PATENT DOCUMENT

Publication Number: US-9603094-B2
Application Number: US-201414268780-A
Country: US
Kind Code: B2

Title: Non-waking push notifications

Abstract:
In some implementations, a mobile device can be configured to monitor environmental, system and user events. The occurrence of one or more events can trigger adjustments to system settings. In some implementations, the mobile device can be configured to keep frequently invoked applications up to date based on a forecast of predicted invocations by the user. In some implementations, the mobile device can receive push notifications associated with applications that indicate that new content is available for the applications to download. The mobile device can launch the applications associated with the push notifications in the background and download the new content. In some implementations, before running an application or accessing a network interface, the mobile device can be configured to check energy and data budgets and environmental conditions of the mobile device to preserve a high quality user experience.

Claims:
What is claimed is: 
     
       1. A method comprising:
 receiving, by a notification server from a mobile device, a first list of application identifiers and a second list of application identifiers, where each of the application identifiers in the first list of application identifiers corresponds to a respective application on the mobile device that the mobile device has automatically determined should not be launched on the mobile device, and where each of the application identifiers in the second list of application identifiers corresponds to a respective application on the mobile device that the mobile device has automatically determined should be allowed to launch on the mobile device; 
 receiving, by the notification server, a low priority push notification for the mobile device at the notification server; 
 comparing, by the notification server, an application identifier in the low priority push notification to the first list of application identifiers associated with the mobile device and the second list of application identifiers associated with the mobile device; 
 determining, by the notification server, whether the application identifier is in the first list of application identifiers or in the second list application identifiers; 
 delaying to send the low priority push notification and storing, at the notification server, the low priority push notification if the application identifier is in the first list of application identifiers based on the determination; and 
 transmitting the low priority push notification to the mobile device if the application identifier is in the second list of application identifiers based on the determination. 
 
     
     
       2. The method of  claim 1 , further comprising:
 determining that the application identifier has been moved from the first list of application identifiers to the second list of application identifiers; and 
 transmitting the low priority push notification to the mobile device based on the determination. 
 
     
     
       3. The method of  claim 1 , further comprising:
 determining that a network connection has been created between the mobile device and the notification server; and 
 transmitting the low priority push notification to the mobile device based on the determination. 
 
     
     
       4. The method of  claim 1 , further comprising:
 receiving a high priority push notification for the mobile device at the notification server; and 
 sending the high priority push notification to the mobile device when an application associated with the high priority push notification is identified in the first list or the second list. 
 
     
     
       5. The method of  claim 4 , wherein sending the high priority push notification to the mobile device includes sending the stored low priority push notification to the mobile device. 
     
     
       6. A non-transitory computer-readable medium including one or more sequences of instructions which, when executed by one or more processors, causes:
 receiving, by a notification server from a mobile device, a first list of application identifiers and a second list of application identifiers, where each of the application identifiers in the first list of application identifiers corresponds to a respective application on the mobile device that the mobile device has automatically determined should not be launched on the mobile device, and where each of the application identifiers in the second list of application identifiers corresponds to a respective application on the mobile device that the mobile device has automatically determined should be allowed to launch on the mobile device: 
 receiving, by the notification server, a low priority push notification for the mobile device at the notification server; 
 comparing, by the notification server, an application identifier in the low priority push notification to the first list of applications identifiers associated with the mobile device and the second list of application identifiers associated with the mobile device: 
 determining, by the notification server, that whether the application identifier is in the first list of application identifiers or in the second list of application identifiers; 
 delaying to send the low priority push notification and storing, at the notification server, the low priority push notification if the application identifier is in the first list of application identifiers based on the determination; and 
 transmitting the low priority push notification to the mobile device if the application identifier is in the second list of application identifiers based on the determination. 
 
     
     
       7. The non-transitory computer-readable medium of  claim 6 , wherein the instructions cause:
 determining that the application identifier has been moved from the first list of application identifiers to the second list of application identifiers; and 
 transmitting the low priority push notification to the mobile device based on the determination. 
 
     
     
       8. The non-transitory computer-readable medium of  claim 6 , wherein the instructions cause:
 determining that a network connection has been created between the mobile device and the notification server; and 
 transmitting the low priority push notification to the mobile device based on the determination. 
 
     
     
       9. The non-transitory computer-readable medium of  claim 6 , wherein the instructions cause:
 receiving a high priority push notification for the mobile device at the notification server; and 
 sending the high priority push notification to the mobile device when an application associated with the high priority push notification is identified in the first list or the second list. 
 
     
     
       10. The non-transitory computer-readable medium of  claim 9 , wherein the instructions cause sending the high priority push notification to the mobile device include instructions that cause sending the stored low priority push notification to the mobile device. 
     
     
       11. A system comprising:
 one or more processors; and 
 a non-transitory computer-readable medium including one or more sequences of instructions which, when executed by one or more processors, causes: 
 receiving, by a notification server from a mobile device, a first list of application identifiers and a second list of application identifiers, where each of the application identifiers in the first list of application identifiers corresponds to a respective application on the mobile device that the mobile device has automatically determined should not be launched on the mobile device, and where each of the application identifiers in the second list of application identifiers corresponds to a respective application on the mobile device that the mobile device has automatically determined should be allowed to launch on the mobile device: 
 receiving, by the notification server, a low priority push notification for the mobile device at the notification server; 
 comparing, by the notification server, an application identifier in the low priority push notification to the first list of application identifiers associated with the mobile device and the second list of application identifiers associated with the mobile device; 
 determining, by the notification server, whether the application identifier is in the first list of application identifiers or in the second list of application identifiers; and 
 delaying to send the low priority push notification and storing, at the notification server, the low priority push notification if the application identifier is in the first list of application identifiers based on the determination; and 
 transmitting the low priority push notification to the mobile device if the application identifier is in the second list of application identifiers based on the determination. 
 
     
     
       12. The system of  claim 11 , wherein the instructions cause:
 determining that the application identifier has been moved from the first list of application identifiers to the second list of application identifiers; and 
 transmitting the low priority push notification to the mobile device based on the determination. 
 
     
     
       13. The system of  claim 11 , wherein the instructions cause:
 determining that a network connection has been created between the mobile device and the notification server; and 
 transmitting the low priority push notification to the mobile device based on the determination. 
 
     
     
       14. The system of  claim 11 , wherein the instructions cause:
 receiving a high priority push notification for the mobile device at the notification server; and 
 sending the high priority push notification to the mobile device when an application associated with the high priority push notification is identified in the first list or the second list. 
 
     
     
       15. The system of  claim 14 , wherein the instructions that cause sending the high priority push notification to the mobile device include instructions that cause sending the stored low priority push notification to the mobile device.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims priority to U.S. Provisional Patent Application No. 61/832,926, entitled “Non-Waking Push Notifications,” filed on Jun. 9, 2013, the entire contents of which are incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     The disclosure generally relates to adjusting components of a computer system based on user behavior. 
     BACKGROUND 
     Mobile computing devices are typically battery operated. Some mobile computing devices can wirelessly access network resources over cellular data and/or Wi-Fi network connections. These mobile devices are often constrained by battery capacity and limits on cellular data. 
     Some mobile computing devices allow a user to run applications that access data from network resources. The user typically invokes an application and then must wait for the application to retrieve data from the network resources so that the application can present current updated content. 
     SUMMARY 
     In some implementations, a mobile device can be configured to monitor environmental, system and user events. The mobile device can be configured to detect the occurrence of one or more events that can trigger adjustments to system settings. 
     In some implementations, the mobile device can be configured to keep frequently invoked applications up to date. The mobile device can keep track of when applications are invoked by the user. Based on the invocation information, the mobile device can forecast when during the day the applications are invoked. The mobile device can then preemptively launch the applications and download updates so that the user can invoke the applications and view current updated content without having to wait for the application to download updated content. 
     In some implementations, the mobile device can receive push notifications associated with applications that indicate that new content is available for the applications to download. The mobile device can launch the applications associated with the push notifications in the background and download the new content. After the content is downloaded, the mobile device can present a graphical interface indicating to the user that the push notification was received. The user can then invoke the applications and view the updated content. 
     In some implementations, the mobile device can be configured to perform out of process downloads and/or uploads of content for applications on the mobile device. For example, a dedicated process can be configured on the mobile device for downloading and/or uploading content for applications on the mobile device. The applications can be suspended or terminated while the upload/download is being performed. The applications can be invoked when the upload/download is complete. 
     In some implementations, before running an application or accessing a network interface, the mobile device can be configured to check battery power and cellular data usage budgets to ensure that enough power and data is available for user invoked operations. Before launching an application in the background, the mobile device can check usage statistics to determine whether the application is likely to be invoked by a user in the near future. 
     Particular implementations provide at least the following advantages: Battery power can be conserved by dynamically adjusting components of the mobile device in response to detected events. The user experience can be improved by anticipating when the user will invoke applications and downloading content so that the user will view updated content upon invoking an application. 
     Details of one or more implementations are set forth in the accompanying drawings and the description below. Other features, aspects, and potential advantages will be apparent from the description and drawings, and from the claims. 
    
    
     
       DESCRIPTION OF DRAWINGS 
         FIG. 1  illustrates a mobile device  100  configured to perform dynamic adjustment of the mobile device. 
         FIG. 2  illustrates an example process for invoking heuristic processes. 
         FIG. 3  illustrates a process for adjusting the settings of a mobile device using a heuristic process. 
         FIG. 4  illustrates an example system for performing background fetch updating of applications. 
         FIG. 5  illustrates example diagrams depicting time series modeling for determining user invocation probabilities for applications on mobile device. 
         FIG. 6  is a flow diagram of an example process for predictively launching applications to perform background updates. 
         FIG. 7  is a flow diagram of an example process for determining when to launch applications on a mobile device. 
         FIG. 8  is a flow diagram illustrating state transitions for an entry in a trending table. 
         FIG. 9  is a block diagram illustrating a system for providing push notifications to a mobile device. 
         FIG. 10  is a flow diagram of an example process for performing non-waking pushes at a push notification server. 
         FIG. 11  is a flow diagram of an example process for performing background updating of an application in response to a low priority push notification. 
         FIG. 12  is a flow diagram of an example process for performing background updating of an application in response to a high priority push notification. 
         FIG. 13  is a block diagram an example system for performing background downloading and/or uploading of data on a mobile device. 
         FIG. 14  is flow diagram of an example process for performing background downloads and uploads. 
         FIG. 15  illustrates an example graphical user interface (GUI) for enabling and/or disabling background updates for applications on a mobile device. 
         FIG. 16  is a block diagram of an example computing device that can implement the features and processes of  FIGS. 1-15 . 
     
    
    
