Modeling of information technology failures of enterprise computing systems
US12169794B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 11, 2020 |
| Grant date | Dec 17, 2024 |
| Priority date | — |
| Expiry date | Oct 18, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F3/04842
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Disclosed are techniques for using machine learning models to more reliably predict likelihoods of application failure. A model is trained to identify and display events that may cause high severity application failures. Logistic regression may be used to fit the model such that application features are mapped to a high severity event flag. Significant features of applications that relate to the high severity flag may be selected using stepwise regression. The identified applications may be displayed on a graphical user interface for review and reprioritization. Information may be ranked and displayed according to multiple different ranking criteria, such as one ranking generated by a first model, and another determined by one or more users. The multiple ranking criteria may be used to inform steps taken, and/or to retrain or tune the parameters of the model for subsequent predictions or classifications.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.