Patents by Inventor Nicholas Cote
Nicholas Cote 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: 11953879Abstract: An agent engine allocates a collection of agents to scan the surface of an object model. Each agent operates autonomously and implements particular behaviors based on the actions of nearby agents. Accordingly, the collection of agents exhibits swarm-like behavior. Over a sequence of time steps, the agents traverse the surface of the object model. Each agent acts to avoid other agents, thereby maintaining a relatively consistent distribution of agents across the surface of the object model over all time steps. At a given time step, the agent engine generates a slice through the object model that intersects each agent in a group of agents. The slice associated with a given time step represents a set of locations where material should be deposited to fabricate a 3D object. Based on a set of such slices, a robot engine causes a robot to fabricate the 3D object.Type: GrantFiled: September 8, 2020Date of Patent: April 9, 2024Assignee: AUTODESK, INC.Inventors: Evan Patrick Atherton, David Thomasson, Maurice Ugo Conti, Heather Kerrick, Nicholas Cote, Hui Li
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Patent number: 11679506Abstract: One embodiment of the present invention sets forth a technique for generating simulated training data for a physical process. The technique includes receiving, as input to at least one machine learning model, a first simulated image of a first object, wherein the at least one machine learning model includes mappings between simulated images generated from models of physical objects and real-world images of the physical objects. The technique also includes performing, by the at least one machine learning model, one or more operations on the first simulated image to generate a first augmented image of the first object. The technique further includes transmitting the first augmented image to a training pipeline for an additional machine learning model that controls a behavior of the physical process.Type: GrantFiled: March 10, 2022Date of Patent: June 20, 2023Assignee: AUTODESK, INC.Inventors: Hui Li, Evan Patrick Atherton, Erin Bradner, Nicholas Cote, Heather Kerrick
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Patent number: 11654565Abstract: One embodiment of the present invention sets forth a technique for controlling the execution of a physical process. The technique includes receiving, as input to a machine learning model that is configured to adapt a simulation of the physical process executing in a virtual environment to a physical world, simulated output for controlling how the physical process performs a task in the virtual environment and real-world data collected from the physical process performing the task in the physical world. The technique also includes performing, by the machine learning model, one or more operations on the simulated output and the real-world data to generate augmented output. The technique further includes transmitting the augmented output to the physical process to control how the physical process performs the task in the physical world.Type: GrantFiled: July 27, 2020Date of Patent: May 23, 2023Assignee: AUTODESK, INC.Inventors: Hui Li, Evan Patrick Atherton, Erin Bradner, Nicholas Cote, Heather Kerrick
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Publication number: 20220346934Abstract: The present invention provides delivery systems for positioning a gastrointestinal implant in a patient, for example, for treatment of a metabolic disease. Also provided are methods for assembling the delivery systems, methods of positioning a gastrointestinal implant, and methods of treatment of metabolic diseases, such as type 2 diabetes, non-alcoholic steatohepatitis (NASH), non-alcoholic fatty liver disease (NAFLD), obesity, and related comorbidities thereof.Type: ApplicationFiled: April 29, 2022Publication date: November 3, 2022Applicant: GI Dynamics, Inc.Inventors: Nicholas COTE, Ryan HANLON, Ronald B. LAMPORT, Barry MAXWELL, John PANEK, Ian PARKER, Scott SCHORER, Nicholas WILLIAMS
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Publication number: 20220193912Abstract: One embodiment of the present invention sets forth a technique for generating simulated training data for a physical process. The technique includes receiving, as input to at least one machine learning model, a first simulated image of a first object, wherein the at least one machine learning model includes mappings between simulated images generated from models of physical objects and real-world images of the physical objects. The technique also includes performing, by the at least one machine learning model, one or more operations on the first simulated image to generate a first augmented image of the first object. The technique further includes transmitting the first augmented image to a training pipeline for an additional machine learning model that controls a behavior of the physical process.Type: ApplicationFiled: March 10, 2022Publication date: June 23, 2022Inventors: Hui LI, Evan Patrick ATHERTON, Erin BRADNER, Nicholas COTE, Heather KERRICK
