Patents by Inventor Nibedita SARMAH

Nibedita SARMAH 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).

  • Continuous learning-based application related trade-off resolution and implementation
    Patent number: 11972251
    Abstract: In some examples, continuous learning-based application related trade-off resolution and implementation may include generating, based on a plurality of historical tradeoff instances, an application feature matrix. Further, association rules for historical tradeoff instances for which decisions are not known, and a decision tree for historical tradeoff instances for which decisions are known may be generated. Decision rules may be induced, and default rules may be applied to a cold start scenario. The decision rules and the default rules may be refined to generate refined rules, and a confidence level may be determined for the refined rules. The refined rules may be prioritized based on the confidence level and applied to a new tradeoff instance to generate a resolution associated with the new tradeoff instance. The resolution may be implemented with respect to the new tradeoff instance.
    Type: Grant
    Filed: April 21, 2021
    Date of Patent: April 30, 2024
    Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITED
    Inventors: Janardan Misra, Vikrant Kaulgud, Adam Patten Burden, Sanjay Podder, Narendranath Sukhavasi, Nibedita Sarmah
  • CONTINUOUS LEARNING-BASED APPLICATION RELATED TRADE-OFF RESOLUTION AND IMPLEMENTATION
    Publication number: 20210334090
    Abstract: In some examples, continuous learning-based application related trade-off resolution and implementation may include generating, based on a plurality of historical tradeoff instances, an application feature matrix. Further, association rules for historical tradeoff instances for which decisions are not known, and a decision tree for historical tradeoff instances for which decisions are known may be generated. Decision rules may be induced, and default rules may be applied to a cold start scenario. The decision rules and the default rules may be refined to generate refined rules, and a confidence level may be determined for the refined rules. The refined rules may be prioritized based on the confidence level and applied to a new tradeoff instance to generate a resolution associated with the new tradeoff instance. The resolution may be implemented with respect to the new tradeoff instance.
    Type: Application
    Filed: April 21, 2021
    Publication date: October 28, 2021
    Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITED
    Inventors: Janardan MISRA, Vikrant KAULGUD, Adam Patten BURDEN, Sanjay PODDER, Narendranath SUKHAVASI, Nibedita SARMAH