Amazon Technologies, Inc.

Tidyware, LLC

Brian Hayward

Jamie Plenderleith

Raman Chandrasekar

Ray Sun

Monika Marta Gnyp

Alvin G Solidum

Patrick Dean Kennedy

Sarah Rose Quigley

Travis T Okahara

Suzie Cuddy

Philip F Carmichael

William L Richman

Methods, systems, and computer-readable media for energy-optimizing placement of resources in data centers are disclosed. A resource placement manager determines information descriptive of energy usage by one or more data centers. The one or more data centers comprise a plurality of computing resources in a plurality of corresponding locations. The resource placement manager selects, from the plurality of computing resources in the plurality of corresponding locations, a particular computing resource in a particular location for performing one or more computing tasks. The particular computing resource in the particular location is selected based at least in part on reducing energy usage associated with the one or more data centers according to the information descriptive of energy usage. The particular computing resource in the particular location is used to perform the one or more computing tasks.

Energy-optimizing placement of resources in data centers

Systems and methods for training a personalized Machine Learning (ML) model used to detect fall events are described herein. The methods may be implemented by one or more computing devices and may include obtaining sensor data associated with one or more activities of a user. A processed or unprocessed version of at least a copy of the sensor data having been fed to a personalized ML model associated with the user and that has been determined not to be associated with a fall event; and using the obtained sensor data training the personalized ML model.

Systems and methods for training a personalized machine learning model for fall detection

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Seattle, WA, United States of America

Brian Hayward