     Like reference symbols in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
     Overview 
     According to implementations, described herein is a system architecture for enabling adaptation of a mobile device to user behavior to facilitate tradeoffs between battery lifetime, power requirements, thermal management and performance. The system provides the underlying event and statistics gathering architecture and a set of heuristic processes that learn from a user&#39;s network conditions and application usage patterns over time to maximize battery life without noticeable degradation in the user experience. This system can anticipate a user&#39;s future behavior as well as the user&#39;s expectation of device performance based on dynamically-gathered statistics and/or explicitly-specified user intent. The system can determine which hardware and software control parameters to set and to what values to set the parameters in order to improve the user experience for the anticipated user behavior. The system leverages user monitoring and hardware control to achieve an overall improvement in the user experience while extending system and network resources available to the mobile device. Thus, the system can maximize system and network resources while minimizing the impact to the user experience. 
     Data Collection—User Centric Statistics 
       FIG. 1  illustrates a mobile device  100  configured to perform dynamic adjustment of the mobile device  100 . In some implementations, mobile device  100  can include a sampling daemon  102  that collects events related to device conditions, network conditions and user behavior. For example, sampling daemon  102  can collect statistics related to applications, sensors and user input received by mobile device  100  and store the statistics in event data store  104 . All of the statistics generated or collected can include a timestamp that indicates the time and time zone when the statistic was generated or collected and/or a geographical location. The geographical location can be determined based on global navigation satellite system signals, cellular transmission signals, Wi-Fi signals or any other location determining methodology. 
     In some implementations, sampling daemon  102  can receive application usage statistics from application manager process  106 . For example, application manager  106  can be a process that starts, stops and monitors applications (e.g., application  108 ) on mobile device  100 . In some implementations, application manager  106  can report start and stop times for applications running on mobile device  100  to sampling daemon  102 . For example, when a user or other process invokes or launches an application, application manager  106  can notify sampling daemon  102  of the application invocation. Additionally, application manager  106  can indicate to sampling daemon  102  that the application launch is initiated in response to a push notification, user invocation or a predicted or forecasted user application invocation. When an application terminates, application manager  106  can notify sampling daemon  102  that the application is no longer running. The application manager  106  can provide the name or other identifier of the application and the start time or end time to sampling daemon  102 , for example. 
     In some implementations, sampling daemon  102  can use the application start and end notifications to generate a history of usage times per application. For example, the history of usage times per application can include for each execution of an application an amount of time that has passed since the last execution of the application and execution duration. Sampling daemon  102  can maintain a separate history of user invoked application launches and/or system launched applications. Thus, sampling daemon  102  can maintain usage statistics for all applications that are run on mobile device  100 . 
     In some implementations, sampling daemon  102  can receive power statistics from power monitor process  108 . For example, power monitor  108  can monitor battery capacity, discharge, usage, and charging characteristics for mobile device  100 . Power monitor can determine when the mobile device  100  is plugged into external power sources and when the mobile device  100  is on battery power. Power monitor  108  can notify sampling daemon  102  when the mobile device  100  is plugged into external power. For example, power monitor  108  can send a message to sampling daemon  102  when power monitor detects that mobile device  100  is plugged into an external power source. The message can include the battery charge at the time when the external power source is connected. 
     Power monitor  108  can notify sampling daemon  102  when the mobile device  100  is disconnected from external power. For example, power monitor  108  can send a message to sampling daemon  102  when power monitor detects that mobile device  100  is disconnected from an external power source. The message can include the battery charge at the time when the external power source is disconnected. Thus, sampling daemon  102  can maintain statistics describing the charging distribution (e.g., charge over time) of the batteries of the mobile device  100 . The charging distribution statistics can include an amount of time since the last charge (e.g., time since plugged into external power) and the change in battery charge attributable to the charging (e.g., start level of charge, end level of charge). 
     In some implementations, power monitor  108  can notify sampling daemon  102  of changes in battery charge throughout the day. For example, power monitor  108  can be notified when applications start and stop and in response to the notifications, determine the amount of battery power discharged during the period and the amount of charge remaining in the battery and transmit this information to sampling daemon  102 . 
     In some implementations, sampling daemon  102  can receive device temperature statistics from thermal management process  110 . For example, thermal management process  110  can monitor the operating temperature conditions of the mobile device  100  using one or more temperature sensors. Thermal management process  110  can be configured to periodically report temperature changes to sampling daemon  102 . For example, thermal management process  110  can determine the operating temperature of mobile device  100  every five seconds and report the temperature to sampling daemon  102 . Sampling daemon  102  can store the reported temperatures in event data store  104 . 
     In some implementations, sampling daemon  102  can receive device settings statistics from device settings process  112 . For example, device settings process  112  can be a function or process of the operating system of mobile device  100 . Device settings process  112  can, for example, receive user input to adjust various device settings, such as turning on/off airplane mode, turning on/off Wi-Fi, turning on/off roaming, etc. Device settings process  112  can report changes to device settings to sampling daemon  102 . For example, device settings process  112  can notify sampling daemon  102  when the user turns on or off airplane mode on the mobile device  100 . Sampling daemon  102  can generate and store statistics for the device settings based on the received notifications. For example, for each time a setting is enabled (or disabled), sampling daemon  102  can store data that indicates the amount of time that has passed since the setting was previously enabled and the amount of time (e.g., duration) that the setting was enabled. 
     Similarly, in some implementations, sampling daemon  102  can receive notifications from other mobile device  100  components (e.g., device sensors  114 ) when other events occur. For example, sampling daemon  102  can receive notifications when the mobile device&#39;s idle screen is turned on or off, when the mobile device  100  is held next to the user&#39;s face, when a cell tower handoff is detected, when the baseband processor is in a search mode, when the mobile device  100  has detected that the user is walking, running and/or driving. In each case, the sampling daemon  102  can receive a notification at the start and end of the event. In each case, the sampling daemon  102  can generate and store statistics indicating the amount of time that has passed since the event was last detected and the duration of the event. The sampling daemon  102  can receive other event notifications and generate other statistics as described further below with respect to specific use cases and scenarios. 
     Application Events 
     In some implementations, sampling daemon  102  can receive event information from applications on mobile device  100 . For example, sampling daemon  102  can receive calendar events (e.g., appointments, meetings, reminders, etc.) from calendar application  116 . Sampling daemon  102  can store the event name, event duration and/or time when the event is scheduled to occur, for example. Sampling daemon  102  can receive clock events from clock application  118 . For example, sampling daemon  102  can store the event name (e.g., alarm name) and/or time when the event is scheduled to occur. Sampling daemon  102  can receive event information from other applications (e.g., media application, passbook application, etc.) as described further below. 
     Application Statistics 
     In some implementations, sampling daemon  102  can collect application statistics across application launch events. For example, sampling daemon  102  can collect statistics for each application across many invocations of the application. For example, each application can be identified with a hash of its executable&#39;s filesystem path and the executable&#39;s content&#39;s hash so that different versions of the same application can be handled as distinct applications. 
     In some implementations, sampling daemon  102  can maintain a counter that tracks background task completion assertion events for each application. For example, each time an application is run as a background task (e.g., not visible in the foreground and/or currently in use by the user) the application or application manager  106  can notify sampling daemon  102  when the application is terminated or is suspended and the sampling daemon  102  can increment the counter. Sampling daemon  102  can maintain a counter that tracks the cumulative number of seconds across application launches that the application has run in the background. In some implementations, sampling daemon  102  can maintain separate counters that count the number of data connections, track the amount of network data traffic (e.g., in bytes), track the duration and size of filesystem operations and/or track the number of threads associated with each application. Sampling daemon  102  can maintain a count of the cumulative amount of time an application remains active across application launches, for example. These are just a limited example of the types of application statistics that can be tracked by sampling daemon  102 . Other statistics can be generated or collected as described further below. 
     Heuristics 
     In some implementations, mobile device  100  can be configured with heuristic processes that can adjust settings of device components based on events detected by sampling daemon  102 . For example, heuristic processes  120  can include one or more processes that are configured (e.g., programmed) to adjust various system settings (e.g., CPU power, baseband processor power, display lighting, etc.) in response to one or more trigger events and/or based on the statistics collected or generated by sampling daemon  102 . 
     In some implementations, heuristic process  120  can register with sampling daemon  102  to be invoked or activated when a predefined set of criteria is met (e.g., the occurrence of some trigger event). Trigger events might include the invocation of a media player application or detecting that the user has started walking, running, driving, etc. The trigger event can be generalized to invoke a heuristic process  120  when some property, data, statistic, event, etc. is detected in event data  104  or by sampling daemon  102 . For example, a heuristic process  120  can be invoked when sampling daemon  102  receives an application start notification or a temperature above a certain threshold value. A heuristic process  120  can register to be invoked when a single event occurs or statistic is observed. A heuristic process  120  can register to be invoked when a combination of events, data and/or statistics are observed or detected. Heuristic process  120  can be triggered or invoked in response to specific user input (e.g., changing a device setting to airplane mode). When sampling process  102  detects the events for which a heuristic process  120  registered, sampling process  102  can invoke the heuristic process  120 . 
     In some implementations, when a heuristic process  120  is invoked, the heuristic process  120  can communicate with sampling daemon  102  to retrieve data from event data  104 . The heuristic process  120  can process the event data and/or other data that the heuristic process  120  collects on its own to determine how to adjust system settings to improve the performance of mobile device  100 , improve the user&#39;s experience while using mobile device  100  and/or avert future problems with mobile device  100 . 
     In some implementations, heuristic process  120  can make settings recommendations that can cause a change in the settings of various device components  122  of mobile device  100 . For example, device components can include CPU, GPU, baseband processor, display, GPS, Bluetooth, Wi-Fi, vibration motor and other components. 
     In some implementations, heuristic process  120  can make settings recommendations to control multiplexer  124 . For example, control multiplexer  124  can be a process that arbitrates between component settings provided by heuristic processes  120  and other processes and/or functions of mobile device  100  that influence or change the settings of the components of mobile device  100 . For example, thermal management process  110  can be configured to make adjustments to CPU power, display brightness, baseband processor power and other component settings based on detecting that the mobile device  100  is in the middle of a thermal event (e.g., above a threshold temperature). However, a heuristic process  120  can be configured to make adjustments to CPU power, display brightness, baseband processor power and other component settings as well. Thus, in some implementations, heuristic process  120  and thermal management process  110  can make settings adjustment recommendations to control multiplexer  124  and control multiplexer  124  can determine which settings adjustments to make. For example, control multiplexer  124  can prioritize processes and perform adjustments based on the priority of the recommending process. Thus, if thermal management process  110  is a higher priority process than heuristic process  120 , control multiplexer  124  can adjust the settings of the CPU, display, baseband processor, etc. according to the recommendations of thermal management process  110  instead of heuristic process  120 . 
     In some implementations, a mobile device  100  can be configured with multiple heuristic processes  120 . The heuristic processes  120  can be configured or reconfigured over the air. For example, the parameters (e.g., triggers, threshold values, criteria, and output) of each heuristic process  120  can be set or adjusted over the network (e.g., cellular data connection, Wi-Fi connection, etc.). In some implementations, new heuristic processes  120  can be added to mobile device  100 . For example, over time new correlations between trigger events, statistical data and device settings can be determined by system developers. As these new correlations are identified, new heuristic processes  120  can be developed to adjust system settings to account for the newly determined relationships. In some implementations, new heuristic processes  120  can be added to mobile device  100  over the network. For example, the new heuristic processes  120  can be downloaded or installed on mobile device  100  over the air (e.g., cellular data connection, Wi-Fi connection, etc.). 
     Example Heuristic Processes 
     In some implementations, a heuristic process  120  can be configured to adjust system settings of the mobile device  100  to prevent the mobile device  100  from getting too hot when in the user&#39;s pocket. For example, this hot-in-pocket heuristic process can be configured to register with sampling daemon  102  to be invoked when the mobile device&#39;s display is off and when the mobile device  100  is not playing any entertainment media (e.g., music, movies, video, etc.). When invoked, the hot-in-pocket heuristic can make recommendations to reduce CPU power and GPU power, for example. 
     In some implementations, a heuristic process  120  can be configured to adjust location accuracy when the mobile device&#39;s display is not being used. For example, if the mobile device&#39;s display is not being used (e.g., the display is turned off), the mobile device  100  cannot display map information or directions to the user. Thus, the user is not likely using the location services of the mobile device  100  and the location services (e.g., GPS location, Wi-Fi location, cellular location, etc.) can be adjusted to use less power. The location accuracy heuristic process can register with sampling daemon  102  to be invoked when the mobile device&#39;s display is off. When invoked, the heuristic process can adjust the power levels of the GPS processor, Wi-Fi transmitter, cellular transmitter, baseband processor or terminate processes used to determine a location of the mobile device  100 . 
