Jun S Gong, 51707 Walnut Ave, Burlingame, CA 94010

2013006, Mar 14, 2013

Sep 10, 2012

13/608047

OTHAR HANSSON - Sunnyvale CA, US
NUNDU JANAKIRAM - San Francisco CA, US
ROBERT ENNALS - San Francisco CA, US
JONATHAN TANG - Mountain View CA, US
JUN GONG - San Jose CA, US
WANDA HUNG - Los Gatos CA, US

G06F 15/16

709204, 709217

A system, computer-readable storage medium storing at least one program, and a computer-implemented method for confirming authorship of documents is presented. A first document hosted on a first website of a first domain is accessed, the first document being linked to a second document through at least one link, and a respective link including a first predefined authorship attribute asserting authorship of a respective document including the respective link by a respective entity associated with a respective target document of the respective link. Authorship of the first document by an entity associated with the second document is conditionally confirmed when the second document includes a second link to the first website of the first domain, the second link including a second predefined authorship attribute indicating that the entity associated with the second document is an author of or contributor to content at the first website of the first domain.

2021012, Apr 29, 2021

Oct 29, 2019

16/667424

- Mountain View CA, US
Jun Gong - Mountain View CA, US
Marc Gendron-Bellemare - Montreal, CA
Marlos Cholodovskis Machado - Montreal, CA

LOON LLC - Mountain View CA

G01C 21/20
G06N 3/08
G06N 3/04

The technology relates to navigating aerial vehicles using deep reinforcement learning techniques to generate flight policies. A computing system may include a simulator configured to produce simulations of a flight of the aerial vehicle in a region of an atmosphere, a replay buffer configured to store frames of the simulations, and a learning module having a deep reinforcement learning architecture configured to, by a reinforcement learning algorithm, process an input of a set of frames, and output a neural network encoding a learned flight policy. A meta-learning system may include stacks of learning systems, a coordinator configured to provide an instruction to the learning systems that includes a parameter and a start time, and an evaluation server configured to evaluate resulting rewards from learned flight policies generated by the learning systems.

2021012, Apr 29, 2021

Oct 29, 2019

16/667441

- Mountain View CA, US
Jun Gong - Mountain View CA, US
Marc Gendron-Bellemare - Montreal, CA

LOON LLC - Mountain View CA

G05D 1/00
G06N 3/04
G06N 3/08
G05D 1/02
B64B 1/00

The technology relates to navigating aerial vehicles using deep reinforcement learning techniques to generate flight policies. An operational system for controlling flight of an aerial vehicle may include a computing system configured to process an input vector representing a state of the aerial vehicle and output an action, an operation-ready policies server configured to store a trained neural network encoding a learned flight policy, and a controller configured to control the aerial vehicle. The input vector may be processed using the trained neural network encoding the learned flight policy. A method for navigating an aerial vehicle may include selecting a trained neural network encoding a learned flight policy from an operation policies server, generating an input vector comprising a set of characteristics representing a state of the aerial vehicle, selecting an action, by the trained neural network, based on the input vector, converting the action into a set of commands, by a flight computer, the set of commands configured to cause the aerial vehicle to perform the action, and causing, by a controller, the aerial vehicle to perform the action using the set of commands.

2020019, Jun 18, 2020

Dec 17, 2018

16/222309

- Mountain View CA, US
Jun Gong - Mountain View CA, US
Jacob Buswell Roberts - San Francisco CA, US
Vincent Carroll - Mountain View CA, US

G01C 21/00
G01C 21/20
B64C 39/02

The technology relates to generating a flight map for an aircraft. For instance, this may include, running a plurality of simulations by placing a simulated aircraft within each cell of a grid representing areas of the earth and using predicted wind data. Each simulations identifies a cell in which each aircraft is located at the end of the simulation. A connection graph may be generated using any identified cells. The connection graph may be used to determine a flight map for an actual aircraft using a cost function and iterating from a destination cell to an initial cell. The flight map may be used to determine a route for the actual aircraft. In some examples, the flight map may be refined by running further simulations. The refined flight map may then be used to determine a route for the actual aircraft.

2015016, Jun 11, 2015

Jun 24, 2014

14/313856

- Mountain View CA, US
Jun Gong - San Jose CA, US
Murali Krishna Viswanathan - Mountain View CA, US
Daphne Dembo - Palo Alto CA, US
Pravir K. Gupta - Mountain View CA, US
Tal Cohen - Haifa, IL
Lev Finkelstein - Netanya, IL
Adi Mano - Haifa, IL
Evan B. Roseman - Mountain View CA, US

G06F 17/30

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for triggering music answer boxes. In one aspect, a method includes receiving a query, obtaining a plurality of search results responsive to the query, the search results being results from a search of web resources on the Internet, and determining from the plurality of search results that the query is a music query. Music data for a song responsive to the query is obtained, where the music data comprises a Uniform Resource Locator (URL) of song content of the song on the Internet. A music answer box is generated for the query, where the music answer box comprises the music data and a link to the URL of the song content, and the music answer box is provided in addition to search results.