Samsara Inc.

Samsara Networks Inc.

Samsara Networks Lac.

Ava O'Neill

John Charles Bicket

Meelap Shah

Derrek Harrison

Ingo Gerhard Wiegand

Cassandra Lee Rommel

Casey Takahashi

Matthew Lee Basham

Mathew Chasan Calmer

David Liang

Justin Joel Delegard

Sabrina Quinn Shemet

Kenshiro Nakagawa

Matthew Hsu

Salil Gupta

Aaron Zeisler

Xicheng Xiong

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

An improved system and method of defining speeding severity levels, receiving over a wireless network from a vehicle device speed data and metadata, determining associated speeding severity levels, based at least in part on the determined associated speeding severity levels, determining whether an alert communication is to be transmitted the vehicle device, and in response to determining that an alert communication is to be transmitted the vehicle device, transmitting the alert communication via a wireless interface to the vehicle device, the alert communication configured to cause the vehicle device to provide a corresponding in-vehicle alert.

Vehicle speed management

Methods for performing improving facial recognition of a driver in a vehicle are disclosed. A set of images is received. Each of the set of images includes a portion that is identified as a face. The identification is based on an application of a first machine-learned model to each of the set of images. The application of the first machine-learned model is performed by an application associated with a client camera device mounted in a vehicle. Based on a determination that the set of images matches one or more reference images stored in a database with a confidence level that is equal to or greater than a confidence threshold, a person corresponding to the one or more reference images is associated as a driver of the vehicle during a time period in which the set of images was captured.

Facial recognition for drivers

Growing community of inventors

San Francisco, CA, United States of America

Ava O'Neill