Patents by Inventor Eric Edward Peter SEIFERT

Eric Edward Peter SEIFERT 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).

  • Auto transformation of network data models using neural machine translation
    Patent number: 11681880
    Abstract: Described herein are systems and methods for neural machine translation (NMT) of languages between network operation systems. The languages may be a query language, such as a XML path, to navigate through elements and attributes in an XML document. An NMT model comprises an encoder and a decoder to implement the machine translation. The encoder encodes a source sentence as a sequence of encoder hidden states. The decoder may incorporate attention mechanism to generate a target sentence, conditioned on the encoder hidden states. The NMT model may also use a modified beam search with variable beam width and search scope for each search step to speed up search process with a balance of accuracy and processing cost. Evaluation results demonstrate that embodiments of the present disclosure may be used in a recommender system for XPath auto-generation between different network operation systems.
    Type: Grant
    Filed: July 7, 2020
    Date of Patent: June 20, 2023
    Assignee: DELL PRODUCTS L.P.
    Inventors: Vinay Sawal, Brian Gerard Pierce, Geetha Sekar, Bernard Sarpong Duffour, Eric Edward Peter Seifert, Srividhya Nadhadhur Murali
  • AUTO TRANSFORMATION OF NETWORK DATA MODELS USING NEURAL MACHINE TRANSLATION
    Publication number: 20220012433
    Abstract: Described herein are systems and methods for neural machine translation (NMT) of languages between network operation systems. The languages may be a query language, such as a XML path, to navigate through elements and attributes in an XML document. An NMT model comprises an encoder and a decoder to implement the machine translation. The encoder encodes a source sentence as a sequence of encoder hidden states. The decoder may incorporate attention mechanism to generate a target sentence, conditioned on the encoder hidden states. The NMT model may also use a modified beam search with variable beam width and search scope for each search step to speed up search process with a balance of accuracy and processing cost. Evaluation results demonstrate that embodiments of the present disclosure may be used in a recommender system for XPath auto-generation between different network operation systems.
    Type: Application
    Filed: July 7, 2020
    Publication date: January 13, 2022
    Applicant: DELL PRODUCTS L.P.
    Inventors: Vinay SAWAL, Brian Gerard PIERCE, Geetha SEKAR, Bernard Sarpong DUFFOUR, Eric Edward Peter SEIFERT, Srividhya Nadhadhur MURALI