US)
Dennis W. Yerby of Richmond VA (US)
Peter J. Opachan of Summerfield NC (US)
Joseph Lynn Thompson of Clayton NC (US)
EFFICIENT GENERATION OF AN EXTRAPOLATED INFERENCE ASSOCIATED WITH A SUBSEQUENT EVENT WITH INCREASED FIDELITY AND REDUCED REDUNDANCY - A simplified explanation of the abstract
This abstract first appeared for US patent application 18064720 titled 'EFFICIENT GENERATION OF AN EXTRAPOLATED INFERENCE ASSOCIATED WITH A SUBSEQUENT EVENT WITH INCREASED FIDELITY AND REDUCED REDUNDANCY
Simplified Explanation: The patent application describes a method for generating an extrapolated indication for a subsequent event based on input data and a determined variable that is strongly correlated with indications of previous events.
- **Key Features and Innovation:**
* Utilizes bivariate analysis and data brackets to identify the determined variable. * Generates the extrapolated indication using the determined variable and associated determined value. * Improves the fidelity and reduces redundancy of the extrapolated indication compared to traditional methods.
Potential Applications: This technology could be applied in various fields such as finance, weather forecasting, and predictive maintenance in industries.
Problems Solved: This technology addresses the challenge of accurately predicting future events based on historical data by identifying a strongly correlated variable.
Benefits: The benefits of this technology include improved accuracy in predicting future events, reduced redundancy in data analysis, and increased efficiency in decision-making processes.
Commercial Applications: The technology could be used in financial markets for predicting stock prices, in meteorology for forecasting weather patterns, and in manufacturing for optimizing maintenance schedules.
Prior Art: Readers can explore prior research on bivariate analysis, data brackets, and multiple variable algorithms in the context of predictive modeling.
Frequently Updated Research: Stay informed about advancements in bivariate analysis techniques and applications in various industries.
Questions about the Technology: 1. What are the potential limitations of using bivariate analysis in predictive modeling? 2. How does this technology compare to traditional extrapolation methods in terms of accuracy and efficiency?
Original Abstract Submitted
A method for generating an extrapolated indication includes receiving input data associated with a subsequent event and indicating a determined variable for the subsequent event. The determined variable is strongly correlated with indications of previous events and is identified utilizing bivariate analysis and data brackets correlated with the indications of the previous events, as identified using a multiple variable algorithm and the previous events. The method also includes generating the extrapolated indication associated with the subsequent event utilizing the input data, the determined variable, and an associated determined value, which separates ranges of data associated with the determined variable, which is strongly correlated with indications of the previous events and is generated utilizing bivariate analysis and the data brackets. The extrapolated indication has increased fidelity or reduced redundancy relative to an extrapolated indication generated from input data not indicating the determined variable for the subsequent event.
