Google Inc.

Bodymedia, Inc.

X Development LLC

Waymo LLC

Wing Aviation LLC

Loon LLC

Softbank Corporation

Other

Carnegie Mellon University

Verily Life Sciences LLC

Zivio Technologies LLC

Hewlett-packard Development Company, L.p.

Aliphcom

Tidalx Ai Inc.

Flux Factory, Inc.

Eric Teller

Richard Wayne Devaul

John Michael Stivoric

Clifford L Biffle

Christopher D Pacione

William Graham Patrick

David Andre

Eric Peeters

Christopher D Kasabach

Joshua L Weaver

Josh Weaver

Jonathan Farringdon

Peter Craig Lombrozo

Johnny Hsienchow Lee

Suresh Vishnubhatla

Raymond Pelletier

Michael Cassidy

Scott A Safier

Bradley Templeton

Scott Klaus Boehmke

Anton Valdemar Staaf

John L Moss

Craig B Liden

Martin Towle King

Claes-Fredrik Urban Mannby

Jesse Blake

Richard Wayne DeVaul

Francine Duskey Gemperle

Cliff L Biffle

Chris Dominick Kasabach

Margaret A McCormack

Nicholas Chim

Cyrus Behroozi

James A Gasbarro

Andre Prager

James Schmalzried

Matthew Nubbe

Alena Rose Fong

Scott Velez

Augusto Roman

Michelle Kaufmann

Kevin Anthony Furr

John A Monocello, Iii

Jennifer Carlile

Cedric Dupont

Eli Attia

James Hanlon

Brian Douglas Cullinane

Nathan M Pletcher

Babak Amirparviz

Bradley J Rhodes

Gregory T Kovacs

Anthony Scott Levandowski

Sergey Brin

Keith Allen Bonawitz

Edward Allen Keyes

Michael J Smith

Daniel M Aminzade

Chris Urmson

Waleed Kadous

Stacy L Wolff

Kevin L Massaro

Valiuddin Y Ali

Baris Ibrahim Erkmen

André Prager

Adam Woodworth

Devin Brinkley

Laura Valentine Chrobak

Neil Davé

Giulia Pantalone

Andrew Rossignol

Dan P Piponi

Martin Kubie

Nisarg Vyas

Jean-Laurent Plateau

Jacob Huffman

Mark Handel

Ealgoo Kim

Steve Menke

Eric Hsiung

Neal Spruce

Steve Shassberger

Anthony B Rorke

Ajay Chaturvedi

Andrè Prager

Balaprakash Kasiviswanathan

Thomas Moore

Bradley Schumitsch

Brad Rhodes

Donna Wolf

Scott Safier

Dan Piponi

Margaret A Mccormack

Cliff Biffle

Bradley Rhodes

Anton Staaf

Richard Wa Ne Devaul

A vehicle configured to operate in an autonomous mode can obtain sensor data from one or more sensors observing one or more aspects of an environment of the vehicle. At least one aspect of the environment of the vehicle that is not observed by the one or more sensors could be inferred based on the sensor data. The vehicle could be controlled in the autonomous mode based on the at least one inferred aspect of the environment of the vehicle.

Inferring state of traffic signal and other aspects of a vehicle's environment based on surrogate data

An example method involves a computing device: receiving point-of-view (POV) image data that is generated by an image capture device of a head-mountable device (HMD); using one or more real-world aspects detected in the POV image data as a basis for determining a context of the HMD; making a threshold determination as to whether or not the context of the HMD is appropriate for display of media content in the HMD; and when it is determined that the context is appropriate for display of media content in the HMD, then: (a) selecting first media content for display at the HMD, wherein the first media content is selected based at least in part on a combination of the determined context and a user-profile associated with the HMD; and (b) sending an indication to play out the first media content at the HMD.

Measurement method and system

In one aspect, there is provided a marine sensor platform that includes: a line having a plurality of slots that are spaced apart on the line, a plurality of sensor nodes, each sensor node configured to couple to each of the slots on the line, and each sensor node including at least one sensor, a power source coupled to the plurality of sensor nodes and configured to supply power to the plurality of sensor nodes on the line, and a controller coupled to the power source and the plurality of sensor nodes on the line, wherein the controller is configured to identify a type of the at least one sensor included in each of the sensor nodes when each of the sensor nodes is coupled to the respective slot on the line.

