Patents by Inventor Adam Estrada

Adam Estrada 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: 20200380308
    Abstract: Techniques for recommending a prediction model from among a number of different prediction models are provided. Each of these prediction models has been trained based on a respective training data set, and each performs in accordance with a respective theoretical performance manifold. An indication of a region definable in relation to the theoretical performance manifolds of the different prediction models is received as input. For each of the different prediction models, the indication of the region is linked to features parameterizing the respective performance manifold; and one or more portions of the respective performance manifold is/are identified based on the features determined by the linking, the portion(s) having a volume and a shape that collectively denote an expected performance of the respective model for the input. The expected performance of the prediction models for the input is compared. Based on the comparison, one or more of the models is/are suggested.
    Type: Application
    Filed: May 29, 2020
    Publication date: December 3, 2020
    Inventors: Arnold BOEDIHARDJO, Adam ESTRADA, Andrew JENKINS, Nathan CLEMENT, Alan SCHOEN
  • Publication number: 20200380307
    Abstract: Techniques for quantifying accuracy of a prediction model that has been trained on a data set parameterized by multiple features are provided. The model performs in accordance with a theoretical performance manifold over an intractable input space in connection with the features. A determination is made as to which of the features are strongly correlated with performance of the model. Based on the features determined to be strongly correlated with performance of the model, parameterized sub-models are created such that, in aggregate, they approximate the intractable input space. Prototype exemplars are generated for each of the created sub-models, with the prototype exemplars for each created sub-model being objects to which the model can be applied to result in a match with the respective sub-model. The accuracy of the model is quantified using the generated prototype exemplars. A recommendation engine is provided for when there are particular areas of interest.
    Type: Application
    Filed: May 29, 2020
    Publication date: December 3, 2020
    Inventors: Arnold BOEDIHARDJO, Adam ESTRADA, Andrew JENKINS, Nathan CLEMENT, Alan SCHOEN
  • Patent number: 10803310
    Abstract: A system for simplified generation of systems for analysis of satellite images to geolocate one or more objects of interest. A plurality of training images labeled for a study object or objects with irrelevant features loaded into a preexisting feature identification subsystem causes automated generation of models for the study object. This model is used to parameterize pre-engineered machine learning elements that are running a preprogrammed machine learning protocol. Training images with the study are used to train object recognition filters. This filter is used to identify the study object in unanalyzed images. The system reports results in a requestor's preferred format.
    Type: Grant
    Filed: August 6, 2019
    Date of Patent: October 13, 2020
    Assignee: DIGITALGLOBE, INC.
    Inventors: Adam Estrada, Kevin Green, Andrew Jenkins
  • Patent number: 10733759
    Abstract: A system for automated geospatial image analysis comprising a deep learning model that receives orthorectified geospatial images, pre-labeled to demarcate objects of interest. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to a convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives orthorectified geospatial images from one or more geospatial image caches. Using a multi-scale sliding window submodule, image analysis software scans geospatial images, detects objects present and geospatially locates them.
    Type: Grant
    Filed: August 27, 2019
    Date of Patent: August 4, 2020
    Assignee: DIGITALGLOBE, INC.
    Inventors: Adam Estrada, Andrew Jenkins, Benjamin Brock, Chris Mangold
  • Patent number: 10636169
    Abstract: A system for broad area geospatial object recognition, identification, classification, location and quantification, comprising an image manipulation module to create synthetically-generated images to imitate and augment an existing quantity of orthorectified geospatial images; together with a deep learning module and a convolutional neural network serving as an image analysis module, to analyze a large corpus of orthorectified geospatial images, identify and demarcate a searched object of interest from within the corpus, locate and quantify the identified or classified objects from the corpus of geospatial imagery available to the system. The system reports results in a requestor's preferred format.
    Type: Grant
    Filed: December 18, 2018
    Date of Patent: April 28, 2020
    Assignee: DIGITALGLOBE, INC.
    Inventors: Adam Estrada, Christopher Burd, Andrew Jenkins, Joseph Newbrough, Scott Szoko, Melanie Vinton
  • Publication number: 20200118292
    Abstract: A system for automated geospatial image analysis comprising a deep learning model that receives orthorectified geospatial images, pre-labeled to demarcate objects of interest. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to a convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives orthorectified geospatial images from one or more geospatial image caches. Using a multi-scale sliding window submodule, image analysis software scans geospatial images, detects objects present and geospatially locates them.
    Type: Application
    Filed: August 27, 2019
    Publication date: April 16, 2020
    Inventors: Adam Estrada, Andrew Jenkins, Benjamin Brock, Chris Mangold
  • Publication number: 20200089930
    Abstract: A system for simplified generation of systems for analysis of satellite images to geolocate one or more objects of interest. A plurality of training images labeled for a study object or objects with irrelevant features loaded into a preexisting feature identification subsystem causes automated generation of models for the study object. This model is used to parameterize pre-engineered machine learning elements that are running a preprogrammed machine learning protocol. Training images with the study are used to train object recognition filters. This filter is used to identify the study object in unanalyzed images. The system reports results in a requestor's preferred format.
