Patents by Inventor Peter Lorenzen

Peter Lorenzen 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: 20200226373
    Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.
    Type: Application
    Filed: March 27, 2020
    Publication date: July 16, 2020
    Inventors: Ryan Kottenstette, Peter Lorenzen, Suat Gedikli
  • Patent number: 10643072
    Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.
    Type: Grant
    Filed: March 14, 2019
    Date of Patent: May 5, 2020
    Assignee: Cape Analytics, Inc.
    Inventors: Ryan Kottenstette, Peter Lorenzen, Suat Gedikli
  • Patent number: 10366288
    Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.
    Type: Grant
    Filed: March 14, 2019
    Date of Patent: July 30, 2019
    Assignee: CAPE ANALYTICS, INC.
    Inventors: Ryan Kottenstette, Peter Lorenzen, Suat Gedikli
  • Publication number: 20190213413
    Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.
    Type: Application
    Filed: March 14, 2019
    Publication date: July 11, 2019
    Inventors: Ryan KOTTENSTETTE, Peter LORENZEN, Suat GEDIKLI
  • Publication number: 20190213412
    Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.
    Type: Application
    Filed: March 14, 2019
    Publication date: July 11, 2019
    Inventors: Ryan KOTTENSTETTE, Peter LORENZEN, Suat GEDIKLI
  • Patent number: 10311302
    Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.
    Type: Grant
    Filed: August 31, 2016
    Date of Patent: June 4, 2019
    Assignee: Cape Analytics, Inc.
    Inventors: Ryan Kottenstette, Peter Lorenzen, Suat Gedikli
  • Patent number: 9710696
    Abstract: Apparatuses, methods, and systems for automated cell classification, embryo ranking, and/or embryo categorization are provided. An apparatus includes a classification module configured to apply classifiers to images of one or more cells to determine, for each image, a classification probability associated with each classifier. Each classifier is associated with a distinct first number of cells, and is configured to determine the classification probability for each image based on cell features including one or more machine learned cell features. The classification probability indicates an estimated likelihood that the distinct first number of cells is shown in each image. The classification module is further configured to classify each image as showing a second number of cells based on the distinct first number of cells and the classification probabilities associated therewith. The classification module is implemented in at least one of a memory or a processing device.
    Type: Grant
    Filed: September 17, 2015
    Date of Patent: July 18, 2017
    Assignee: Progyny, Inc.
    Inventors: Yu Wang, Farshid Moussavi, Peter Lorenzen, Stephen Gould
  • Publication number: 20170076438
    Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.
    Type: Application
    Filed: August 31, 2016
    Publication date: March 16, 2017
    Inventors: Ryan KOTTENSTETTE, Peter LORENZEN, Suat GEDIKLI
  • Patent number: 9542591
    Abstract: Apparatuses, methods, and systems for automated, non-invasive evaluation of cell activity are provided. In one embodiment, an apparatus includes a hypothesis selection module configured to select a hypothesis from a plurality of hypotheses characterizing one or more cells shown in an image. Each of the plurality of hypotheses includes an inferred characteristic of the one or more cells based on geometric features of the one or more cells shown in the image. The hypothesis selection module is implemented in at least one of a memory or a processing device.
    Type: Grant
    Filed: February 28, 2014
    Date of Patent: January 10, 2017
    Assignee: PROGYNY, INC.
    Inventors: Farshid Moussavi, Yu Wang, Peter Lorenzen, Stephen Gould
  • Publication number: 20160078275
    Abstract: Apparatuses, methods, and systems for automated cell classification, embryo ranking, and/or embryo categorization are provided. An apparatus includes a classification module configured to apply classifiers to images of one or more cells to determine, for each image, a classification probability associated with each classifier. Each classifier is associated with a distinct first number of cells, and is configured to determine the classification probability for each image based on cell features including one or more machine learned cell features. The classification probability indicates an estimated likelihood that the distinct first number of cells is shown in each image. The classification module is further configured to classify each image as showing a second number of cells based on the distinct first number of cells and the classification probabilities associated therewith. The classification module is implemented in at least one of a memory or a processing device.
