18064742. INTEGRATED GENERATION OF A HI-FI EXTRAPOLATED INDICATION ASSOCIATED WITH A SUBSEQUENT EVENT EFFICIENTLY WITHOUT REDUNDANCY simplified abstract (Truist Bank)

INTEGRATED GENERATION OF A HI-FI EXTRAPOLATED INDICATION ASSOCIATED WITH A SUBSEQUENT EVENT EFFICIENTLY WITHOUT REDUNDANCY

Organization Name

Truist Bank

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)

INTEGRATED GENERATION OF A HI-FI EXTRAPOLATED INDICATION ASSOCIATED WITH A SUBSEQUENT EVENT EFFICIENTLY WITHOUT REDUNDANCY - A simplified explanation of the abstract

This abstract first appeared for US patent application 18064742 titled 'INTEGRATED GENERATION OF A HI-FI EXTRAPOLATED INDICATION ASSOCIATED WITH A SUBSEQUENT EVENT EFFICIENTLY WITHOUT REDUNDANCY

Simplified Explanation

The patent application describes a system that uses statistical algorithms to determine variables based on previous events and generate associated values for future events with increased accuracy.

• The system receives data on previous events and uses statistical algorithms to identify data brackets with correlations to these events.
• Through bivariate analysis, the system determines variables with strong correlations to previous events and generates associated values.
• These determined variables and values are then used to provide more accurate predictions for future events.

Key Features and Innovation

• Utilizes statistical algorithms to determine variables based on previous events.
• Generates associated values for future events with increased accuracy.
• Incorporates bivariate analysis to identify strong correlations between variables and previous events.

Potential Applications

The technology can be applied in various fields such as finance, weather forecasting, and predictive maintenance in industries.

Problems Solved

• Enhances the accuracy of predictions for future events.
• Provides a systematic approach to analyzing correlations between variables and previous events.

Benefits

• Improves decision-making processes based on historical data.
• Reduces redundancy in predictions for future events.

Commercial Applications

Predictive analytics software for businesses to optimize operations and make informed decisions based on historical data analysis.

Prior Art

Further research can be conducted in the field of statistical algorithms and predictive analytics to explore similar technologies.

Frequently Updated Research

Stay updated on advancements in statistical algorithms and predictive analytics to enhance the accuracy of future event predictions.

Questions about the Technology

How does the system determine the strength of correlations between variables and previous events?

The system utilizes bivariate analysis to identify variables with strong correlations to previous events.

What industries can benefit the most from this technology?

Various industries such as finance, weather forecasting, and predictive maintenance can benefit from the technology's accurate predictions based on historical data.

Original Abstract Submitted

A system with a processor configured to execute a front-end variable determination program including steps to receive determination data indicative of previous events; to identify, utilizing a multiple variable statistical algorithm, data brackets, each having interpolated correlation with indications of the previous events; to identify, utilizing bivariate analysis and the data brackets, a determined variable defining a strong interpolated correlation with indications of the previous events; and to generate, utilizing bivariate analysis, an associated determined value that separates ranges of data associated with the determined variable based on the strong interpolated correlation within the determination data. The processor is further configured to execute a back-end indication program including steps to receive the determined variable and each determined value from the front-end determination program; receive input data indicating the determined variable for subsequent event; and generate the extrapolated indication for the subsequent event having increased fidelity or reduced redundancy.