    Type: Application
    Filed: August 6, 2019
    Publication date: March 19, 2020
    Inventors: Adam Estrada, Kevin Green, Andrew Jenkins
  • Publication number: 20190385338
    Abstract: A system for broad area geospatial object recognition, identification, classification, location and quantification, comprising an image manipulation module to create synthetically-generated images to imitate and augment an existing quantity of orthorectified geospatial images; together with a deep learning module and a convolutional neural network serving as an image analysis module, to analyze a large corpus of orthorectified geospatial images, identify and demarcate a searched object of interest from within the corpus, locate and quantify the identified or classified objects from the corpus of geospatial imagery available to the system. The system reports results in a requestor's preferred format.
    Type: Application
    Filed: December 18, 2018
    Publication date: December 19, 2019
    Inventors: Adam Estrada, Christopher Burd, Andrew Jenkins, Joseph Newbrough, Scott Szoko, Melanie Vinton
  • Patent number: 10395388
    Abstract: A system for automated geospatial image analysis comprising a deep learning model that receives orthorectified geospatial images, pre-labeled to demarcate objects of interest. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to a convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives orthorectified geospatial images from one or more geospatial image caches. Using a multi-scale sliding window submodule, image analysis software scans geospatial images, detects objects present and geospatially locates them.
    Type: Grant
    Filed: July 3, 2018
    Date of Patent: August 27, 2019
    Assignee: DigitalGlobe, Inc.
    Inventors: Adam Estrada, Andrew Jenkins, Benjamin Brock, Chris Mangold
  • Patent number: 10372985
    Abstract: A system for simplified generation of systems for analysis of satellite images to geolocate one or more objects of interest. A plurality of training images labeled for a study object or objects with irrelevant features loaded into a preexisting feature identification subsystem causes automated generation of models for the study object. This model is used to parameterize pre-engineered machine learning elements that are running a preprogrammed machine learning protocol. Training images with the study are used to train object recognition filters. This filter is used to identify the study object in unanalyzed images. The system reports results in a requestor's preferred format.
    Type: Grant
    Filed: February 27, 2018
    Date of Patent: August 6, 2019
    Assignee: DigitalGlobe, Inc.
    Inventors: Adam Estrada, Kevin Green, Andrew Jenkins
  • Publication number: 20190043217
    Abstract: A system for automated geospatial image analysis comprising a deep learning model that receives orthorectified geospatial images, pre-labeled to demarcate objects of interest. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to a convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives orthorectified geospatial images from one or more geospatial image caches. Using a multi-scale sliding window submodule, image analysis software scans geospatial images, detects objects present and geospatially locates them.
    Type: Application
    Filed: July 3, 2018
    Publication date: February 7, 2019
    Inventors: Adam Estrada, Andrew Jenkins, Benjamin Brock, Chris Mangold
  • Patent number: 10157479
    Abstract: A system for broad area geospatial object recognition, identification, classification, location and quantification, comprising an image manipulation module to create synthetically-generated images to imitate and augment an existing quantity of orthorectified geospatial images; together with a deep learning module and a convolutional neural network serving as an image analysis module, to analyze a large corpus of orthorectified geospatial images, identify and demarcate a searched object of interest from within the corpus, locate and quantify the identified or classified objects from the corpus of geospatial imagery available to the system. The system reports results in a requestor's preferred format.
    Type: Grant
    Filed: September 19, 2017
    Date of Patent: December 18, 2018
    Assignee: DigitalGlobe, Inc.
    Inventors: Adam Estrada, Christopher Burd, Andrew Jenkins, Joseph Newbrough, Scott Szoko, Melanie Vinton
  • Publication number: 20180189544
    Abstract: A system for simplified generation of systems for analysis of satellite images to geolocate one or more objects of interest. A plurality of training images labeled for a study object or objects with irrelevant features loaded into a preexisting feature identification subsystem causes automated generation of models for the study object. This model is used to parameterize pre-engineered machine learning elements that are running a preprogrammed machine learning protocol. Training images with the study are used to train object recognition filters. This filter is used to identify the study object in unanalyzed images. The system reports results in a requestor's preferred format.
    Type: Application
    Filed: February 27, 2018
    Publication date: July 5, 2018
    Inventors: Adam Estrada, Kevin Green, Andrew Jenkins
  • Patent number: 10013774
    Abstract: A system for automated geospatial image analysis comprising a deep learning model module and a convolutional neural network serving as an automated image analysis software module. The deep learning module receives a plurality of orthorectified geospatial images, pre-labeled to demarcate objects of interest, and optimized for the purpose of training the neural network of the image analysis software module. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to the convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives a plurality of orthorectified geospatial images from one or more geospatial image caches. Using multi-scale sliding window submodule, image analysis modules scan geospatial images, detect objects present and locate them on the geographical latitude-longitude system.
