Patents by Inventor Craig Allan ESTEPP

Craig Allan ESTEPP has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20230376855
    Abstract: Systems and methods are provided for detecting anomalies on multiple layers of a computer system, such as a compute server. For example, the system can detect anomalies from the lower firmware layer up to the upper application layer of the compute server. The system collects train data from the computer system that is under testing. The train data includes features that affect performance metrics, as defined by a selected benchmark. This train data is used in training machine learning (ML) models. The ML models create a train snapshot corresponding to the selected benchmark. Additionally with every new release, a test snapshot can be created corresponding to the selected benchmark or workload. The system can detect an anomaly based on the train snapshot and the test snapshot. Also, the system can recommend tunings for a best set of features based upon data collected over generations of compute server.
    Type: Application
    Filed: July 28, 2023
    Publication date: November 23, 2023
    Inventors: Klaus-Dieter Lange, Mukund Kumar, Prateek Bhatnagar, Nalamati Sai Rajesh, Nishant Rawtani, Craig Allan Estepp
  • Patent number: 11755955
    Abstract: Systems and methods are provided for detecting anomalies on multiple layers of a computer system, such as a compute server. For example, the system can detect anomalies from the lower firmware layer up to the upper application layer of the compute server. The system collects train data from the computer system that is under testing. The train data includes features that affect performance metrics, as defined by a selected benchmark. This train data is used in training machine learning (ML) models. The ML models create a train snapshot corresponding to the selected benchmark. Additionally with every new release, a test snapshot can be created corresponding to the selected benchmark or workload. The system can detect an anomaly based on the train snapshot and the test snapshot. Also, the system can recommend tunings for a best set of features based upon data collected over generations of compute server.
    Type: Grant
    Filed: April 8, 2021
    Date of Patent: September 12, 2023
    Assignee: Hewlett Packard Enterprise Development LP
    Inventors: Klaus-Dieter Lange, Mukund Kumar, Prateek Bhatnagar, Nalamati Sai Rajesh, Nishant Rawtani, Craig Allan Estepp
  • Publication number: 20210406146
    Abstract: Systems and methods are provided for detecting anomalies on multiple layers of a computer system, such as a compute server. For example, the system can detect anomalies from the lower firmware layer up to the upper application layer of the compute server. The system collects train data from the computer system that is under testing. The train data includes features that affect performance metrics, as defined by a selected benchmark. This train data is used in training machine learning (ML) models. The ML models create a train snapshot corresponding to the selected benchmark. Additionally with every new release, a test snapshot can be created corresponding to the selected benchmark or workload. The system can detect an anomaly based on the train snapshot and the test snapshot. Also, the system can recommend tunings for a best set of features based upon data collected over generations of compute server.
    Type: Application
    Filed: April 8, 2021
    Publication date: December 30, 2021
    Inventors: Klaus-Dieter LANGE, Mukund KUMAR, Prateek BHATNAGAR, Nalamati SAI RAJESH, Nishant RAWTANI, Craig Allan ESTEPP