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公开(公告)号：US20230065981A1
公开(公告)日：2023-03-02
申请号：US18047127
申请日：2022-10-17
Applicant: X Development LLC
Inventor： Neil David Treat , Thomas Peter Hunt , Artem Goncharuk , Karen R Davis , Vikram Neal Sahney
Abstract: Techniques for improving a blast pattern at a mining site include conducting an initial blast and recording the initial blast as a high speed optical video. The high speed optical video, and the blast pattern used in the initial blast are sent as inputs to a machine learning model, which correlates one or more characteristics of the region being blasted with measurements associated with characteristics of the region being blasted obtained from the high speed optical video. The machine learning model can then determine an improved blast pattern based on the correlation made. This improved blast pattern can be displayed on a user computing device, or transmitted to a drilling system to automatically drill the improved blast pattern for subsequent blasts.
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公开(公告)号：US11351576B2
公开(公告)日：2022-06-07
申请号：US16892861
申请日：2020-06-04
Applicant: X Development LLC
Inventor： Thomas Peter Hunt , Neil David Treat , Karen R Davis , Artem Goncharuk , Vikram Neal Sahney
IPC: B07C5/34 , B07C5/342 , B07C5/344 , B07C5/346 , G01N23/20091 , G01N23/223 , G01N23/207 , G01N33/24 , G06N3/08 , G06V10/42 , G06V20/00
Abstract: Techniques for processing ore include the steps of causing an imaging capture system to record a plurality of images of a stream of ore fragments en route from a first location in an ore processing facility to a second location in the ore processing facility; correlating the plurality of images of the stream of ore fragments with at least one or more characteristics of the ore fragments using a machine learning model that includes a plurality of ore parameter measurements associated with the one or more characteristics of the ore fragments; determining, based on the correlation, at least one of the one or more characteristics of the ore fragments; and generating, for display on a user computing device, data indicating the one or more characteristics of the ore fragments or data indicating an action or decision based on the one or more characteristics of the ore fragments.
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公开(公告)号：US11592594B2
公开(公告)日：2023-02-28
申请号：US17229391
申请日：2021-04-13
Applicant: X Development LLC
Inventor： Kenton Lee Prindle , Artem Goncharuk , Neil David Treat , Kevin Forsythe Smith , Thomas Peter Hunt , Karen R Davis , Allen Richard Zhao
Abstract: This disclosure describes a system and method for generating a subsurface model representing lithological characteristics and attributes of the subsurface of a celestial body or planet. By automatically ingesting data from many sources, a machine learning system can infer information about the characteristics of regions of the subsurface and build a model representing the subsurface rock properties. In some cases, this can provide information about a region using inferred data, where no direct measurements have been taken. Remote sensing data, such as aerial or satellite imagery, gravimetric data, magnetic field data, electromagnetic data, and other information can be readily collected or is already available at scale. Lithological attributes and characteristics present in available geoscience data can be correlated with related remote sensing data using a machine learning model, which can then infer lithological attributes and characteristics for regions where remote sensing data is available, but geoscience data is not.
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公开(公告)号：US11475659B2
公开(公告)日：2022-10-18
申请号：US16991757
申请日：2020-08-12
Applicant: X Development LLC
Inventor： Neil David Treat , Thomas Peter Hunt , Artem Goncharuk , Karen R Davis , Vikram Neal Sahney
Abstract: Techniques for improving a blast pattern at a mining site include conducting an initial blast and recording the initial blast as a high speed optical video. The high speed optical video, and the blast pattern used in the initial blast are sent as inputs to a machine learning model, which correlates one or more characteristics of the region being blasted with measurements associated with characteristics of the region being blasted obtained from the high speed optical video. The machine learning model can then determine an improved blast pattern based on the correlation made. This improved blast pattern can be displayed on a user computing device, or transmitted to a drilling system to automatically drill the improved blast pattern for subsequent blasts.
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Patent Agency Ranking
公开(公告)号：US11867608B2
公开(公告)日：2024-01-09
申请号：US17832910
申请日：2022-06-06
Applicant: X Development LLC
Inventor： Thomas Peter Hunt , Neil David Treat , Karen R Davis , Artem Goncharuk , Vikram Neal Sahney
IPC: B07C5/34 , G01N15/14 , B07C5/342 , B07C5/344 , B07C5/346 , G01N23/20091 , G01N23/207 , G01N23/223 , G01N33/24 , G06N3/08 , G06T7/00
CPC classification number: G01N15/1475 , B07C5/344 , B07C5/346 , B07C5/3425 , B07C5/3427 , G01N23/207 , G01N23/20091 , G01N23/223 , G01N33/24 , G06N3/08 , G06T7/001 , G06T2207/20081
Abstract: Techniques for processing ore include the steps of causing an imaging capture system to record a plurality of images of a stream of ore fragments en route from a first location in an ore processing facility to a second location in the ore processing facility; correlating the plurality of images of the stream of ore fragments with at least one or more characteristics of the ore fragments using a machine learning model that includes a plurality of ore parameter measurements associated with the one or more characteristics of the ore fragments; determining, based on the correlation, at least one of the one or more characteristics of the ore fragments; and generating, for display on a user computing device, data indicating the one or more characteristics of the ore fragments or data indicating an action or decision based on the one or more characteristics of the ore fragments.