    Type: Grant
    Filed: March 7, 2017
    Date of Patent: July 3, 2018
    Assignee: DigitalGlobe, Inc.
    Inventors: Adam Estrada, Andrew Jenkins, Benjamin Brock, Chris Mangold
  • Publication number: 20180158210
    Abstract: A system for broad area geospatial object recognition, identification, classification, location and quantification, comprising an image manipulation module to create synthetically-generated images to imitate and augment an existing quantity of orthorectified geospatial images; together with a deep learning module and a convolutional neural network serving as an image analysis module, to analyze a large corpus of orthorectified geospatial images, identify and demarcate a searched object of interest from within the corpus, locate and quantify the identified or classified objects from the corpus of geospatial imagery available to the system. The system reports results in a requestor's preferred format.
    Type: Application
    Filed: September 19, 2017
    Publication date: June 7, 2018
    Inventors: Adam Estrada, Christopher Burd, Andrew Jenkins, Joseph Newbrough, Scott Szoko, Melanie Vinton
  • Patent number: 9904849
    Abstract: A system for simplified generation of systems for analysis of satellite images to geolocate one or more objects of interest. A plurality of training images labeled for a study object or objects with irrelevant features loaded into a preexisting feature identification subsystem causes automated generation of models for the study object. This model is used to parameterize pre-engineered machine learning elements that are running a preprogrammed machine learning protocol. Training images with the study are used to train object recognition filters. This filter is used to identify the study object in unanalyzed images. The system reports results in a requestor's preferred format.
    Type: Grant
    Filed: May 30, 2017
    Date of Patent: February 27, 2018
    Assignee: DIGITALGLOBE, INC.
    Inventors: Adam Estrada, Kevin Green, Andrew Jenkins
  • Publication number: 20170364733
    Abstract: A system for simplified generation of systems for analysis of satellite images to geolocate one or more objects of interest. A plurality of training images labeled for a study object or objects with irrelevant features loaded into a preexisting feature identification subsystem causes automated generation of models for the study object. This model is used to parameterize pre-engineered machine learning elements that are running a preprogrammed machine learning protocol. Training images with the study are used to train object recognition filters. This filter is used to identify the study object in unanalyzed images. The system reports results in a requestor's preferred format.
    Type: Application
    Filed: May 30, 2017
    Publication date: December 21, 2017
    Inventors: Adam Estrada, Kevin Green, Andrew Jenkins
  • Publication number: 20170301108
    Abstract: A system for automated geospatial image analysis comprising a deep learning model module and a convolutional neural network serving as an automated image analysis software module. The deep learning module receives a plurality of orthorectified geospatial images, pre-labeled to demarcate objects of interest, and optimized for the purpose of training the neural network of the image analysis software module. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to the convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives a plurality of orthorectified geospatial images from one or more geospatial image caches. Using multi-scale sliding window submodule, image analysis modules scan geospatial images, detect objects present and locate them on the geographical latitude-longitude system.
    Type: Application
    Filed: March 7, 2017
    Publication date: October 19, 2017
    Inventors: Adam Estrada, Andrew Jenkins, Benjamin Brock, Chris Mangold
  • Patent number: 9767565
    Abstract: A system for broad area geospatial object recognition, identification, classification, location and quantification, comprising an image manipulation module to create synthetically-generated images to imitate and augment an existing quantity of orthorectified geospatial images; together with a deep learning module and a convolutional neural network serving as an image analysis module, to analyze a large corpus of orthorectified geospatial images, identify and demarcate a searched object of interest from within the corpus, locate and quantify the identified or classified objects from the corpus of geospatial imagery available to the system. The system reports results in a requestor's preferred format.
    Type: Grant
    Filed: June 27, 2016
    Date of Patent: September 19, 2017
    Assignee: DigitalGlobe, Inc.
    Inventors: Adam Estrada, Christopher Burd, Andrew Jenkins, Joseph Newbrough, Scott Szoko, Melanie Vinton
  • Patent number: 9589210
    Abstract: A system for automated geospatial image analysis comprising a deep learning model module and a convolutional neural network serving as an automated image analysis software module. The deep learning module receives a plurality of orthorectified geospatial images, pre-labeled to demarcate objects of interest, and optimized for the purpose of training the neural network of the image analysis software module. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to the convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives a plurality of orthorectified geospatial images from one or more geospatial image caches. Using multi-scale sliding window submodule, image analysis modules scan geospatial images, detect objects present and locate them on the geographical latitude-longitude system.
    Type: Grant
    Filed: August 26, 2015
    Date of Patent: March 7, 2017
    Assignee: DigitalGlobe, Inc.
    Inventors: Adam Estrada, Andrew Jenkins, Benjamin Brock, Chris Mangold