Google Inc.

Marc Stogaitis

Brian Patrick Williams

Mohammed Waleed Kadous

Zhentao Sun

Andrew Ames Bunner

Nadav Aharony

Pascal Tom Getreuer

Alan Lee Gardner, Iii

Ami Patel

Thomas Chao

Richard Daniel Webb

Benjamin Razon

Tony Weber

Liem Tran

Joseph A Farfel

Brian Duff

Daniel Estrada Alva

Richard Francis Lyon

Zhengrong Ji

Ryan Michael Rifkin

Angana Ghosh

Etienne Le Grand

Andrew Robert Kirmse

Denise Ho

Dale Kris Hawkins

Arunesh Mishra

Stephen David Zelinka

Tsuwei Chen

Ralph Jacob Cressman

David Hawkey

David James Hawkey

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

Example methods and systems for determining media files based on activity levels are described. An example method includes receiving information indicative of a first speed of the computing device, and receiving information indicative of a geography of a location of the computing device. The method further includes determining, from a plurality of media files tagged with respective tempo identifiers, a first media file based on the geography of the location of the computing device and also having a tempo that substantially matches to the first speed of the computing device. The method includes providing an indication of the first media file to a media player, and based on a change in the first speed of the computing device to a second speed, determining from the plurality of media files tagged with respective tempo identifiers, a second media file having a tempo that substantially matches to the second speed.

Methods and devices for determining media files based on activity levels

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 present disclosure describes methods, systems, and apparatuses for determining the likelihood that two wireless scans of a mobile computing device were performed in the same location. The likelihood is determined by scanning for wireless networks with a computing device. The scanning includes a receiving a plurality of network attributes for each wireless networks within the range of the mobile computing device. Further, the likelihood is determined by comparing the plurality of network attributes from the scanning with a reference set of network attributes. The comparing of network attributes is used to determine an attribute comparison. Finally, the likelihood between a position associated with the reference set of network attributes and the computing device, based on the attribute comparison, determines a position associated with the network.

Use of a trained classifier to determine if a pair of wireless scans came from the same location

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

Systems and methods are provided for estimating the locations and coverage ranges of wireless transmission stations based on the existing location and coverage range data. The system divides the transmitting stations into a plurality of pairs and estimates new coverage range and location for each transmitting station in each pair based on confidence level, distance to another transmitting station in the same pair and coverage range overlap with other transmitting stations. Systems and methods are provided to estimate the location and accuracy range of the location of a client device based on the intersections of the coverage ranges of wireless transmission stations detected by the client device. The system repeatedly removes the transmission station with fewer number of intersections from further process if the number of intersections for each remaining transmission station is not the same.

System and method of location estimation based on intersections and range estimation

Methods and systems for modifying a location history of a computing device are described. An example method may include receiving sensor data collected by one or more sensors of a computing device, and receiving a history of geographic locations of the computing device determined based on the sensor data collected by the computing device and additional data from one or more sources. The method may also include receiving updated additional data from the one or more sources, and reprocessing at least a portion of the sensor data with the updated additional data to determine one or more updates to the history of geographic locations of the computing device. The method may further include modifying the history of geographic locations of the computing device based on the one or more updates to provide an updated history of geographic locations of the computing device.

Modifying a history of geographic locations of a computing device

Methods and systems for grouping computing devices together based on the devices being colocated with one another or being associated with complementary usage contexts, and then using the location or usage context of one device in the group to estimate the location or usage context of another device in the group are described. An example method may include receiving first sensor data from sensors of a first computing device; receiving second sensor data from sensors of a second computing device; determining, based on the received sensor data, that the first and second computing devices are colocated with one another; identifying, based on the first sensor data, a context associated with the first computing device; and determining, based at least in part on the context associated with the first computing device, a context associated with the second computing device.

Systems and techniques for colocation and context determination

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

In one example, a method includes determining, by a processor operating in a first power mode and based on first motion data, a first activity of a user, transitioning from operating in the first power mode to operating in a second power mode, wherein the processor consumes less power while operating in the second power mode than in the first power mode, responsive to determining, while the processor is operating in the second power mode and based on second motion data, that a change in an angle relative to gravity satisfies a threshold, transitioning from operating in the second power mode to operating in the first power mode, determining, by the processor and based on second motion data, a second activity of the user, and, responsive to determining that the second activity is different from the first activity, performing an action.

Detecting transitions between physical activity

In one example, a method includes determining, based on first motion data, that a change in an angle of a mobile computing device relative to gravity satisfies a threshold amount of change, and, responsive to determining that the change in the angle satisfies the threshold amount of change, initiating, at a first position of the mobile computing device, storage of second motion data, wherein the first position is a reference position. The method may also include determining, based on an external signal, a second position of the mobile computing device, wherein the second position is a current position of the mobile computing device that is different from the reference position, and, responsive to determining the second position of the mobile computing device, determining, based on the second motion data and the current position, the reference position of the mobile computing device.

Growing community of inventors

San Mateo, CA, United States of America

Marc Stogaitis