Patents by Inventor Sherry Towers

Sherry Towers 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).

  • Patent number: 12073341
    Abstract: Disclosed herein is a visual analytics system and method that provides a proactive and predictive environment in order to assist decision makers in making effective resource allocation and deployment decisions. The challenges involved with such predictive analytics processes include end-users' understanding, and the application of the underlying statistical algorithms at the right spatiotemporal granularity levels so that good prediction estimates can be established. In the disclosed approach, a suite of natural scale templates and methods are provided allowing users to focus and drill down to appropriate geospatial and temporal resolution levels. The disclosed forecasting technique is based on the Seasonal Trend decomposition based on Loess (STL) method applied in a spatiotemporal visual analytics context to provide analysts with predicted levels of future activity.
    Type: Grant
    Filed: February 17, 2020
    Date of Patent: August 27, 2024
    Assignee: Purdue Research Foundation
    Inventors: David Scott Ebert, Abish Malik, Sherry Towers, Ross Maciejewski
  • Publication number: 20200242523
    Abstract: Disclosed herein is a visual analytics system and method that provides a proactive and predictive environment in order to assist decision makers in making effective resource allocation and deployment decisions. The challenges involved with such predictive analytics processes include end-users' understanding, and the application of the underlying statistical algorithms at the right spatiotemporal granularity levels so that good prediction estimates can be established. In the disclosed approach, a suite of natural scale templates and methods are provided allowing users to focus and drill down to appropriate geospatial and temporal resolution levels. The disclosed forecasting technique is based on the Seasonal Trend decomposition based on Loess (STL) method applied in a spatiotemporal visual analytics context to provide analysts with predicted levels of future activity.
    Type: Application
    Filed: February 17, 2020
    Publication date: July 30, 2020
    Applicant: Purdue Research Foundation
    Inventors: David Scott Ebert, Abish Malik, Sherry Towers, Ross Maciejewski
  • Publication number: 20170011299
    Abstract: Disclosed herein is a visual analytics system and method that provides a proactive and predictive environment in order to assist decision makers in making effective resource allocation and deployment decisions. The challenges involved with such predictive analytics processes include end-users' understanding, and the application of the underlying statistical algorithms at the right spatiotemporal granularity levels so that good prediction estimates can be established. In the disclosed approach, a suite of natural scale templates and methods are provided allowing users to focus and drill down to appropriate geospatial and temporal resolution levels. The disclosed forecasting technique is based on the Seasonal Trend decomposition based on Loess (STL) method applied in a spatiotemporal visual analytics context to provide analysts with predicted levels of future activity.
    Type: Application
    Filed: November 13, 2015
    Publication date: January 12, 2017
    Applicant: PURDUE RESEARCH FOUNDATION
    Inventors: David Scott Ebert, Abish Malik, Sherry Towers, Ross Maciejewski