Abstract:
Techniques are provided for contacting emergency response services when certain conditions are met. In some instances, it may be determined, by a wearable device, whether a user has responded to a first user interface prompt displayed in response to detection of a physical event associated with the user. In accordance with a determination that the user has not responded to the first user interface prompt after expiration of a first time period, certain actions may be performed. For example, a countdown timer may begin to run for a second timer period. During the second period, an audio alert and a haptic alert may be output by the wearable device. In accordance with a determination that the user has not responded to a second user interface prompt prior to expiration of the second time period, a communication channel request may be transmitted to an emergency response service by the wearable device.
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:
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:
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:
Systems, methods and computer program products are disclosed for machine learning to determine preferential device behavior. In some implementations, a server receives inputs, including attributes from a client device, crowd-sourced data from a number of other devices and a priori knowledge. The server includes a concept engine that applies machine-learning process to the inputs. The output of the machine learning process is transported to the client device. At the client device, a client engine associates attributes observed at the device to the machine learning output to determine a user profile. Applications may access the user profile to determine preferential device behavior, such as provide targeted information to the user or take action on the device that is personalized to the user of the device.
Abstract:
A method for using a location to refine network-provided time zone information is disclosed. The method can include a wireless communication device receiving a time zone information message from a network; determining multiple candidate time zones matching a set of time zone identification parameters included in the received time zone information message; deriving a location of the wireless communication device; and using the location to select a current time zone for the wireless communication device from the candidate time zones matching the set of time zone identification parameters.
Abstract:
Systems and methods for activating a mobile device for use with a service provider are described. In one exemplary method, a mobile device having a currently inserted SIM card may be prepared for activation using a signing process in which an activation server generates a signed activation ticket that uniquely corresponds to the combination of the device and SIM card, and that is securely stored on the mobile device. In another exemplary method the mobile device may be activated in an activation process in which the device verifies an activation ticket against information specific to the device and SIM card, and initiates activation when the verification of the activation ticket is successful.
Abstract:
A parameter related to the Earth's magnetic field can be used to determine accuracy of a magnetometer of a mobile device. In one aspect, a first instance of a parameter related to Earth's magnetic field is determined using data generated by the magnetometer. The magnetometer data can be based in part on a position of the mobile device with respect to the Earth. A second instance of the parameter can be determined using data generated by a model of Earth's magnetic field. The model data can also be based in part on the position of the mobile device with respect to the Earth. The first instance of the parameter can be compared with the second instance of the parameter. An accuracy metric for the magnetometer can be determined based on a result of the comparison. An indication of the accuracy metric can be presented by the mobile device.
Abstract:
A journaling subsystem on a mobile device stores event data related to applications or other subsystems running on the mobile device. The event data can be stored and indexed in a journal database so that a timeline of past events can be reconstructed in response to search queries. In some implementations, a timeline can be reconstructed with markers on a map display based on search results. When the user interacts with a marker on the map display, the event data collected by the mobile device is made available to the user.
Abstract:
A device in an automated environment can detect patterns in the user's interactions with accessories in the automated environment and can provide feedback to the user based on the patterns. Examples include: suggesting automation of particular actions based on the patterns; suggesting actions that conform to the pattern when the user performs part of the pattern; or suggesting changes to a pattern to conform to a preferred pattern. A state of the group of accessory devices can be changed together based on the pattern of accessory state changes for the group of accessory devices.