Abstract: This disclosure describes embodiments for harvesting and serving labels for locations. In an embodiment, a method comprises: receiving, by one or more server computers, location data including wireless access point data and location labels associated with significant locations, the location data being harvested from a plurality of devices operating at a plurality of geographic locations; and updating, by the one or more server computers, a plurality of fingerprints representing the plurality of geographic locations, the updating including associating at least one of the received location labels with at least one of the plurality of fingerprints.

Abstract: This disclosure describes embodiments for harvesting and serving labels for locations. In an embodiment, a method comprises: receiving, by one or more server computers, location data including wireless access point data and location labels associated with significant locations, the location data being harvested from a plurality of devices operating at a plurality of geographic locations; and updating, by the one or more server computers, a plurality of fingerprints representing the plurality of geographic locations, the updating including associating at least one of the received location labels with at least one of the plurality of fingerprints.

Abstract: 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.

Abstract: 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.

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 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: Techniques for predictive user assistance are described. A mobile device can learn movement patterns of the mobile device. The mobile device can construct a state model that is an abstraction of locations where the mobile device stayed for sufficient amount of time. The state model can include states representing the locations, and transitions representing movement of the mobile device between the locations. The mobile device can use the state model, a current location of the mobile device, and a current time to determine a predicted future location of the mobile device at a given future time. Based on the predicted location and the given future time, the mobile device can predict what assistance a user of the mobile device may request. The mobile device can then provide the assistance to the user before the given future time.

Abstract: Methods, systems and computer program products for user settlement detection are disclosed. A mobile device configured to perform an action immediately upon entering or exiting a geofenced environment can delay performing the action until a user of the mobile device has settled into the environment. The mobile device can detect a settled user state by observing the environment of the mobile device, including measuring one or more environment variables using one or more sensors of the mobile device. The mobile device can detect a settled user state even when the mobile device is in motion. The mobile device can perform the action upon detecting a settled user state.

Abstract: Techniques for lifestyle-based social groups are described. A user device can learn movement patterns of the user device. Based on the movement pattern, and a user activity history, a computer system can determine a lifestyle of a user, or a meaning of a location. The system can create a social group based on the lifestyle and the meaning of location. The system can designate the lifestyle or meaning as a theme of the social group. The social group can be an ad hoc social network. For example, the social group can be created without an explicit user request, can be anonymous, and can be lifestyle and location based.

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: 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: Methods, systems and computer program products for user settlement detection are disclosed. A mobile device configured to perform an action immediately upon entering or exiting a geofenced environment can delay performing the action until a user of the mobile device has settled into the environment. The mobile device can detect a settled user state by observing the environment of the mobile device, including measuring one or more environment variables using one or more sensors of the mobile device. The mobile device can detect a settled user state even when the mobile device is in motion. The mobile device can perform the action upon detecting a settled user state.

Abstract: 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.

Abstract: Some embodiments of the invention provide a mobile device with a novel route prediction engine that (1) can formulate predictions about current or future destinations and/or routes to such destinations for the device's user, and (2) can relay information to the user about these predictions. In some embodiments, this engine includes a machine-learning engine that facilitates the formulation of predicted future destinations and/or future routes to destinations based on stored, user-specific data. The user-specific data is different in different embodiments. In some embodiments, the stored, user-specific data includes data about any combination of the following (1) previous destinations traveled to by the user, (2) previous routes taken by the user, (3) locations of calendared events in the user's calendar, (4) locations of events for which the user has electronic tickets, and (5) addresses parsed from recent e-mails and/or messages sent to the user.

Abstract: Techniques for modeling significant locations are described. A significant location can be a location that is significant to a user of a mobile device for a variety of reasons. The mobile device can determine that a place or region is a significant location upon determining that, with sufficient certainty, the mobile device has stayed at the place or region for a sufficient amount of time. The mobile device can construct a state model that is an abstraction of one or more significant locations. The state model can include states representing the significant locations, and transitions representing movement of the mobile device between the locations. The mobile device can use the state model to provide predictive user assistance.

Abstract: Some embodiments of the invention provide a mobile device with a novel route prediction engine that (1) can formulate predictions about current or future destinations and/or routes to such destinations for the device's user, and (2) can relay information to the user about these predictions. In some embodiments, this engine includes a machine-learning engine that facilitates the formulation of predicted future destinations and/or future routes to destinations based on stored, user-specific data. The user-specific data is different in different embodiments. In some embodiments, the stored, user-specific data includes data about any combination of the following (1) previous destinations traveled to by the user, (2) previous routes taken by the user, (3) locations of calendared events in the user's calendar, (4) locations of events for which the user has electronic tickets, and (5) addresses parsed from recent e-mails and/or messages sent to the user.

Abstract: Some embodiments of the invention provide a novel prediction engine that (1) can formulate predictions about current or future destinations and/or routes to such destinations for a user, and (2) can relay information to the user about these predictions. In some embodiments, this engine includes a machine-learning engine that facilitates the formulation of predicted future destinations and/or future routes to destinations based on stored, user-specific data. The user-specific data is different in different embodiments. In some embodiments, the stored, user-specific data includes data about any combination of the following: (1) previous destinations traveled to by the user, (2) previous routes taken by the user, (3) locations of calendared events in the user's calendar, (4) locations of events for which the user has electronic tickets, and (5) addresses parsed from recent e-mails and/or messages sent to the user.

Abstract: A mobile device enables its user to retroactively “check in,” on social media, to locations to which the device has previously been. The mobile device automatically tracks the locations to which it goes during some time interval. As the mobile device goes to each location, the mobile device stores data that specifies that location. Following the time interval, and potentially in response to a request by the device's user to view the locations previously visited, the mobile device presents a list of at least some of the locations on its display. The device's user can select one or more of the presented locations. The selection of a location causes the mobile device to post, to an Internet-based social media service, information pertaining to the selected location. For example, such information can indicate that the device's user had been at the selected location.

Abstract: 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.