Patents by Inventor Muneeza Azmat

Muneeza Azmat 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).

  • Forecasting land-based environmental variables using similarity analysis and temporal graph convolutional neural networks
    Patent number: 12639916
    Abstract: Embodiments are directed to a computer-implemented method of analyzing a land region that has been decomposed into a plurality of regular or irregular sub-regions. The computer-implemented method includes applying, using a processor system, a feature extraction process that extracts a set of sub-region environmental descriptors for each of the plurality of sub-regions. The processor system applies a similarity analysis to the set of sub-region environmental descriptors to generate groups of the plurality of sub-regions. The processor system creates a plurality of group-based graphs by encoding each of the groups into a corresponding group-based graph. A spatio-temporal neural network is used to train a model based at least in part on the plurality of group-based graphs.
    Type: Grant
    Filed: October 4, 2022
    Date of Patent: May 26, 2026
    Assignee: International Business Machines Corporation
    Inventors: Fearghal O'Donncha, Malvern Madondo, Muneeza Azmat, Michael Jacobs, Raya Horesh
  • FORECASTING LAND-BASED ENVIRONMENTAL VARIABLES USING SIMILARITY ANALYSIS AND TEMPORAL GRAPH CONVOLUATIONAL NEURAL NETWORKS
    Publication number: 20240112442
    Abstract: Embodiments are directed to a computer-implemented method of analyzing a land region that has been decomposed into a plurality of regular or irregular sub-regions. The computer-implemented method includes applying, using a processor system, a feature extraction process that extracts a set of sub-region environmental descriptors for each of the plurality of sub-regions. The processor system applies a similarity analysis to the set of sub-region environmental descriptors to generate groups of the plurality of sub-regions. The processor system creates a plurality of group-based graphs by encoding each of the groups into a corresponding group-based graph. A spatio-temporal neural network is used to train a model based at least in part on the plurality of group-based graphs.
    Type: Application
    Filed: October 4, 2022
    Publication date: April 4, 2024
    Inventors: Fearghal O'Donncha, Malvern Madondo, Muneeza Azmat, Michael Jacobs, Raya Horesh