    Type: Application
    Filed: September 17, 2015
    Publication date: March 17, 2016
    Inventors: Yu Wang, Farshid Moussavi, Peter Lorenzen, Stephen Gould
  • Patent number: 9177192
    Abstract: Apparatuses, methods, and systems for automated cell classification, embryo ranking, and/or embryo categorization are provided. An apparatus includes a classification module configured to apply classifiers to images of one or more cells to determine, for each image, a classification probability associated with each classifier. Each classifier is associated with a distinct first number of cells, and is configured to determine the classification probability for each image based on cell features including one or more machine learned cell features. The classification probability indicates an estimated likelihood that the distinct first number of cells is shown in each image. The classification module is further configured to classify each image as showing a second number of cells based on the distinct first number of cells and the classification probabilities associated therewith. The classification module is implemented in at least one of a memory or a processing device.
    Type: Grant
    Filed: February 28, 2014
    Date of Patent: November 3, 2015
    Assignee: Progyny, Inc.
    Inventors: Yu Wang, Farshid Moussavi, Peter Lorenzen, Stephen Gould
  • Publication number: 20150268227
    Abstract: Methods, compositions and kits for determining the developmental potential of one or more embryos are provided. These methods, compositions and kits find use in identifying embryos in vitro that are most useful in treating infertility in humans.
    Type: Application
    Filed: March 20, 2015
    Publication date: September 24, 2015
    Inventors: Lei Tan, Martin Chian, Alice Chen Kim, Peter Lorenzen
  • Publication number: 20140247972
    Abstract: Apparatuses, methods, and systems for automated cell classification, embryo ranking, and/or embryo categorization are provided. An apparatus includes a classification module configured to apply classifiers to images of one or more cells to determine, for each image, a classification probability associated with each classifier. Each classifier is associated with a distinct first number of cells, and is configured to determine the classification probability for each image based on cell features including one or more machine learned cell features. The classification probability indicates an estimated likelihood that the distinct first number of cells is shown in each image. The classification module is further configured to classify each image as showing a second number of cells based on the distinct first number of cells and the classification probabilities associated therewith. The classification module is implemented in at least one of a memory or a processing device.
    Type: Application
    Filed: February 28, 2014
    Publication date: September 4, 2014
    Inventors: YU WANG, FARSHID MOUSSAVI, PETER LORENZEN, STEPHEN GOULD
  • Publication number: 20140247973
    Abstract: Apparatuses, methods, and systems for automated, non-invasive evaluation of cell activity are provided. In one embodiment, an apparatus includes a hypothesis selection module configured to select a hypothesis from a plurality of hypotheses characterizing one or more cells shown in an image. Each of the plurality of hypotheses includes an inferred characteristic of the one or more cells based on geometric features of the one or more cells shown in the image. The hypothesis selection module is implemented in at least one of a memory or a processing device.
    Type: Application
    Filed: February 28, 2014
    Publication date: September 4, 2014
    Inventors: Farshid MOUSSAVI, Yu WANG, Peter LORENZEN, Stephen GOULD
  • Publication number: 20140149174
    Abstract: A method for predicting and quantifying risk in information technology (IT) service contracts includes comparing features of a new IT service contract with similar features from one or more previous IT service contracts selected from a plurality of previous IT service contracts to calculate a similarity value between each pair of the new IT service contract and one of the one or more previous IT service contracts, aggregating the similarity values, and using the aggregated similarity values with a prediction model to predict risk factors affecting the new IT service contract and to quantify an impact of each predicted risk factor on an expected gross profit margin.
    Type: Application
    Filed: November 26, 2012
    Publication date: May 29, 2014
    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Geraldine L. Abbott, Sherif A. Goma, Allen D. Grussing, Richard D. Howard, Sinem Guven Kaya, Peter Lorenzen, Sergey Makogon, Satya Nitta, Anatoli Olkhovets, Natalia M. Ruderman, Shu Tao
  • Publication number: 20140149175
    Abstract: A method for predicting and quantifying risk in information technology (IT) service contracts includes comparing features of a new IT service contract with similar features from one or more previous IT service contracts selected from a plurality of previous IT service contracts to calculate a similarity value between each pair of the new IT service contract and one of the one or more previous IT service contracts, aggregating the similarity values, and using the aggregated similarity values with a prediction model to predict risk factors affecting the new IT service contract and to quantify an impact of each predicted risk factor on an expected gross profit margin.
    Type: Application
    Filed: February 8, 2013
    Publication date: May 29, 2014
    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: GERALDINE L. ABBOTT, SHERIF A. GOMA, ALLEN D. GRUSSING, RICHARD D. HOWARD, SINEM GUVEN KAYA, PETER LORENZEN, SERGEY MAKOGON, SATYA NITTA, ANATOLI OLKHOVETS, NATALIA M. RUDERMAN, SHU TAO