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Patent number: 11318008Abstract: The present invention provides delivery systems for positioning a gastrointestinal implant in a patient, for example, for treatment of a metabolic disease. Also provided are methods for assembling the delivery systems, methods of positioning a gastrointestinal implant, and methods of treatment of metabolic diseases, such as type 2 diabetes, non-alcoholic steatohepatitis (NASH), non-alcoholic fatty liver disease (NAFLD), obesity, and related comorbidities thereof.Type: GrantFiled: January 30, 2017Date of Patent: May 3, 2022Assignee: GI Dynamics, Inc.Inventors: Nicholas Cote, Ryan Hanlon, Ronald B. Lamport, Barry Maxwell, John Panek, Ian K. Parker, Scott Schorer, Nicholas Williams
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Patent number: 11273553Abstract: One embodiment of the present invention sets forth a technique for generating simulated training data for a physical process. The technique includes receiving, as input to at least one machine learning model, a first simulated image of a first object, wherein the at least one machine learning model includes mappings between simulated images generated from models of physical objects and real-world images of the physical objects. The technique also includes performing, by the at least one machine learning model, one or more operations on the first simulated image to generate a first augmented image of the first object. The technique further includes transmitting the first augmented image to a training pipeline for an additional machine learning model that controls a behavior of the physical process.Type: GrantFiled: May 31, 2018Date of Patent: March 15, 2022Assignee: AUTODESK, INC.Inventors: Hui Li, Evan Patrick Atherton, Erin Bradner, Nicholas Cote, Heather Kerrick
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Patent number: 11181886Abstract: A robot system is configured to fabricate three-dimensional (3D) objects using closed-loop, computer vision-based control. The robot system initiates fabrication based on a set of fabrication paths along which material is to be deposited. During deposition of material, the robot system captures video data and processes that data to determine the specific locations where the material is deposited. Based on these locations, the robot system adjusts future deposition locations to compensate for deviations from the fabrication paths. Additionally, because the robot system includes a 6-axis robotic arm, the robot system can deposit material at any locations, along any pathway, or across any surface. Accordingly, the robot system is capable of fabricating a 3D object with multiple non-parallel, non-horizontal, and/or non-planar layers.Type: GrantFiled: April 24, 2017Date of Patent: November 23, 2021Assignee: AUTODESK, INC.Inventors: Evan Atherton, David Thomasson, Maurice Ugo Conti, Heather Kerrick, Nicholas Cote
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Publication number: 20210208563Abstract: A robot system is configured to fabricate three-dimensional (3D) objects using closed-loop, computer vision-based control. The robot system initiates fabrication based on a set of fabrication paths along which material is to be deposited. During deposition of material, the robot system captures video data and processes that data to determine the specific locations where the material is deposited. Based on these locations, the robot system adjusts future deposition locations to compensate for deviations from the fabrication paths. Additionally, because the robot system includes a 6-axis robotic arm, the robot system can deposit material at any locations, along any pathway, or across any surface. Accordingly, the robot system is capable of fabricating a 3D object with multiple non-parallel, non-horizontal, and/or non-planar layers.Type: ApplicationFiled: March 22, 2021Publication date: July 8, 2021Inventors: Evan ATHERTON, David THOMASSON, Maurice Ugo CONTI, Heather KERRICK, Nicholas COTE
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Publication number: 20210205065Abstract: The present invention provides delivery systems for positioning a gastrointestinal implant in a patient, for example, for treatment of a metabolic disease. Also provided are methods for assembling the delivery systems, methods of positioning a gastrointestinal implant, and methods of treatment of metabolic diseases, such as type 2 diabetes, non-alcoholic steatohepatitis (NASH), non-alcoholic fatty liver disease (NAFLD), obesity, and related comorbidities thereof.Type: ApplicationFiled: January 30, 2017Publication date: July 8, 2021Applicants: GI Dynamics, Inc., GI Dynamics, Inc.Inventors: Nicholas COTE, Ryan HANLON, Ronald B. LAMPORT, Barry MAXWELL, John PANEK, Ian K. PARKER, Scott SCHORER, Nicholas WILLIAMS
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Patent number: 10955814Abstract: A robot system is configured to fabricate three-dimensional (3D) objects using closed-loop, computer vision-based control. The robot system initiates fabrication based on a set of fabrication paths along which material is to be deposited. During deposition of material, the robot system captures video data and processes that data to determine the specific locations where the material is deposited. Based on these locations, the robot system adjusts future deposition locations to compensate for deviations from the fabrication paths. Additionally, because the robot system includes a 6-axis robotic arm, the robot system can deposit material at any locations, along any pathway, or across any surface. Accordingly, the robot system is capable of fabricating a 3D object with multiple non-parallel, non-horizontal, and/or non-planar layers.Type: GrantFiled: April 24, 2017Date of Patent: March 23, 2021Assignee: AUTODESK, INC.Inventors: Evan Atherton, David Thomasson, Maurice Ugo Conti, Heather Kerrick, Nicholas Cote
