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公开(公告)号：US11983851B2
公开(公告)日：2024-05-14
申请号：US18091235
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 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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公开(公告)号：US11854181B2
公开(公告)日：2023-12-26
申请号：US17989432
申请日：2022-11-17
Inventor： Ryan Knuffman , Bradley A. Sliz , Lucas Allen
IPC: G06T7/00 , G06Q40/08 , G07C5/00 , G06N20/00 , B64C39/02 , G05D1/00 , G06Q10/20 , G06Q30/0283 , G06Q10/1093 , G06Q30/0601 , G07C5/08 , B64U101/30
CPC classification number: G06T7/0004 , B64C39/024 , G05D1/0038 , G05D1/0088 , G06N20/00 , G06Q10/1097 , G06Q10/20 , G06Q30/0283 , G06Q30/0633 , G06Q40/08 , G06T7/0008 , G07C5/006 , G07C5/0808 , B64U2101/30 , B64U2201/10 , B64U2201/20 , G05D2201/0207 , G06T7/0002 , G06T2207/10032 , G06T2207/20081 , G06T2207/30248 , G06T2207/30252 , G06T2207/30268
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：US11694333B1
公开(公告)日：2023-07-04
申请号：US17031612
申请日：2020-09-24
Inventor： Ryan Knuffman
CPC classification number: G06T7/11 , G06N3/04 , G06N3/08 , G06T2207/10028 , G06T2207/20081 , G06T2207/20084
Abstract: A deep artificial neural network (DNN) for generating a semantically-segmented three-dimensional (3D) point cloud is manufactured by a process including obtaining a 3D point cloud, establishing a DNN topology, training the DNN to output labels by subdividing the point cloud, pre-processing the subdivisions, updating weights, and storing weights. Training a DNN includes obtaining a 3D point cloud, establishing a topology of the DNN, training the DNN to output point labels by subdividing, pre-processing the subdivisions, analyzing the features and respective labels of the point cloud to update DNN weights, and storing the weights. A server includes a processor and a memory storing instructions that, when executed by the processor, cause the server to obtain a 3D point cloud, establish a DNN topology, train the DNN to output labels by subdividing, pre-process the subdivisions, analyze the features and respective labels of the point cloud to update weights, and store the weights.
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公开(公告)号：US11972541B2
公开(公告)日：2024-04-30
申请号：US18091254
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 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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5.发明公开公开(公告)号：US20240087107A1
公开(公告)日：2024-03-14
申请号：US18516828
申请日：2023-11-21
Inventor： Ryan Knuffman , Bradley A. Sliz , Lucas Allen
IPC: G06T7/00 , B64C39/02 , G05D1/00 , G06N20/00 , G06Q10/1093 , G06Q10/20 , G06Q30/0283 , G06Q30/0601 , G06Q40/08 , G07C5/00 , G07C5/08
CPC classification number: G06T7/0004 , B64C39/024 , G05D1/0038 , G05D1/0088 , G06N20/00 , G06Q10/1097 , G06Q10/20 , G06Q30/0283 , G06Q30/0633 , G06Q40/08 , G06T7/0008 , G07C5/006 , G07C5/0808 , B64U2101/30 , G05D2201/0207 , G06T7/0002 , G06T2207/10032 , G06T2207/20081 , G06T2207/30248 , G06T2207/30252 , G06T2207/30268
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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Patent Agency Ranking
公开(公告)号：US11080841B1
公开(公告)日：2021-08-03
申请号：US16668837
申请日：2019-10-30
Inventor： Ryan Knuffman , Bradley A. Sliz , Lucas Allen
Abstract: One or more processing elements may be trained to identify vehicle damages or vehicle damages based upon training data. 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 using the trained processing elements to identify a damage to the vehicle or defect of the vehicle.
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7.发明公开公开(公告)号：US20240338803A1
公开(公告)日：2024-10-10
申请号：US18746878
申请日：2024-06-18
Inventor： Ryan Knuffman
CPC classification number: G06T5/77 , G06N3/045 , G06N3/088 , G06T7/579 , G06T2207/10028 , G06T2207/20081 , G06T2207/20084
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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公开(公告)号：US12056859B2
公开(公告)日：2024-08-06
申请号：US18091227
申请日：2022-12-29
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 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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公开(公告)号：US12051179B2
公开(公告)日：2024-07-30
申请号：US18091183
申请日：2022-12-29
Inventor： Ryan Knuffman
CPC classification number: G06T5/77 , G06N3/045 , G06N3/088 , G06T7/579 , G06T2207/10028 , G06T2207/20081 , G06T2207/20084
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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公开(公告)号：US20230289974A1
公开(公告)日：2023-09-14
申请号：US18199267
申请日：2023-05-18
Inventor： Ryan Knuffman
CPC classification number: G06T7/11 , G06N3/08 , G06N3/04 , G06T2207/20084 , G06T2207/10028 , G06T2207/20081
Abstract: A computer-implemented method of training a deep artificial neural network includes receiving a three-dimensional point cloud and training the deep artificial neural network by subdividing the three-dimensional point cloud, and updating weights of the deep artificial neural network. A computing system includes a processor; and a memory having stored thereon computer-executable instructions that, when executed by the processor, cause the computing system to receive a three-dimensional point cloud and train the deep artificial neural network by subdividing the three-dimensional point cloud, and updating weights of the deep artificial neural network. In yet another aspect, a non-transitory computer-readable medium includes computer-executable instructions that when executed, cause a computer to receive a three-dimensional point cloud and train the deep artificial neural network by subdividing the three-dimensional point cloud, and updating weights of the deep artificial neural network.
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