Patents by Inventor Ryan Kottenstette
Ryan Kottenstette 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: 11568639Abstract: 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: GrantFiled: September 15, 2021Date of Patent: January 31, 2023Assignee: Cape Analytics, Inc.Inventors: Ryan Kottenstette, Peter Lorenzen, Suat Gedikli
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Publication number: 20220004762Abstract: 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: ApplicationFiled: September 15, 2021Publication date: January 6, 2022Inventors: Ryan Kottenstette, Peter Lorenzen, Suat Gedikli
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Patent number: 11151378Abstract: 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: GrantFiled: March 27, 2020Date of Patent: October 19, 2021Assignee: Cape Analytics, Inc.Inventors: Ryan Kottenstette, Peter Lorenzen, Suat Gedikli
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Publication number: 20200226373Abstract: 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: ApplicationFiled: March 27, 2020Publication date: July 16, 2020Inventors: Ryan Kottenstette, Peter Lorenzen, Suat Gedikli
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Patent number: 10643072Abstract: 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: GrantFiled: March 14, 2019Date of Patent: May 5, 2020Assignee: Cape Analytics, Inc.Inventors: Ryan Kottenstette, Peter Lorenzen, Suat Gedikli
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Patent number: 10366288Abstract: 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: GrantFiled: March 14, 2019Date of Patent: July 30, 2019Assignee: CAPE ANALYTICS, INC.Inventors: Ryan Kottenstette, Peter Lorenzen, Suat Gedikli
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Publication number: 20190213413Abstract: 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: ApplicationFiled: March 14, 2019Publication date: July 11, 2019Inventors: Ryan KOTTENSTETTE, Peter LORENZEN, Suat GEDIKLI
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Publication number: 20190213412Abstract: 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: ApplicationFiled: March 14, 2019Publication date: July 11, 2019Inventors: Ryan KOTTENSTETTE, Peter LORENZEN, Suat GEDIKLI
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Patent number: 10311302Abstract: 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: GrantFiled: August 31, 2016Date of Patent: June 4, 2019Assignee: Cape Analytics, Inc.Inventors: Ryan Kottenstette, Peter Lorenzen, Suat Gedikli
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Publication number: 20170076438Abstract: 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: ApplicationFiled: August 31, 2016Publication date: March 16, 2017Inventors: Ryan KOTTENSTETTE, Peter LORENZEN, Suat GEDIKLI
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Publication number: 20120045670Abstract: Provided are novel electrochemical cells that include positive electrodes, negative electrodes containing high capacity active materials such as silicon, and auxiliary electrodes containing lithium. An auxiliary electrode is provided in the cell at least prior to its formation cycling and is used to supply lithium to the negative electrode. The auxiliary electrode may be then removed from the cell prior or after formation. The transfer of lithium to the negative electrode may be performed using a different electrolyte, a higher temperature, and/or a slower rate than during later operational cycling of the cell. After this transfer, the negative electrode may remain pre-lithiated during later cycling at least at a certain predetermined level. This pre-lithiation helps to cycle the cell at more optimal conditions and to some degree maintain this cycling performance over the operating life of the cell. Also provided are methods of fabricating such cells.Type: ApplicationFiled: September 26, 2011Publication date: February 23, 2012Applicant: AMPRIUS, INC.Inventors: Constantin I. Stefan, Rainer J. Fasching, Gregory Alan Roberts, Ryan Kottenstette, Song Han, Ghyrn E. Loveness