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公开(公告)号：US11828582B2
公开(公告)日：2023-11-28
申请号：US18047127
申请日：2022-10-17
Applicant: X Development LLC
Inventor： Neil David Treat , Thomas Peter Hunt , Artem Goncharuk , Karen R Davis , Vikram Neal Sahney
CPC classification number: F42D1/00 , F42D3/04 , G06F18/214 , G06N3/08 , G06T17/00 , G06V10/25 , G06V20/13
Abstract: Techniques for improving a blast pattern at a mining site include conducting an initial blast and recording the initial blast as a high speed optical video. The high speed optical video, and the blast pattern used in the initial blast are sent as inputs to a machine learning model, which correlates one or more characteristics of the region being blasted with measurements associated with characteristics of the region being blasted obtained from the high speed optical video. The machine learning model can then determine an improved blast pattern based on the correlation made. This improved blast pattern can be displayed on a user computing device, or transmitted to a drilling system to automatically drill the improved blast pattern for subsequent blasts.
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公开(公告)号：US12007519B2
公开(公告)日：2024-06-11
申请号：US18167988
申请日：2023-02-13
Applicant: X Development LLC
Inventor： Kenton Lee Prindle , Artem Goncharuk , Neil David Treat , Kevin Forsythe Smith , Thomas Peter Hunt , Karen R Davis , Allen Richard Zhao
Abstract: This disclosure describes a system and method for generating a subsurface model representing lithological characteristics and attributes of the subsurface of a celestial body or planet. By automatically ingesting data from many sources, a machine learning system can infer information about the characteristics of regions of the subsurface and build a model representing the subsurface rock properties. In some cases, this can provide information about a region using inferred data, where no direct measurements have been taken. Remote sensing data, such as aerial or satellite imagery, gravimetric data, magnetic field data, electromagnetic data, and other information can be readily collected or is already available at scale. Lithological attributes and characteristics present in available geoscience data can be correlated with related remote sensing data using a machine learning model, which can then infer lithological attributes and characteristics for regions where remote sensing data is available, but geoscience data is not.
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公开(公告)号：US20230194750A1
公开(公告)日：2023-06-22
申请号：US18167988
申请日：2023-02-13
Applicant: X Development LLC
Inventor： Kenton Lee Prindle , Artem Goncharuk , Neil David Treat , Kevin Forsythe Smith , Thomas Peter Hunt , Karen R Davis , Allen Richard Zhao
CPC classification number: G01V99/005 , G01V1/282
Abstract: This disclosure describes a system and method for generating a subsurface model representing lithological characteristics and attributes of the subsurface of a celestial body or planet. By automatically ingesting data from many sources, a machine learning system can infer information about the characteristics of regions of the subsurface and build a model representing the subsurface rock properties. In some cases, this can provide information about a region using inferred data, where no direct measurements have been taken. Remote sensing data, such as aerial or satellite imagery, gravimetric data, magnetic field data, electromagnetic data, and other information can be readily collected or is already available at scale. Lithological attributes and characteristics present in available geoscience data can be correlated with related remote sensing data using a machine learning model, which can then infer lithological attributes and characteristics for regions where remote sensing data is available, but geoscience data is not.
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公开(公告)号：US20210049344A1
公开(公告)日：2021-02-18
申请号：US16991757
申请日：2020-08-12
Applicant: X Development LLC
Inventor： Neil David Treat , Thomas Peter Hunt , Artem Goncharuk , Karen R Davis , Vikram Neal Sahney
Abstract: Techniques for improving a blast pattern at a mining site include conducting an initial blast and recording the initial blast as a high speed optical video. The high speed optical video, and the blast pattern used in the initial blast are sent as inputs to a machine learning model, which correlates one or more characteristics of the region being blasted with measurements associated with characteristics of the region being blasted obtained from the high speed optical video. The machine learning model can then determine an improved blast pattern based on the correlation made. This improved blast pattern can be displayed on a user computing device, or transmitted to a drilling system to automatically drill the improved blast pattern for subsequent blasts.
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