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公开(公告)号：US20230136766A1
公开(公告)日：2023-05-04
申请号：US18091183
申请日：2022-12-29
Inventor： Ryan Knuffman
Abstract: A method includes receiving a navigation data set; generating a combined data set using a trained generative adversarial network; and generating a high resolution map that includes spatial data. A computing system includes: one or more processors, and one or more memories having stored thereon computer-executable instructions that, when executed, cause the computing system to: receive a navigation data set; generate a combined data set using a trained generative adversarial network; and generate a high resolution map that includes spatial data. A non-transitory computer-readable medium includes computer-executable instructions that, when executed, cause a computer to: receive a navigation data set; generate a combined data set using a trained generative adversarial network; and generate a high resolution map that includes spatial data.
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22.发明申请公开(公告)号：US20230100483A1
公开(公告)日：2023-03-30
申请号：US17989432
申请日：2022-11-17
Inventor： Ryan Knuffman , Bradley Sliz , Lucas Allen
IPC: G06T7/00 , G06Q40/08 , G07C5/00 , G06N20/00 , B64C39/02 , G05D1/00 , G06Q10/00 , G06Q30/02 , G06Q10/10 , G06Q30/06 , G07C5/08
Abstract: A remotely-controlled (RC) and/or autonomously operated inspection device, such as a ground vehicle or drone, may capture one or more sets of imaging data indicative of at least a portion of an automotive vehicle, such as all or a portion of the undercarriage. The one or more sets of imaging data may be analyzed based upon data indicative of at least one of vehicle damage or a vehicle defect being shown in the one or more sets of imaging data. Based upon the analyzing of the one or more sets of imaging data, damage to the vehicle or a defect of the vehicle may be identified. The identified damage or defect may be compared to a claimed damage or defect to determine whether the claimed damage or defect occurred.
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23.发明授权公开(公告)号：US10825097B1
公开(公告)日：2020-11-03
申请号：US15843761
申请日：2017-12-15
Inventor： Ryan Knuffman , Bradley A. Sliz , Lucas Allen
Abstract: A remotely-controlled (RC) and/or autonomously operated inspection device, such as a ground vehicle or drone, may capture one or more sets of imaging data indicative of at least a portion of an automotive vehicle, such as all or a portion of the undercarriage. The one or more sets of imaging data may be analyzed based upon data indicative of at least one of vehicle damage or a vehicle defect being shown in the one or more sets of imaging data. Based upon the analyzing of the one or more sets of imaging data, damage to the vehicle or a defect of the vehicle may be identified. The identified damage or defect may be compared to a claimed damage or defect to determine whether the claimed damage or defect occurred.
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公开(公告)号：US20240265511A1
公开(公告)日：2024-08-08
申请号：US18637054
申请日：2024-04-16
Inventor： Ryan Knuffman
CPC classification number: G06T5/77 , G06N3/045 , G06N3/088 , G06T7/579 , G06T2207/10028 , G06T2207/20081 , G06T2207/20084
Abstract: A method for using a trained generative adversarial network to improve underwriting, claim handling and retail operations includes receiving a 3D point cloud; and generating a gap-filled semantically-segmented 3D point cloud using a trained generative adversarial network. A computing system for using a trained generative adversarial network to improve vehicle orientation and navigation includes one or more processors, and one or more memories having stored thereon computer-executable instructions that, when executed, cause the computing system to: receive a 3D point cloud; and generate a gap-filled semantically-segmented 3D point cloud using the trained generative adversarial network. A non-transitory computer-readable medium having stored thereon computer-executable instructions that, when executed, cause a computer to: receive a 3D point cloud; and generate a gap-filled semantically-segmented 3D point cloud using a trained generative adversarial network.
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25.发明公开公开(公告)号：US20240242316A1
公开(公告)日：2024-07-18
申请号：US18618673
申请日：2024-03-27
Inventor： Ryan Knuffman
CPC classification number: G06T5/77 , G06N3/045 , G06N3/088 , G06T7/579 , G06T2207/10028 , G06T2207/20081 , G06T2207/20084
Abstract: A computer-implemented method for using a trained generative adversarial network to improve construction and urban planning includes receiving a semantically-segmented point cloud corresponding to a construction site; determining a volumetric soil measurement; and generating a cost estimate. A computing system for using a trained generative adversarial network to improve vehicle orientation and navigation includes one or more processors, and one or more memories having stored thereon computer-executable instructions that, when executed, cause the computing system to: receive a semantically-segmented point cloud corresponding to a construction site; determine a volumetric soil measurement; and generate a cost estimate. A non-transitory computer-readable medium includes computer-executable instructions that, when executed, cause a computer to: receive a semantically-segmented point cloud corresponding to a construction site; determine a volumetric soil measurement; and generate a cost estimate.
