18650609. SYSTEMS AND METHODS FOR USING A PREDICTIVE ENGINE TO PREDICT FAILURES IN MACHINE-LEARNING TRAINED SYSTEMS FOR DISPLAY VIA GRAPHICAL USER INTERFACE simplified abstract (Capital One Services, LLC)
SYSTEMS AND METHODS FOR USING A PREDICTIVE ENGINE TO PREDICT FAILURES IN MACHINE-LEARNING TRAINED SYSTEMS FOR DISPLAY VIA GRAPHICAL USER INTERFACE
Organization Name
Inventor(s)
Jason Hoover of Grapevine TX (US)
Stephen Wylie of Carrollton TX (US)
Qiaochu Tang of Frisco TX (US)
Geoffrey Dagley of McKinney TX (US)
SYSTEMS AND METHODS FOR USING A PREDICTIVE ENGINE TO PREDICT FAILURES IN MACHINE-LEARNING TRAINED SYSTEMS FOR DISPLAY VIA GRAPHICAL USER INTERFACE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18650609 titled 'SYSTEMS AND METHODS FOR USING A PREDICTIVE ENGINE TO PREDICT FAILURES IN MACHINE-LEARNING TRAINED SYSTEMS FOR DISPLAY VIA GRAPHICAL USER INTERFACE
The abstract describes a computer-implemented method using a machine-learning trained predictive engine to predict failures based on electronic transaction data.
- The method involves receiving electronic transaction data from a user via a graphical user interface.
- The data includes an item and candidate transaction terms, with the candidate transaction terms containing a first transaction term.
- A machine learning predictive engine determines the likelihood of success of the candidate transaction based on the data.
- The predictive engine also identifies a second transaction term that, when combined with the first transaction term, increases the likelihood of success above a predetermined threshold.
- The second transaction term and the likelihood of success are displayed on the graphical user interface.
Potential Applications: - Predictive maintenance in industrial settings - Fraud detection in financial transactions - Personalized recommendations in e-commerce platforms
Problems Solved: - Anticipating failures before they occur - Enhancing decision-making based on predictive analytics - Improving user experience through tailored suggestions
Benefits: - Increased efficiency and cost savings - Enhanced accuracy in forecasting outcomes - Customized solutions for individual users
Commercial Applications: Predictive maintenance software for manufacturing companies to optimize equipment performance and reduce downtime.
Questions about the technology: 1. How does the machine learning predictive engine determine the likelihood of success of a candidate transaction? 2. What are the key factors considered when identifying the second transaction term to increase the likelihood of success?
Original Abstract Submitted
A computer-implemented method for using a machine-learning trained predictive engine to predict failures, comprising receiving, from a user using a graphical user interface, electronic transaction data comprising an item and candidate transaction terms, the candidate transaction terms comprising a first transaction term, the electronic transaction data being associated with a candidate transaction. The method may determine, by a machine learning predictive engine, a likelihood of success of the candidate transaction based on the electronic transaction data, and may determine, by the machine learning predictive engine, based on the first transaction term, a second transaction term that, together with the first transaction term, increases the likelihood of success of the candidate transaction above a predetermined threshold. An indication of the second transaction term and an indication of the likelihood of success of the candidate transaction may be displayed on the graphical user interface.
