Chen C Chen, 57324 San Pedro Ave, Pacifica, CA 94044

8588383, Nov 19, 2013

Dec 17, 2010

12/971195

Surojit Chatterjee - Fremont CA, US
Anshul Kothari - Sunnyvale CA, US
Chen Chen - Sunnyvale CA, US

Google Inc. - Mountain View CA

H04M 15/00

37911405, 37920101, 4554141, 709217

Various methods, systems, and computer program products are disclosed for controlling a number of calls based on promotional message impressions. The method may include receiving a request to communicate at least one promotional message, wherein the at least one promotional message is associated with one or more phone numbers. The method may include communicating the at least one promotional message and the one or more phone numbers. The method may include receiving one or more calls associated with the at least one promotional message. The method may include determining a call through rate for the at least one promotional message based on the one or more calls. The method may include determining a number of impressions of the at least one promotional message based on the determined call through rate.

8619952, Dec 31, 2013

Jan 6, 2011

12/985745

Surojit Chatterjee - Fremont CA, US
Anshul Kothari - Sunnyvale CA, US
Chen Chen - Sunnyvale CA, US

Google Inc. - Mountain View CA

H04M 11/00

379 9317, 379 9001, 705 1455

Various methods, systems, and computer program products are disclosed for determining promotional messages that led to a phone call. The method may include communicating a promotional message, wherein the promotional message is associated with a phone number. The method may include receiving an indication that the promotional message was selected. The method may include receiving a phone call via the phone number, the phone call indicating the phone number that was called. The method may include associating the phone call with the promotional message based on the phone number. The method may include determining that the communicated promotional message led to the phone call based on the associated phone call and promotional message.

2013033, Dec 12, 2013

Aug 12, 2013

13/964775

Surojit Chatterjee - Fremont CA, US
Chen Chen - Sunnyvale CA, US
Jian Zhou - Milpitas CA, US

Google Inc. - Mountain View CA

G06Q 30/02

705 1442, 37911206, 705 1441

This specification relates to tracking phone calls related to online content items. In general, one aspect of the subject matter described in this specification can be embodied in methods that include the actions of determining a probability that a call to a phone number resulted from a first interaction of a set of interactions. In general, another aspect of the subject matter described in this specification can be embodied in methods that include the actions of determining a rank score for an online content item, including for each call of a plurality of phone calls to the phone number, attributing the call to an online content interaction of a set of online content interactions; determining, using the rank score, whether to provide the online content item to a client device; and providing the online content item to the client device upon determining to do so using the rank score.

2023003, Feb 2, 2023

May 23, 2022

17/664653

- Santa Clara CA, US
Gregory William Coombe - Mountain View CA, US
Di Zeng - Sunnyvale CA, US
Jeff Adachi - El Cerrito CA, US
Chen Chen - San Jose CA, US

G01C 21/16
G05D 1/02
G01S 19/39
G01S 19/47
G01S 19/46
G01S 19/52
G01C 21/00

A vehicle computing system validates location data received from a Global Navigation Satellite System receiver with other sensor data. In one embodiment, the system calculates velocities with the location data and the other sensor data. The system generates a probabilistic model for velocity with a velocity calculated with location data and variance associated with the location data. The system determines a confidence score by applying the probabilistic model to one or more of the velocities calculated with other sensor data. In another embodiment, the system implements a machine learning model that considers features extracted from the sensor data. The system generates a feature vector for the location data and determines a confidence score for the location data by applying the machine learning model to the feature vector. Based on the confidence score, the system can validate the location data. The validated location data is useful for navigation and map updates.