     In some implementations, a heuristic process  120  can be configured to adjust the settings of the mobile device&#39;s ambient light sensor in response to the user&#39;s behavior. For example, this user-adaptive ambient light sensor (ALS) heuristic process can be invoked by sampling daemon  102  when sampling daemon  102  receives data indicating that the ambient light sensor has detected a change in the ambient light surrounding mobile device  100 , that the ambient light sensor system has adjusted the brightness of the display and/or that the user has provided input to adjust the brightness of the display. 
     When invoked, the user-adaptive ALS heuristic can request additional information from sampling daemon  102  with respect to ALS display adjustments and user initiated display adjustments to determine if there is a pattern of user input that indicates that when the ALS adjusts the display brightness up or down and the user adjusts the display brightness in the opposite direction. For example, the user may ride the bus or the train to work. The bus lights may be turned on and off during the ride. The ambient light sensor can detect the change in ambient light and increase the display brightness when the lights come on. Since the lights only come on temporarily, the user may decrease the display brightness when the lights turn off again. This pattern of user input can be correlated to time of day, calendar or alarm entry, or travel pattern by the heuristic process to determine under what circumstances or context the user adjusts the display brightness in response to an ALS display adjustment. Once the user-adaptive ALS heuristic process determines the pattern of input and context, the heuristic process can adjust the settings of the ALS to be more or less aggressive. For example, the ALS can be adjusted to check the level of ambient light more or less frequently during the determined time of day, calendar or alarm entry, or travel pattern and adjust the display brightness accordingly. 
     The above heuristic processes are a few examples of heuristic processes and how they might be implemented in the system described herein. Other heuristic processes can be implemented and added to the system as they are developed over time. For example, additional heuristic processes can be configured or programmed to adjust CPU, GPU, baseband processors or other components of the mobile device in response to detecting events or patterns of events related to temperature measurements, user input, clock events (e.g., alarms), calendar events and/or other events occurring and detected on the mobile device. 
     Example Heuristic Registration and Invocation Processes 
       FIG. 2  illustrates an example process  200  for invoking heuristic processes. At step  202 , the sampling daemon  102  can be initialized. For example, sampling daemon  102  can be initialized during startup of the mobile device  100 . 
     At step  204 , the sampling daemon  102  can invoke the heuristic processes configured on the mobile device  100  during initialization of the sampling daemon  102 . For example, sampling daemon  102  can cause each heuristic process  120  to execute on mobile device  100  and run through their initialization subroutines. 
     At step  206 , the sampling daemon  102  can receive event registration messages from each heuristic process  120 . For example, during the initialization subroutines of the heuristic processes  120 , the heuristic processes  120  can send information to sampling daemon  102  indicating which events should trigger an invocation of heuristic processes  120 . Sampling daemon  102  can store the registration information in a database, such as event data store  104 , for example. For example, the registration information can include an identification of the heuristic process (e.g., executable name, file system path, etc.) and event criteria (identification of events, values, threshold, ranges, etc.). 
     At step  208 , the sampling daemon  102  can receive event data. For example, sampling daemon  102  can receive event data from various system components, including the application manager  106 , sensors  114 , calendar  116  and clock  118 , as described above. 
     At step  210 , the sampling daemon  102  can compare the received event data to the heuristic registration data. For example, as event data is reported to sampling daemon  102 , sampling daemon  102  can compare the event data, or the statistics generated from the event data, to the registration information received from the heuristic processes  120 . 
     At step  212 , the sampling daemon  102  can invoke a heuristic process based on the comparison performed at step  210 . For example, if the event data and/or statistics, meet the criteria specified in the heuristic registration data for a heuristic process  120 , then the sampling daemon  102  can invoke the heuristic process  120 . For example, if the event data and/or statistics data cross some threshold value specified for an event by the heuristic process during registration, then the heuristic process can be invoked by sampling daemon  102 . 
       FIG. 3  illustrates a process  300  for adjusting the settings of a mobile device  100  using a heuristic process  120 . At step  302 , the heuristic process  120  is initialized. For example, the heuristic process  120  can be invoked by sampling daemon  102  so that the heuristic process  120  can run through its initialization subroutines. For example, the invocation can be parameterized to indicate that the heuristic process  120  should run through its initialization subroutines during this invocation. 
     At step  304 , the heuristic process  120  can register with sampling daemon  102  for system events. For example, during initialization, the heuristic process  120  can send a message to sampling daemon  102  that includes an identification of events, thresholds, values or other criteria for invoking the heuristic process  120 . When the event occurs and/or the criteria are met, sampling daemon  102  can invoke the heuristic process  120 . 
     At step  306 , the heuristic process  120  can shut down or terminate. For example, the heuristic process  120  is not needed by the system until the registration criteria are met for the heuristic process  120 . Thus, to conserve device resources (e.g., battery power, processing power, etc.), the heuristic process  120  is terminated, shutdown or suspended until it is needed. 
     At step  308 , the heuristic process  120  can be restarted. For example, sampling daemon  102  can invoke the heuristic process  120  when sampling daemon  102  determines that the criteria specified by the heuristic process  120  in the registration message have been met. 
     At step  310 , the heuristic process  120  can obtain event data from sampling daemon  102 . For example, once restarted, the heuristic process  120  can query sampling daemon  102  for additional event data. The heuristic process  120  can be configured to interact with other system resources, processes, sensors, etc. to collect data, as needed. 
     At step  312 , the heuristic process  120  can process event data to determine component settings. For example, the heuristic process  120  can use the event data and/or statistics from the sampling daemon  102  and/or the data collected from other components of the system to determine how to adjust the settings of various components of the mobile device  100 . 
     At step  314 , the heuristic process  120  can transmit the determined component settings to the control multiplexer  124 . For example, the control multiplexer  124  can arbitrate device settings recommendations received from the heuristic process  120  and other system components (e.g., thermal management process  110 ). The control multiplexer  124  can then adjust various components (e.g., CPU, GPU, baseband processor, display, etc.) of the mobile device  100  according to the received settings recommendations. 
     Keep Applications Up to Date—Fetching Updates 
       FIG. 4  illustrates an example system  400  for performing background fetch updating of applications. In some implementations, mobile device  100  can be configured to predictively launch applications as background processes of the mobile device  100  so that the applications can download content and update their interfaces in anticipation of a user invoking the applications. For example, the user application launch history data maintained by sampling daemon  102  can be used to forecast when the user will invoke applications of the mobile device  100 . These applications can be launched by the application manager  106  prior to user invocation so that the user will not be required to wait for a user invoked application to download current content and update the graphical interfaces of the applications. 
     Determining when to Launch Applications 
     In some implementations, application manager  106  can request an application invocation forecast from sampling daemon  102 . For example, sampling daemon  102  can provide an interface that allows the application manager  106  to request information that indicate when to launch applications of the mobile device  100 . Sampling daemon  102  can maintain statistics that indicate when the user invokes applications on the mobile device  100 , as described above. When application manager  106  calls the “when to launch” interface, sampling daemon  102  can process the user application invocation statistics to determine when during the day each application is typically invoked by the user. For example, sampling daemon  102  can calculate a probability that a particular time of day or time period will include an application invocation by a user. 
     In some implementations, application manager  106  can invoke the “when to launch” interface of sampling daemon  102  during initialization of the application manager  106 . For example, application manager  106  can be invoked or launched during startup of mobile device  100 . While application manager  106  is initializing, application manager  106  can request a forecast of application invocations for the next 24 hours. Once the initial 24 hour period has passed, application manager  106  can request another 24 hour forecast. This 24 forecast cycle can continue until the mobile device  100  is turned off, for example. 
     In some implementations, sampling daemon  102  can generate an application invocation forecast for a 24 hour period. For example, sampling daemon  102  can divide the 24 hour period into 96 fifteen minute timeslots. Sampling daemon  102  can determine which applications have been invoked and at what time the applications were invoked over a number (e.g., 1 to 7) of previous days of operation based on the application launch history data collected by sampling daemon  102  and stored in event data store  104 . In some implementations, sampling daemon  102  will exclude an application from the invocation forecast when background updates have been disabled for the application. 
     In some implementations, each 15 minute timeslot can be ranked according to a probability that an (e.g., any) application will be invoked in the 15 minute timeslot. For example, if the sampling daemon  102  is using the previous seven days of user invoked application launch history data to determine the probabilities for the 15 minute timeslots, sampling daemon  102  can calculate how many user invoked application launches occurred over the previous seven days (e.g., total app launches) and determine how many application launches occurred within each 15 minute timeslot (e.g., n timeslot). For example, if a 15 minute window corresponds to the time period 12:00 pm-12:15 pm, then the number of applications launched within the 15 minute window will include all user initiated application launches occurring from 12:00 pm-12:15 pm on each of the last seven days. Sampling daemon  102  can then determine the probability of an application being invoked by a user within a given 15 minute timeslot by dividing the number of application launches occurring within a given 15 minute window of each of the seven days by the total application launches over the seven day period (e.g., n timeslot/total app launches). 
     Once the application invocation probabilities for each of the 96 timeslots is calculated, sampling daemon  102  will select a number (e.g., up to 64) of the timeslots having the largest non-zero probabilities and return information identifying the timeslots to application manager  106 . For example, sampling daemon  102  can send application manager  106  a list of times (e.g., 12:00 pm, 1:45 pm, etc.) that correspond to the start of 15 minute timeslots that correspond to probable user invoked application launches. 
     In some implementations, each of the 96 timeslots can be ranked based on recurring application invocations within each respective timeslot. For example, a timeslot can be highly ranked if a user invokes the same application in the same timeslot during each day over the last number (e.g., seven) of days. A timeslot can be lowly ranked if a user invoked different applications (e.g., the same application is not invoked more than once) in the same timeslot during each day over the last number of days. For example, if during a timeslot (e.g., 12:00-12:15 pm) for the last seven days, application ‘A’ is invoked by the user, the timeslot can have a ranking or a score based on the ratio of days the application is invoked over the total number of days considered (e.g., 7/7=1). If during a timeslot (e.g., 12:00-12:15 pm) for the last seven days, application ‘B’ is invoked by the user on only one day, then the timeslot can have a ranking or score based on the ratio of days the application is invoked over the total number of days considered (e.g., 1/7=0.14). If application A and application B are invoked in the 12:00-12:15 pm timeslot over the seven day period, then the timeslot can be ranked or scored according to the highest score generated. For example, the score for the timeslot using application A is 1.0, the score for the timeslot using application B is 0.14, thus, the score for the timeslot is 1.0. Timeslots having no application invocations can be assigned a score of zero. 
     In some implementations, sampling daemon  102  can select a number (e.g., 64) of timeslots having the highest score and send them to application manager  106 . For example, sampling daemon  102  can send application manager  106  a list of times (e.g., 12:00 pm, 1:45 pm, etc.) that correspond to the start of 15 minute timeslots that correspond to probable user invoked application launches. 
     In some implementations, application manager  106  can set timers based on the timeslots provided by sampling daemon  102 . For example, application manager  106  can create or set one or more timers (e.g., alarms) that correspond to the timeslots identified by sampling daemon  102 . When each timer goes off (e.g., at 12:00 pm), application manager  106  can wake (e.g., if sleeping, suspended, etc.) and determine which applications should be launched for the current 15 minute timeslot. Thus, the timers can trigger a fetch background update for applications that are likely to be invoked by a user within the corresponding timeslot. 
     In some implementations, other events can trigger a fetch background update for applications. For example, turning on a cellular radio, baseband processor or establishing a network connection (e.g., cellular or Wi-Fi) can trigger a background application launch so that the application update can take advantage of an active network connection. Unlocking the mobile device  100 , turning on the display and/or other interactions can trigger a background application launch and fetch update, as described further below. In some implementations, application manager  106  will not trigger a background application launch and fetch update if any background updates were performed within a previous number (e.g., seven) of minutes. 
     Determining What Applications to Launch 
     In some implementations, application manager  106  can request that sampling daemon  102  provide a list of applications to launch for the current times. For example, when a timer goes off (e.g., expires) for a 15 minute timeslot, application manager can call a “what to launch” interface of sampling daemon  102  so that sampling daemon  102  can determine which applications to launch for the current timeslot. Sampling daemon  102  can then generate a list of applications and corresponding scores indicating the probability that each application will be invoked by the user at about the current time, as described further below. In some implementations, sampling daemon  102  will exclude an application from the list of applications when background updates have been disabled for the application. 
     In some implementations, sampling daemon  102  can determine the probability that each application will be invoked by a user for a current (e.g., 15 minute) timeslot. For example, in response to an invocation of the “what to launch” interface, sampling daemon  102  can determine the likelihood that each application will be invoked by the user. 
       FIG. 5  illustrates example diagrams  500 ,  530  and  560  depicting time series modeling for determining user invocation probabilities for applications on mobile device  100 . In some implementations, sampling daemon  102  can use time series modeling to determine the user invocation probabilities for each application on mobile device  100 . If an application does not show up in the time series, the application can be assigned a zero probability value. 
     In some implementations, user invocation probabilities can be generated based on recent application invocations. For example, user invocation probabilities can be generated using application launch data for the previous two hours (e.g., user initiated application launches within the last two hours). As illustrated by diagram  500 , application launch history data can indicate a number (e.g., four) of applications were launched in the previous two hours. For example, the dots and circles can represent applications where the empty circles can represent a single particular application (e.g., email, social networking application, etc.). The probability associated with the particular application using recent history can be calculated by dividing the number of invocations of the particular application (e.g., 2) by the total number of application invocations (e.g., 4) within the previous two hours. In the illustrated case, the probability associated with the particular application using recent application launch history data is 2/4 or 50%. 