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Publication number: 20210073445Abstract: A robotic assembly cell is configured to generate a physical mesh of physical polygons based on a simulated mesh of simulated triangles. A control application configured to operate the assembly cell selects a simulated polygon in the simulated mesh and then causes a positioning robot in the cell to obtain a physical polygon that is similar to the simulated polygon. The positioning robot positions the polygon on the physical mesh, and a welding robot in the cell then welds the polygon to the mesh. The control application captures data that reflects how the physical polygon is actually positioned on the physical mesh, and then updates the simulated mesh to be geometrically consistent with the physical mesh. In doing so, the control application may execute a multi-objective solver to generate an updated simulated mesh that meets specific design criteria.Type: ApplicationFiled: November 24, 2020Publication date: March 11, 2021Inventors: Evan Patrick Atherton, David Thomasson, Maurice Ugo Conti, Heather Kerrick, Nicholas Cote
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Publication number: 20200401105Abstract: An agent engine allocates a collection of agents to scan the surface of an object model. Each agent operates autonomously and implements particular behaviors based on the actions of nearby agents. Accordingly, the collection of agents exhibits swarm-like behavior. Over a sequence of time steps, the agents traverse the surface of the object model. Each agent acts to avoid other agents, thereby maintaining a relatively consistent distribution of agents across the surface of the object model over all time steps. At a given time step, the agent engine generates a slice through the object model that intersects each agent in a group of agents. The slice associated with a given time step represents a set of locations where material should be deposited to fabricate a 3D object. Based on a set of such slices, a robot engine causes a robot to fabricate the 3D object.Type: ApplicationFiled: September 8, 2020Publication date: December 24, 2020Inventors: Evan Patrick ATHERTON, David THOMASSON, Maurice Ugo CONTI, Heather KERRICK, Nicholas COTE, Hui LI
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Patent number: 10853539Abstract: A robotic assembly cell is configured to generate a physical mesh of physical polygons based on a simulated mesh of simulated triangles. A control application configured to operate the assembly cell selects a simulated polygon in the simulated mesh and then causes a positioning robot in the cell to obtain a physical polygon that is similar to the simulated polygon. The positioning robot positions the polygon on the physical mesh, and a welding robot in the cell then welds the polygon to the mesh. The control application captures data that reflects how the physical polygon is actually positioned on the physical mesh, and then updates the simulated mesh to be geometrically consistent with the physical mesh. In doing so, the control application may execute a multi-objective solver to generate an updated simulated mesh that meets specific design criteria.Type: GrantFiled: May 26, 2017Date of Patent: December 1, 2020Assignee: AUTODESK, INC.Inventors: Evan Patrick Atherton, David Thomasson, Maurice Ugo Conti, Heather Kerrick, Nicholas Cote
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Publication number: 20200353621Abstract: One embodiment of the present invention sets forth a technique for controlling the execution of a physical process. The technique includes receiving, as input to a machine learning model that is configured to adapt a simulation of the physical process executing in a virtual environment to a physical world, simulated output for controlling how the physical process performs a task in the virtual environment and real-world data collected from the physical process performing the task in the physical world. The technique also includes performing, by the machine learning model, one or more operations on the simulated output and the real-world data to generate augmented output. The technique further includes transmitting the augmented output to the physical process to control how the physical process performs the task in the physical world.Type: ApplicationFiled: July 27, 2020Publication date: November 12, 2020Inventors: Hui LI, Evan Patrick ATHERTON, Erin BRADNER, Nicholas COTE, Heather KERRICK
