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公开(公告)号：US12137143B1
公开(公告)日：2024-11-05
申请号：US18169031
申请日：2023-02-14
Applicant: Samsara Inc.
Inventor： Saleh ElHattab , Justin Joel Delegard , Bodecker John DellaMaria , Brian Tuan , Jennifer Winnie Leung , Sylvie Lee , Jesse Michael Chen , Sean Kyungmok Bae , Angel Manalastas Lim , Timothy John Passaro
IPC: H04L67/12 , G06V20/40 , G07C5/00 , H04L41/069
Abstract: Example embodiments described herein therefore relate to an event detection system that comprises a plurality of sensor devices, to perform operations that include: generating sensor data at the plurality of sensor devices; accessing the sensor data generated by the plurality of sensor devices; detecting an event, or precursor to an event, based on the sensor data, wherein the detected event corresponds to an event category; accessing an object model associated with the event type in response to detecting the event, wherein the object model defines a procedure to be applied by the event detection system to the sensor data; and streaming at least a portion of a plurality of data streams generated by the plurality of sensor devices to a server system based on the procedure, wherein the server system may perform further analysis or visualization based on the portion of the plurality of data streams.
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公开(公告)号：US12128919B2
公开(公告)日：2024-10-29
申请号：US18188173
申请日：2023-03-22
Applicant: Samsara Inc.
Inventor： Mathew Chasan Calmer , Justin Delegard , Justin Pan , Sabrina Shemet , Meelap Shah , Kavya Joshi , Brian Tuan , Sharan Srinivasan , Muhammad Ali Akhtar , John Charles Bicket , Margaret Finch , Vincent Shieh , Bruce Kellerman , Mitch Lin , Marvin Arroz , Siddhartha Datta Roy , Jason Symons , Tina Quach , Cassandra Lee Rommel , Saumya Jain
IPC: G06V20/56 , B60W40/08 , B60W50/14 , G06F3/01 , G06N3/02 , G06T7/70 , G06V10/44 , G06V10/74 , G06V20/40 , G06V20/59 , G07C5/00 , G07C5/08 , H04N19/132 , H04N23/51
CPC classification number: B60W50/14 , B60W40/08 , G06F3/012 , G06N3/02 , G06T7/70 , G06V10/44 , G06V10/761 , G06V20/44 , G06V20/56 , G06V20/597 , G07C5/008 , G07C5/0866 , H04N19/132 , H04N23/51 , B60W2050/143 , B60W2520/105 , B60W2540/229 , G06V2201/07 , G06V2201/10
Abstract: A vehicle dash cam may be configured to execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. Detection of a safety event may trigger an in-cab alert to make the driver aware of the safety risk. The dash cam may include logic for determining which asset data to transmit to a backend server in response to detection of a safety event, as well as which asset data to transmit to the backend server in response to analysis of sensor data that did not trigger a safety event. The asset data transmitted to the backend server may be further analyzed to determine if further alerts should be provided to the driver and/or to a safety manager.
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公开(公告)号：US11995546B1
公开(公告)日：2024-05-28
申请号：US17811512
申请日：2022-07-08
Applicant: Samsara Inc.
Inventor： Sharan Srinivasan , Brian Tuan , John Bicket , Jing Wang , Muhammad Ali Akhtar , Abner Ayala Acevedo , Bruce Kellerman , Vincent Shieh
CPC classification number: G06N3/08 , B60W40/09 , B60W50/14 , G06N7/01 , G06N20/20 , B60W2420/403 , B60W2420/54 , B60W2540/223 , B60W2540/225 , B60W2540/229
Abstract: A vehicle device may execute one or more neural networks (and/or other artificial intelligence), based on input from one or more of the cameras and/or other sensors, to intelligently detect safety events in real-time. The one or more neural networks may be an ensemble neural network that includes neural networks for detecting a head and hand of a user, neural networks for detecting hand actions of the user, neural networks for detecting the head pose of the user, neural networks for predicting an occurrence of an event, and neural networks for predicting a start time and end time of the event. Further, the neural networks can be segmented into a modular neural network based on metadata. The segmentation of the neural network can define a thin layer of the modular neural network to enable independent tuning of the thin layer of the modular neural network.