     User invocation probabilities can be generated based on a daily history of application launches (e.g., which applications were launched at the current time+−2 hours for each of the previous seven days). Diagram  530  represents using a daily history to determine a user invocation probability for an application. For example, each box of diagram  530  represents time windows (e.g., current time +−2 hours) in each of a number (e.g., 7) of previous days that can be analyzed to determine the user invocation probability for a particular application (e.g., empty circle). The probability associated with the particular application using daily history data can be calculated by dividing the number of invocations of the particular application in all windows (e.g., 6) by the total number of application invocations in all windows (e.g., 22). In the illustrated case, the probability associated with the particular application using daily launch history data is 6/22 or 27%. 
     User invocation probabilities can be generated based on a weekly history of application launches (e.g., which applications were launched at the current time+−2 hours seven days ago). Diagram  560  represents using a weekly history to determine a user invocation probability for an application. For example, if the current day and time is Wednesday at 1 pm, the user invocation probability for an application can be based on applications launched during the previous Wednesday during a time window at or around 1 pm (e.g., +−2 hours). In the illustrated case, the probability associated with the particular application (e.g., empty circle) using weekly application launch history data is 1/4 or 25%. 
     In some implementations, the recent, daily and weekly user invocation probabilities can be combined to generate a score for each application. For example, the recent, daily and weekly probabilities can be combined by calculating a weighted average of the recent (r), daily (d) and weekly (w) probabilities. Each probability can have an associated weight and each weight can correspond to an empirically determined predefined importance of each probability. The sum of all weights can equal one. For example, the weight for probability based on recent launches can be 0.6, the weight for the daily probability can be 0.3, and the weight for the weekly probability can be 0.1. Thus, the combined probability score can be the sum of 0.6(r), 0.3(d) and 0.1(w) (e.g., score=0.6r+0.3d+0.1w). 
     Referring back to  FIG. 4 , once the probability score is determined for each application based on the recent, daily and weekly probabilities, the sampling daemon  102  can recommend a configurable number (e.g., three) of applications having the highest non-zero probability scores to the application manager  106  for launching to perform background fetch downloads/updates. 
     In some implementations, sampling daemon  102  can exclude from the “what to launch” analysis described above applications that do not support background updates (e.g., fetching) application updates, applications where the user has turned off background updates, applications that have opted out of background updates, and/or whichever application is currently being used by the user or is in the foreground on the display of the mobile device  100  since it is likely that the foreground application is already up to date. 
     Determining that it is Ok to Launch an Application 
     In some implementations, when application manager  106  receives the list of applications to launch and application scores, application manager  106  can call an “ok to launch” interface of sampling daemon  102  to determine if it is ok to launch each application. For example, sampling daemon  102  can analyze network conditions, device conditions, environmental conditions, data and energy budgets and other data to determine if it is ok for application manager  106  to launch a particular application. Application manager  106  can send sampling daemon  102  an application identifier when calling the “ok to launch” interface, for example. 
     In some implementations, other applications on mobile device  102  can call the “ok to launch” interface of sampling daemon  102  to determine whether it is ok to launch an application in the background of mobile device  100 . For example, the push service daemon described below can invoke the “ok to launch” interface to determine if it is ok to launch applications in response to receiving a push notification. In some implementations, sampling daemon  102  will return a “no” reply value when background updates have been disabled for an application identified in an “ok to launch” request. 
     Environmental Conditions 
     In some implementations, sampling daemon  102  can determine whether to allow launching the identified application based on the environmental conditions of the mobile device  100 . For example, environmental conditions can include the current state of the mobile device  100 , the state of network connections and/or other conditions as described below. In some implementations, when the “ok to launch” interface is called by application manager  106  (or another application, process or daemon), sampling daemon  102  can return values indicating that it is “never” ok to launch the identified application, “no” it is not ok to launch the application, or “yes” it is ok to launch the application. For example, a “yes” value will be returned unless one of the conditions below indicates that the application should not be launched. Sampling daemon  102  will return a “never” value when the identified application has not been recently used (e.g., not used in the past 8 weeks), when the user has disabled the application and/or when the application was explicitly closed by the user. 
     In some implementations, sampling daemon  102  can indicate that application manager  106  should not launch an application when the mobile device  100  is connected to a voice call and not connected to a Wi-Fi network connection. For example, to prevent background updating processes (e.g., fetch processes) from interfering with or reducing the quality of voice calls, the sampling daemon  102  will not allow application manager  106  to launch a background updating process when the user is connected to a voice call and not connected to a Wi-Fi connection. Thus, sampling daemon  102  will return a “no” value in response to an “ok to launch” request when the mobile device  100  is connected to a call and not connected to Wi-Fi. 
     In some implementations, sampling daemon  102  can indicate that application manager  106  should not launch an application when the mobile device  100  has detected a thermal event. For example, the thermal management process  110  can monitor the temperature of the mobile device  100  and report temperature values to sampling daemon  102 . When sampling daemon  102  determines that the temperature of mobile device  100  is above a threshold temperature value, sampling daemon  102  can prevent application manager  106  from launching additional applications that may increase the operating temperature of mobile device  100  further by returning a “no” value when application manager makes an “ok to launch” request. 
     In some implementations, sampling daemon  102  can indicate that application manager  106  should not launch an application when the mobile device  100  has a poor quality cellular network connection. A poor quality cellular connection can be determined when transfer rate and/or throughput are below predefined threshold values. For example, if the mobile device  100  has a poor quality cellular network connection and is not connected to Wi-Fi, the sampling daemon  102  can prevent application manager  106  from launching an application that will waste battery energy and cellular data by attempting to download or upload data over a poor cellular connection by returning a “no” value when application manager makes an “ok to launch” request. 
     In some implementations, sampling daemon  102  can indicate that application manager  106  should not launch an application when mobile device  100  is using more than a threshold amount (e.g., 90%) of memory resources. For example, if mobile device  100  is already running many applications or processes that are using most of the memory resources of the mobile device  100 , launching additional applications in the background will only reduce the performance of the mobile device  100  by using up remaining memory resources. Thus, when sampling daemon  102  determines that memory usage exceeds a threshold value (e.g., 75%), sampling daemon  102  can prevent application manager  106  from launching additional applications by returning a “no” value when application manager makes an “ok to launch” request. 
     In some implementations, sampling daemon  102  can indicate that application manager  106  should not launch an application when an application has opted out of background updates or a user has turned off background updates for an application. For example, an application can programmatically (e.g., dynamically) determine (based on programmer defined criteria) that the application should opt out of background updates for a period of time. Alternatively, a user can interact with a settings user interface to turn off background updates for an application. In either case, sampling daemon  102  can determine that background updates have been disabled for the application and prevent application manager  106  from launching the application by returning a “no” value when application manager  106  makes an “ok to launch” request for an application. 
     Accounting for Budgets and Rate Limits 
     In some implementations, sampling daemon  102  can determine whether it is ok to launch an application based on an energy budget, a data budget and/or application launch rate limits configured for mobile device  100 . Sampling daemon  102  can store budget and rate limit information in accounting data store  402 , including counters for keeping track of remaining data and energy budgets for the current time period (e.g., current hour). 
     Energy Budget 
     In some implementations, sampling daemon  102  can determine whether it is ok to launch an application based on an energy budget. For example, the energy budget can be a percentage (e.g., 5%) of the capacity of the mobile device&#39;s battery in milliamp hours. The energy budget can be divided between predictive fetch applications (e.g., 2%), as described above, and push applications and background download/upload (e.g., 3%), as described below. 
     In some implementations, the energy budgets can be distributed among each hour in a 24 hour period. For example, sampling daemon  102  can utilize the battery charging statistics collected and stored in event data store  104  to determine a distribution that reflects a typical historical battery usage for each hour in the 24 hour period. For example, each hour can be assigned a percentage of the energy budget based on the historically or statistically determined energy use distribution or application usage forecast, as described above. Each hour will have at least a minimum amount of energy budget that is greater than zero (e.g., 0.1%, 1%, etc.). For example, 10% of the energy budget can be distributed among hours with no use data and the remaining 90% of the energy budget can be distributed among active use hours according to historical energy or application use. As each hour passes, the current energy budget will be replenished with the energy budget for the new/current hour. Any energy budget left over from a previous hour will be added to the current hour&#39;s budget. 
     In some implementations, accounting data store  402  can include a counter for determining how much energy budget remains for fetch applications (e.g., predictively launched applications). For example, accounting data store  402  can include one or more counters that are initialized with the energy budgets (e.g., fetch budget, push and background download/upload) for the current hour. When the energy budget is used by an application, the corresponding (e.g., fetch or push) energy budget can be decremented by a corresponding amount. For example, application manager  106  can notify sampling daemon  102  when an application is launched or terminated. In turn, sampling daemon  102  can notify power monitor  108  when an application is launched and when the application is terminated. Based on the start and stop times, power monitor  108  can determine how much energy was used by the application. Power monitor  108  can transmit the amount of power used by the application to sampling daemon  102  and sampling daemon  102  can decrement the appropriate counter by the amount of power used. 
     In some implementations, when no energy budget remains for the current hour, sampling daemon  102  can respond with a “no” reply to an ok to launch request. For example, when the energy budget counters in accounting data store  402  are decremented to zero, no energy budget remains and no additional background applications will be launched. 
     In some implementations, sampling daemon  102  will not base an “ok to launch” determination on the energy budget when the mobile device  100  is plugged into external power. For example, a remaining energy budget of zero will not prevent applications from launching when the mobile device  100  is plugged into an external power source. 
     Data Budget 
     In some implementations, sampling daemon  102  can determine whether it is ok to launch an application based on a data budget. For example, sampling daemon  102  can determine an average amount of network data consumed by the mobile device  100  based on statistical data collected by sampling daemon  102  and stored in event data store  104 . The network data budgets (e.g., fetch/push budget, background download/upload budget) can be calculated as a percentage of average daily network data consumed by the user/mobile device  100 . Alternatively, the network data budgets can be predefined or configurable values (e.g., 15 MB per day for fetch/push budget, 5 MB per day for background download/upload). 
     In some implementations, the network data budgets can be distributed among each hour in a 24 hour period. For example, each hour can be allocated a minimum budget (e.g., 0.2 MB). The remaining amount of the network data budget can be distributed among each of the 24 hours according to historical network data use. For example, sampling daemon  102  can determine based on historical statistical data how much network data is consumed in each hour of the day and assign percentages according to the amounts of data consumed in each hour. As each hour passes, the current data budget will be replenished with the data budget for the new/current hour. Any data budget left over from a previous hour will be added to the current hour&#39;s data budget. 
     In some implementations, accounting data store  402  can maintain data counters for network data budgets. For example, accounting data store  402  can maintain a data counter for fetch/push applications. Accounting data store  402  can maintain a data counter for background download/upload operations. As network data is consumed, the data counters can be decremented according to the amount of network data consumed. For example, the amount of network data consumed can be determined based on application start and stop times provided to sampling daemon  102  by application manager  106 . Alternatively, the amount of network data consumed can be provided by the process utilizing the network interface (e.g., the background download/upload daemon described below). 
     In some implementations, sampling daemon  102  can keep track of which network interface (e.g., cellular or Wi-Fi) is used to consume network data and determine the amount of network data consumed based on the network interface. The amount of network data consumed can be adjusted according to weights or coefficients assigned to each interface. For example, network data consumed on a cellular data interface can be assigned a coefficient of one (1). Network data consumed on a Wi-Fi interface can be assigned a coefficient of one tenth (0.1). The total network data consumed can be calculated by adding the cellular data consumed to Wi-Fi data consumed divided by ten (e.g., total data=1*cellular data+0.1*Wi-Fi). Thus, data consumed over Wi-Fi will impact the data budget much less than data consumed over a cellular data connection. 
     In some implementations, when no data budget remains for the current hour, sampling daemon  102  can respond with a “no” reply to an ok to launch request. For example, when the data budget counters in accounting data store  402  are decremented to zero, no data budget remains and no additional background applications will be launched. 
     Global Application Launch Rate Limit 
     In some implementations, sampling daemon  102  can determine whether it is ok to launch an application based on a global application launch rate limit. For example, sampling daemon  102  can be configured with a number of background applications (e.g., fetch and/or push background applications) that application manager  106  can launch per hour. In some implementations, launch rate limits will only be considered for push notification triggered application launches, as described below. For example, application manager  106  can be limited to launching  64  background applications per hour. Sampling daemon  102  can maintain a counter that tracks the number of background application launches that have been performed in the current hour. Each hour the global application launch counter can be reset to allow more application launches. However, if the global application launch rate for the current hour is exceeded, then no additional background applications can be launched and sampling daemon  102  will return a “no” reply to an ok to launch request. 
     Per Application Launch Rate Limit 
