Patents by Inventor Sandeep Narayanaswami

Sandeep Narayanaswami 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).

  • Generation of synthetic data using agent-based simulations
    Patent number: 11847390
    Abstract: A system, method, and computer-readable medium for generating factual and/or counterfactual data are described. This may have the effect of improving the complexity of data available for training machine learning models. The models may include, but not limited to, a probabilistic graphical model (PGM) and/or an agent-based model (ABM). Further aspects may provide for scrubbing actual data to create a data model that does not reveal the content of the underlying source data. Yet further aspects may provide for validating a data model.
    Type: Grant
    Filed: January 5, 2021
    Date of Patent: December 19, 2023
    Assignee: Capital One Services, LLC
    Inventors: Francisco Gutierrez, Matthew Tomaszewicz, Sandeep Narayanaswami, Eiran Shalev
  • Extensible Agents in Agent-Based Generative Models
    Publication number: 20220215142
    Abstract: A system, method, and computer-readable medium for generating factual and/or counterfactual data are described. This may have the effect of improving the complexity of data available for training machine learning models. The models may include agent-based models (ABMs) in which the agent definitions are decoupled from the simulation. In one or more aspects, some agents may have attributes and associated behaviors that permit them to be reused in different ABMs to simulate different systems.
    Type: Application
    Filed: April 26, 2021
    Publication date: July 7, 2022
    Inventors: Francisco Gutierrez, Matthew Tomaszewicz, Sandeep Narayanaswami, Eiran Shalev
  • Augmenting Datasets with Synthetic Data
    Publication number: 20220215262
    Abstract: A system, method, and computer-readable medium for generating factual and/or counterfactual data are described. This may have the effect of improving the complexity of data available for training machine learning models. The models may include, but not limited to, a probabilistic graphical model (PGM) and/or an agent-based model (ABM). Further aspects may provide for scrubbing actual data to create a data model that does not reveal the content of the underlying source data. Yet further aspects may provide for validating a data model.
    Type: Application
    Filed: January 5, 2021
    Publication date: July 7, 2022
    Inventors: Sandeep Narayanaswami, Omar Sharifali, Eiran Shalev, Francisco Gutierrez, Matthew Tomaszewicz, Nicholas Mccurry
  • Generation of Secure Synthetic Data Based On True-Source Datasets
    Publication number: 20220215242
    Abstract: A system, method, and computer-readable medium for generating factual and/or counterfactual data are described. This may have the effect of improving the complexity of data available for training machine learning models. The models may include, but not limited to, a probabilistic graphical model (PGM) and/or an agent-based model (ABM). Further aspects may provide for scrubbing actual data to create a data model that does not reveal the content of the underlying source data. Yet further aspects may provide for validating a data model.
    Type: Application
    Filed: January 5, 2021
    Publication date: July 7, 2022
    Inventors: Eiran Shalev, Sandeep Narayanaswami, Nicholas McCurry, Matthew Tomaszewicz, Omar Sharifali, Jesse Anderson, Daniel Finn, Francisco Gutierrez
  • Risk-Reliability Framework for Evaluating Synthetic Data Models
    Publication number: 20220215243
    Abstract: A system, method, and computer-readable medium for generating factual and/or counterfactual data are described. This may have the effect of improving the complexity of data available for training machine learning models. The models may include, but not limited to, a probabilistic graphical model (PGM) and/or an agent-based model (ABM). Further aspects may provide for scrubbing actual data to create a data model that does not reveal the content of the underlying source data. Yet further aspects may provide for validating a data model.
    Type: Application
    Filed: January 5, 2021
    Publication date: July 7, 2022
    Inventors: Sandeep Narayanaswami, Omar Sharifali, Matthew Tomaszewicz, Eiran Shalev
  • Generation of Synthetic Data using Agent-Based Simulations
    Publication number: 20220215141
    Abstract: A system, method, and computer-readable medium for generating factual and/or counterfactual data are described. This may have the effect of improving the complexity of data available for training machine learning models. The models may include, but not limited to, a probabilistic graphical model (PGM) and/or an agent-based model (ABM). Further aspects may provide for scrubbing actual data to create a data model that does not reveal the content of the underlying source data. Yet further aspects may provide for validating a data model.
    Type: Application
    Filed: January 5, 2021
    Publication date: July 7, 2022
    Inventors: Francisco Gutierrez, Matthew Tomaszewicz, Sandeep Narayanaswami, Eiran Shalev