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公开(公告)号：US11875683B1
公开(公告)日：2024-01-16
申请号：US18053985
申请日：2022-11-09
Applicant: Samsara Inc.
Inventor： Evaline Shin-Tin Tsai , Alan Guihong Liu , Ijeoma Emeagwali , Ishaan Kansal , Saleh ElHattab , Bodecker John DellaMaria , Eliott Ray Chapuis , Jason Noah Laska , Jennifer Kao , Sean Kyungmok Bae , Sylvie Lee , Brian Tuan
CPC classification number: G08G1/20 , B60R11/04 , G06N20/00 , G06T7/74 , G06V20/59 , G06V40/173 , B60R2300/8006 , G06T2200/24 , G06T2207/30201 , G06T2207/30268
Abstract: Methods for improving compliance with regulations pertaining to vehicle driving records are disclosed. One or more digital images from a camera mounted in a vehicle are received. Based on a determination that the vehicle has hours of service that have not been assigned to a driver, a subset of the one or more digital images corresponding to the hours of service are identified based on the timestamps. The subset of the one or more digital images are processed to identify a correspondence between a face of a person included in the one or more digital images and a face of a known person. Based on the correspondence transgressing a threshold level of correspondence, a user interface is generated for presentation on a device. The user interface includes an interactive user interface element for accepting a recommendation to assign the known person as the driver for the unassigned hours of service.
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公开(公告)号：US11866055B1
公开(公告)日：2024-01-09
申请号：US17662622
申请日：2022-05-09
Applicant: Samsara Inc.
Inventor： Sharan Srinivasan , Brian Tuan , John Bicket , Jing Wang , Muhammad Ali Akhtar , Abner Ayala Acevedo , Bruce Kellerman , Vincent Shieh
CPC classification number: B60W40/09 , G06N3/045 , G06N3/082 , G06V40/20 , B60W2400/00
Abstract: A vehicle device may execute one or more neural networks (and/or other artificial intelligence), based on input from one or more of the cameras and/or other sensors, to intelligently detect safety events in real-time. The one or more neural networks may be an ensemble neural network that includes neural networks for detecting a head and hand of a user, neural networks for detecting hand actions of the user, neural networks for detecting the head pose of the user, neural networks for predicting an occurrence of an event, and neural networks for predicting a start time and end time of the event. Further, the neural networks can be segmented into a modular neural network based on metadata. The segmentation of the neural network can define a thin layer of the modular neural network to enable independent tuning of the thin layer of the modular neural network.
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Patent Agency Ranking
公开(公告)号：US11780446B1
公开(公告)日：2023-10-10
申请号：US17661689
申请日：2022-05-02
Applicant: Samsara Inc.
Inventor： Sharan Srinivasan , Brian Tuan , John Bicket , Jing Wang , Muhammad Ali Akhtar , Abner Ayala Acevedo , Bruce Kellerman , Vincent Shieh
CPC classification number: B60W40/09 , G06N3/045 , G06T7/73 , G06V20/597
Abstract: A vehicle device may execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. The vehicle device may further pass the input to a backend server for further analysis and the backend server can detect safety events based on the input. The vehicle device may analyze the output of the vehicle device and the output of the backend server to determine whether the output of the vehicle device is correct. If the output of the vehicle device is incorrect, the vehicle device can adjust how the vehicle device identifies safety events.
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公开(公告)号：US11386325B1
公开(公告)日：2022-07-12
申请号：US17454799
申请日：2021-11-12
Applicant: Samsara Inc.
Inventor： Sharan Srinivasan , Brian Tuan , John Bicket , Jing Wang , Muhammad Ali Akhtar , Abner Ayala Acevedo , Bruce Kellerman , Vincent Shieh
Abstract: A vehicle device may execute one or more neural networks (and/or other artificial intelligence), based on input from one or more of the cameras and/or other sensors, to intelligently detect safety events in real-time. The one or more neural networks may be an ensemble neural network that includes neural networks for detecting a head and hand of a user, neural networks for detecting hand actions of the user, neural networks for detecting the head pose of the user, neural networks for predicting an occurrence of an event, and neural networks for predicting a start time and end time of the event. Further, the neural networks can be segmented into a modular neural network based on metadata. The segmentation of the neural network can define a thin layer of the modular neural network to enable independent tuning of the thin layer of the modular neural network.
