Patents by Inventor Susanna Maria Ricco
Susanna Maria Ricco 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: 11763466Abstract: A system comprising an encoder neural network, a scene structure decoder neural network, and a motion decoder neural network. The encoder neural network is configured to: receive a first image and a second image; and process the first image and the second image to generate an encoded representation of the first image and the second image. The scene structure decoder neural network is configured to process the encoded representation to generate a structure output characterizing a structure of a scene depicted in the first image. The motion decoder neural network configured to process the encoded representation to generate a motion output characterizing motion between the first image and the second image.Type: GrantFiled: December 23, 2020Date of Patent: September 19, 2023Assignee: Google LLCInventors: Cordelia Luise Schmid, Sudheendra Vijayanarasimhan, Susanna Maria Ricco, Bryan Andrew Seybold, Rahul Sukthankar, Aikaterini Fragkiadaki
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Patent number: 11688077Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing a machine-learned object tracking policy. One of the methods includes receiving a current video frame by a user device having a plurality of installed object trackers, wherein each object tracker is configured to perform a different object tracking procedure on the current video frame rent video frame. The current video frame and one or more object tracks previously generated by the one or more object trackers are provided as input to a trained policy engine that implements a reinforcement learning model to generate a particular object tracking plan. A particular object tracking plan is selected based on the output of the reinforcement learning model, and the selected object tracking plan is performed on the current video frame to generate one or more updated object tracks for the current video frame.Type: GrantFiled: December 15, 2017Date of Patent: June 27, 2023Assignee: Google LLCInventors: Susanna Maria Ricco, Caroline Rebecca Pantofaru, Kevin Patrick Murphy, David A. Ross
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Patent number: 11669977Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an optical flow object localization system and a novel object localization system. In a first aspect, the optical flow object localization system is trained to process an optical flow image to generate object localization data defining locations of objects depicted in a video frame corresponding to the optical flow image. In a second aspect, a novel object localization system is trained to process a video frame to generate object localization data defining locations of novel objects depicted in the video frame.Type: GrantFiled: March 26, 2021Date of Patent: June 6, 2023Assignee: Google LLCInventors: Susanna Maria Ricco, Bryan Andrew Seybold
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Publication number: 20210217197Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an optical flow object localization system and a novel object localization system. In a first aspect, the optical flow object localization system is trained to process an optical flow image to generate object localization data defining locations of objects depicted in a video frame corresponding to the optical flow image. In a second aspect, a novel object localization system is trained to process a video frame to generate object localization data defining locations of novel objects depicted in the video frame.Type: ApplicationFiled: March 26, 2021Publication date: July 15, 2021Inventors: Susanna Maria Ricco, Bryan Andrew Seybold
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Publication number: 20210166402Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing a machine-learned object tracking policy. One of the methods includes receiving a current video frame by a user device having a plurality of installed object trackers, wherein each object tracker is configured to perform a different object tracking procedure on the current video frame rent video frame. The current video frame and one or more object tracks previously generated by the one or more object trackers are provided as input to a trained policy engine that implements a reinforcement learning model to generate a particular object tracking plan. A particular object tracking plan is selected based on the output of the reinforcement learning model, and the selected object tracking plan is performed on the current video frame to generate one or more updated object tracks for the current video frame.Type: ApplicationFiled: December 15, 2017Publication date: June 3, 2021Inventors: Susanna Maria Ricco, Caroline Rebecca Pantofaru, Kevin Patrick Murphy, David A. Ross
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Patent number: 10991122Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an optical flow object localization system and a novel object localization system. In a first aspect, the optical flow object localization system is trained to process an optical flow image to generate object localization data defining locations of objects depicted in a video frame corresponding to the optical flow image. In a second aspect, a novel object localization system is trained to process a video frame to generate object localization data defining locations of novel objects depicted in the video frame.Type: GrantFiled: January 31, 2019Date of Patent: April 27, 2021Assignee: Google LLCInventors: Susanna Maria Ricco, Bryan Andrew Seybold
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Publication number: 20210118153Abstract: A system comprising an encoder neural network, a scene structure decoder neural network, and a motion decoder neural network. The encoder neural network is configured to: receive a first image and a second image; and process the first image and the second image to generate an encoded representation of the first image and the second image. The scene structure decoder neural network is configured to process the encoded representation to generate a structure output characterizing a structure of a scene depicted in the first image. The motion decoder neural network configured to process the encoded representation to generate a motion output characterizing motion between the first image and the second image.Type: ApplicationFiled: December 23, 2020Publication date: April 22, 2021Inventors: Cordelia Luise Schmid, Sudheendra Vijayanarasimhan, Susanna Maria Ricco, Bryan Andrew Seybold, Rahul Sukthankar, Aikaterini Fragkiadaki
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Patent number: 10878583Abstract: A system comprising an encoder neural network, a scene structure decoder neural network, and a motion decoder neural network. The encoder neural network is configured to: receive a first image and a second image; and process the first image and the second image to generate an encoded representation of the first image and the second image. The scene structure decoder neural network is configured to process the encoded representation to generate a structure output characterizing a structure of a scene depicted in the first image. The motion decoder neural network configured to process the encoded representation to generate a motion output characterizing motion between the first image and the second image.Type: GrantFiled: December 1, 2017Date of Patent: December 29, 2020Assignee: Google LLCInventors: Cordelia Luise Schmid, Sudheendra Vijayanarasimhan, Susanna Maria Ricco, Bryan Andrew Seybold, Rahul Sukthankar, Aikaterini Fragkiadaki
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Publication number: 20200349722Abstract: A system comprising an encoder neural network, a scene structure decoder neural network, and a motion decoder neural network. The encoder neural network is configured to: receive a first image and a second image; and process the first image and the second image to generate an encoded representation of the first image and the second image. The scene structure decoder neural network is configured to process the encoded representation to generate a structure output characterizing a structure of a scene depicted in the first image. The motion decoder neural network configured to process the encoded representation to generate a motion output characterizing motion between the first image and the second image.Type: ApplicationFiled: December 1, 2017Publication date: November 5, 2020Inventors: Cordelia Luise Schmid, Sudheendra Vijayanarasimhan, Susanna Maria Ricco, Bryan Andrew Seybold, Rahul Sukthankar, Aikaterini Fragkiadaki
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Publication number: 20200151905Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an optical flow object localization system and a novel object localization system. In a first aspect, the optical flow object localization system is trained to process an optical flow image to generate object localization data defining locations of objects depicted in a video frame corresponding to the optical flow image. In a second aspect, a novel object localization system is trained to process a video frame to generate object localization data defining locations of novel objects depicted in the video frame.Type: ApplicationFiled: January 31, 2019Publication date: May 14, 2020Inventors: Susanna Maria Ricco, Bryan Andrew Seybold