Patents by Inventor Prasanth Kambhatla

Prasanth Kambhatla 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).

  • Publication number: 20240151850
    Abstract: An apparatus, method, and computer program product are provided for the improved and automatic prediction of an elevation of an architectural feature of a structure at a particular geographic location. Some example implementations employ predictive, machine-learning modeling to facilitate the use of LiDAR-derived ground-elevation data, additional location context data, and elevation data from comparator locations to extrapolate and otherwise predict the elevation or other position of a given architectural feature of structure.
    Type: Application
    Filed: November 15, 2023
    Publication date: May 9, 2024
    Inventors: Rajiv MATTA, Ron BRUSKY, Mathew SCHMITT, Prasanth KAMBHATLA
  • Patent number: 11948200
    Abstract: Systems and methods for estimating current roof conditions and potential underwriting risk using insurance claim data. Historical claims for confirmed weather-damaged homes may be clustered by claims stemming from one or more weather events to determine potential areas where damage has occurred, but a claim has not yet been filed. In certain implementation, claims for weather-related vehicle damage may be utilized to predict weather-related roof damage.
    Type: Grant
    Filed: August 24, 2021
    Date of Patent: April 2, 2024
    Assignee: LexisNexis Risk Solutions, Inc.
    Inventors: Prasanth Kambhatla, John Beal, George Hosfield, Veli-Heikki Vesanto, Daniel Bang
  • Patent number: 11852728
    Abstract: An apparatus, method, and computer program product are provided for the improved and automatic prediction of an elevation of an architectural feature of a structure at a particular geographic location. Some example implementations employ predictive, machine-learning modeling to facilitate the use of LiDAR-derived ground-elevation data, additional location context data, and elevation data from comparator locations to extrapolate and otherwise predict the elevation or other position of a given architectural feature of structure.
    Type: Grant
    Filed: September 16, 2022
    Date of Patent: December 26, 2023
    Assignee: ASSURANT, INC.
    Inventors: Rajiv Matta, Ron Brusky, Mathew Schmitt, Prasanth Kambhatla
  • Publication number: 20230083833
    Abstract: An apparatus, method, and computer program product are provided for the improved and automatic prediction of an elevation of an architectural feature of a structure at a particular geographic location. Some example implementations employ predictive, machine-learning modeling to facilitate the use of LiDAR-derived ground-elevation data, additional location context data, and elevation data from comparator locations to extrapolate and otherwise predict the elevation or other position of a given architectural feature of structure.
    Type: Application
    Filed: September 16, 2022
    Publication date: March 16, 2023
    Inventors: Rajiv MATTA, Ron BRUSKY, Mathew SCHMITT, Prasanth KAMBHATLA
  • Publication number: 20230060374
    Abstract: Systems and methods for estimating current roof conditions and potential underwriting risk using insurance claim data. Historical claims for confirmed weather-damaged homes may be clustered by claims stemming from one or more weather events to determine potential areas where damage has occurred, but a claim has not yet been filed. In certain implementation, claims for weather-related vehicle damage may be utilized to predict weather-related roof damage.
    Type: Application
    Filed: August 24, 2021
    Publication date: March 2, 2023
    Inventors: Prasanth Kambhatla, John Beal, George Hosfield, Veli-Heikki Vesanto, Daniel Bang
  • Patent number: 11480683
    Abstract: An apparatus, method, and computer program product are provided for the improved and automatic prediction of an elevation of an architectural feature of a structure at a particular geographic location. Some example implementations employ predictive, machine-learning modeling to facilitate the use of LiDAR-derived ground-elevation data, additional location context data, and elevation data from comparator locations to extrapolate and otherwise predict the elevation or other position of a given architectural feature of structure.
    Type: Grant
    Filed: May 3, 2019
    Date of Patent: October 25, 2022
    Assignee: Assurant, Inc.
    Inventors: Rajiv Matta, Ron Brusky, Mathew Schmitt, Prasanth Kambhatla