Amazon Technologies, Inc.

Tellme Networks, Inc.

Microsoft Technology Licensing, LLC

Brandon William Porter

Gur Kimchi

Daniel Buchmueller

Brian C Beckman

Darren Ernest Canavor

Jon Arron McClintock

Amir Navot

Jesper Mikael Johansson

Eckart Walther

Varadarajan Gopalakrishnan

Andrew Jay Roths

Fabian Hensel

Hadi Partovi

Anton Vladilenovich Goldberg

John Giannandrea

Joseph L Ellsworth

Scott A Green

Angus Macdonald Davis

Severan Sylvain Jean-Michel Rault

Daniel Wade Hitchcock

Christopher Richard Newcombe

Michael S McCue

Roderick Steven Brathwaite

Daphne Huetu Luong

Corrinne Yu

Michael Vogelsong

Benjamin Lev Snyder

Charles Edward Cartwright

Leonardo Ruggiero Bachega

Jeffrey P Bezos

Ricky Dean Welsh

Frederik Schaffalitzky

Michael James McInerny

Chris A Suver

Matthew T Marx

Zhe Li

Avi Bar-Zeev

Christopher Allen Suver

Jeromey Russell Goetz

Michael Bodell

Jeff Bradley Beal

Alan Stuart Robbins

James Domit Mackraz

Jeff C Kunins

Donghui Zhuo

Anthony Accardi

Aram Grigoryan

Jane Mooney

Harsha Ramalingam

Louis LeRoi LeGrand, Iii

Korwin Jon Smith

Lisa Joy Stifelman

Grant Alexander Macdonald McAlister

Luan Khai Nguyen

Kyle Bradley Peterson

Dominique Imjya Brezinski

Darin Keith McAdams

James Robert Everingham

Logan Luyet Dillard

Bhavnish H Lathia

Jack Erdozain, Jr

Joshua White Traube

Walker Chamberlain Robb

Terrence R Nightingale

Olga I Kuznetsova

Erik Castedo Ellerman

Jason Roselander

William Alexander Strand

Paul A Viola

James Christopher Curlander

Jon Stuart Battles

Simon Kurt Johnston

Joseph Adam Ruff

Madhuvanesh Parthasarathy

Vishal Parakh

Leon Robert Warman

Jacob Carr

David Henry Clark

Melissa Elaine Davis

Peter Varnum Commons

Martin Robert Frank

Karen M Cross

Grant A M McAlister

Llewellyn James Mason

Ajit Ashok Varangaonkar

Daniel Dwight Longley

Stephen T Campbell

Gary Clayton

Aaron Ben Fernandes

Donald C Jackson

Morgan Akers

Terry Ray Weissman

Tyson Christopher Trautmann

Eugene Koh

David John Edwards, Jr

Matthew Oshry

Elton Victor Pinto

Karthik Tamilmani

Scott Dresser

Scott James McKee

Recent location and control information received from 'lead' vehicles that traveled over a segment of land, sea, or air is captured to inform, via aggregated data, subsequent “trailing” vehicles that travel over that same segment of land, sea, or air. The aggregated data may provide the trailing vehicles with annotated road information that identifies obstacles. In some embodiments, at least some sensor control data may be provided to the subsequent vehicles to assist those vehicles in identifying the obstacles and/or performing other tasks. Besides, obstacles, the location and control information may enable determining areas traveled by vehicles that are not included in conventional maps, as well as vehicle actions associated with particular locations, such as places where vehicles park or make other maneuvers.

Annotated virtual track to inform autonomous vehicle control

This disclosure describes a distributed automated mobile vehicle ('automated mobile vehicle') system for autonomously delivering orders of items to various delivery locations and/or autonomously returning items to a return location. In some implementations, each user may own or be assigned their own automated mobile vehicle that is associated with the user and an automated mobile vehicle control system maintained by the user. When the user orders an item, the user owned or controlled automated mobile vehicle navigates to a materials handling facility, retrieves the ordered item and delivers it to the user.

Distributed automated mobile vehicle routing based on characteristic information satisfying a minimum requirement

The disclosure describes an automated aerial vehicle (AAV) and system for automatically detecting a contact or an imminent contact between a propeller of the AAV and an object (e.g., human, pet, or other animal). When a contact or an imminent contact is detected, a safety profile may be executed to reduce or avoid any potential harm to the object and/or the AAV. For example, if a contact with a propeller of the AAV by an object is detected, the rotation of the propeller may be stopped to avoid harming the object. Likewise, an object detection component may be used to detect an object that is nearing a propeller, stop the rotation of the propeller, and/or navigate the AAV away from the detected object.

Propeller safety for automated aerial vehicles

A machine learning system builds and uses computer models for controlling robotic performance of a task. Such computer models may be first trained using feedback on computer simulations of the robotic system performing the task, and then refined using feedback on real-world trials of the robot performing the task. Some examples of the computer models can be trained to automatically evaluate robotic task performance and provide the feedback. This feedback can be used by a machine learning system, for example an evolution strategies system or reinforcement learning system, to generate and refine the controller.

