Patents by Inventor Shibal Ibrahim

Shibal Ibrahim 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).

  • Knowledge graphs in machine learning decision optimization
    Patent number: 12536428
    Abstract: Training a machine learning model can include receiving time series data. A knowledge graph structure can be received including nodes and edges, the nodes representing entities associated with the time series data, the edges representing relationships between the nodes connected by the edges. A machine learning model can be structured to forecast a prediction using the time series data. The machine learning model can be structured to integrate the knowledge graph structure as an error term in the machine learning model. The machine learning model can be trained to forecast the prediction based on the time series data and the knowledge graph structure. The error term representing the knowledge graph structure can be regularized for sparsity during training.
    Type: Grant
    Filed: February 24, 2021
    Date of Patent: January 27, 2026
    Assignee: International Business Machines Corporation
    Inventors: Yada Zhu, Yang Zhang, Pin-Yu Chen, Rahul Mazumder, Shibal Ibrahim, Wenyu Chen
  • KNOWLEDGE GRAPHS IN MACHINE LEARNING DECISION OPTIMIZATION
    Publication number: 20220269936
    Abstract: Training a machine learning model can include receiving time series data. A knowledge graph structure can be received including nodes and edges, the nodes representing entities associated with the time series data, the edges representing relationships between the nodes connected by the edges. A machine learning model can be structured to forecast a prediction using the time series data. The machine learning model can be structured to integrate the knowledge graph structure as an error term in the machine learning model. The machine learning model can be trained to forecast the prediction based on the time series data and the knowledge graph structure. The error term representing the knowledge graph structure can be regularized for sparsity during training.
    Type: Application
    Filed: February 24, 2021
    Publication date: August 25, 2022
    Inventors: Yada Zhu, Yang Zhang, Pin-Yu Chen, Rahul Mazumder, Shibal Ibrahim, Wenyu Chen