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Patent number: 10768606Abstract: An agent engine allocates a collection of agents to scan the surface of an object model. Each agent operates autonomously and implements particular behaviors based on the actions of nearby agents. Accordingly, the collection of agents exhibits swarm-like behavior. Over a sequence of time steps, the agents traverse the surface of the object model. Each agent acts to avoid other agents, thereby maintaining a relatively consistent distribution of agents across the surface of the object model over all time steps. At a given time step, the agent engine generates a slice through the object model that intersects each agent in a group of agents. The slice associated with a given time step represents a set of locations where material should be deposited to fabricate a 3D object. Based on a set of such slices, a robot engine causes a robot to fabricate the 3D object.Type: GrantFiled: June 2, 2017Date of Patent: September 8, 2020Assignee: AUTODESK, INC.Inventors: Evan Patrick Atherton, David Thomasson, Maurice Ugo Conti, Heather Kerrick, Nicholas Cote, Hui Li
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Patent number: 10751879Abstract: One embodiment of the present invention sets forth a technique for controlling the execution of a physical process. The technique includes receiving, as input to a machine learning model that is configured to adapt a simulation of the physical process executing in a virtual environment to a physical world, simulated output for controlling how the physical process performs a task in the virtual environment and real-world data collected from the physical process performing the task in the physical world. The technique also includes performing, by the machine learning model, one or more operations on the simulated output and the real-world data to generate augmented output. The technique further includes transmitting the augmented output to the physical process to control how the physical process performs the task in the physical world.Type: GrantFiled: May 31, 2018Date of Patent: August 25, 2020Assignee: AUTODESK, INC.Inventors: Hui Li, Evan Patrick Atherton, Erin Bradner, Nicholas Cote, Heather Kerrick
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Publication number: 20200147794Abstract: An assembly engine is configured to generate, based on a computer-aided design (CAD) assembly, a set of motion commands that causes the robot to manufacture a physical assembly corresponding to the CAD assembly. The assembly engine analyzes the CAD assembly to determine an assembly sequence for various physical components to be included in the physical assembly. The assembly sequence indicates the order in which each physical component should be incorporated into the physical assembly and how those physical components should be physically coupled together. The assembly engine further analyzes the CAD assembly to determine different component paths that each physical component should follow when being incorporated into the physical assembly. Based on the assembly sequence and the component paths, the assembly engine generates a set of motion commands that the robot executes to assemble the physical components into the physical assembly.Type: ApplicationFiled: October 29, 2019Publication date: May 14, 2020Inventors: Heather KERRICK, Erin BRADNER, Hui LI, Evan Patrick ATHERTON, Nicholas COTE
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Patent number: 10579046Abstract: A robot system is configured to fabricate three-dimensional (3D) objects using closed-loop, computer vision-based control. The robot system initiates fabrication based on a set of fabrication paths along which material is to be deposited. During deposition of material, the robot system captures video data and processes that data to determine the specific locations where the material is deposited. Based on these locations, the robot system adjusts future deposition locations to compensate for deviations from the fabrication paths. Additionally, because the robot system includes a 6-axis robotic arm, the robot system can deposit material at any locations, along any pathway, or across any surface. Accordingly, the robot system is capable of fabricating a 3D object with multiple non-parallel, non-horizontal, and/or non-planar layers.Type: GrantFiled: April 24, 2017Date of Patent: March 3, 2020Assignee: AUTODESK, INC.Inventors: Evan Atherton, David Thomasson, Maurice Ugo Conti, Heather Kerrick, Nicholas Cote
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Publication number: 20180345496Abstract: One embodiment of the present invention sets forth a technique for controlling the execution of a physical process. The technique includes receiving, as input to a machine learning model that is configured to adapt a simulation of the physical process executing in a virtual environment to a physical world, simulated output for controlling how the physical process performs a task in the virtual environment and real-world data collected from the physical process performing the task in the physical world. The technique also includes performing, by the machine learning model, one or more operations on the simulated output and the real-world data to generate augmented output. The technique further includes transmitting the augmented output to the physical process to control how the physical process performs the task in the physical world.Type: ApplicationFiled: May 31, 2018Publication date: December 6, 2018Inventors: Hui LI, Evan Patrick ATHERTON, Erin BRADNER, Nicholas COTE, Heather KERRICK