Samsara Inc.

Cisco Technology, Inc.

Samsara Networks Inc.

Meraki, Inc.

Other

Meraki Networks, Inc.

Samsara Networks Lac.

John Charles Bicket

Brian Tuan

James Michael Rowson

Sanjit Zubin Biswas

Justin Joel Delegard

Muhammad Ali Akhtar

Benjamin Avery Chambers

Sharan Srinivasan

Bruce Kellerman

Vincent Shieh

Kavya U Joshi

Sabrina Quinn Shemet

Jing Wang

Jason Frederic Symons

Meelap Shah

Abner Ayala Acevedo

Chase Phillips

Ingo Gerhard Wiegand

James Duffield

Derrek Harrison

Bodecker John DellaMaria

Clifford Arthur Frey

James Robert Saunders

Matthew Lee Basham

Margaret Irene Finch

Anubhav Jain

Mathew Chasan Calmer

Adam Eric Funkenbusch

Cassandra Lee Rommel

James Michael Roewe

Kenshiro Nakagawa

Benjamin Arthur Calderon

Ava O'Neill

David Liang

Jason Noah Laska

Robert Tristan Shanks

Patrick Douglas Verkaik

Sean Kyungmok Bae

Ishaan Kansal

Evaline Shin-Tin Tsai

Justin Pan

Arthur Pohsiang Huang

Kieran K Gupta

James Chen

Dan Aguayo

Ryan Reading

Kong Kee Poon

Christian Peter Almer

Varun Sagar Malhotra

Morgan Davis Teachworth

Sylvie Lee

Sven Eberhardt

Kevin R Lloyd

Noah Paul Gonzales

Kathryn Siegel

Siri Amrit Ramos

Karthikram Rajadurai

Somasundara Pandian

Saumya Jain

Ye-Sheng Kuo

Brian Westphal

Katherine Lee Heddleston

Thomer Gil

Vignesh Bhuvaneshwar Iyer

Casey Takahashi

Matthew Hsu

Andrew William Deagon

Kevin Paul Herbert

Tina Quach

Siddhartha Datta Roy

Marvin Arroz

Mitch Lin

Peter Lee Atkin

Abhishyant Khare

Yu Kang Chen

William Waldman

Ruben Vardanyan

Sushant Thakur

Naveen Chinya Krishnamurthy

Jennifer Julia Zhang

Nathan Hurst

Kelsey Michelle Lam

Joyce Tian Chen

Rhea Lin

Glen Phillip McGillan

Evan Welbourne

Kenneth Kai Tai Lee

Seamus Feider-Sullivan

Matthew Chasan

Abhinav Acharya

Allen Lu

Eric Klaus Allseits

Lucas Glenat

Harry Lu

Sanjit Biswas

Yangyong Zhang

Bodecker John Dellamaria

Kavya Joshi

A baseline event detection system to detect events by performing operations that include: generating a baseline data set; accessing a data stream; performing a comparison of the baseline data set and the data stream; and detecting an event based on the comparison.

Baseline event detection system

An Applicant Assistant System may receive, from a user device, a user input indicating a query or task associated with information displayed in a user interface. The device may determine one or more components associated with the user interface and access component metadata of the determined one or more components, the component metadata for each of the one or more components indicating one or more of a fact, a data link, or an action. A prompt comprising at least some of the component metadata, at least some context information, and an indication of one or more available response elements may be provided to a large language model (LLM). The LLM may response with one or more response elements that may be processed by the System, such as to cause updates to the user interface on the user device.

Artificial intelligence application assistant

An inspection application may display, on a user device, a user interface including at least a portion of a vehicle inspection report including a plurality of inspection categories. The inspection application may configure the user device to obtain inspection information associated with a vehicle, the inspection information comprising photographs, videos, audio, and/or text. The inspection application and/or a network-accessible inspection assistant system may generate a prompt including at least a portion of the inspection information and information indicating potential vehicle defects. The prompt may be transmitted to a large language model that returns a response indicating any potential vehicle defects identified in the inspection information. The vehicle inspection report may then be updated to indicate the potential vehicle defects identified by the language model.

Artificial intelligence inspection assistant

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.

Refining event triggers using machine learning model feedback

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.

Dash cam with artificial intelligence safety event detection

An Internet of Things (IoT) or vehicle dash cam may store both a high-resolution and low-resolution video stream on a device. The video streams are selectively accessible by remote devices. Because of the relatively smaller storage requirements of low-resolution video files, retaining of additional video data on the vehicle device (beyond what would be possible with only high-resolution video) is possible. The user may be provided an option to adjust the amount of low-resolution and high-resolution video to store on the device. A combined media file may be generated by a device to include time-synced high-resolution video, low-resolution video, and/or metadata for a particular time period.

Dual-stream video management

Methods and systems for offload of data from a wireless sensing device to a gateway device. A certificate that is generated by the management server in response to a determination that the gateway device is associated with a wireless sensing device is received during an initial connection with the management server. In response to confirming, based on the certificate, that the gateway device is authorized to connect to the wireless sensing device, the certificate is transmitted to the wireless sensing device; and data is received from the wireless sensing device in response to confirming that the wireless sensing device is authorized to connect with the gateway device based on the certificate.

Authentication of a gateway device in a sensor network

A device orientation calibration system to perform operations that include: accessing inertial measurement unit (IMU) data generated by an IMU mounted upon an object that corresponds with a first reference frame, the IMU data indicating a second reference frame that comprises a Z-axis, a Y-axis, and an X-axis; performing a calibration of the IMU data to align the second reference frame of the IMU with the first reference frame of the object, the performing the calibration comprising: determining a gravitational vector based on a first portion of the IMU data; applying a first rotation to the IMU data to align the Z-axis of the second reference frame with the gravitational vector; determining a forward motion vector based on a second portion of the IMU data; and applying a second rotation to the IMU data to align the X-axis of the second reference frame with the forward motion vector.

Device orientation calibration system

An event detection system to operations that include: detecting an event based on at least a portion of the sensor data, the event comprising a plurality of event attributes; determining one or more event attributes from the plurality of event attributes of the event transgress a threshold value; and causing display of a notification that includes a presentation of at least the portion of the sensor data in response to the determining that the one or more event attributes from among the plurality of event attributes of the event transgress the threshold value.

Event notification system

Techniques for managing access control policies are described herein. According to one embodiment, access control policies (ACPs) and access control rules (ACRs) are downloaded from a management server to a network access device (NAD) over the Internet, where the network access device is one of a plurality of network access devices managed by the management server over the Internet. In response to a request from a network client device for entering a network, a device type of the network client device is detected and an ACP identifier is determined based on the device type using the ACRs An ACP is selected from the ACPs based on the ACP identifier and enforced against the network client device. At least the selected ACP is reported to the management server to distribute the selected ACP to other network access devices.

Method for automatically applying access control policies based on device types of networked computing devices

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.

Ensemble neural network state machine for detecting distractions

Tuning layers of a modular neural network

A map-based notification system configured to perform operations that include: accessing a base-map that comprises a map-tile, the map-tile defining a speed limit of a location; receiving vehicle data that includes speed data; determining that the speed data from the vehicle data transgresses the speed limit associated with the location defined by the map-tile; and causing display of a notification in response to determining that the speed data from the vehicle data transgresses the speed limit associated with the location defined by the map-tile.

Map-based notification system

Growing community of inventors

Burlingame, CA, United States of America

John Charles Bicket