Google Inc.

Airbnb, Inc.

Zhentao Sun

Mohammed Waleed Kadous

Marc Stogaitis

Brian Patrick Williams

Andrew Ames Bunner

Nadav Aharony

Pascal Tom Getreuer

Alan Lee Gardner, Iii

Ami Patel

Richard Daniel Webb

Thomas Chao

Brian Duff

Daniel Estrada Alva

Benjamin Razon

Tony Weber

Liem Tran

Joseph A Farfel

Richard Francis Lyon

Ryan Michael Rifkin

Shike Mei

Andriy Sergiyenko

Sean Odonnell

Greg Thompson

Dawei Wang

Noah Martin

Denise Ho

Jaikumar Ganesh

Ralph Jacob Cressman

Laurent Tu

Network site users can be selected to receive a communication based on a network site event, such as incomplete registration. A hybrid user interaction machine learning scheme can select a portion of the selected users based on user interaction estimates and network sampling data. The electronic document sent to the users can have portions that undergo two-pass ranking for ordering of content items to be included in the electronic document, such as an email.

User electronic message system

The disclosure includes a system and method for detecting fine grain copresence between users. The system includes a processor and a memory storing instructions that when executed cause the system to: transmit a wakeup signal to a plurality of devices based on coarse grain location information; send a request to a first device of the plurality of devices to transmit a token using a first communication technology to determine fine grain copresence; receive a first token acknowledgment from a first subset of the plurality of devices; send a request to a second device of the first subset of the plurality of devices to transmit the token using a second communication technology to determine fine grain copresence; receive a second token acknowledgment from a second subset of the plurality of devices; and refine copresence based on receiving the first and second token acknowledgment.

Server orchestrated connectivity

The disclosure includes a system and method for detecting fine grain copresence between users. The system includes a processor and a memory storing instructions that when executed cause the system to receive user input regarding copresence detection settings for a first user device, the copresence detection settings comprising a location and/or a user access control list, and determine a current location of the first user device. The system may determine whether copresence detection of the first user device is enabled at the current location based on the copresence detection settings and the current location. Based on determining that copresence detection is enabled, the system may refine copresence and perform an action based on fine grain copresence of the first and second user device.

Copresence permission model

The disclosure includes a system and method for detecting fine grain copresence between users. The system includes a processor and a memory storing instructions that when executed cause the system to: process one or more signals to determine coarse grain location information of a first device and a second device; determine whether the first device and the second device are copresent based on the coarse grain location information; in response to determining that the first device and the second device are copresent based on the coarse grain location information, transmit a signal to the second device to alert the second device to listen for a fine grain copresence token from the first device; and refine copresence based on receiving an indication that the second device has received the fine grain copresence token.

Two-pass copresence

Methods and apparatus are directed to geofencing-related heuristics for computing devices. A computing device with a plurality of sensors can receive a plurality of heuristics. Each heuristic can be configured to generate command(s) for the sensors based on one or more heuristic inputs. The heuristic input(s) can include an input related to a geofence. The computing device can receive a plurality of signals from the sensors. The computing device can determine, based on the plurality of signals, an activity class for the computing device. The activity class can specify an activity associated with the computing device. The computing device can select a heuristic from the plurality of heuristics at least based on the activity class. The computing device can execute the selected heuristic to generate the command(s) for the sensors.

Sending geofence-related heuristics to multiple separate hardware components of mobile devices

The present disclosure provides example methods operable by computing device. An example method may include querying a location of a computing device with a first frequency. The method may also include accumulating data on the computing device over a time period. The data may include respective locations of the computing device during the time period with respect to the time of day and at increments based on the first frequency. The method may also include determining a model of the location of the computing device with respect to the time of day based on the accumulated data. The method may also include comparing the current location of the computing device to the model. If the current location of the computing device is consistent with the model, the method may also include querying the location of the computing device with a second frequency.

Methods and systems for adjusting location querying frequency of a computing device

The subject matter of this specification can be implemented in, among other things, a method for determining a wireless access point location. The method includes a step for receiving session data from at least one mobile device, wherein each instance of the received session data includes one or more global positioning system (GPS) data points, one or more sensor data points, and one or more WiFi scan data points, wherein the one or more WiFi scan data points are associated with one or more wireless access points (WAPs). The method also includes a step for calculating a location of at least one of the one or more WAPs using at least a portion from each of the received one or more global positioning system (GPS) data points, the one or more sensor data points, and the one or more WiFi scan data points.

Determining a WiFi scan location

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Sunnyvale, CA, United States of America

Zhentao Sun