     In some implementations, sampling daemon  102  can determine whether it is ok to launch an application based on an individual application launch rate limit. For example, sampling daemon  102  can be configured with a number times that individual background applications (e.g., fetch and/or push background applications) can be launched per hour. For example, application manager  106  can be limited to launching the same application 15 times per hour. Sampling daemon  102  can maintain a counter that tracks the number of times individual applications are launched in the background per hour. Each hour the individual application launch counter can be reset to allow more application launches. However, if the individual application launch rate for the current hour is exceeded for a particular application, then the particular application cannot be launched again in the current hour and sampling daemon  102  will return a “no” reply to an ok to launch request that identifies the particular application. 
     Launching a Background Fetch Application 
     In some implementations, when application manager  106  makes an “ok to launch” call to sampling daemon  102  and receives a “yes” reply, application manager  106  can invoke or launch the identified application (e.g., application  108 ) in the background of the operating environment of mobile device  100 . For example, the application  108  can be launched in the background such that it is not apparent to the user that application  108  was launched. The application  108  can then communicate over a network (e.g., the internet) with content server  404  to download updated content for display to the user. Thus, when the user subsequently invokes application  108  (e.g., brings the application to the foreground), the user will be presented with current and up-to-date content without having to wait for application  108  to download the content and refresh the application&#39;s user interfaces. 
     In some implementations, application manager  106  can be configured to launch background fetch enabled applications when the mobile device  100  is charging and connected to Wi-Fi. For example, sampling daemon  102  can determine when mobile device  100  is connected to an external power source and connected to the network (e.g., internet) over Wi-Fi and send a signal to application manager  106  to cause application manager  106  to launch fetch enabled applications that have been used within a previous amount of time (e.g., seven days). 
     Example Background Fetch Processes 
       FIG. 6  is a flow diagram of an example process  600  for predictively launching applications to perform background updates. For example, process  600  can be performed by application manager  106  to determine when to launch background applications configured to fetch data updates from network resources, such as content server  404  of  FIG. 4 . Additional description related to the steps of process  600  can be found with reference to  FIG. 4  above. 
     At step  602 , application manager  106  can receive an application invocation forecast from sampling daemon  102 . For example, application manager  106  can be launched during startup of mobile device  100 . During its initialization, application manager  106  can request a forecast of applications likely to be invoked by a user of the mobile device  100  over the next 24 hour period. This forecast can indicate when to launch applications, for example. The 24 hour period can be divided into 15 minute blocks and each 15 minute block can be associated with a probability that the user will invoke an application during the 15 minute block. The forecast returned to application manager  106  can identify up to 64 15 minute blocks of time when the user is likely to invoke an application. 
     At step  604 , application manager  106  can set timers based on the application launch forecast. For example, application manager  106  can set a timer or alarm for each of the 15 minute blocks identified in the application launch forecast returned to the application manager  106  by sampling daemon  102 . 
     At step  606 , application manager  106  can request sampling daemon  102  identify what applications to launch. For example, when a timer expires or alarm goes off, application manager can wake, if sleeping or suspended, and request from sampling daemon  102  a list of applications to launch for the current 15 minute block of time. Sampling daemon  102  can return a list of applications that should be launched in the background on mobile device  100 . 
     At step  607 , application manager  106  can send a request to sampling daemon  102  asking if it is ok to launch an application. For example, for each application identified by sampling daemon  102  in response to the “what to launch” request, application manager  106  can ask sampling daemon  102  whether it is ok to launch the application. Sampling daemon  102  can return “yes” if it is ok to launch the application, “no” if it is not ok to launch the application or “never” if it is never ok to launch the application. 
     At step  610 , application manager  106  can launch an application. For example, if sampling daemon  102  returns an “ok” response for the “ok to launch” request, application manager  106  will launch the application as a background process of mobile device  100 . If sampling daemon  102  returns a “no” or “never” response to the “ok to launch” request, application manager  106  will not launch the application. 
     At step  612 , application manager  106  can transmit an application launch notification to sampling daemon  102 . For example, sampling daemon  102  can use the application launch notification to generate application launch statistics, data usage statistics, energy use statistics and/or other application related statistics as needed. 
     At step  614 , application manager  106  can detect that the launched application has terminated. For example, application manager  106  can determine when the launched application is no longer running on mobile device  100 . 
     At step  616 , application manager  106  can transmit an application termination notification to sampling daemon  102 . For example, sampling daemon  102  can use the application termination notification to generate application launch statistics, data usage statistics, energy use statistics and/or other application related statistics as needed. 
       FIG. 7  is a flow diagram of an example process  700  for determining when to launch applications on a mobile device  100 . For example, process  700  can be used to determine when to launch applications, what applications should be launched and if it is ok to launch applications based on application use statistics, data and energy budgets and mobile device conditions, as described above in detail with reference to  FIG. 4   
     At step  702 , sampling daemon  102  can receive an application launch forecast request from application manager  106 . For example, application manager  106  can request an application launch forecast for the next 24 hours. Once the 24 hour period has passed, application manager  106  can request an application launch forecast for the subsequent 24 hour period. For example, application manager  106  can request an application launch forecast every 24 hours. 
     At step  704 , sampling daemon  102  can determine an application launch forecast. For example, the application launch forecast be used to predict when user initiated application launches are likely to occur during a 24 hour period. For example, sampling daemon  102  can determine an application launch forecast for a 24 hour period. The 24 hour period can be divided into 15 minute time blocks. For each 15 minute time block (e.g., there are 96 15 minute time blocks in a 24 hour period), sampling daemon  102  can use historical user invocation statistics determine a probability that a user initiated application launch will occur in the 15 minute time block, as described above with reference to  FIG. 4 . 
     At step  706 , sampling daemon  102  can transmit the application launch forecast to application manager  106 . For example, sampling daemon  102  can select up to 64 15 minute blocks having the highest non-zero probability of a user initiated application launch. Each of the selected 15 minute blocks can be identified by a start time for the 15 minute block (e.g., 12:45 pm). Sampling daemon  102  can send the list of 15 minute block identifiers to application manager  106  as the application launch forecast. 
     At step  708 , sampling daemon  102  can receive a request for what applications to launch at a current time. For example, application manager  106  can send a request to sampling daemon  102  for sampling daemon  102  to determine which applications should be launched at or around the current time. 
     At step  710 , sampling daemon  102  can score applications for the current time based on historical user data. Sampling daemon  102  can determine which applications that the user is likely to launch in the near future based on historical user initiated application launch data collected by sampling daemon  102 . Sampling daemon  102  can utilize recent application launch data, daily application launch data and/or weekly application launch data to score applications based on the historical likelihood that the user will invoke the application at or around the current time, as described above with reference to  FIG. 4  and  FIG. 5 . 
     At step  712 , sampling daemon  102  can transmit the applications and application scores to application manager  106 . For example, sampling daemon  102  can select a number (e.g., three) of applications having the highest scores (e.g., highest probability of being invoked by the user) to transmit to application manager  106 . Sampling daemon  102  can exclude applications that have been launched within a previous period of time (e.g., the previous 5 minutes). Sampling daemon  102  can transmit information that identifies the highest scored applications and their respective scores to application manager  106 , as described above with reference to  FIG. 4 . 
     At step  714 , sampling daemon  102  can receive a request from application manager  106  to determine whether it is ok to launch an application. For example, sampling daemon  102  can receive an “ok to launch” request that identifies an application. 
     At step  716 , sampling daemon  102  can determine that current mobile device conditions and budgets allow for an application launch. For example, sampling daemon  102  can analyze the environmental conditions of the mobile device  100 , data and energy budgets, application launch rate limits, network conditions and other data to determine if the current time is a good time to launch an application, as described in detail above with reference to  FIG. 4 . 
     At step  718 , sampling daemon  102  can transmit a reply to application manger  106  indicating that it is ok to launch the identified application. For example, if conditions are good for a background application launch, sampling daemon  102  can return a “yes” value to application manager  106  so that application manager  106  can launch the identified application. 
     Short Term Trending 
     In some implementations, sampling daemon  102  can be configured to detect when applications are trending and predictively launch the applications in the background on mobile device  100  based on the detecting trend. For example, an application is trending if the application is being repeatedly invoked by a user of mobile device  100 . In some cases, the trending application is a new application or, prior to the trend, a rarely used application that may not be included in the “what to launch” application forecasting described above. Thus, the trending application may not be kept up to date using the application launch forecasting methods described above. 
     The purpose of application launch trend detection is to detect applications which are being launched repeatedly by the user and to determine an approximate cadence (e.g., periodicity) with which the applications are being launched, erring on reporting a smaller cadence. Applications that are being invoked repeatedly by a user are said to be “trending.” The determined cadence can then be used by application manager  106  to set timers that will trigger the application manager  106  to launch the trending applications in the background so that the applications will be updated when the user invokes the applications, as described above. For example, if the cadence is 5 minutes for an application, application manager  106  can set a timer that will expire every 4 minutes and cause application manager  106  to launch the application so that the application can receive updated content and update the application&#39;s interfaces before being invoked again by the user. In some implementations, the trend detection mechanisms described herein can be used to detect other system event trends beyond application launches, such as repeated software or network notifications, application crashes, etc. 
     In some implementations, sampling daemon  102  can maintain a trending table that can be used to track the behavior of a number of applications. The trending table can include an application identification field (APPID), a state field (STATE), a last launch timestamp (LLT), an inter-launch cadence (ILC) that indicates the amount of time between launches, and a confidence field (C). 
       FIG. 8  is a flow diagram  800  illustrating state transitions for an entry (e.g., application) in the trending table. Initially at step  802 , the trending table can include empty entries (e.g., records) where the APPID, LLT, ILC and C fields are empty (e.g., N/A) and the STATE is set to “invalid” (I). When an application is launched at time t, the trending table is scanned for an available entry (e.g., an entry in state I). Among the possible invalid entries, several methods can be used for selecting an entry to use. For example, a random invalid entry can be selected. Alternatively, an invalid entry can be selected such that all the empty entries in the trending table are kept in consecutive order. If no invalid entry exists, the oldest entry (or a random entry) in transient (T) state can be selected to track the newly launched application. If no I or T state entries exist, the oldest new (N) state entry can be selected to track the newly launched application. 
     At step  804 , once the trending table entry is selected, the STATE field of the selected entry for tracking the newly launched application can be set to new (N), the APPID can be set to an identifier for the newly launched application, the LLT field can be set to the current time t (e.g., wall clock time) and the ILC and C fields are set to predefined minimum values ILC_MIN (e.g., 1 minute) and C_MIN (e.g., zero). 
     At step  806 , on the next launch of the same application at time t′, the entry in the table for the application is found, if it still exists and has not been evicted (e.g., selected to track another application). The STATE of the entry is set to transient (T), the ILC is set to the difference between the LLT and the current system time (e.g., t′−t or t′−LLT), and the C field is incremented (e.g., by predefined value C_DELTA). Alternatively, the ILC field can be set to some other function of its old and new values, such as the running average. 
     At step  808 , on the next launch of the same application at time t″, the entry in the table for the application is found, if it still exists and has not been evicted (e.g., selected to track another application). The STATE of the entry can remain set to transient (T), the ILC is set to the difference between the LLT and the current wall clock time (e.g., t″−t′ or t″−LLT), and the C field is incremented again (e.g., by predefined value C_DELTA). 
     At step  810 , if, after several launches of the application, the C value of the trending table entry reaches (e.g., equals) a threshold value (e.g., C_HIGHTHRESHOLD), at step  811 , the state of the application entry can be changed to STATE=A. If, at step  810 , the C value of the trending table entry does not reach the threshold value (e.g., C_HIGHTHRESHOLD), the values of the entry can be updated according to step  808 . 
     Whenever the application is launched while in state “A”, if the time between the last launch and the time of launch is within some amount of time (e.g., ILC_EPSILON=5 minutes), then the application entry&#39;s confidence (C) field is incremented until it reaches a predefined maximum value (e.g., C_MAX). When an application entry in the trending table is in the active (A) state, the entry&#39;s ILC value can be used as an estimation of the rate of launch (e.g., cadence) and the entry&#39;s APPID can be used to identify the trending application. 
     In some implementations, sampling daemon  102  can send the application identifier (APPID) and cadence value (ILC) to application manager  106  so that application manager  106  can launch the identified application in the background in anticipation of a user invocation of the application so that the application can receive updated content prior the user launching the application, as described above. For example, application manager  106  can start a timer based on the cadence value that will wake the application manager  106  to launch the application in anticipation of a user invoking the application. 
     In some implementations, sampling daemon  102  can send application manager  106  a signal or notification indicating that a trending application should be launched by application manager  106 . For example, application manager  106  can register interest in an application by sending sampling daemon  102  an application identifier. Sampling daemon  102  can monitor the application for user invocation to determine whether the application is trending, as described above. If the application is trending, sampling daemon  102  can determine the cadence of invocation, as described above, and send a notification or signal to application manager  106  at a time determined based on the cadence. For example, if the cadence is four minutes, sampling daemon  102  can send a signal to application manager  106  every 3 minutes to cause application manager  106  to launch the application. If the cadence changes to six minutes, sampling daemon  102  can detect the cadence change and adjust when application manager  106  is signaled. For example, sampling daemon  102  can signal application manager  106  to launch the application every 5 minutes instead of every 3 minutes to adjust for the decreased cadence (e.g., increased time period between invocations). 
