Apple Inc.

Hyo Jeong Shin

Richard B Warren

Ronald Keryuan Huang

Lili Cao

Michael P Dal Santo

Brian J Andrich

Daniel R Delling

Marcos Regis Vescovi

Hengbin Luo

Krishna M Behara

Xufeng Han

Patrick James Coleman

Ethan T Bold

Dennis Schieferdecker

Patrick Thomas Dillon

Michael Wegner

Rami Khawandi

Liviu Teodor Popescu

Lukas Michael Marti

Matthaeus Krenn

Pejman Lotfali Kazemi

David R Stites

Jason J Gauci

Zehua Zhou

Danil Yuryevich Zvyagintsev

Daniel C Gross

Scott Joseph Adler

Samuel C Cates

Computer-implemented methods, computer-readable storage media storing instructions and computer systems for labeling significant locations based on contextual data can be implemented to perform operations that include determining a location of a computing device, and determining a label for the determined location based on contextual data associated with the significant location. The location can be a significant location that has meaning to a user of the device.

Labeling a significant location based on contextual data

In some implementations, a computing device can proactively determine a destination and request traffic information for routes from a starting location to the destination. In some implementations, a computing device can identify some routes between a starting location and a destination as non-recommended routes and recommend other routes. In some implementations, a computing device can rank routes between a starting location and a destination based on automatically-determined user interest. In some implementations, a computing device can determine a user is familiar with a route and adjust the information presented to the user about the route accordingly.

Providing light navigation guidance

Presenting suggested routes based on local route ranking

In an example method, a computer system receives a query from a mobile device, including an indication of a location of the mobile device, and an environmental measurement obtained by the mobile device at the location. A set of candidate points of interest in geographical proximity to the location is determined. For each of one or more candidate points of interest of the set, a location fingerprint of the candidate point of interest and contextual data regarding the candidate point of interest are obtained. A similarity between the environmental measurement and each location fingerprint is determined. A particular candidate point of interest is selected from among the set based on the similarity, and based on an assessment of the contextual data. A label of the selected point of interest is associated with the location and transmitted to the mobile device.

Harvesting labels for significant locations based on candidate points of interest and contextual data

Presenting non-recommended routes

An event can be detected by an input device. The event may be determined to be a triggering event by comparing the event to a group of triggering events. A first prediction model corresponding to the event is then selected. Contextual information about the device specifying one or more properties of the computing device in a first context is then received, and a set of one or more applications is identified. The set of one or more applications may have at least a threshold probability of being accessed by the user when the event occurs in the first context. Thereafter, a user interface is provided to a user for interacting with the set of one or more applications.

Application recommendation based on detected triggering events

In an example method, a mobile device receives a first calendar item associated with a first event. The first calendar item includes a first text string. The mobile device determines a correlation between the first text string and one or more locations associated with one or more second events. The mobile device determines a suggested location for the first event based on the correlation.

Suggested locations for calendar events

Growing community of inventors

Santa Clara, CA, United States of America

Hyo Jeong Shin