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公开(公告)号：US20250002033A1
公开(公告)日：2025-01-02
申请号：US18883478
申请日：2024-09-12
Applicant: Samsara Inc.
Inventor： Mathew Chasan Calmer , Justin Delegard , Justin Pan , Sabrina Shemet , Meelap Shah , Kavya Joshi , Brian Tuan , Sharan Srinivasan , Muhammad Ali Akhtar , John Charles Bicket , Margaret Finch , Vincent Shieh , Bruce Kellerman , Mitch Lin , Marvin Arroz , Siddhartha Datta Roy , Jason Symons , Tina Quach , Cassandra Lee Rommel , Saumya Jain
IPC: B60W50/14 , B60W40/08 , G06F3/01 , G06N3/02 , G06T7/70 , G06V10/44 , G06V10/74 , G06V20/40 , G06V20/56 , G06V20/59 , G07C5/00 , G07C5/08 , H04N19/132 , H04N23/51
Abstract: A vehicle dash cam may be configured to execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. Detection of a safety event may trigger an in-cab alert to make the driver aware of the safety risk. The dash cam may include logic for determining which asset data to transmit to a backend server in response to detection of a safety event, as well as which asset data to transmit to the backend server in response to analysis of sensor data that did not trigger a safety event. The asset data transmitted to the backend server may be further analyzed to determine if further alerts should be provided to the driver and/or to a safety manager.
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公开(公告)号：US12165519B1
公开(公告)日：2024-12-10
申请号：US18528031
申请日：2023-12-04
Applicant: Samsara Inc.
Inventor： Evaline Shin-Tin Tsai , Alan Guihong Liu , Ijeoma Emeagwali , Ishaan Kansal , Saleh ElHattab , Bodecker John DellaMaria , Eliott Ray Chapuis , Jason Noah Laska , Jennifer Kao , Sean Kyungmok Bae , Sylvie Lee , Brian Tuan
Abstract: Methods for improving compliance with regulations pertaining to vehicle driving records are disclosed. One or more digital images from a camera mounted in a vehicle are received. Based on a determination that the vehicle has hours of service that have not been assigned to a driver, a subset of the one or more digital images corresponding to the hours of service are identified based on the timestamps. The subset of the one or more digital images are processed to identify a correspondence between a face of a person included in the one or more digital images and a face of a known person. Based on the correspondence transgressing a threshold level of correspondence, a user interface is generated for presentation on a device. The user interface includes an interactive user interface element for accepting a recommendation to assign the known person as the driver for the unassigned hours of service.
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公开(公告)号：US20230219592A1
公开(公告)日：2023-07-13
申请号：US18188173
申请日：2023-03-22
Applicant: Samsara Inc.
Inventor： Mathew Chasan Calmer , Justin Delegard , Justin Pan , Sabrina Shemet , Meelap Shah , Kavya Joshi , Brian Tuan , Sharan Srinivasan , Muhammad Ali Akhtar , John Charles Bicket , Margaret Finch , Vincent Shieh , Bruce Kellerman , Mitch Lin , Marvin Arroz , Siddhartha Datta Roy , Jason Symons , Tina Quach , Cassandra Lee Rommel , Saumya Jain
CPC classification number: G06V20/56 , G06V20/44 , G06V20/597 , G06T7/70 , G06V10/44 , G06V10/761 , G06V2201/07
Abstract: A vehicle dash cam may be configured to execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. Detection of a safety event may trigger an in-cab alert to make the driver aware of the safety risk. The dash cam may include logic for determining which asset data to transmit to a backend server in response to detection of a safety event, as well as which asset data to transmit to the backend server in response to analysis of sensor data that did not trigger a safety event. The asset data transmitted to the backend server may be further analyzed to determine if further alerts should be provided to the driver and/or to a safety manager.
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