Abstract: Systems and methods are disclosed for advising a user when an energy storage device in a computing system needs charging. State of charge data of the energy storage device can be measured and stored at regular intervals. The historic state of charge data can be queried over a plurality of intervals and a state of charge curve generated that is representative of a user's charging habits over time. The state of charge curve can be used to generate a rate of charge histogram and an acceleration of charge histogram. These can be used to predict when a user will charge next, and whether the energy storage device will have an amount of energy below a predetermined threshold amount before the next predicted charging time. A first device can determine when a second device typically charges and whether the energy storage device in the second device will have an amount of energy below the predetermined threshold amount before the next predicted charge time for the second device.

Abstract: The present disclosure describes systems and methods associated with selective powering of an electronic display in response to sensed data indicating that the electronic display is not visible. By selectively enabling the electronic display between power states based on whether the electronic display is visible, a perceivably always-on electronic display may be provided without the electronic display being always-on, thereby balancing consumer interests with technical requirements of a relatively long battery life and correspondingly low power consumption.

Abstract: The present disclosure describes systems and methods associated with selective powering of an electronic display in response to operating conditions, additional data, and/or predicting that the electronic display is not going to be viewed or visible. By selectively enabling the electronic display between power states based on a prediction of when a viewer is expected to view the electronic display, a perceivably always-on electronic display may be provided without the electronic display being always-on, thereby balancing consumer interests with technical requirements of a relatively long battery life and correspondingly low power consumption.

Abstract: In some implementations, a user device can maintain a multi-device context store. For example, the user device can receive device and/or user context information from multiple devices and store the context information in a local data store. The user device can collect local device and/or user context information and store the context information in the local context store. The user device can receive user/device context queries from client processes and send the client processes user/device context information from multiple devices in response to the queries.

Abstract: In some implementations, a user device can maintain a multi-device context store. For example, the user device can receive device and/or user context information from multiple devices and store the context information in a local data store. The user device can collect local device and/or user context information and store the context information in the local context store. The user device can receive user/device context queries from client processes and send the client processes user/device context information from multiple devices in response to the queries.

Abstract: Certain embodiments disclosed herein provide application-specific launch optimization. Aspects of the present disclosure include one or more cost functions for each application, where each cost function corresponds to a likelihood that a particular application should be placed into a particular pre-activation state. For each of the inactive applications, a respective one of the pre-activation states is selected based on comparing cost values obtained by evaluating the cost functions. Each of the inactive applications can be moved to or maintained in the respectively-selected pre-activation state to more efficiently provide an expedited application launch experience for a user.

Abstract: Systems and methods are disclosed for advising a user when an energy storage device in a computing system needs charging. State of charge data of the energy storage device can be measured and stored at regular intervals. The historic state of charge data can be queried over a plurality of intervals and a state of charge curve generated that is representative of a user's charging habits over time. The state of charge curve can be used to generate a rate of charge histogram and an acceleration of charge histogram. These can be used to predict when a user will charge next, and whether the energy storage device will have an amount of energy below a predetermined threshold amount before the next predicted charging time. A first device can determine when a second device typically charges and whether the energy storage device in the second device will have an amount of energy below the predetermined threshold amount before the next predicted charge time for the second device.

Abstract: In some implementations, a user device can schedule tasks based on user behavior. For example, the user device can receive a task request that includes a time window and user/device context parameters for performing the task. The user device can predict a time when the user/device context is optimal for performing the task during the time window based on historical context data. For example, the user device can generate an optimal context score for the task based on the context parameters and the historical context data. The user device can execute the requested task at a current time within the time window when a context score for the current context exceeds a threshold determined based on the optimal context score.

