Abstract: 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).

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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).

Abstract: 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).

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems, methods, devices and computer-readable mediums are disclosed for parking event detection and location estimation. In some implementations, a method comprises: determining, by a processor of a mobile device, a first activity state indicative of a possible parking event; obtaining, by the processor, a speed of the mobile device from a global navigation satellite system (GNSS) of the mobile device; obtaining, by the processor, pedometer data from a digital pedometer of the mobile device; determining, by the processor, a second activity state indicative of a possible parking event based at least in part on the GNSS speed and pedometer data; and responsive to the second activity state, estimating, by the processor, a location of the vehicle.

Abstract: Crowd-sourced localized application usage data is collected from mobile devices at a usage location and sent to a network-based service. The network-based service analyzes the data to determine a single most relevant application correlated to the usage location. Once the most relevant application is determined, a recommendation for the application is sent to client devices operating at the usage location. In some implementations the data is processed to determine whether the usage location is a chained venue, a large venue or an event. Once the usage location has been determined, the most relevant application can be selected for recommendation.

Abstract: 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.

Abstract: Systems, methods, devices and computer-readable mediums are disclosed for parking event detection and location estimation. In some implementations, a method comprises: determining, by a processor of a mobile device, a first activity state indicative of a possible parking event; obtaining, by the processor, a speed of the mobile device from a global navigation satellite system (GNSS) of the mobile device; obtaining, by the processor, pedometer data from a digital pedometer of the mobile device; determining, by the processor, a second activity state indicative of a possible parking event based at least in part on the GNSS speed and pedometer data; and responsive to the second activity state, estimating, by the processor, a location of the vehicle.

Abstract: Systems, methods, and program products for determining a location of a calendar item are described. A mobile device can receive a calendar item including a description and a time. The mobile device can determine that, at the time specified in the calendar item, the mobile device is located at a location that is estimated to be significant to a user. The mobile device can store the description in association with the significant location. Upon receive a new calendar item containing at least one term in the description, the mobile device can predict that the user will visit the significant location at the time specified in the new calendar item. The mobile device can provide user assistance based on the prediction.

Abstract: 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).

Abstract: 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).

Abstract: 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).

Abstract: 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.

Abstract: 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.

Abstract: 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.