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Patent Agency Ranking
公开(公告)号：US11995805B2
公开(公告)日：2024-05-28
申请号：US17982174
申请日：2022-11-07
Inventor： Ryan Knuffman
CPC classification number: G06T5/77 , G06N3/045 , G06N3/088 , G06T7/579 , G06T2207/10028 , G06T2207/20081 , G06T2207/20084
Abstract: A non-transitory computer readable storage medium includes instructions that, when executed by one or more processors, cause a computer to: generate a loss value; update one or more weights of a generative adversarial network; and store the updated weights on a non-transitory computer readable storage medium. A computer-implemented method includes generating a loss value; updating one or more weights of a generative adversarial network; and storing the updated weights on a non-transitory computer readable storage medium. A computing system for training a generative adversarial network includes generating a loss value; updating one or more weights of a generative adversarial network; and storing the updated weights on a non-transitory computer readable storage medium.
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公开(公告)号：US11983817B2
公开(公告)日：2024-05-14
申请号：US17543105
申请日：2021-12-06
Inventor： Bryan Nussbaum , Jeremy Carnahan , Ryan Knuffman
CPC classification number: G06T17/205 , G06F16/5866 , G06T19/006 , G06V20/20 , H04W4/021
Abstract: A computer-implemented method for labeling a three-dimensional (3D) model using virtual reality (VR) techniques implemented by a computer system including a processor is provided herein. The method may include (i) receiving a 3D model including an environmental feature that is unlabeled, (ii) displaying, through a VR device in communication with the processor, a VR environment to a user representing the 3D model, (iii) prompting a user to input labeling data for the environmental feature displayed within the VR environment of the VR device by prompting the user to select the environmental feature through user interaction with the VR device, and input labeling data for the environmental feature, wherein the labeling data identifies the environmental feature, and (iv) generating a labeled 3D model by embedding the labeling data associated with the selected environmental feature into the 3D model.
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28.发明公开公开(公告)号：US20230360244A1
公开(公告)日：2023-11-09
申请号：US18355336
申请日：2023-07-19
Inventor： Ryan Knuffman , Jeremy Carnahan
CPC classification number: G06T7/579 , G06T15/205 , G06T2207/10032 , G06T2207/20084 , G06T2207/20081
Abstract: A server includes a processor and a memory storing instructions that, when executed by the processor, cause the server to receive two-dimensional (2D) images, analyze the images using a trained deep network to generate points, process the labeled points to identify tie points, and combine the 2D dimensional images into a three-dimensional (3D) point cloud using structure-from-motion. A method for generating a semantically-segmented 3D point cloud from 2D data includes receiving 2D images, analyzing the images using a trained deep network to generate labeled points, processing the points to identify tie points, and combining the 2D images into a 3D point cloud using structure-from-motion. A non-transitory computer readable storage medium stores executable instructions that, when executed by a processor, cause a computer to receive 2D images, analyze the images using a trained deep network to generate labeled points, process the points to identify and combine tie points using structure-from-motion.
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29.发明授权公开(公告)号：US11748901B1
公开(公告)日：2023-09-05
申请号：US17031643
申请日：2020-09-24
Inventor： Ryan Knuffman , Jeremy Carnahan
CPC classification number: G06T7/579 , G06T15/205 , G06T2207/10032 , G06T2207/20081 , G06T2207/20084
Abstract: A server includes a processor and a memory storing instructions that, when executed by the processor, cause the server to receive two-dimensional (2D) images, analyze the images using a trained deep network to generate points, process the labeled points to identify tie points, and combine the 2D dimensional images into a three-dimensional (3D) point cloud using structure-from-motion. A method for generating a semantically-segmented 3D point cloud from 2D data includes receiving 2D images, analyzing the images using a trained deep network to generate labeled points, processing the points to identify tie points, and combining the 2D images into a 3D point cloud using structure-from-motion. A non-transitory computer readable storage medium stores executable instructions that, when executed by a processor, cause a computer to receive 2D images, analyze the images using a trained deep network to generate labeled points, process the points to identify and combine tie points using structure-from-motion.
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30.发明公开公开(公告)号：US20230141319A1
公开(公告)日：2023-05-11
申请号：US18091227
申请日：2022-12-29
Inventor： Ryan Knuffman
CPC classification number: G06T5/005 , G06N3/045 , G06N3/088 , G06T7/579 , G06T2207/10028 , G06T2207/20081 , G06T2207/20084
Abstract: A method for using a trained generative adversarial network to improve peril modeling includes receiving a semantically-segmented 3D point cloud; generating a gap-filled point cloud; and generating a digital map. A computing system for using a trained generative adversarial network to improve vehicle orientation and navigation includes one or more processors, and one or more memories having stored thereon computer-executable instructions that, when executed, cause the computing system to: receive a semantically-segmented 3D point cloud; generate a gap-filled point cloud; and generate a digital map. A non-transitory computer-readable medium includes computer-executable instructions that, when executed, cause a computer to: receive a semantically-segmented 3D point cloud; generate a gap-filled point cloud; and generate a digital map.
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