Patents by Inventor Daniel Clymer

Daniel Clymer 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).

  • METHOD FOR OBJECT DETECTION USING HIERARCHICAL DEEP LEARNING
    Publication number: 20240169748
    Abstract: A hierarchical deep-learning object detection framework provides a method for identifying objects of interest in high-resolution, high pixel count images, wherein the objects of interest comprise a relatively a small pixel count when compared to the overall image. The method uses first deep-learning model to analyze the high pixel count images, in whole or as a patchwork, at a lower resolution to identify objects, and a second deep-learning model to analyze the objects at a higher resolution to classify the objects.
    Type: Application
    Filed: December 21, 2023
    Publication date: May 23, 2024
    Applicant: CARNEGIE MELLON UNIVERSITY
    Inventors: JONATHAN CAGAN, PHILIP LeDUC, DANIEL CLYMER
  • Method for object detection using hierarchical deep learning
    Patent number: 11893811
    Abstract: A hierarchical deep-learning object detection framework provides a method for identifying objects of interest in high-resolution, high pixel count images, wherein the objects of interest comprise a relatively a small pixel count when compared to the overall image. The method uses first deep-learning model to analyze the high pixel count images, in whole or as a patchwork, at a lower resolution to identify objects, and a second deep-learning model to analyze the objects at a higher resolution to classify the objects.
    Type: Grant
    Filed: May 21, 2021
    Date of Patent: February 6, 2024
    Assignee: Carnegie Mellon University
    Inventors: Daniel Clymer, Jonathan Cagan, Philip LeDuc, Liron Pantanowitz, Janet Catov
  • Method for object detection using hierarchical deep learning
    Patent number: 11367189
    Abstract: A hierarchical deep-learning object detection framework provides a method for identifying objects of interest in high-resolution, high pixel count images, wherein the objects of interest comprise a relatively a small pixel count when compared to the overall image. The method uses first deep-learning model to analyze the high pixel count images, in whole or as a patchwork, at a lower resolution to identify objects, and a second deep-learning model to analyze the objects at a higher resolution to classify the objects.
    Type: Grant
    Filed: October 16, 2020
    Date of Patent: June 21, 2022
    Assignee: CARNEGIE MELLON UNIVERSITY
    Inventors: Daniel Clymer, Jonathan Cagan, Philip LeDuc
  • METHOD FOR OBJECT DETECTION USING HIERARCHICAL DEEP LEARNING
    Publication number: 20210406602
    Abstract: A hierarchical deep-learning object detection framework provides a method for identifying objects of interest in high-resolution, high pixel count images, wherein the objects of interest comprise a relatively a small pixel count when compared to the overall image. The method uses first deep-learning model to analyze the high pixel count images, in whole or as a patchwork, at a lower resolution to identify objects, and a second deep-learning model to analyze the objects at a higher resolution to classify the objects.
    Type: Application
    Filed: October 16, 2020
    Publication date: December 30, 2021
    Inventors: Daniel Clymer, Jonathan Cagan, Philip LeDuc
  • METHOD FOR OBJECT DETECTION USING HIERARCHICAL DEEP LEARNING
    Publication number: 20210327061
    Abstract: A hierarchical deep-learning object detection framework provides a method for identifying objects of interest in high-resolution, high pixel count images, wherein the objects of interest comprise a relatively a small pixel count when compared to the overall image. The method uses first deep-learning model to analyze the high pixel count images, in whole or as a patchwork, at a lower resolution to identify objects, and a second deep-learning model to analyze the objects at a higher resolution to classify the objects.
    Type: Application
    Filed: May 21, 2021
    Publication date: October 21, 2021
    Inventors: Daniel Clymer, Jonathan Cagan, Philip LeDuc, Liron Pantanowitz, Janet Catov
  • HINGE PIN FOR ENCLOSURE
    Publication number: 20080295285
    Abstract: A hinge pin for use with a hinge section having a bore. The hinge pin includes a hinge pin body dimensioned to fit within a bore of a hinge section and a clip having a first end attached to the hinge pin body, the clip extending along the hinge pin body and configured to be located outside the hinge section when the hinge pin body is within the bore.
    Type: Application
    Filed: May 31, 2007
    Publication date: December 4, 2008
    Applicant: Hoffman Enclosures, Inc
    Inventors: Joshua Daniel Clymer, David Lee Schaefbauer, Marcus Adrian Koukkari