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).
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Patent number: 11776242Abstract: A computer implemented method for recognizing a hand gesture using a random forest model includes training the random forest model. The method also includes obtaining image data. The method further includes clustering a plurality of pixels from the image data to generate a plurality of clusters. Moreover, the method includes analyzing the plurality of clusters using a rejection cascade to generate a plurality of selected candidates. In addition, the method includes analyzing the plurality of selected candidates using a classification decision tree from the random forest model. The method also includes skeletonizing the plurality of selected candidates to generate a one dimension plus branches hand model. The method further includes analyzing the one dimension plus branches hand model using a regression decision tree from the random forest model.Type: GrantFiled: June 14, 2019Date of Patent: October 3, 2023Assignee: Magic Leap, Inc.Inventor: Douglas Bertram Lee
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Patent number: 11775836Abstract: 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: GrantFiled: May 20, 2020Date of Patent: October 3, 2023Assignee: Magic Leap, Inc.Inventors: Prajwal Chidananda, Ayan Tuhinendu Sinha, Adithya Shricharan Srinivasa Rao, Douglas Bertram Lee, Andrew Rabinovich
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Patent number: 11657286Abstract: 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: GrantFiled: February 23, 2021Date of Patent: May 23, 2023Assignee: Magic Leap, Inc.Inventors: Andrew Rabinovich, Vijay Badrinarayanan, Daniel DeTone, Srivignesh Rajendran, Douglas Bertram Lee, Tomasz Malisiewicz
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Publication number: 20210327085Abstract: 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: ApplicationFiled: April 2, 2021Publication date: October 21, 2021Inventors: Adrian Kaehler, Douglas Bertram Lee, Vijay Badrinarayanan
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Publication number: 20210248358Abstract: A computer implemented method for recognizing a hand gesture using a random forest model includes training the random forest model. The method also includes obtaining image data. The method further includes clustering a plurality of pixels from the image data to generate a plurality of clusters. Moreover, the method includes analyzing the plurality of clusters using a rejection cascade to generate a plurality of selected candidates. In addition, the method includes analyzing the plurality of selected candidates using a classification decision tree from the random forest model. The method also includes skeletonizing the plurality of selected candidates to generate a one dimension plus branches hand model. The method further includes analyzing the one dimension plus branches hand model using a regression decision tree from the random forest model.Type: ApplicationFiled: June 14, 2019Publication date: August 12, 2021Applicant: MAGIC LEAP, INC.Inventor: Douglas Bertram LEE
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Publication number: 20210182636Abstract: 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: ApplicationFiled: February 23, 2021Publication date: June 17, 2021Applicant: MAGIC LEAP, INC.Inventors: Andrew Rabinovich, Vijay Badrinarayanan, Daniel DeTone, Srivignesh Rajendran, Douglas Bertram Lee, Tomasz Malisiewicz
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Patent number: 10977820Abstract: 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: GrantFiled: May 21, 2020Date of Patent: April 13, 2021Assignee: Magic Leap, Inc.Inventors: Adrian Kaehler, Douglas Bertram Lee, Vijay Badrinarayanan
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Patent number: 10963758Abstract: 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: GrantFiled: March 27, 2019Date of Patent: March 30, 2021Assignee: Magic Leap, Inc.Inventors: Andrew Rabinovich, Vijay Badrinarayanan, Daniel Detone, Srivignesh Rajendran, Douglas Bertram Lee, Tomasz Malisiewicz
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Publication number: 20200372246Abstract: 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: ApplicationFiled: May 20, 2020Publication date: November 26, 2020Applicant: MAGIC LEAP, INC.Inventors: Prajwal CHIDANANDA, Ayan Tuhinendu SINHA, Adithya Shricharan Srinivasa RAO, Douglas Bertram LEE, Andrew RABINOVICH
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Publication number: 20200286251Abstract: 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: ApplicationFiled: May 21, 2020Publication date: September 10, 2020Inventors: Adrian Kaehler, Douglas Bertram Lee, Vijay Badrinarayanan
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Patent number: 10719951Abstract: 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: GrantFiled: September 18, 2018Date of Patent: July 21, 2020Assignee: Magic Leap, Inc.Inventors: Adrian Kaehler, Douglas Bertram Lee, Vijay Badrinarayanan
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Publication number: 20190286951Abstract: 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: ApplicationFiled: March 27, 2019Publication date: September 19, 2019Applicant: MAGIC LEAP, INC.Inventors: Andrew RABINOVICH, Vijay BADRINARAYANAN, Daniel DETONE, Srivignesh RAJENDRAN, Douglas Bertram LEE, Tomasz MALISIEWICZ
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Patent number: 10255529Abstract: 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: GrantFiled: March 13, 2017Date of Patent: April 9, 2019Assignee: Magic Leap, Inc.Inventors: Andrew Rabinovich, Vijay Badrinarayanan, Daniel DeTone, Srivignesh Rajendran, Douglas Bertram Lee, Tomasz Malisiewicz
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Patent number: 9832437Abstract: 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: GrantFiled: January 11, 2016Date of Patent: November 28, 2017Assignee: MAGIC LEAP, INC.Inventors: Michael Kass, Douglas Bertram Lee
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Publication number: 20170262737Abstract: 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: ApplicationFiled: March 13, 2017Publication date: September 14, 2017Applicant: Magic Leap, Inc.Inventors: Andrew Rabinovich, Vijay Badrinarayanan, Daniel DeTone, Srivignesh Rajendran, Douglas Bertram Lee, Tomasz Malisiewicz
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Publication number: 20160241827Abstract: 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: ApplicationFiled: January 11, 2016Publication date: August 18, 2016Applicant: Magic Leap, Inc.Inventors: Michael Kass, Douglas Bertram Lee