     At each inspection of the trending table for any reason (e.g., adding a new entry, updating an existing entry, etc.), all entries in STATE=T or STATE=A whose time since last launch is greater than their ILC by ILC_EPSILON will have their C values decremented. Any entry whose C value at that point falls below a minimum threshold value (e.g., C_LOWTHRESHOLD) is demoted. An entry can be demoted from state A to state T or from state T to state I, for example. 
     In some implementations, the trend detection mechanism described above can be used to detect trending events other than application invocations or launches. For example, the trend detection method and trending table described above can be used to detect and track any recurring event on mobile device  100 . A trending event can include screen touches, network connections, application failures, the occurrence of network intrusions and/or any other event that can be reported or signaled to sampling daemon  102 . 
     Push Notifications 
       FIG. 9  is a block diagram  900  illustrating a system for providing push notifications to a mobile device  100 . In some implementations, mobile device  100  can be configured to receive push notifications. For example, a push notification can be a message that is initiated by a push provider  902  and sent to a push service daemon  904  running on mobile device  100  through push notification server  906 . 
     In some implementations, push provider  902  can receive authorization to send push notifications to mobile device  100  through a user authorization request presented to a user of mobile device  100  by application  908 . For example, push provider  902  can be a server owned, operated and/or maintained by the same vendor that created (e.g., programmed, developed) application  908 . Push provider  902  can receive authorization from a user to send push notifications to mobile device  100  (e.g., push service daemon  904 ) when application  908  presents a user interface on mobile device  100  requesting authorization for push provider  902  to send push notifications to mobile device  100  and the user indicates that push notifications are authorized. For example, the user can select a button on the user interface presented by application  908  to indicate that push notifications are authorized for the push provider  902  and/or application  908 . Push provider  902  can then receive a device token that identifies mobile device  100  and that can be used to route push notifications to mobile device  100 . For example, push notification server  906  can receive a device token with a push notification and use the device token to determine which mobile device  100  should receive the push notification. 
     In some implementations, mobile device  100  can send information identifying authorized push applications to push notification server  906 . For example, mobile device  100  can send a message  926  containing push notification filters  914  and the device token for mobile device  100  to push notification server  906 . Push notification server  906  can store a mapping of device tokens (e.g., identifier for mobile device  100 ) to push filters  914  for each mobile device serviced by push notification server  906 . Push filters  914  can include information identifying applications that have received authorization to receive push notifications on mobile device  100 , for example. 
     In some implementations, push filters  914  can be used by push notification server  906  to filter out (e.g., prevent sending) push notifications to applications that have not been authorized by a user of mobile device  100 . Each push notification sent by push provider  902  to push notification server  906  can include information (e.g., an identifier) that identifies the application  908  associated with push provider  902  and the mobile device  100  (e.g., device token). 
     When notification server  906  receives a push notification, notification server  906  can use the mobile device identification information (e.g., device token) to determine which push filters  914  to apply to the received push notification. Notification server  906  can compare application identification information in the push notification to the push filters  914  for the identified mobile device to determine if the application associated with push provider  902  and identified in the push notification is identified in the push filter  914 . If the application associated with the push notification is identified in the push filters  914 , then the notification server  906  can transmit the push notification received from push provider  902  to mobile device  100 . If the application identified in the push notification is not identified in the push filters  914 , then the notification server will not transmit the push notification received from push provider  902  to mobile device  100  and can delete the push notification. 
     Non-Waking Push Notifications 
     In some implementations, notification server  906  can be configured to process high priority push notifications and low priority push notifications. For example, push provider  902  can send a high priority push notification  910  and/or a low priority push notification  912  to push notification server  906 . Push provider  902  can identify a push notification as high or low priority by specifying the priority of the push notification in data contained within the push notification sent to push notification server  906  and mobile device  100 , for example. 
     In some implementations, push notification server  906  can process low priority push notification  912  differently than high priority push notification  910 . For example, push notification server  906  can be configured to compare application identification information contained in high priority push  910  with authorized application identification information in push filters  914  to determine if high priority push notification  910  can be transmitted to mobile device  100 . If the application identification information in high priority push notification  910  matches an authorized application identifier in push filters  914 , then push notification server  906  can transmit the high priority push notification to mobile device  100 . If the application identification information in high priority push notification  910  does not match an authorized application identifier in push filters  914 , then push notification server  906  will not transmit the high priority push notification to mobile device  100 . 
     In some implementations, push notification server  906  can be configured to delay delivery of low priority push notifications. For example, when mobile device  100  receives a push notification from push notification server  906 , the receipt of the push notification causes mobile device  100  to wake up (e.g., if in a sleep or low power state). When mobile device  100  wakes, mobile device  100  will turn on various subsystems and processors that can drain the battery, use cellular data, cause the mobile device  100  to heat up or otherwise effect the mobile device  100 . By preventing or delaying the delivery of low priority push notifications to mobile device  100 , mobile device  100  can conserve network (e.g., cellular data) and system (e.g., battery) resources, for example. 
     In some implementations, push notification filters  914  can include a wake list  916  and a no wake list  918 . The wake list  916  can identify applications for which low priority push notifications should be delivered to mobile device  100 . In some implementations, when an application is authorized to receive push notifications at mobile device  100 , the application identification information is added to the wake list  916  by default. The no wake list  918  can identify authorized applications for which low priority push notifications should be delayed. The specific mechanism for populating no wake list  918  and/or manipulating wake list  916  and no wake list  918  is described in detail below when describing push notification initiated background updates. In some implementations, high priority push notifications will not be delayed at the push notification server  906  and will be delivered to mobile device  100  as long as the application identified in the high priority push notification is identified in push filters  914  (e.g., wake list  916  and/or no wake list  918 ). 
     In some implementations, when push notification server  906  receives a low priority push notification  912 , push notification server  906  can compare the application identifier in low priority push notification  912  to wake list  916  and/or no wake list  918 . For example, if the application identification information in the low priority push notification  912  matches an authorized application identifier in the wake list  916 , the low priority push notification  912  will be delivered to the mobile device  100  in a notification message  920 . 
     In some implementations, delivery of low priority push notifications associated with applications identified in the no wake list  918  can be delayed. For example, if an application identified in low priority push notification  912  is also identified in no wake list  918 , then low priority push notification  912  can be stored in push notification data store  922  and not immediately delivered to mobile device  100 . In some implementations, if the mobile device  100  identified by a push notification (high or low priority) is not currently connected to push notification server  906 , the push notification for the disconnected mobile device  100  can be stored in push notification data store  922  for later delivery to mobile device  100 . 
     In some implementations, push notifications stored in push data store  922  will remain in push data store  922  until the application identifier associated with a stored push notification is moved from the no wake list  918  to wake list  916  or until a network connection is established between push notification server  906  and mobile device  100 . For example, a network connection between push notification server  906  and mobile device  100  can be established when another (high or low priority) push notification is delivered to mobile device  100  or when mobile device  100  sends other transmissions  924  (e.g., status message, heartbeat message, keep alive message, etc.) to push notification server  906 . For example, mobile device  100  can send a message  924  to push notification server  905  indicating that the mobile device  100  will be active for a period of time (e.g., 5 minutes) and push notification server  906  can send all received push notifications to mobile device  100  during the specified active period of time. In some implementations, when a network connection is established between mobile device  100  and push notification server  906  all push notifications stored in push notification store  922  will be delivered to mobile device  100 . For example, push notifications stored in push notification data store  922  can be transmitted through connections created by other transmissions between mobile device  100  an push notification server  906 . 
     In some implementations, mobile device  100  can establish two different communication channels with push notification server  906 . For example, the two communication channels can be established simultaneously or at different times. The mobile device  100  can have a cellular data connection and/or a Wi-Fi connection to push notification server  906 , for example. In some implementations, mobile device  100  can generate and transmit to push notification server  906  different push filters  914  for each communication channel. For example, a cellular data connection can be associated with first set of push filters  914  for determining when to send high and low priority push notifications across the cellular data connection. A Wi-Fi data connection can be associated with a second set of push filters  914  that are the same or different than the cellular data push filters for determining when to send high and low priority push notifications across the Wi-Fi data connection. When push notification server  906  receives a push notification, push notification server can compare the application identified in the push notification to the push notification filters for the communication channel (e.g., Wi-Fi, cellular) that the push notification server  906  will use to transmit the push notification to the mobile device  100 . 
     Push Initiated Background Updates 
     In some implementations, receipt of push notifications by mobile device  100  can trigger a background update of applications on the mobile device  100 . For example, when mobile device  100  (e.g., push service daemon  904 ) receives a push notification message  920  from push notification server  906 , push service daemon  904  can compare the application identifier in the push notification message  920  to push filters  928  stored on mobile device  100  to determine if the push notification message  920  was properly delivered or should have been filtered (e.g., not delivered) by push notification server  906 . For example, push filters  928 , wake list  930  and no wake list  932  can correspond to push filters  914 , wake list  916  and no wake list  918 , respectively. In some implementations, if push service daemon  904  determines that the push notification message  920  should not have been delivered to mobile device  100 , the push notification message  920  will be deleted. 
     Low Priority Push Notifications 
     In some implementations, the push notification message  920  received by mobile device  100  can include a low priority push notification. For example, the low priority push notification can indicate that content updates are available for the application associated with the push notification. Thus, when the low priority push notification causes an launch of an application  908 , the application  908  can download updated content from one or more network resources (e.g., push provider  902 ). 
     In some implementations, when push service daemon  904  receives a low priority push notification associated with an application (e.g., application  908 ) on mobile device  100 , push service daemon  904  can ask sampling daemon  102  if it is ok to launch the application associated with the received low priority push notification. For example, push service daemon  904  can invoke the “ok to launch” interface of sampling daemon  102  by sending sampling daemon  102  an identifier for the application associated with the received low priority push notification. Sampling daemon  102  can check data budgets, energy budgets, environmental conditions and rate limits, as described above with reference to  FIG. 4 , and returns to push service daemon  904  a value indicating whether it is ok to launch the application identified by the low priority push notification. 
     In some implementations, if the value returned from the “ok to launch” request indicates “yes” it is ok to launch the application, push service daemon  904  will send the low priority push notification to application manager  106  and application manager  106  can invoke the application (e.g., application  908 ). Application  908  can then communicate with push provider  902  over the network (e.g., the internet) to receive updated content from push provider  902 . 
     In some implementations, if the value returned from the “ok to launch” request indicates “no” it is not ok to launch the application, push service daemon  904  will store the low priority push notification in push notification data store  934 . For example, when storing a low priority push notification, push service daemon  904  will only store the last push notification received for the application identified in the push notification. 
     In some implementations, when sampling daemon  102  indicates that push service daemon  904  should not launch an application right now (e.g., the “ok to launch” reply is “no”), push service daemon  904  can move the application identifier for the application from wake list  930  to no wake list  932 . For example, if sampling daemon  102  determines that the budgets, limits and/or conditions of the mobile device do not allow for launching the application, allowing the push notification server  906  to wake mobile device  100  for additional low priority push notifications associated with the application will just further consume the data and energy budgets of the mobile device  100  or make environmental conditions worse (e.g., cause the device to heat up). Thus, by moving the application identifier into the no wake list  932  and sending a message  926  to push notification server  906  that includes the updated filters  928  (e.g., wake list  930  and no wake list  932 ), notification server  906  can update its own push filters  914 , wake list  916  and no wake list  918  to reflect the changes to push filters  928  and to prevent additional low priority push notifications for the application from being delivered to mobile device  100 . 
     In some implementations, if the value returned from the “ok to launch” request indicates that it is “never” ok to launch the application, push service daemon  904  will delete the low priority push notification and remove the application identifier associated with the push notification from push filters  928 . The updated push filters can be transmitted to push notification server  906  and push filters  914  on push notification server  906  can be updated to prevent push notification server  906  from sending any more push notifications associated with the application identifier. 
