Patents by Inventor Douglas Bertram Lee

Douglas Bertram Lee 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: 20200372246
    Abstract: A neural network in multi-task deep learning paradigm for machine vision includes an encoder that further includes a first, a second, and a third tier. The first tier comprises a first-tier unit having one or more first-unit blocks. The second tier receives a first-tier output from the first tier at one or more second-tier units in the second tier, a second-tier unit comprises one or more second-tier blocks, the third tier receives a second-tier output from the second tier at one or more third-tier units in the third tier, and a third-tier block comprises one or more third-tier blocks. The neural network further comprises a decoder operatively the encoder to receive an encoder output from the encoder as well as one or more loss function layers that are configured to backpropagate one or more losses for training at least the encoder of the neural network in a deep learning paradigm.
    Type: Application
    Filed: May 20, 2020
    Publication date: November 26, 2020
    Applicant: MAGIC LEAP, INC.
    Inventors: Prajwal CHIDANANDA, Ayan Tuhinendu SINHA, Adithya Shricharan Srinivasa RAO, Douglas Bertram LEE, Andrew RABINOVICH
  • Publication number: 20200286251
    Abstract: Disclosed herein is a wearable display system for capturing retraining eye images of an eye of a user for retraining a neural network for eye tracking. The system captures retraining eye images using an image capture device when user interface (UI) events occur with respect to UI devices displayed at display locations of a display. The system can generate a retraining set comprising the retraining eye images and eye poses of the eye of the user in the retraining eye images (e.g., related to the display locations of the UI devices) and obtain a retrained neural network that is retrained using the retraining set.
    Type: Application
    Filed: May 21, 2020
    Publication date: September 10, 2020
    Inventors: Adrian Kaehler, Douglas Bertram Lee, Vijay Badrinarayanan
  • Patent number: 10719951
    Abstract: Disclosed herein is a wearable display system for capturing retraining eye images of an eye of a user for retraining a neural network for eye tracking. The system captures retraining eye images using an image capture device when user interface (UI) events occur with respect to UI devices displayed at display locations of a display. The system can generate a retraining set comprising the retraining eye images and eye poses of the eye of the user in the retraining eye images (e.g., related to the display locations of the UI devices) and obtain a retrained neural network that is retrained using the retraining set.
    Type: Grant
    Filed: September 18, 2018
    Date of Patent: July 21, 2020
    Assignee: Magic Leap, Inc.
    Inventors: Adrian Kaehler, Douglas Bertram Lee, Vijay Badrinarayanan
  • Publication number: 20190286951
    Abstract: The present disclosure provides an improved approach to implement structure learning of neural networks by exploiting correlations in the data/problem the networks aim to solve. A greedy approach is described that finds bottlenecks of information gain from the bottom convolutional layers all the way to the fully connected layers. Rather than simply making the architecture deeper, additional computation and capacitance is only added where it is required.
    Type: Application
    Filed: March 27, 2019
    Publication date: September 19, 2019
    Applicant: MAGIC LEAP, INC.
    Inventors: Andrew RABINOVICH, Vijay BADRINARAYANAN, Daniel DETONE, Srivignesh RAJENDRAN, Douglas Bertram LEE, Tomasz MALISIEWICZ
  • Patent number: 10255529
    Abstract: The present disclosure provides an improved approach to implement structure learning of neural networks by exploiting correlations in the data/problem the networks aim to solve. A greedy approach is described that finds bottlenecks of information gain from the bottom convolutional layers all the way to the fully connected layers. Rather than simply making the architecture deeper, additional computation and capacitance is only added where it is required.
    Type: Grant
    Filed: March 13, 2017
    Date of Patent: April 9, 2019
    Assignee: Magic Leap, Inc.
    Inventors: Andrew Rabinovich, Vijay Badrinarayanan, Daniel DeTone, Srivignesh Rajendran, Douglas Bertram Lee, Tomasz Malisiewicz
  • Patent number: 9832437
    Abstract: Described are improved approaches to implement color sequential displays that can mitigate problems with conventional display technologies. Color-breakup is mitigated by modifying the original color channels and adding one or more additional color channels derived from the original ones.
    Type: Grant
    Filed: January 11, 2016
    Date of Patent: November 28, 2017
    Assignee: MAGIC LEAP, INC.
    Inventors: Michael Kass, Douglas Bertram Lee
  • Publication number: 20170262737
    Abstract: The present disclosure provides an improved approach to implement structure learning of neural networks by exploiting correlations in the data/problem the networks aim to solve. A greedy approach is described that finds bottlenecks of information gain from the bottom convolutional layers all the way to the fully connected layers. Rather than simply making the architecture deeper, additional computation and capacitance is only added where it is required.
    Type: Application
    Filed: March 13, 2017
    Publication date: September 14, 2017
    Applicant: Magic Leap, Inc.
    Inventors: Andrew Rabinovich, Vijay Badrinarayanan, Daniel DeTone, Srivignesh Rajendran, Douglas Bertram Lee, Tomasz Malisiewicz
  • Publication number: 20160241827
    Abstract: Described are improved approaches to implement color sequential displays that can mitigate problems with conventional display technologies. Color-breakup is mitigated by modifying the original color channels and adding one or more additional color channels derived from the original ones.
    Type: Application
    Filed: January 11, 2016
    Publication date: August 18, 2016
    Applicant: Magic Leap, Inc.
    Inventors: Michael Kass, Douglas Bertram Lee