2020016, May 28, 2020

Jan 31, 2020

16/779118

- Menlo Park CA, US
Shayne Mikel Sweeney - Palo Alto CA, US
Peter William Hunt - San Francisco CA, US
Nicholas Patrick Shortway - Daly City CA, US
Richard William Branson - San Francisco CA, US
Alexandre Karpenko - Atherton CA, US
Chen Chen - San Francisco CA, US
Francois Claude Fabien Jean Jules Deliége - San Francisco CA, US

H04L 29/06
H04N 19/40
H04L 29/08

In one embodiment, a method includes receiving from a client computing device a request to upload a multimedia file from the client computing device to a service. The method includes identifying transcoding servers configured to receive and transcode multimedia files from client computing devices and store the transcoded multimedia files. The method includes sending a reply to the client computing device that includes job configuration information that includes an access token authorizing the client computing device to access the transcoding servers. The information specifies upload policies that include failover instructions for if a failure occurs during an upload from the client computing device to a first of the transcoding servers. The failover instructions include sending the access token to the first of the transcoding servers to resume the upload or sending the access token to a second of the transcoding servers to restart the upload.

2020008, Mar 12, 2020

Sep 9, 2019

16/564303

- Palo Alto CA, US
Jeff Adachi - El Cerrito CA, US
Chen Chen - San Jose CA, US
Gregory William Coombe - Mountain View CA, US
Di Zeng - Sunnyvale CA, US

G01S 19/39
G01S 19/47
G01S 19/46
G01S 19/52
G05D 1/02

A vehicle computing system validates location data received from a Global Navigation Satellite System receiver with other sensor data. In one embodiment, the system calculates velocities with the location data and the other sensor data. The system generates a probabilistic model for velocity with a velocity calculated with location data and variance associated with the location data. The system determines a confidence score by applying the probabilistic model to one or more of the velocities calculated with other sensor data. In another embodiment, the system implements a machine learning model that considers features extracted from the sensor data. The system generates a feature vector for the location data and determines a confidence score for the location data by applying the machine learning model to the feature vector. Based on the confidence score, the system can validate the location data. The validated location data is useful for navigation and map updates.

2020005, Feb 13, 2020

Aug 13, 2019

16/539234

- Waterloo, CA
Chen CHEN - San Francisco CA, US

G06Q 10/04
H04M 7/00
G06Q 30/02

An example communications routing system includes a prediction engine to, for each user account, generate a predicted cost of communications events using a gamma-gamma cost model, generate a predicted revenue of presented advertisements using a gamma-gamma revenue model, generate a margin based on the predicted cost and the predicted revenue, and, when the margin for a given user account is below a margin threshold, restrict a status associated with the given user account.

2019017, Jun 13, 2019

Nov 26, 2018

16/200616

- Greenbelt MD, US
Chen CHEN - Burlingame CA, US
Qinyi Xu - College Park MD, US
Beibei Wang - Clarksville MD, US
Chenshu Wu - Greenbelt MD, US
Hangfang Zhang - Greenbelt MD, US
Chau-Wai Wong - Raleigh NC, US
David N. Claffey - Somerville MA, US
Chun-I Chen - Brookeville MD, US
Hung-Quoc Duc Lai - Parkville MD, US
Min Wu - Clarksville MD, US
Yi Han - Ellicott City MD, US
Oscar Chi-Lim Au - San Jose CA, US

ORIGIN WIRELESS, INC. - Greenbelt MD

G01S 5/06
G01S 5/02

Methods, apparatus and systems for detecting and monitoring vital signs in real time are disclosed. In one example, a system for monitoring a repeating motion in a venue is disclosed. The system comprises a transmitter, a receiver, and a repeating motion monitor. The transmitter is located at a first position in the venue and configured for transmitting a wireless signal through a wireless multipath channel impacted by the repeating motion of an object in the venue. The receiver is located at a second position in the venue and configured for: receiving the wireless signal through the wireless multipath channel impacted by the repeating motion of the object in the venue, and obtaining a time series of channel information (CI) of the wireless multipath channel based on the wireless signal. The repeating motion monitor is configured for: monitoring a periodic characteristics of the repeating motion of the object based on the time series of CI.