Patents by Inventor Julius Simonelli

Julius Simonelli 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: 20250131710
    Abstract: Methods, non-transitory computer readable media, and building analysis systems are disclosed that analyze an overhead image to generate a building outline for a building associated with a property represented by the overhead image. The overhead image is included in imagery data obtained from an overhead imagery server based on a received request comprising a geographic location for the property. The building outline is then shifted or rotated. Segment(s) of the building outline are slid to match identified wall(s) of the building. The building outline is then modified based on property feature(s) detected based on an application of one or more trained machine learning classifiers to the overhead image. At least a portion of the overhead image is output with a graphical overlay comprising the building outline via a user interface in response to the received request.
    Type: Application
    Filed: October 20, 2023
    Publication date: April 24, 2025
    Inventors: Julius Simonelli, Ilsoo Seong, Jason Janofsky, Nicholas Molyneux, David Tobias, David Lyman, Peter Tran, Hiroshi Kobayashi
  • Publication number: 20250078499
    Abstract: Methods, non-transitory computer-readable media, and property analysis systems are disclosed that extract vegetation location information from overhead and LIDAR images associated with a geographic location. The images are aligned based on the extracted vegetation information. Models are applied to the aligned images based on vegetation height information extracted from the LIDAR images. A graph is then generated based on a result of the application of the models. The graph represents a relationship between vegetation, one or more buildings, and one or more fire pathways associated with the geographic location. A wildfire risk score generated based on the graph is then output for the buildings via a GUI. Thus, the disclosed technology applies wildfire spread models and graphs to vegetation and buildings identified in aligned overhead and LIDAR imagery to provide relatively accurate wildfire risk assessment to insurers and homeowners and thereby facilitate informed decision-making and preventative measures.
    Type: Application
    Filed: August 29, 2023
    Publication date: March 6, 2025
    Inventors: Julius Simonelli, Ilsoo Seong
  • Publication number: 20250037455
    Abstract: Methods, non-transitory computer readable media, and roof analysis systems are disclosed that preprocess overhead images in imagery data obtained based on a request comprising a geographic location. The overhead images depict a building at different historical points in time identified in the imagery data. A neural network is applied to input data structures into which the overhead images are converted. The neural network is trained to extract relationships between features from the input data structures indicating changes in a roof of the building and generate an output data structure representing the changes. Patterns in the output data structure are analyzed to determine instances of change of the roof. A roof age is then output via a user interface in response to the request. The roof age is generated based on a likelihood of the instances of change or a time interval between the overhead images and a current time.
    Type: Application
    Filed: July 15, 2024
    Publication date: January 30, 2025
    Inventors: Julius Simonelli, Ilsoo Seong, Jason Janofsky, Nicholas Molyneux, David Tobias, David Lyman
  • Publication number: 20250037454
    Abstract: Methods, non-transitory computer readable media, and roof analysis systems are disclosed that preprocess overhead images in imagery data obtained based on a request comprising a geographic location. The overhead images depict a building at different historical points in time identified in the imagery data. A neural network is applied to input data structures into which the overhead images are converted. The neural network is trained to extract relationships between features from the input data structures indicating changes in a roof of the building and generate an output data structure representing the changes. Patterns in the output data structure are analyzed to determine instances of change of the roof. A roof age is then output via a user interface in response to the request. The roof age is generated based on a likelihood of the instances of change or a time interval between the overhead images and a current time.
    Type: Application
    Filed: July 26, 2023
    Publication date: January 30, 2025
    Inventors: Julius Simonelli, Ilsoo Seong, Jason Janofsky, Nicholas Molyneux, David Tobias, David Lyman