Amazon Technologies, Inc.

Samsara Inc.

Maisie Wang

Sven Eberhardt

Yan Wang

Nathan Hurst

Sung Chun Lee

Kevin Lai

Kenshiro Nakagawa

Shirish Nair

Cole Jurden

Rachel Demerly

Kunal Pramod Ghogale

Jovanna Bubar

Akanksha Gupta

Aravindh Ramesh

Soyoung Kim

Justin Levine

Olamide Akintewe

Charles Edwin Ashton Brett

Hongyang Wang

Dustin D Clark

Suhel Banerjee

Jin Long Lee

Ryan H Fink

Systems and methods for device configuration usage optimization include identifying device configurations that are likely to result in device usage cessation within a predetermined period of time. When device usage cessation is determined to be likely, alternative device configurations are determined that are likely to increase the chances of device usage. One or more recommendations for the alternative device configurations may be sent to user devices, and when accepted, devices may be reconfigured, device functionalities may be enabled, and/or additional devices may be acquired.

Device configuration usage optimization

Systems and methods for device state reversion are disclosed. For example, a requested and/or scheduled device state change may occur, and prior to the device state change, devices may be queried for their device states. This prior device state data may be saved. A user may provide an undo request and the prior device state data may be utilized along with current device state data to select a device to revert device state on, as well as the device state to revert to. In more complex situations and/or when prior state data is unavailable, machine learning techniques may be utilized to select the target device and device state.

Device state reversion

Techniques are presented for detecting when drivers drive while drowsy. In some implementations, a drowsiness model is trained with data associated with inward videos and outward videos captured during a trip. The inward videos capture the inside of the cabin with the driver, and the outward videos capture the view in front of the vehicle in the direction of travel. Further, a device at the vehicle periodically calculates a drowsiness scale index value that indicates the level of drowsiness of the driver. Calculating the drowsiness scale index value includes obtaining a set of inward frames from the inward videos; for each inward frame, creating a face image by cropping the inward frame; obtaining a set of outward frames from the outward videos; calculating inward embeddings of the face images and outward embeddings of the outward frames; and calculating, by the drowsiness model, the drowsiness scale index value.

Drowsy driving detection

Systems and methods for personalized device routines include determining devices associated with user account data and generating device usage data indicating aspects of smart device usage over a period of time. The usage data may then be utilized to identify candidate smart devices, time indicators, and trigger event types to associate with candidate routines. One or more of the candidate routines may be recommended to a user based at least in part on the usage data and the trigger event types at issue to personalize the device routine recommendations.

Personalized device routines

Systems and methods for device functionality control utilizing activity prediction are disclosed. For example, an activity model is utilized to identify activities associated with sensor data collected from connected devices within an environment. Target device usage data is then associated with the activities to determine probability values that the target device will be operated when the identified activities are detected. Commands may be generated and sent to the target device to perform a function when the identified activities are detected.

Device functionality control utilizing activity prediction

Growing community of inventors

Seattle, WA, United States of America

Maisie Wang