View source for 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==

[[:Category:Capital One Services, LLC|Capital One Services, LLC]]


[[Category:Capital One Services, LLC]]

==Inventor(s)==

[[:Category:Jason Hoover of Grapevine TX (US)|Jason Hoover of Grapevine TX (US)]][[Category:Jason Hoover of Grapevine TX (US)]]

[[:Category:Micah Price of Plano TX (US)|Micah Price of Plano TX (US)]][[Category:Micah Price of Plano TX (US)]]

[[:Category:Stephen Wylie of Carrollton TX (US)|Stephen Wylie of Carrollton TX (US)]][[Category:Stephen Wylie of Carrollton TX (US)]]

[[:Category:Qiaochu Tang of Frisco TX (US)|Qiaochu Tang of Frisco TX (US)]][[Category:Qiaochu Tang of Frisco TX (US)]]

[[:Category:Geoffrey Dagley of McKinney TX (US)|Geoffrey Dagley of McKinney TX (US)]][[Category: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.



[[Category:G06N20/00]]
[[Category:G06N5/04]]
[[Category:G06Q50/18]]
[[Category:CPC_G06N20/00]]