Patents by Inventor Sonal Bhutani

Sonal Bhutani 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).

  • Network node failure predictive system
    Patent number: 9483338
    Abstract: In an example, network node failures may be predicted by extracting performance metrics for the network nodes from a plurality of data sources. A fail condition may be defined for the network nodes and input variables related to the fail condition for the network nodes may then be derived from the extracted performance metrics. A plurality of models may then be trained to predict the fail condition for the network nodes using a training set from the extracted performance metrics with at least one of the identified input variables. Each of the plurality of trained models may be validated using a validation set from the extracted performance metrics and may be rated according to predefined criteria. As a result, a highest rated model of the validated models may be selected to predict the fail condition for the network nodes.
    Type: Grant
    Filed: November 7, 2014
    Date of Patent: November 1, 2016
    Assignee: ACCENTURE GLOBAL SERVICES LIMITED
    Inventors: Anuj Bhalla, Madan Kumar Singh, Christopher Scott Lucas, Ravi Teja, Sachin Sehgal, Mayank Kant, Sonal Bhutani
  • NETWORK NODE FAILURE PREDICTIVE SYSTEM
    Publication number: 20150135012
    Abstract: In an example, network node failures may be predicted by extracting performance metrics for the network nodes from a plurality of data sources. A fail condition may be defined for the network nodes and input variables related to the fail condition for the network nodes may then be derived from the extracted performance metrics. A plurality of models may then be trained to predict the fail condition for the network nodes using a training set from the extracted performance metrics with at least one of the identified input variables. Each of the plurality of trained models may be validated using a validation set from the extracted performance metrics and may be rated according to predefined criteria. As a result, a highest rated model of the validated models may be selected to predict the fail condition for the network nodes.
    Type: Application
    Filed: November 7, 2014
    Publication date: May 14, 2015
    Applicant: Accenture Global Services Limited
    Inventors: Anuj Bhalla, Madan Kumar Singh, Christopher Scott Lucas, Ravi Teja, Sachin Sehgal, Mayank Kant, Sonal Bhutani