     In some implementations, sampling daemon  102  can transmit a “stop” signal to push service daemon  904  to temporarily prevent future low priority push notifications from being sent from push notification server  906  to mobile device  100 . For example, sampling daemon  102  can send a stop signal to push service daemon  904  when sampling daemon determines that the global application launch rate limit has been exceeded, the push data budget is exhausted for the current hour, the push energy budget is exhausted for the current hour, the system is experiencing a thermal event (e.g., mobile device  100  is too hot), the mobile device  100  has a poor cellular connection and the mobile device  100  is not connected to Wi-Fi and/or that the mobile device  100  is connected to a voice call and not connected to Wi-Fi. When push service daemon  904  receives a stop signal, push service daemon  904  can move the application identifiers in wake list  930  to no wake list  932  and transmit the updated push filters  928  to push notification server  906  to update push filters  914 . Thus, push notification server  906  will temporarily prevent future low priority push notifications from waking mobile device  100  and impacting the budgets, limits and operating conditions of mobile device  100 . 
     In some implementations, sampling daemon  102  can transmit an “ok to retry” signal to push service daemon  904 . For example, sampling daemon  102  can monitor the status of the budgets, network connections, limits and device conditions and will send an “ok to retry” message to push service daemon  904  when the push data budget is not exhausted, when the energy budget is not exhausted, when the mobile device  100  is not experiencing a thermal event, when the mobile device  100  has a good quality cellular connection or is connected to Wi-Fi, when mobile device  100  is not connected to a voice call and when the launch rate limits have been reset. Once the push service daemon  904  receives the “ok to retry” signal, push service daemon  904  will send an “ok to launch” request to sampling daemon  102  for each push notification in push notification data store  934  to determine if it is ok to launch each application associated with the stored push notifications. 
     If sampling daemon  102  returns a “yes” from the ok to launch request, push service daemon  904  can send the push notification to application manager  106  and application manager  106  can launch the application associated with the push notification as a background process on mobile device  100 , as described above. Once the application is launched, the application can download content or data updates and update the applications user interfaces based on the downloaded data. Application manager  106  will not ask sampling daemon  102  if it is ok to launch an application associated with a low priority push notification. 
     High Priority Push Notifications 
     In some implementations, the push notification message  920  received by mobile device  100  can include a high priority push notification. For example, the high priority push notification can indicate that content updates are available for the application associated with the push notification. Thus, when the high priority push notification causes an invocation of an application, the application can download updated content from one or more network resources. In some implementations, when a high priority push notification is received by push service daemon  904 , push service daemon  904  will send the high priority push notification to application manager  106  without making an “ok to launch” request to sampling daemon  102 . 
     In some implementations, when application manager  106  receives a push notification associated with an application, application manager  106  will make an “ok to launch” request to sampling daemon  102 . In response to the “ok to launch” request, sampling daemon  102  can reply with “yes,” “no,” or “never” responses as described above. When application manager  106  receives a “yes” reply to the ok to launch request, application manager  106  can launch the application associated with the received high priority push notification as a background process on mobile device  100 . 
     In some implementations, when application manager  106  receives a “no” reply to an “ok to launch” request, application manager  106  can store the high priority push notification in high priority push notification store  936 . When application manager  106  receives a “never” response, application manager  106  can delete the high priority push notification and delete any push notifications stored in push notification data store  936  for the application associated with the push notification. 
     In some implementations, sampling daemon  102  can send an “ok to retry” signal to application manager  106 . For example, when application manager  106  receives an “ok to retry” message from sampling daemon  102 , application manager  106  can make an “ok to launch” request for the applications associated with each high priority push notification in high priority push notification data store  936  and launch the respective applications as background processes when a “yes” reply is received in response to the “ok to launch” request. 
     Delaying Display of Push Notifications 
     In some implementations, high priority push notifications can cause a graphical user interface to be displayed on mobile device  100 . For example, receipt of a high priority push notification can cause a banner, balloon or other graphical object to be displayed on a graphical user interface of mobile device  100 . The graphical object can include information indicating the subject matter or content of the received push notification, for example. 
     In some implementations, when application manager  106  receives a high priority push notification, application manager  106  can cause the notification to be displayed on a graphical user interface of the mobile device  100 . However, when the high priority push notification indicates that there are data updates to be downloaded to the application associated with the high priority push notification, the application can be launched in the background of mobile device  100  before the push notification is displayed. For example, application manager  106  can be configured with an amount of time (e.g., 30 seconds) to delay between launching an application associated with the high priority push notification and displaying the graphical object (e.g., banner) that announces the push notification to the user. The delay can allow the application enough time to download content updates and update the application&#39;s user interfaces before being invoked by the user, for example. Thus, when the user provides input to the graphical object or otherwise invokes the application associated with the high priority push notification, the application&#39;s user interfaces will be up to date and the user will not be forced to wait for updates to the application. In some implementations, if application manager  106  is unable to launch the application associated with the high priority push notification, the mobile device  100  will display the graphical object (e.g., banner) to notify the user that the high priority push notification was received. 
     Example Push Notification Processes 
       FIG. 10  is a flow diagram of an example process  1000  for performing non-waking pushes at a push notification server  906 . At step  1002 , push notification server  906  can receive a push notification. For example, push notification server  906  can receive a push notification from a push notification provider  902  (e.g., a server operated by an application vendor). 
     At step  1004 , push notification server  906  can determine that the push notification is a low priority push notification. For example, the push notification provider can include data in the push notification that specifies the priority of the push notification. Push notification server  906  can analyze the contents of the push notification to determine the priority of the push notification. 
     At step  1006 , push notification server  906  can compare the push notification to a push notification filter. For example, the push notification can identify an application installed or configured on mobile device  100  to which the low priority push notification is directed. The push notification can include an application identifier, for example. Push notification server  906  can compare the application identifier in the push notification to application identifiers in the push notification filter&#39;s no wake list  918 . 
     At step  1008 , push notification server  906  can determine that the low priority push notification should be stored. For example, if the application identifier from the low priority push notification is in the push notification filter&#39;s no wake list  918 , the push notification server  906  can determine that the low priority push should be stored in push notification data store  922 . 
     At step  1010 , based on the determination at step  1008 , the low priority push notification will be stored in a database or data store  922  of the push notification server  906  and not immediately sent to the mobile device  100 . 
     At step  1012 , push notification server  906  can determine that a network connection to mobile device  100  has been established. For example, push notification server  906  can create a network connection to mobile device  100  to deliver another high or low priority push. Mobile device  100  can establish a network connection to push notification server  906  to send notification filter changes, periodic status updates, keep alive messages or other messages to push notification server  906 . 
     At step  1014 , push notification server  906  can send the stored push notifications in response to determining that a network connection to mobile device  100  has been established. For example, push notification server  906  can send the low priority push notifications stored at the push notification server  906  to mobile device  100 . 
       FIG. 11  is a flow diagram of an example process  1100  for performing background updating of an application in response to a low priority push notification. At step  1102 , mobile device  100  can receive a low priority push notification from push notification server  906 . 
     At step  1104 , mobile device  100  can determine if it is ok to launch an application associated with the low priority push notification. For example, the application can be launched as a background process on mobile device  100 . Mobile device  100  can determine whether it is ok to launch the application based on data and energy budgets determined for the mobile device  100 . Mobile device  100  can determine whether it is ok to launch the application based on conditions of the mobile device, and/or the condition of the mobile device&#39;s network connections. Mobile device  100  can determine whether it is ok to launch the application based on device-wide (e.g., global) and individual application launch limits (e.g., how many applications can be launched per hour, how many times a single application can be launched per hour). The details for determining whether it is ok to launch an application are described in greater detail with reference to  FIG. 4  above. 
     At step  1106 , mobile device  100  can store the low priority push notification when device conditions, budgets, limits and other data indicate that it is not ok to launch the application. For example, mobile device  100  can store the low priority push notifications in a database or other data store on mobile device  100 . 
     At step  1108 , mobile device  100  can update its push notification filters in response to determining that it is not ok to launch a background application. For example, mobile device  100  can move the application associated with the low priority push notification to the no wake list of the push notification filters on mobile device  100 . 
     At step  1110 , mobile device  100  can transmit the updated notification filters to push notification server  906 . Push notification server  906  can update its own push notification filters based on the filters received from mobile device  100  to determine when to transmit and when to not transmit low priority push notifications to mobile device  100 . 
     At step  1112 , mobile device  100  can determine that it is ok to retry launching applications associated with low priority push notifications. For example, mobile device  100  can determine that the budgets, limits and device conditions, as described above, allow for launching additional background applications on the mobile device  100 . 
     At step  1114 , mobile device  100  can determine whether it is ok to launch a particular application associated with a stored low priority push notification. For example, mobile device  100  can determine that the budgets and limits configured on mobile device  100  have been reset or replenished for the current time and that the environmental conditions of the mobile device  100  and network connections are good enough to launch the particular background application. 
     At step  1116 , mobile device  100  can launch the particular application when the mobile device  100  determines that it is ok to launch the application. For example, the particular application can be launched as a background process to download new content and update the user interfaces of the application before a user invokes the application. This process will allow a user to invoke an application and not have to wait for content updates to be downloaded and for user interfaces of the application to be refreshed. 
       FIG. 12  is a flow diagram of an example process  1200  for performing background updating of an application in response to a high priority push notification. At step  1202 , mobile device  100  can receive a high priority push notification. 
     At step  1204 , mobile device  100  can determine if it is ok to launch an application associated with the high priority push notification. For example, mobile device  100  can determine whether it is ok to launch the application based on budgets and environmental conditions of the mobile device  100  (e.g., device conditions, network conditions, etc.). 
     At step  1206 , mobile device  100  can store the high priority push notification when it is not ok to launch the application associated with the high priority push notification. For example, mobile device  100  can store the high priority push notification in a database, queue, or other appropriate data structure. 
     At step  1208 , mobile device  100  can determine that it is ok to retry launching applications associated with stored high priority push notifications. For example, mobile device  100  can determine that it is ok to retry launching applications when the data and energy budgets have been replenished, device conditions have improved, network conditions have improved or other conditions of the mobile device  100  have changed, as discussed above. 
     At step  1210 , mobile device  100  can determine if it is ok to launch an application associated with a stored high priority push notification. For example, mobile device  100  can determine if it is ok to launch an application based on the criteria discussed above. 
     At step  1212 , mobile device  100  can launch the application in the background on the mobile device  100 . For example, the application can be launched as a background process on the mobile device  100  so that the application can download updated content from a network resource (e.g., a content server) on a network (e.g., the internet). 
     At step  1214 , the mobile device  100  can wait a period of time before presenting the push notification to the user. For example, the mobile device can be configured to allow the application to download content for a period of time before notifying the user of the received high priority push notification. 
     At step  1216 , the mobile device  100  can present the push notification on a user interface of the mobile device  100 . For example, the mobile device  100  can present a graphical object (e.g., a banner) that includes information describing the high priority push notification. The user can select the graphical object to invoke the application, for example. Since the application had time to download content before the user was presented with the notification, when the user invokes the application the application will be able to display updated content to the user without forcing the user to wait for the updated content to be downloaded from the network. 
     Background Uploading/Downloading 
       FIG. 13  is a block diagram an example system  1300  for performing background downloading and/or uploading of data on a mobile device  100 . A background download and/or upload can be a network data transfer that is initiated by an application without explicit input from the user. For example, a background download could be performed to retrieve the next level of a video game while the user is playing the video game application. In contrast, a foreground download or upload can be a network data transfer performed in response to an explicit indication from the user that the download or upload should occur. For example, a foreground download could be initiated by a user selecting a webpage link to download a picture, movie or document. Similarly, background uploads can be distinguished from foreground uploads based on whether or not an explicit user request to upload data to a network resource (e.g. server) was received from the user. 
     In some implementations, foreground downloads/uploads (e.g., downloads/uploads explicitly requested by a user) are performed immediately for the user. For example, the user requested downloads/uploads are performed immediately and are not subject to budgeting constraints or other considerations. Foreground downloads/uploads can be performed over a cellular data connection. In contrast, background downloads and/or uploads can be performed opportunistically and within budgeting constraints and considering environmental conditions, such as the temperature of the mobile device  100 . In some implementations, background downloads and/or uploads can be restricted to Wi-Fi connections. 
