Apple Inc.

Other

Michael P Dal Santo

Lukas Michael Marti

Ronald Keryuan Huang

Lili Cao

Richard B Warren

Hyo Jeong Shin

Patrick Thomas Dillon

Marcos Regis Vescovi

Krishna M Behara

Xufeng Han

Marcel Van Os

Bradford Allen Moore

Seejo K Pylappan

Stephen J Rhee

Christine B McGavran

Gregory Dennis Bolsinga

Rama Krishna Chitta

Pejman Lotfali Kazemi

François M Jouaux

Danil Yuryevich Zvyagintsev

Liviu Teodor Popescu

Bradley Joel Jensen

Zehua Zhou

Nicholas M Holt

Lei Wang

Shannon M Ma

Isaac Thomas Miller

Stephen P Jackson

Aditya Narain Srivastava

William J Bencze

Kumar Gaurav Chhokra

Jay N Bruins

Francois M Jouaux

Adam M Driscoll

Christina Selle

Glenn D Macgougan

Fatemeh Ghafoori

Cheng Jia

Yi-Wen Liao

Alexandru Popovici

Danil Y Zvyagintsev

A mobile device can provide predictive user assistance based on various sensor readings, independently of or in addition to a location of the mobile device. The mobile device can determine a context of an event. The mobile device can store the context and a label of the event on a storage device. The label can be provided automatically by the mobile device or by the external system without user input. At a later time, the mobile device can match new sensor readings with the stored context. If a match is found, the mobile device can predict that the user is about to perform the action or recognize that the user has performed the action again. The mobile device can perform various operations, including, for example, providing user assistance, based on the prediction or recognition.

Context driven routine prediction assistance

Systems, methods, and program products for providing services to a user by a mobile device based on the user's daily routine of movement. The mobile device determines whether a location cluster indicates a significant location for the user based on one or more hints that indicate an interest of the user in locations in the cluster. The mobile device can perform adaptive clustering to determine a size of area of the significant location based on how multiple locations converge in the location cluster. The mobile device can provide location-based services for calendar items, including predicting a time of arrival at an estimated location of a calendar item. The mobile device can provide various services related to a location of the mobile device or a significant location of the user through an application programming interface (API).

Determining a significant user location for providing location-based services

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

Methods, systems, and computer program products for determining transit routes through crowd-sourcing, for determining an estimated time of arrival (ETA) of a vehicle of the transit route at a given location, and for providing predictive reminders to a user for catching a vehicle of the transit route. A server receives signal source information about wireless signal sources detected by user devices, including information about a first wireless signal source detected by some devices. The server determines that the first wireless signal source is moving. The server determines that the first wireless signal source is associated with a public transit route upon determining that the signal source information satisfies one or more selection criteria. The server stores information associating the first wireless signal source with the public transit route as transit movement data corresponding to the public transit route.

Providing transit information

Described herein are techniques to enable a mobile device to perform multi-source estimation of an altitude for a location. A baseline altitude may be determined at ground level for a location and used to calibrate a barometric pressure sensor on the mobile device. The calibrated barometric pressure sensor can then estimate changes in altitude relative to ground level based on detected pressure differentials, allowing a relative altitude to ground to be determined. Baseline calibration for the barometric sensor calibration can be performed to determine an ambient ground-level barometric pressure.

Location of interest altitude and determining calibration points

A mobile device with a route prediction engine is provided that can predict current/future destinations or routes to destinations for the user, and can relay prediction information to the user. The engine includes a machine-learning engine that facilitates the formulation of predicted future destinations and/or future routes to destinations based on user-specific data. The user-specific data includes data about (1) previous destinations traveled, (2) previous routes taken, (3) locations of calendared events, (4) locations of events for which the user has electronic tickets, and/or (5) addresses parsed from e-mails and/or messages. The prediction engine relies on one or more of user-specific data stored on the device and data stored outside of the device by external devices/servers.

Mobile device with predictive routing engine

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

Data-driven context determination

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San Francisco, CA, United States of America

Michael P Dal Santo