Abstract: Systems and methods are disclosed for advising a user when an energy storage device in a computing system needs charging. State of charge data of the energy storage device can be measured and stored at regular intervals. The historic state of charge data can be queried over a plurality of intervals and a state of charge curve generated that is representative of a user's charging habits over time. The state of charge curve can be used to generate a rate of charge histogram and an acceleration of charge histogram. These can be used to predict when a user will charge next, and whether the energy storage device will have an amount of energy below a predetermined threshold amount before the next predicted charging time. A first device can determine when a second device typically charges and whether the energy storage device in the second device will have an amount of energy below the predetermined threshold amount before the next predicted charge time for the second device.

Abstract: In some implementations, a user device can schedule tasks based on user behavior. For example, the user device can receive a task request that includes a time window and user/device context parameters for performing the task. The user device can predict a time when the user/device context is optimal for performing the task during the time window based on historical context data. For example, the user device can generate an optimal context score for the task based on the context parameters and the historical context data. The user device can execute the requested task at a current time within the time window when a context score for the current context exceeds a threshold determined based on the optimal context score.

Abstract: Systems and methods are disclosed for advising a user when an energy storage device in a computing system needs charging. State of charge data of the energy storage device can be measured and stored at regular intervals. The historic state of charge data can be queried over a plurality of intervals and a state of charge curve generated that is representative of a user's charging habits over time. The state of charge curve can be used to generate a rate of charge histogram and an acceleration of charge histogram. These can be used to predict when a user will charge next, and whether the energy storage device will have an amount of energy below a predetermined threshold amount before the next predicted charging time. A first device can determine when a second device typically charges and whether the energy storage device in the second device will have an amount of energy below the predetermined threshold amount before the next predicted charge time for the second device.

Abstract: In some implementations, a user device can schedule tasks based on user behavior. For example, the user device can receive a task request that includes a time window and user/device context parameters for performing the task. The user device can predict a time when the user/device context is optimal for performing the task during the time window based on historical context data. For example, the user device can generate an optimal context score for the task based on the context parameters and the historical context data. The user device can execute the requested task at a current time within the time window when a context score for the current context exceeds a threshold determined based on the optimal context score.

Abstract: Systems and methods are disclosed for advising a user when an energy storage device in a computing system needs charging. State of charge data of the energy storage device can be measured and stored at regular intervals. The historic state of charge data can be queried over a plurality of intervals and a state of charge curve generated that is representative of a user's charging habits over time. The state of charge curve can be used to generate a rate of charge histogram and an acceleration of charge histogram. These can be used to predict when a user will charge next, and whether the energy storage device will have an amount of energy below a predetermined threshold amount before the next predicted charging time. A first device can determine when a second device typically charges and whether the energy storage device in the second device will have an amount of energy below the predetermined threshold amount before the next predicted charge time for the second device.

Abstract: In some implementations, a user device can maintain a multi-device context store. For example, the user device can receive device and/or user context information from multiple devices and store the context information in a local data store. The user device can collect local device and/or user context information and store the context information in the local context store. The user device can receive user/device context queries from client processes and send the client processes user/device context information from multiple devices in response to the queries.

Abstract: In some implementations, a user device can schedule tasks based on user behavior. For example, the user device can receive a task request that includes a time window and user/device context parameters for performing the task. The user device can predict a time when the user/device context is optimal for performing the task during the time window based on historical context data. For example, the user device can generate an optimal context score for the task based on the context parameters and the historical context data. The user device can execute the requested task at a current time within the time window when a context score for the current context exceeds a threshold determined based on the optimal context score.

Abstract: Systems and methods are disclosed for advising a user when an energy storage device in a computing system needs charging. State of charge data of the energy storage device can be measured and stored at regular intervals. The historic state of charge data can be queried over a plurality of intervals and a state of charge curve generated that is representative of a user's charging habits over time. The state of charge curve can be used to generate a rate of charge histogram and an acceleration of charge histogram. These can be used to predict when a user will charge next, and whether the energy storage device will have an amount of energy below a predetermined threshold amount before the next predicted charging time. A first device can determine when a second device typically charges and whether the energy storage device in the second device will have an amount of energy below the predetermined threshold amount before the next predicted charge time for the second device.