Simulation-real world feedback loop for learning robotic control policies

Recent location and control information received from 'lead' vehicles that traveled over a segment of land, sea, or air is captured to inform, via aggregated data, subsequent 'trailing' vehicles that travel over that same segment of land, sea, or air. The aggregated data may provide the trailing vehicles with annotated road information that identifies obstacles. In some embodiments, at least some sensor control data may be provided to the subsequent vehicles to assist those vehicles in identifying the obstacles and/or performing other tasks. Besides, obstacles, the location and control information may enable determining areas traveled by vehicles that are not included in conventional maps, as well as vehicle actions associated with particular locations, such as places where vehicles park or make other maneuvers.

An unmanned aerial vehicle (UAV) expandable landing marker system may include a an expandable volume. The landing marker may be expanded prior to arrival of a UAV delivering an item to be received by the landing marker. The landing marker may be expanded by regulating an amount of fluid in the volume. An anchor may be coupled to the landing marker to restrain movement of the expanded landing marker. An optional retraction mechanism may retract the landing marker. The landing marker can be retracted with the deposited item, moving the item to a location for later retrieval.

Unmanned aerial vehicle expandable landing marker system

Various embodiments of a system and method for tracking service requests are described. Embodiments may include call tree generation logic configured to receive multiple request identifiers associated with a respective one of multiple service requests. Each given request identifier may include an origin identifier, a depth value, and a request stack comprising one or more interaction identifiers. The call tree generation logic may also be configured to, based on multiple request identifiers that each include an origin identifier associated with a particular root request, generating a data structure that specifies a hierarchy of services called to fulfill that particular root request. Based on one or more of the interaction identifiers and one or more of the depth values, the generated data structure may specify for each given service of the hierarchy: a parent service that called the given service, and one or more child services called by the given service.

System and method for tracking service requests

A system incorporating volume activated communications. A system with local devices linked to a remote server that is capable of processing voice commands is capable of linking local devices in a communications link where the link is initiated upon high volume speech being detected by a receiving device. A target device is determined based on an estimated connection between users at the respective devices. Audio is then sent from the receiving device to the target device resulting in a communication link between the two devices, allowing communication between the users.

Volume initiated communications

Pattern based detection of data usage is facilitated using data injection. Data values are injected in one or more storage locations accessible to a plurality of services or included in service requests. Service interactions among the services are compared to a set of patterns. The set of patterns are configured to match the data values. By comparing the service interactions to the patterns, one or more of the service interactions are determined to include individual ones of the data values. Data are generated indicating a presence of the data values in the services.

Pattern-based detection using data injection

Techniques described herein include systems and methods for throttling requests for content to reduce stress on a check out pipeline associated with an electronic marketplace thereby avoiding overstressing a server to the point of no longer processing requests from users. In embodiments, first information may be maintained that identifies a ranking for a plurality of items based on a score. A predicted velocity of content requests about the plurality of items may be maintained and second information about an actual velocity of content requests about the plurality of items may be received. In response to a request for content, a portion of items may be identified based on the scores associated with said portion and partition the portion into a number of groups or partitions based on the predicted velocity and the second information. A data object that comprises the portion of items associated with a partition may be generated.

Method and system for dynamic traffic shaping of deals to reduce server stress

Hybrid sortation systems and methods may include a floor having a plurality of transfer locations, a plurality of container drive units or pods that may be positioned at respective transfer locations to receive packages, and a plurality of overdrive units that may travel over and sort packages to respective transfer locations. The overdrive units may be configured to travel over container drive units or pods, such that operations or movements of container drive units or pods may be substantially decoupled or independent from operations or movements of overdrive units. In this manner, sort density and throughput of such hybrid sortation systems may be increased, while also reducing congestion associated with operations or movements of robotic drive units.

Robotic overdrive units for hybrid sortation systems and methods

A system and method for operating an automated aerial vehicle are provided wherein influences of ground effects (e.g., which may increase the effective thrusts of propellers by interfering with the respective airflows) are utilized for sensing the ground or other surfaces. In various implementations, operating parameters of the automated aerial vehicle are monitored to determine when ground effects are influencing the parameters associated with the propellers, which correspondingly indicate proximities to a surface (e.g., the ground). Such ground effect sensing techniques may be utilized as a backup to other sensors (e.g., which may be determined to not be functioning properly and/or may be otherwise inhibited due factors such as to rain, snow, fog, reflections, bright sunlight, etc.).

Ground effect based surface sensing utilized with other sensing technologies in automated aerial vehicles

Two unmanned vehicles come within communication range of one another. The unmanned vehicles exchange logs of messages each has received. Each of the unmanned vehicles analyzes the messages that it received from the other unmanned vehicle to determine whether any of the received messages warrants changing a set of tasks it was planning to perform. When a message indicates that a task should be changed, the task is updated accordingly.

Unmanned vehicle message exchange

Described is an airborne monitoring station ('AMS') for use in monitoring a coverage area and/or unmanned aerial vehicles ('UAVs') positioned within a coverage area of the AMS. For example, the AMS may be an airship that remains at a high altitude (e.g., 45,000 feet) that monitors a coverage area that is within a line-of-sight of the AMS. As UAVs enter, navigate within and exit the coverage area, the AMS may wirelessly communicate with the UAVs, facilitate communication between the UAVs and one or more remote computing resources, and/or monitor a position of the UAVs.

Airborne unmanned aerial vehicle monitoring station with adjustable image capture devices

Growing community of inventors

Yarrow Point, WA, United States of America

Brandon William Porter