Abstract: A system for dynamic content delivery is provided. The system includes a control component to determine location, preferences, and state of a user. A delivery component dynamically selects and provides content to the user as a function of the user's location, preferences, and state.

Abstract: Personalization data such as settings and/or state is migrated across devices including mobile phones. Device personalizations are pushed to a cloud service or other mechanism for backup and subsequent transmission to other devices. The personalization data can later be employed to configure one or more other devices in the same manner as a first device.

Abstract: Managing power-consuming resources on a first computing device by adjusting data delivery from a plurality of second computing devices based on a state of the first computing device. The state of the first computing device is provided to the second computing devices to alter the data delivery. In some embodiments, the first computing device provides the second computing devices with actions or commands relating to data delivery based on the device state. For example, the second computing devices are instructed to store the data, forward the data, forward only high priority data, or perform other actions. Managing the data delivery from the second computing devices preserves battery life of the first computing device.

Abstract: A system for dynamic content delivery is provided. The system includes a control component to determine location, preferences, and state of a user. A delivery component dynamically selects and provides content to the user as a function of the user's location, preferences, and state.

Abstract: A system for dynamic content delivery is provided. The system includes a control component to determine location, preferences, and state of a user. A delivery component dynamically selects and provides content to the user as a function of the user's location, preferences, and state.

Abstract: Personalization data such as settings and/or state is migrated across devices including mobile phones. Device personalizations are pushed to a cloud service or other mechanism for backup and subsequent transmission to other devices. The personalization data can later be employed to configure one or more other devices in the same manner as a first device.

Abstract: Personalization data such as settings and/or state is migrated across devices including mobile phones. Device personalizations are pushed to a cloud service or other mechanism for backup and subsequent transmission to other devices. The personalization data can later be employed to configure one or more other devices in the same manner as a first device.

Abstract: Personalization data such as settings and/or state is migrated across devices including mobile phones. Device personalizations are pushed to a cloud service or other mechanism for backup and subsequent transmission to other devices. The personalization data can later be employed to configure one or more other devices in the same manner as a first device.

Abstract: A continual learning process is applied to a class of risk estimate-based algorithms and associated risk thresholds used for deciding when to initiate a handoff between different types of network connections that are available to a mobile device having telephony functionality. The process is implemented as a virtuous loop providing ongoing tuning and adjustment to improve call handoff algorithms and risk thresholds so that handoffs can be performed with the goals of minimizing dropped calls and unacceptable degradation in call quality as well as avoiding premature handoffs. Device characteristics, environmental context, connection measurements, and outcomes of call handoff decisions are crowd-sourced from a population of mobile devices into a cloud-based handoff decision enabling service.

Abstract: Personalization data such as settings and/or state is migrated across devices including mobile phones. Device personalizations are pushed to a cloud service or other mechanism for backup and subsequent transmission to other devices. The personalization data can later be employed to configure one or more other devices in the same manner as a first device.

Abstract: Managing power-consuming resources on a first computing device by adjusting data delivery from a plurality of second computing devices based on a state of the first computing device. The state of the first computing device is provided to the second computing devices to alter the data delivery. In some embodiments, the first computing device provides the second computing devices with actions or commands relating to data delivery based on the device state. For example, the second computing devices are instructed to store the data, forward the data, forward only high priority data, or perform other actions. Managing the data delivery from the second computing devices preserves battery life of the first computing device.

Abstract: A continual learning process is applied to a class of risk estimate-based algorithms and associated risk thresholds used for deciding when to initiate a handoff between different types of network connections that are available to a mobile device having telephony functionality. The process is implemented as a virtuous loop providing ongoing tuning and adjustment to improve call handoff algorithms and risk thresholds so that handoffs can be performed with the goals of minimizing dropped calls and unacceptable degradation in call quality as well as avoiding premature handoffs. Device characteristics, environmental context, connection measurements, and outcomes of call handoff decisions are crowd-sourced from a population of mobile devices into a cloud-based handoff decision enabling service.

Abstract: A continual learning process is applied to a class of risk estimate-based algorithms and associated risk thresholds used for deciding when to initiate a handoff between different types of network connections that are available to a mobile device having telephony functionality. The process is implemented as a virtuous loop providing ongoing tuning and adjustment to improve call handoff algorithms and risk thresholds so that handoffs can be performed with the goals of minimizing dropped calls and unacceptable degradation in call quality as well as avoiding premature handoffs. Device characteristics, environmental context, connection measurements, and outcomes of call handoff decisions are crowd-sourced from a population of mobile devices into a cloud-based handoff decision enabling service.

Abstract: Personalization data such as settings and/or state is migrated across devices including mobile phones. Device personalizations are pushed to a cloud service or other mechanism for backup and subsequent transmission to other devices. The personalization data can later be employed to configure one or more other devices in the same manner as a first device.

Abstract: An inter-process messaging security management may be provided. A message comprising an operation to be performed may be sent from a process operating in a process chamber to a second process operating in another chamber. Before the message is allowed to be delivered, the validity of the operation contained in the message may be verified and a security policy may be examined to determine whether the message is permitted to be sent from the first process to the second process. If the security policy permits the second process to execute the operation requested by the first process, the message may be delivered to the second process. If the operation is not permitted, the message may not be delivered and an error message may be returned to the first process.

Abstract: Managing power-consuming resources on a first computing device by adjusting data delivery from a plurality of second computing devices based on a state of the first computing device. The state of the first computing device is provided to the second computing devices to alter the data delivery. In some embodiments, the first computing device provides the second computing devices with actions or commands relating to data delivery based on the device state. For example, the second computing devices are instructed to store the data, forward the data, forward only high priority data, or perform other actions. Managing the data delivery from the second computing devices preserves battery life of the first computing device.

Abstract: Personalization data such as settings and/or state is migrated across devices including mobile phones. Device personalizations are pushed to a cloud service or other mechanism for backup and subsequent transmission to other devices. The personalization data can later be employed to configure one or more other devices in the same manner as a first device.

Abstract: A methodology for automatic selection of a connection, on behalf of a device user, to a hybrid network for calls is provided in which a combination of a variety of inputs is utilized to populate a filtered list of available connections that is sorted by connection cost in which the least costly connection is at the top of the list. The methodology is executed periodically, or in response to event occurrence such as a call event, and the connection at the top of the filtered sorted list is utilized as the preferred connection for calls. Preferred status may be persisted until the next execution of the methodology when a potentially different connection is selected as preferred.

Abstract: A continual learning process is applied to a class of risk estimate-based algorithms and associated risk thresholds used for deciding when to initiate a handoff between different types of network connections that are available to a mobile device having telephony functionality. The process is implemented as a virtuous loop providing ongoing tuning and adjustment to improve call handoff algorithms and risk thresholds so that handoffs can be performed with the goals of minimizing dropped calls and unacceptable degradation in call quality as well as avoiding premature handoffs. Device characteristics, environmental context, connection measurements, and outcomes of call handoff decisions are crowd-sourced from a population of mobile devices into a cloud-based handoff decision enabling service.

Abstract: A system that facilitates different levels of operating system (OS) boot so as to provide users with rapid access to certain mobile device functionalities, security, applications, etc. is provided. An OS can be loaded in phases or stages commensurate with policies, preferences, actions, context, etc. Similarly, a particular OS or group of OSs can be loaded to address a particular scenario or requirement.