Abstract:
A mobile computing device can be used to locate a vehicle parking location. In particular, the mobile device can automatically identify when a vehicle in which the mobile device is located has entered into a parked state. The mobile device can determine that the vehicle is in a parked state by analyzing one or more parameters that indicate a parked state or a transit state. The location of the mobile device at a time corresponding to when the vehicle is identified as being parked can be associated with an identifier for the current parking location.
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. In some embodiments, the prediction engine only relies on user-specific data stored on the device on which this engine executes. Alternatively, in other embodiments, it relies only on user-specific data stored outside of the device by external devices/servers. In still other embodiments, the prediction engine relies on user-specific data stored both by the device and by other devices/servers.
Abstract:
Systems and methods are provided for displaying a portion of a map on a mobile device of a user while the user is traveling along a route. The mobile device can use a selected route and a current location of the device to load map tiles for parts of the map that are upcoming along the route. In this manner, the user can have quick access to the portions of the map that the user likely will want to view. For example, the map tiles can be loaded for the next 50 Km, and then when the stored tiles reaches only 25 Km ahead, another 25 Km of tiles can be retrieved. The amount of tiles loaded (e.g., minimum and maximum amounts) can vary based on a variety of factors, such as network state, distance traveled along the route, and whether the mobile device is charging.
Abstract:
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.
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. The device's prediction engine only relies on user-specific data stored on the device in some embodiments, relies only on user-specific data stored outside of the device by external devices/servers in other embodiments, and relies on user-specific data stored both by the device and by other devices/servers in other embodiments.
Abstract:
A context-aware voice guidance method is provided that interacts with other voice services of a user device. The voice guidance does not provide audible guidance while the user is making a verbal request to any of the voice-activated services. Instead, the voice guidance transcribes its output on the screen while the verbal requests from the user are received. In some embodiments, the voice guidance only provides a short warning sound to get the user's attention while the user is speaking on a phone call or another voice-activated service is providing audible response to the user's inquires. The voice guidance in some embodiments distinguishes between music that can be ducked and spoken words, for example from an audiobook, that the user wants to pause instead of being skipped. The voice guidance ducks music but pauses spoken words of an audio book in order to provide voice guidance to the user.
Abstract:
Embodiments may include receiving signal strength information reported by multiple client communication devices. The signal strength information reported by a given client device may indicate one or more locations detected by the given client device. The signal strength information may also indicate, for each location, a respective measure of signal strength for a communication signal detected at that location by the client device. Embodiments may also include generating a signal strength map for a region based on the client-reported signal strength information. Generating the signal strength map may include, for each location of multiple locations within the region, generating an expected signal strength value for that location based on an evaluation of the signal strength information received for that location. The generation of the signal strength map for the region may also be based on the expected signal strength values for the locations within the region.
Abstract:
Embodiments may include determining a navigation route between an origination and a destination; the route may span multiple portions of a map. Embodiments may also include receiving an order of priority in which to receive the multiple portions of the map; the order may be generated based on distinct levels of expected signal strength for each of the multiple portions. For instance, within the order of priority, map portions associated with areas of low signal strength may be ranked higher than areas of higher signal strength. Embodiments may also include acquiring at least some of the portions of the map according to the order of priority, and generating a map display comprising the multiple portions of the map. For instance, map portions associated with areas of poor reception may be downloaded first whereas map portions associated with strong signal strength may be downloaded on-the-fly during route navigation.
Abstract:
Embodiments may include determining a navigation route between an origination and a destination; the route may span multiple portions of a map. Embodiments may also include receiving an order of priority in which to receive the multiple portions of the map; the order may be generated based on distinct levels of expected signal strength for each of the multiple portions. For instance, within the order of priority, map portions associated with areas of low signal strength may be ranked higher than areas of higher signal strength. Embodiments may also include acquiring at least some of the portions of the map according to the order of priority, and generating a map display comprising the multiple portions of the map. For instance, map portions associated with areas of poor reception may be downloaded first whereas map portions associated with strong signal strength may be downloaded on-the-fly during route navigation.
Abstract:
Embodiments may include receiving signal strength information reported by multiple client communication devices. The signal strength information reported by a given client device may indicate one or more locations detected by the given client device. The signal strength information may also indicate, for each location, a respective measure of signal strength for a communication signal detected at that location by the client device. Embodiments may also include generating a signal strength map for a region based on the client-reported signal strength information. Generating the signal strength map may include, for each location of multiple locations within the region, generating an expected signal strength value for that location based on an evaluation of the signal strength information received for that location. The generation of the signal strength map for the region may also be based on the expected signal strength values for the locations within the region.