Abstract: A processor-based personal electronic device (such as a smartphone) is programmed to automatically respond to data sent by various sensors from which the user's activity may be inferred. One or more alarms on the device may be temporarily disabled when sensor data indicates that the user is asleep. One or more of the sensors may be worn by the user and remote from the device. A wireless communication link may be used by the device to obtain remote sensor data. Data from on-board sensors in the device—such as motion sensors, location sensors, ambient light sensors, and the like—may also be used to deduce the user's current activity. User data (such as calendar entries) may also be used to determine likely user activity and set alarms accordingly. Biometric data from a second, nearby person may also be used to automatically select certain alarm modes on a first person's device.

Abstract: A processor-based personal electronic device (such as a smartphone) is programmed to automatically respond to data sent by various sensors from which the user's activity may be inferred. One or more alarms on the device may be temporarily disabled when sensor data indicates that the user is asleep. One or more of the sensors may be worn by the user and remote from the device. A wireless communication link may be used by the device to obtain remote sensor data. Data from on-board sensors in the device—such as motion sensors, location sensors, ambient light sensors, and the like—may also be used to deduce the user's current activity. User data (such as calendar entries) may also be used to determine likely user activity and set alarms accordingly. Biometric data from a second, nearby person may also be used to automatically select certain alarm modes on a first person's device.

Abstract: A processor-based personal electronic device (such as a smartphone) is programmed to automatically respond to data sent by various sensors from which the user's activity may be inferred. One or more alarms on the device may be temporarily disabled when sensor data indicates that the user is asleep. One or more of the sensors may be worn by the user and remote from the device. A wireless communication link may be used by the device to obtain remote sensor data. Data from on-board sensors in the device—such as motion sensors, location sensors, ambient light sensors, and the like—may also be used to deduce the user's current activity. User data (such as calendar entries) may also be used to determine likely user activity and set alarms accordingly. Biometric data from a second, nearby person may also be used to automatically select certain alarm modes on a first person's device.

Abstract: Techniques of category-based fence are described. A category-based fence can correspond to a group of signal sources instead of a point location fixed to latitude and longitude coordinates. The group of signal sources can represent a category of entities, e.g., a particular business chain. The signal sources can be distributed to multiple discrete locations. A category-based fence associated with the group, accordingly, can correspond to multiple locations instead of a single point location and a radius. Each signal source in the group can be associated with a category identifier unique to the group and uniform among signal sources in the group. The category identifier can be programmed into each signal source. A mobile device can enter the category-based fence by entering any of the discrete locations when the mobile device detects the signal identifier. The mobile device can then execute an application program associated with the category-based fence.

Abstract: A processor-based personal electronic device (such as a smartphone) is programmed to automatically respond to data sent by various sensors from which the user's activity may be inferred. One or more alarms on the device may be temporarily disabled when sensor data indicates that the user is asleep. One or more of the sensors may be worn by the user and remote from the device. A wireless communication link may be used by the device to obtain remote sensor data. Data from on-board sensors in the device—such as motion sensors, location sensors, ambient light sensors, and the like—may also be used to deduce the user's current activity. User data (such as calendar entries) may also be used to determine likely user activity and set alarms accordingly. Biometric data from a second, nearby person may also be used to automatically select certain alarm modes on a first person's device.

Abstract: A processor-based personal electronic device (such as a smartphone) is programmed to automatically respond to data sent by various sensors from which the user's activity may be inferred. One or more alarms on the device may be temporarily disabled when sensor data indicates that the user is asleep. One or more of the sensors may be worn by the user and remote from the device. A wireless communication link may be used by the device to obtain remote sensor data. Data from on-board sensors in the device—such as motion sensors, location sensors, ambient light sensors, and the like—may also be used to deduce the user's current activity. User data (such as calendar entries) may also be used to determine likely user activity and set alarms accordingly. Biometric data from a second, nearby person may also be used to automatically select certain alarm modes on a first person's device.

Abstract: A processor-based personal electronic device (such as a smartphone) is programmed to automatically respond to data sent by various sensors from which the user's activity may be inferred. One or more alarms on the device may be temporarily disabled when sensor data indicates that the user is asleep. One or more of the sensors may be worn by the user and remote from the device. A wireless communication link may be used by the device to obtain remote sensor data. Data from on-board sensors in the device—such as motion sensors, location sensors, ambient light sensors, and the like—may also be used to deduce the user's current activity. User data (such as calendar entries) may also be used to determine likely user activity and set alarms accordingly. Biometric data from a second, nearby person may also be used to automatically select certain alarm modes on a first person's device.

Abstract: Techniques of category-based fence are described. A category-based fence can correspond to a group of signal sources instead of a point location fixed to latitude and longitude coordinates. The group of signal sources can represent a category of entities, e.g., a particular business chain. The signal sources can be distributed to multiple discrete locations. A category-based fence associated with the group, accordingly, can correspond to multiple locations instead of a single point location and a radius. Each signal source in the group can be associated with a category identifier unique to the group and uniform among signal sources in the group. The category identifier can be programmed into each signal source. A mobile device can enter the category-based fence by entering any of the discrete locations when the mobile device detects the signal identifier. The mobile device can then execute an application program associated with the category-based fence.

Abstract: Techniques of category-based fence are described. A category-based fence can correspond to a group of signal sources instead of a point location fixed to latitude and longitude coordinates. The group of signal sources can represent a category of entities, e.g., a particular business chain. The signal sources can be distributed to multiple discrete locations. A category-based fence associated with the group, accordingly, can correspond to multiple locations instead of a single point location and a radius. Each signal source in the group can be associated with a category identifier unique to the group and uniform among signal sources in the group. The category identifier can be programmed into each signal source. A mobile device can enter the category-based fence by entering any of the discrete locations when the mobile device detects the signal identifier. The mobile device can then execute an application program associated with the category-based fence.

