Patents by Inventor David FLEET
David FLEET 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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Publication number: 20230385990Abstract: A method includes receiving, by a computing device, training data comprising a plurality of pairs of images, wherein each pair comprises an image and at least one corresponding target version of the image. The method also includes training a neural network based on the training data to predict an enhanced version of an input image, wherein the training of the neural network comprises applying a forward Gaussian diffusion process that adds Gaussian noise to the at least one corresponding target version of each of the plurality of pairs of images to enable iterative denoising of the input image, wherein the iterative denoising is based on a reverse Markov chain associated with the forward Gaussian diffusion process. The method additionally includes outputting the trained neural network.Type: ApplicationFiled: July 27, 2023Publication date: November 30, 2023Inventors: Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David Fleet, Mohammad Norouzi
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Publication number: 20230333035Abstract: There is provided systems and methods for generating 3D structure estimation of at least one target from a set of 2D Cryo-electron microscope particle images. The method includes: receiving the set of 2D particle images of the target from a Cryo-electron microscope; splitting the set of particle images into at least a first half-set and a second half-set; iteratively performing: determining local resolution estimation and local filtering on at least a first half-map associated with the first half-set and a second half-map associated with the second half-set; aligning 2D particles from each of the half-sets using at least one region of the associated half-map; for each of the half-maps, generating an updated half-map using the aligned 2D particles from the associated half-set; and generating a resultant 3D map using all the half-maps.Type: ApplicationFiled: April 28, 2023Publication date: October 19, 2023Inventors: Ali PUNJANI, David Fleet, Haowei Zhang
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Publication number: 20230335216Abstract: Provided are systems and methods for determining 3D structure and 3D motion of a protein molecule from 2D or 3D particle observation images. The method including: initializing pose parameters and unknown model parameters; the parameters of the one or more flow generators; image formation including: generating one or more 3D deformation fields by inputting the latent coordinate vector into the one or more flow generators; performing a convection and projection operation; and performing CTF corruption; fitting the unknown model parameters to the experimental images by gradient-based optimization of an objective function; latent variable search for a given experimental image including: performing the image formation one or more times to generate simulated images; selecting one or more latent coordinate vectors based on similarity; updating the at least one of the unknown model parameters including: generating simulated images; evaluating the objective function; computing the gradient of the objective function.Type: ApplicationFiled: April 21, 2022Publication date: October 19, 2023Inventors: Ali PUNJANI, David FLEET
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Patent number: 11769228Abstract: A method includes receiving, by a computing device, training data comprising a plurality of pairs of images, wherein each pair comprises an image and at least one corresponding target version of the image. The method also includes training a neural network based on the training data to predict an enhanced version of an input image, wherein the training of the neural network comprises applying a forward Gaussian diffusion process that adds Gaussian noise to the at least one corresponding target version of each of the plurality of pairs of images to enable iterative denoising of the input image, wherein the iterative denoising is based on a reverse Markov chain associated with the forward Gaussian diffusion process. The method additionally includes outputting the trained neural network.Type: GrantFiled: August 2, 2021Date of Patent: September 26, 2023Assignee: Google LLCInventors: Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David Fleet, Mohammad Norouzi
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Patent number: 11756166Abstract: A method includes receiving, by a computing device, training data comprising a plurality of pairs of images, wherein each pair comprises an image and at least one corresponding target version of the image. The method also includes training a neural network based on the training data to predict an enhanced version of an input image, wherein the training of the neural network comprises applying a forward Gaussian diffusion process that adds Gaussian noise to the at least one corresponding target version of each of the plurality of pairs of images to enable iterative denoising of the input image, wherein the iterative denoising is based on a reverse Markov chain associated with the forward Gaussian diffusion process. The method additionally includes outputting the trained neural network.Type: GrantFiled: January 17, 2023Date of Patent: September 12, 2023Assignee: Google LLCInventors: Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David Fleet, Mohammad Norouzi
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Patent number: 11680914Abstract: There is provided systems and methods for generating 3D structure estimation of at least one target from a set of 2D Cryo-electron microscope particle images. The method includes: receiving the set of 2D particle images of the target from a Cryo-electron microscope; splitting the set of particle images into at least a first half-set and a second half-set; iteratively performing: determining local resolution estimation and local filtering on at least a first half-map associated with the first half-set and a second half-map associated with the second half-set; aligning 2D particles from each of the half-sets using at least one region of the associated half-map; for each of the half-maps, generating an updated half-map using the aligned 2D particles from the associated half-set; and generating a resultant 3D map using all the half-maps.Type: GrantFiled: October 5, 2018Date of Patent: June 20, 2023Assignee: THE GOVERNING COUNCIL OF THE UNIVERSITY OF TORONTOInventors: Ali Punjani, David Fleet, Haowei Zhang