Daisy chained marine sensor platform

An autonomous vehicle configured for active sensing may also be configured to weigh expected information gains from active-sensing actions against risk costs associated with the active-sensing actions. An example method involves: (a) receiving information from one or more sensors of an autonomous vehicle, (b) determining a risk-cost framework that indicates risk costs across a range of degrees to which an active-sensing action can be performed, wherein the active-sensing action comprises an action that is performable by the autonomous vehicle to potentially improve the information upon which at least one of the control processes for the autonomous vehicle is based, (c) determining an information-improvement expectation framework across the range of degrees to which the active-sensing action can be performed, and (d) applying the risk-cost framework and the information-improvement expectation framework to determine a degree to which the active-sensing action should be performed.

Consideration of risks in active sensing for an autonomous vehicle

An example method involves determining an expected demand level for a first type of a plurality of types of transport tasks for unmanned aerial vehicles (UAVs), the first type of transport tasks associated with a first payload type. Each of the UAVs is physically reconfigurable between at least a first and a second configuration corresponding to the first payload type and a second payload type, respectively. The method also involves determining based on the expected demand level for the first type of transport tasks, (i) a first number of UAVs having the first configuration and (ii) a second number of UAVs having the second configuration. The method further involves, at or near a time corresponding to the expected demand level, providing one or more UAVs to perform the transport tasks, including at least the first number of UAVs.

Anticipatory dispatch of UAVs to pre-staging locations

Methods and systems herein may help to provide an advertisement marketplace for advertisements that are valued according to gaze data from wearable computing devices. An exemplary method involves: receiving a listing request to sell advertisement rights to an advertisement space, wherein the listing request corresponds to a user-account; determining an advertisement value for the advertisement space, wherein the advertisement value is based on occurrences of the advertisement space in gaze data, wherein the gaze data is received from a plurality of wearable computing devices and is indicative of a respective wearer-view associated with each of the devices; in response to the listing request, updating an advertising-space database with a listing for the advertisement space, wherein the listing indicates the advertisement rights are available for purchase at a listing price that is based on the advertisement value; and making the listing for the advertisement space available via a network-based advertisement marketplace.

Marketplace for advertisement space using gaze-data valuation

The optical tracking module includes an optical phased array (OPA), an analog drive, an integrated photodetector, and one or more processors. The OPA includes a plurality of array elements, and a plurality of phase shifters. The analog drive is configured to adjust the plurality of phase shifters. The integrated photodetector is configured to receive light from the OPA. The one or more processors is configured to extract signal information of an incoming beam via the OPA, and control an outgoing beam using the analog drive based on the signal information. The OPA, the analog drive, the integrated photodetector and the one or more processors are in an integrated circuit.

Optical tracking module chip for wireless optical communication terminal

Methods and systems for recipient-assisted recharging during delivery by an unmanned aerial vehicle (UAV) are disclosed herein. During a UAV transport task, a UAV determines that the UAV has arrived at a delivery location specified by a first flight leg of the transport task. The UAV responsively initiates a notification process indicating that a recipient-assisted recharging process should be initiated at or near the delivery location. When the UAV determines that the recipient-assisted recharging process has recharged a battery of the UAV to a target level, and also determines that a non-returnable portion of the payload has been removed from the UAV while a returnable portion of the payload is coupled to or held by the UAV, the UAV initiates a second flight segment of the transport task.

Recipient-assisted vehicle recharging

The present disclosure provides methods operable in a balloon network. The method can include determining that a balloon is at a location associated with a legally-defined geographic area. An area profile of the legally-defined geographic area may identify geographically-restricted data that must not be removed from the legally-defined geographic area. The method can also include determining that the balloon contains at least some of the geographically-restricted data. The method can also include determining that the balloon is likely to move out of the legally-defined geographic area. The method can also include removing the geographically-restricted data from the memory of the balloon.

Method for ensuring data localization on an ad hoc moving data network

Growing community of inventors

San Francisco, CA, United States of America

Eric Teller