Patents by Inventor Sam Muscroft

Sam Muscroft 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).

  • State detection using machine-learning model trained on simulated image data
    Patent number: 10496898
    Abstract: A set of virtual images can be generated based on one or more real images and target rendering specifications, such that the set of virtual images correspond to (for example) different rendering specifications (or combinations thereof) than do the real images. An image style can be transferred to the at least some of the virtual images of the set of virtual images to generate a stylized virtual image. A machine-learning model can be trained using a plurality of stylized virtual images. Another real image can then be processed using the trained machine-learning model. The processing can include segmenting the other real image to detect whether and/or which objects are represented (and/or a state of the object). The object data can then be used to identify (for example) a state of a procedure.
    Type: Grant
    Filed: February 1, 2019
    Date of Patent: December 3, 2019
    Assignee: Digital Surgery Limited
    Inventors: Odysseas Zisimopoulos, Evangello Flouty, Imanol Luengo Muntion, Mark Stacey, Sam Muscroft, Petros Giataganas, Andre Chow, Jean Nehme, Danail Stoyanov
  • STATE DETECTION USING MACHINE-LEARNING MODEL TRAINED ON SIMULATED IMAGE DATA
    Publication number: 20190164012
    Abstract: A set of virtual images can be generated based on one or more real images and target rendering specifications, such that the set of virtual images correspond to (for example) different rendering specifications (or combinations thereof) than do the real images. An image style can be transferred to the at least some of the virtual images of the set of virtual images to generate a stylized virtual image. A machine-learning model can be trained using a plurality of stylized virtual images. Another real image can then be processed using the trained machine-learning model. The processing can include segmenting the other real image to detect whether and/or which objects are represented (and/or a state of the object). The object data can then be used to identify (for example) a state of a procedure.
    Type: Application
    Filed: February 1, 2019
    Publication date: May 30, 2019
    Applicant: Digital Surgery Limited
    Inventors: Odysseas Zisimopoulos, Evangello Flouty, Imanol Luengo Muntion, Mark Stacey, Sam Muscroft, Petros Giataganas, Andre Chow, Jean Nehme, Danail Stoyanov
  • Surgical simulation for training detection and classification neural networks
    Patent number: 10242292
    Abstract: A set of virtual images can be generated based on one or more real images and target rendering specifications, such that the set of virtual images correspond to (for example) different rendering specifications (or combinations thereof) than do the real images. A machine-learning model can be trained using the set of virtual images. Another real image can then be processed using the trained machine-learning model. The processing can include segmenting the other real image to detect whether and/or which objects are represented (and/or a state of the object). The object data can then be used to identify (for example) a state of a procedure.
    Type: Grant
    Filed: June 4, 2018
    Date of Patent: March 26, 2019
    Assignee: Digital Surgery Limited
    Inventors: Odysseas Zisimopoulos, Evangello Flouty, Imanol Luengo Muntion, Mark Stacey, Sam Muscroft, Petros Giataganas, Andre Chow, Jean Nehme, Danail Stoyanov
  • SURGICAL SIMULATION FOR TRAINING DETECTION AND CLASSIFICATION NEURAL NETWORKS
    Publication number: 20180357514
    Abstract: A set of virtual images can be generated based on one or more real images and target rendering specifications, such that the set of virtual images correspond to (for example) different rendering specifications (or combinations thereof) than do the real images. A machine-learning model can be trained using the set of virtual images. Another real image can then be processed using the trained machine-learning model. The processing can include segmenting the other real image to detect whether and/or which objects are represented (and/or a state of the object). The object data can then be used to identify (for example) a state of a procedure.
    Type: Application
    Filed: June 4, 2018
    Publication date: December 13, 2018
    Applicant: Digital Surgery Limited
    Inventors: Odysseas Zisimopoulos, Evangello Flouty, Imanol Luengo Muntion, Mark Stacey, Sam Muscroft, Petros Giataganas, Andre Chow, Jean Nehme, Danail Stoyanov