Patents by Inventor Nicole EICKHOFF

Nicole EICKHOFF 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).

  • DISTRIBUTED LEARNING ANOMALY DETECTOR
    Publication number: 20220092421
    Abstract: In a network discovery and management system, a machine learning (ML) DLAD processor trains, validates, updates, and stores machine learning models. A ML training data preparation program performs operations to process and format input data to generate ML training data that can be used to train ML models. ML training program uses the ML training data to train ML models, thereby generating trained ML models. The ML training program can re-train or update the training of ML models as the system collects additional data and produces additional estimates, predictions, and forecasts. ML model validation program performs validation testing on trained ML models to generate one or more metrics that can indicate accuracy of predictions generated by the trained models. The resulting ML model(s) can be used to manage the network including but not limited to retrieve, instantiate and execute dynamic applications based on predictions made based on the models.
    Type: Application
    Filed: December 2, 2021
    Publication date: March 24, 2022
    Inventors: Shankar ANANTHANARAYANAN, Nicole EICKHOFF, Tim HERRMANN, Matthew LUEBKE, Mathew MALONEY
  • Distributed learning anomaly detector
    Patent number: 11210587
    Abstract: In a network discovery and management system, a machine learning (ML) DLAD processor trains, validates, updates, and stores machine learning models. A ML training data preparation program performs operations to process and format input data to generate ML training data that can be used to train ML models. ML training program uses the ML training data to train ML models, thereby generating trained ML models. The ML training program can re-train or update the training of ML models as the system collects additional data and produces additional estimates, predictions, and forecasts. ML model validation program performs validation testing on trained ML models to generate one or more metrics that can indicate accuracy of predictions generated by the trained models. The resulting ML model(s) can be used to manage the network including but not limited to retrieve, instantiate and execute dynamic applications based on predictions made based on the models.
    Type: Grant
    Filed: April 23, 2020
    Date of Patent: December 28, 2021
    Assignee: ScienceLogic, Inc.
    Inventors: Shankar Ananthanarayanan, Nicole Eickhoff, Tim Herrmann, Matthew Luebke, Mathew Maloney
  • DISTRIBUTED LEARNING ANOMALY DETECTOR
    Publication number: 20200364561
    Abstract: In a network discovery and management system, a machine learning (ML) DLAD processor trains, validates, updates, and stores machine learning models. A ML training data preparation program performs operations to process and format input data to generate ML training data that can be used to train ML models. ML training program uses the ML training data to train ML models, thereby generating trained ML models. The ML training program can re-train or update the training of ML models as the system collects additional data and produces additional estimates, predictions, and forecasts. ML model validation program performs validation testing on trained ML models to generate one or more metrics that can indicate accuracy of predictions generated by the trained models. The resulting ML model(s) can be used to manage the network including but not limited to retrieve, instantiate and execute dynamic applications based on predictions made based on the models.
    Type: Application
    Filed: April 23, 2020
    Publication date: November 19, 2020
    Inventors: Shankar ANANTHANARAYANAN, Nicole EICKHOFF, Tim HERRMAN, Matthew LUEBKE