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Publication number: 20230153959Abstract: A method includes receiving, by a computing device, training data comprising a plurality of pairs of images, wherein each pair comprises an image and at least one corresponding target version of the image. The method also includes training a neural network based on the training data to predict an enhanced version of an input image, wherein the training of the neural network comprises applying a forward Gaussian diffusion process that adds Gaussian noise to the at least one corresponding target version of each of the plurality of pairs of images to enable iterative denoising of the input image, wherein the iterative denoising is based on a reverse Markov chain associated with the forward Gaussian diffusion process. The method additionally includes outputting the trained neural network.Type: ApplicationFiled: January 17, 2023Publication date: May 18, 2023Inventors: Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David Fleet, Mohammad Norouzi
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Publication number: 20230067841Abstract: A method includes receiving, by a computing device, training data comprising a plurality of pairs of images, wherein each pair comprises an image and at least one corresponding target version of the image. The method also includes training a neural network based on the training data to predict an enhanced version of an input image, wherein the training of the neural network comprises applying a forward Gaussian diffusion process that adds Gaussian noise to the at least one corresponding target version of each of the plurality of pairs of images to enable iterative denoising of the input image, wherein the iterative denoising is based on a reverse Markov chain associated with the forward Gaussian diffusion process. The method additionally includes outputting the trained neural network.Type: ApplicationFiled: August 2, 2021Publication date: March 2, 2023Inventors: Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David Fleet, Mohammad Norouzi
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Publication number: 20200333270Abstract: There is provided systems and methods for generating 3D structure estimation of at least one target from a set of 2D Cryo-electron microscope particle images. The method includes: receiving the set of 2D particle images of the target from a Cryo-electron microscope; splitting the set of particle images into at least a first half-set and a second half-set; iteratively performing: determining local resolution estimation and local filtering on at least a first half-map associated with the first half-set and a second half-map associated with the second half-set; aligning 2D particles from each of the half-sets using at least one region of the associated half-map; for each of the half-maps, generating an updated half-map using the aligned 2D particles from the associated half-set; and generating a resultant 3D map using all the half-maps.Type: ApplicationFiled: October 5, 2018Publication date: October 22, 2020Inventors: Ali PUNJANI, David FLEET, Haowei ZHANG
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Publication number: 20180099361Abstract: Apparatus and methods for fastening insulation to a metal panel are disclosed. One apparatus includes a frame; a capacitor discharge welder; a panel support bar, preferably being non-marring of a metal panel; a hopper for receiving weld pins and the weld pins; PLC controls; and a grounding clamp. Another apparatus includes a weld table, a weld pin hopper, a weld head, a welder power supply, a carriage for the weld head, a motor for moving the weld head on the carriage and a motor for moving the carriage the length of the weld table.Type: ApplicationFiled: October 6, 2017Publication date: April 12, 2018Applicant: Gripnail CorporationInventors: David Fleet ASHTON, Louis Steven COSTA, Christopher Allan RYDING, Anthony David OLIVER
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Patent number: 8985927Abstract: Hanger strips for securing insulation material to a surface of a structure formed of sheet metal material including a plurality of bases which are connected by one or more breakable tabs integrally formed between each pair of adjacent bases and wherein first open slots extend from each of the breakable tabs toward opposite side edges of the bases and wherein a shank extends from each of the bases in a common direction and wherein pluralities of strips are oriented in first and second assemblies of rows of strips having their shanks opposing one another and their bases overlapping one another such that tips of the shanks are safely enclosed between the bases of the opposing assemblies and seated within the first open slots.Type: GrantFiled: July 20, 2012Date of Patent: March 24, 2015Assignee: Gripnail CorporationInventors: David Fleet Ashton, Louis Steven Costa, Christopher Allan Ryding, Anthony David Oliver
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Publication number: 20130022425Abstract: Hanger strips for securing insulation material to a surface of a structure formed of sheet metal material including a plurality of bases which are connected by one or more breakable tabs integrally formed between each pair of adjacent bases and wherein first open slots extend from each of the breakable tabs toward opposite side edges of the bases and wherein a shank extends from each of the bases in a common direction and wherein pluralities of strips are oriented in first and second assemblies of rows of strips having their shanks opposing one another and their bases overlapping one another such that tips of the shanks are safely enclosed between the bases of the opposing assemblies and seated within the first open slots.Type: ApplicationFiled: July 20, 2012Publication date: January 24, 2013Applicant: GRIPNAIL CORPORATIONInventors: David Fleet Ashton, Louis Steven Costa, Christopher Allan Ryding, Anthony David Oliver