Abstract: A processor-based personal electronic device (such as a smartphone) is programmed to automatically respond to data sent by various sensors from which the user's activity may be inferred. One or more alarms on the device may be temporarily disabled when sensor data indicates that the user is asleep. One or more of the sensors may be worn by the user and remote from the device. A wireless communication link may be used by the device to obtain remote sensor data. Data from on-board sensors in the device—such as motion sensors, location sensors, ambient light sensors, and the like—may also be used to deduce the user's current activity. User data (such as calendar entries) may also be used to determine likely user activity and set alarms accordingly. Biometric data from a second, nearby person may also be used to automatically select certain alarm modes on a first person's device.

Abstract: Techniques of category-based fence are described. A category-based fence can correspond to a group of signal sources instead of a point location fixed to latitude and longitude coordinates. The group of signal sources can represent a category of entities, e.g., a particular business chain. The signal sources can be distributed to multiple discrete locations. A category-based fence associated with the group, accordingly, can correspond to multiple locations instead of a single point location and a radius. Each signal source in the group can be associated with a category identifier unique to the group and uniform among signal sources in the group. The category identifier can be programmed into each signal source. A mobile device can enter the category-based fence by entering any of the discrete locations when the mobile device detects the signal identifier. The mobile device can then execute an application program associated with the category-based fence.

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: Techniques of category-based fence are described. A category-based fence can correspond to a group of signal sources instead of a point location fixed to latitude and longitude coordinates. The group of signal sources can represent a category of entities, e.g., a particular business chain. The signal sources can be distributed to multiple discrete locations. A category-based fence associated with the group, accordingly, can correspond to multiple locations instead of a single point location and a radius. Each signal source in the group can be associated with a category identifier unique to the group and uniform among signal sources in the group. The category identifier can be programmed into each signal source. A mobile device can enter the category-based fence by entering any of the discrete locations when the mobile device detects the signal identifier. The mobile device can then execute an application program associated with the category-based fence.

Abstract: Techniques of range free proximity determination are described. A mobile device can determine an entry into or exit from a proximity fence upon determining that the mobile device is sufficiently close to a signal source. The proximity fence can be a virtual fence defined by the signal source and associated with a service. The mobile device can detect signals from multiple signal sources. The mobile device can determine that, among the signal sources, one or more signal sources are located closest to the mobile device based on a ranking of the signal sources using signal strength. The mobile device can determine a probability indicating a confident level of the ranking. The mobile device can determine that the mobile device entered or exited a proximity fence associated with a highest ranked signal source satisfying a confidence threshold.

Abstract: Techniques of range free proximity determination are described. A mobile device can determine an entry into or exit from a proximity fence upon determining that the mobile device is sufficiently close to a signal source. The proximity fence can be a virtual fence defined by the signal source and associated with a service. The mobile device can detect signals from multiple signal sources. The mobile device can determine that, among the signal sources, one or more signal sources are located closest to the mobile device based on a ranking of the signal sources using signal strength. The mobile device can determine a probability indicating a confident level of the ranking. The mobile device can determine that the mobile device entered or exited a proximity fence associated with a highest ranked signal source satisfying a confidence threshold.

Abstract: Methods, program products, and systems for estimating a location of a mobile device in a venue are provided. The venue can have pathways represented by a path network that includes segments connected by junctions. Estimating the location can include determining a first set of candidate locations for the mobile device, and mapping some of the candidate locations to updated candidate locations that are on or closer to one or more segments of the path network based on distances between the candidate locations and respective segments, resulting in a second set of candidate locations for the mobile device. The location of the mobile device can be derived from the second set of candidate locations.

Abstract: Techniques of category-based fence are described. A category-based fence can correspond to a group of signal sources instead of a point location fixed to latitude and longitude coordinates. The group of signal sources can represent a category of entities, e.g., a particular business chain. The signal sources can be distributed to multiple discrete locations. A category-based fence associated with the group, accordingly, can correspond to multiple locations instead of a single point location and a radius. Each signal source in the group can be associated with a category identifier unique to the group and uniform among signal sources in the group. The category identifier can be programmed into each signal source. A mobile device can enter the category-based fence by entering any of the discrete locations when the mobile device detects the signal identifier. The mobile device can then execute an application program associated with the category-based fence.

Abstract: Techniques of category-based fence are described. A category-based fence can correspond to a group of signal sources instead of a point location fixed to latitude and longitude coordinates. The group of signal sources can represent a category of entities, e.g., a particular business chain. The signal sources can be distributed to multiple discrete locations. A category-based fence associated with the group, accordingly, can correspond to multiple locations instead of a single point location and a radius. Each signal source in the group can be associated with a category identifier unique to the group and uniform among signal sources in the group. The category identifier can be programmed into each signal source. A mobile device can enter the category-based fence by entering any of the discrete locations when the mobile device detects the signal identifier. The mobile device can then execute an application program associated with the category-based fence.

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: A proximity fence can be a location-agnostic fence defined by signal sources having no geographic location information. The proximity fence can correspond to a group of signal sources instead of a point location fixed to latitude and longitude coordinates. A signal source can be a radio frequency (RF) transmitter broadcasting a beacon signal. The beacon signal can include a payload that includes an identifier indicating a category to which the signal source belongs, and one or more labels indicating one or more subcategories to which the signal source belongs. The proximity fence defined by the group of signal sources can trigger different functions of application programs associated with the proximity fence on a mobile device, when the mobile device moves within the proximity fence and enters and exits different parts of the proximity fence corresponding to the different subcategories.