     In some implementations, system  1300  can include background transfer daemon  1302 . In some implementations, background transfer daemon  1302  can be configured to perform background downloading and uploading of data or content on behalf of applications or processes running on mobile device  100 . For example background transfer daemon  1302  can perform background download and/or uploads between application  1304  and server  1306  on behalf of application  1304 . Thus, the background downloads/uploads can be performed out of process from application  1304  (e.g., not performed in the process requesting the download/upload). 
     In some implementations, application  1304  can initiate a background download/upload by sending a request to background transfer daemon  1302  to download or upload data. For example, a request to download data (e.g., content) can identify a network location from where the data can be downloaded. A request to upload data can identify a network location to which the data can be uploaded and a location where the data is currently stored on the mobile device  100 . The request can also identify application  1304 . Once the request has been made, application  1304  can be shut down or suspended so that the application will not continue consuming computing and/or network resources on mobile device  100  while the background download/upload is being performed by background transfer daemon  1302 . 
     In some implementations, upon receiving a request to perform a background upload or download of data, background transfer daemon  1302  can send a request to sampling daemon  102  to determine if it is ok for background transfer daemon  1302  to perform a data transfer over the network. 
     In response to receiving the “ok to transfer” request from background transfer daemon  1302 , sampling daemon  102  can determine if the data and/or energy budgets for background downloads/uploads have been exhausted for the current hour. However, if sampling daemon  102  determines that the mobile device  100  is connected to an external power source, sampling daemon  102  will not prevent a background download/upload based on the energy budget. Sampling daemon  102  can determine if mobile device  100  is connected to Wi-Fi. Sampling daemon  102  can also determine whether mobile device  100  is in the middle of a thermal event (e.g., operating temperature above a predefined threshold value). If sampling daemon  102  determines that the data budget is exhausted and the mobile device  100  is not connected to Wi-Fi, that the energy budget is exhausted and the mobile device  100  is not connected to an external power source, or that the mobile device  100  is in the middle of a thermal event, then sampling daemon  102  will return a “no” reply to the “ok to transfer” request by process  1302 . 
     In some implementations, when background transfer daemon  1302  receives a “no” reply to the “ok to transfer” request from sampling daemon  102 , process  1302  can store the background download/upload request from application  1304  in request repository  1308 . 
     In some implementations, sampling daemon  102  can send an “ok to retry” signal to background transfer daemon  1302 . For example, sampling daemon  102  can send the ok to retry signal to background transfer daemon  1302  when the data and energy budgets are replenished and when the system is no longer experiencing a thermal event. Sampling daemon  102  can send the ok to retry signal to background transfer daemon  1302  when the mobile device  100  is connected to Wi-Fi, connected to external power and when the system is not experiencing a thermal event. 
     In some implementations, when the “ok to retry” signal is received by background transfer daemon  1302 , background transfer daemon  1302  can send an “ok to transfer” request to sampling daemon  102 . If sampling daemon  102  returns an “ok” reply, background transfer daemon  1302  can perform the background download or upload for application  1304 . Once a background download is completed, background transfer daemon  1302  can wake or invoke application  1304  and provide application  1304  with the downloaded data. 
     In some implementations, background transfer daemon  1302  can notify sampling daemon  102  when the background download/upload starts and ends so that sampling daemon  102  can adjust the budgets and maintain statistics on the background downloads/uploads performed on mobile device  100 . In some implementations, background transfer daemon  1302  can transmit the number of bytes transferred over cellular data, over Wi-Fi and/or in total so that sampling daemon  102  can adjust the budgets and maintain statistics on the background downloads/uploads performed on mobile device  100 . 
     In some implementations, sampling daemon  102  can return a timeout value to background transfer daemon  1302  in response to an “ok to transfer” request. For example, the timeout value can indicate a period of time (e.g., 5 minutes) that the background transfer daemon has to perform the background download or upload. When the timeout period elapses, background transfer daemon  1302  will suspend the background download or upload. 
     In some implementations, the timeout value can be based on remaining energy budgets for the current hour. For example, sampling daemon  102  can determine how much energy is consumed each second while performing a download or upload over Wi-Fi based on historical data collected by sampling daemon  102 . Sampling daemon  102  can determine the time out period by dividing the remaining energy budget by the rate at which energy is consumed while performing a background download or upload (e.g., energy budget/energy consumed/time=timeout period). 
     In some implementations, background downloads and/or uploads are resumable. For example, if mobile device  100  moves out of Wi-Fi range, the background download/upload can be suspended (e.g., paused). When mobile device  100  reenters Wi-Fi range, the suspended download/upload can be resumed. Similarly, if the background download/upload runs out of energy budget (e.g., timeout period elapses), the background download/upload can be suspended. When additional budget is allocated (e.g., in the next hour), the suspended download/upload can be resumed. 
     In some implementations, background downloads/uploads can be suspended based on the quality of the network connection. For example, even though mobile device  100  can have a good cellular data connection between mobile device  100  and the servicing cellular tower and a good data connection between the cellular tower and the server that the mobile device  100  is transferring data to or from, mobile device  100  may not have a good connection to the server. For example, the transfer rate between the mobile device  100  and the server may be slow or the throughput of the cellular interface may be low. If the transfer rate of the background download/upload falls below a threshold transfer rate value and/or the throughput of the background download/upload falls below a threshold throughput value, the background download/upload (e.g., data transfer) can be suspended or paused based on the detected poor quality network connection until a better network connection is available. For example, if a Wi-Fi connection becomes available the suspended background download/upload can be resumed over the Wi-Fi connection. 
     In some implementations, background transfer daemon  1302  can be configured with a limit on the number of background downloads and/or uploads that can be performed at a time. For example, background transfer daemon  1302  can restrict the number of concurrent background downloads and/or uploads to three. 
     Example Background Download/Upload Process 
       FIG. 14  is flow diagram of an example process  1400  for performing background downloads and uploads. For example, background downloads and/or uploads can be performed on behalf of applications on mobile device  100  by background transfer daemon  1302 . 
     At step  1402 , a background transfer request can be received. For example, background transfer daemon  1302  can receive a background download/upload request from an application running on mobile device  100 . Once the application makes the request, the application can be terminated or suspended, for example. The request can identify the application and identify source and/or destination locations for the data. For example, when downloading data the source location can be a network address for a server and the destination location can be a directory in a file system of the mobile device  100 . When uploading data, the source location can be a file system location and the destination can be a network location. 
     At step  1404 , mobile device  100  can determine that budgets and device conditions do not allow for the data transfer. For example, background transfer daemon  1302  can ask sampling daemon  102  if it is ok to perform the requested background transfer. Sampling daemon  102  can determine if energy and data budgets for background download/upload are exhausted and if the mobile device  100  is in the middle of a thermal event. If the background download/upload budgets are exhausted or if the mobile device  100  is in the middle of a thermal event, sampling daemon  102  can send a message to background transfer daemon  1302  indicating that it is not ok to perform the background data transfer. 
     At step  1406 , mobile device  100  can store the background transfer request. For example, background transfer daemon  1302  can store the transfer request in a transfer request repository. 
     At step  1408 , mobile device  100  can determine that it is ok to retry the background transfer. For example, sampling daemon  102  can determine that the data and energy budgets have been replenished and that the mobile device  100  is not in the middle of a thermal event. Sampling daemon  102  can send a retry message to background transfer daemon  1302 . Background transfer daemon  1302  can then attempt to perform the requested transfers stored in the transfer request repository. 
     At step  1410 , mobile device  100  can determine that budgets and conditions of the mobile device  100  allow for background data transfer. For example, background transfer daemon  1302  can ask sampling daemon  102  if it is ok to perform the requested background transfer. Sampling daemon  102  can determine that energy and data budgets for background download/upload are replenished and that the mobile device  100  is not in the middle of a thermal event. If the background download/upload budgets are not exhausted or if the mobile device  100  is not in the middle of a thermal event, sampling daemon  102  can send a message to background transfer daemon  1302  indicating that it is ok to perform the background data transfer. 
     At step  1412 , mobile device  100  can perform the background transfer. For example, background transfer daemon  1302  can perform the requested background download or background upload for the requesting application. Background transfer daemon  1302  can notify sampling daemon  102  when the background transfer begins and ends. Background transfer daemon  1302  can send a message informing sampling daemon of the number of bytes transferred during the background download or upload. Once the background transfer is complete, background transfer daemon  1302  can invoke (e.g., launch or wake) the application that made the background transfer request and send completion status information (e.g., success, error, downloaded data, etc.) to the requesting application. 
     Enabling/Disabling Background Updates 
       FIG. 15  illustrates an example graphical user interface (GUI)  1500  for enabling and/or disabling background updates for applications on a mobile device. For example, GUI  1500  can be an interface presented on a display of mobile device  100  for receiving user input to adjust background update settings for applications on mobile device  100 . 
     In some implementations, user input to GUI  1500  can enable or disable background updates from being performed for applications based on a user invocation forecast, as described above. For example, sampling process  102  and/or application manager  106  can determine whether background updates are enabled or disabled for an application and prevent the application from being launched by application manager  106  or prevent the application from being included in application invocation forecasts generated by sampling process  102 . For example, if background updates are disabled for an application, sampling daemon  102  will not include the application the user invoked application forecast requested by when application manager  106 . Thus, application manager  106  will not launch the application when background updates are disabled. Conversely, if background updates are enabled for the application, the application may be included in the application invocation forecast generated by sampling daemon  102  based on user invocation probabilities, as described above. 
     In some implementations, user input to GUI  1500  can enable or disable background updates from being performed for applications when a push notification is received, as described above. For example, sampling process  102 , application manager  106  and/or push service daemon  904  can determine whether background updates are enabled or disabled for an application and prevent the application from being launched by application manager  106  in response to receiving a push notification. For example, if background updates are disabled for an application and a push notification is received for the application, application manager  106  will not launch the application to download updates in response to the push notification. 
     In some implementations, GUI  1500  can display applications  1502 - 1514  that have been configured to perform background updates. For example, the applications  1502 - 1514  can be configured or programmed to run as background processes on mobile device  100  when launched by application manager  106 . When run as a background process, the applications  1502 - 1514  can communicate with various network resources to download current or updated content. The applications  1502 - 1514  can then update their respective user interfaces to present updated content when invoked by a user of mobile device  100 . In some implementations, applications that are not configured or programmed to perform background updates will not be displayed on GUI  1500 . 
     In some implementations, a user can provide input to GUI  1500  to enable and/or disable background updates for an application. For example, a user can provide input (e.g., touch input) to mobile device  100  with respect to toggle  1516  to tum on or off background updates for application  1502 . A user can provide input (e.g., touch input) to mobile device  100  with respect to toggle  1518  to tum on or off background updates for application  1508 . 
     In some implementations, additional options can be specified for a background update application through GUI  1500 . For example, a user can select graphical object  1510  associated with application  1514  to invoke a graphical user interface (not shown) for specifying additional background update options. The background update options can include, for example, a start time and an end time for turning on and/or off background updates for application  1514 . 
     Example System Architecture 
       FIG. 16  is a block diagram of an example computing device  1600  that can implement the features and processes of  FIGS. 1-15 . The computing device  1600  can include a memory interface  1602 , one or more data processors, image processors and/or central processing units  1604 , and a peripherals interface  1606 . The memory interface  1602 , the one or more processors  1604  and/or the peripherals interface  1606  can be separate components or can be integrated in one or more integrated circuits. The various components in the computing device  1600  can be coupled by one or more communication buses or signal lines. 
     Sensors, devices, and subsystems can be coupled to the peripherals interface  1606  to facilitate multiple functionalities. For example, a motion sensor  1610 , a light sensor  1612 , and a proximity sensor  1614  can be coupled to the peripherals interface  1606  to facilitate orientation, lighting, and proximity functions. Other sensors  1616  can also be connected to the peripherals interface  1606 , such as a global navigation satellite system (GNSS) (e.g., GPS receiver), a temperature sensor, a biometric sensor, magnetometer or other sensing device, to facilitate related functionalities. 
     A camera subsystem  1620  and an optical sensor  1622 , e.g., a charged coupled device (CCD) or a complementary metal-oxide semiconductor (CMOS) optical sensor, can be utilized to facilitate camera functions, such as recording photographs and video clips. The camera subsystem  1620  and the optical sensor  1622  can be used to collect images of a user to be used during authentication of a user, e.g., by performing facial recognition analysis. 
     Communication functions can be facilitated through one or more wireless communication subsystems  1624 , which can include radio frequency receivers and transmitters and/or optical (e.g., infrared) receivers and transmitters. The specific design and implementation of the communication subsystem  1624  can depend on the communication network(s) over which the computing device  1600  is intended to operate. For example, the computing device  1600  can include communication subsystems  1624  designed to operate over a GSM network, a GPRS network, an EDGE network, a Wi-Fi or WiMax network, and a Bluetooth™ network. In particular, the wireless communication subsystems  1624  can include hosting protocols such that the device  100  can be configured as a base station for other wireless devices. 
     An audio subsystem  1626  can be coupled to a speaker  1628  and a microphone  1630  to facilitate voice-enabled functions, such as speaker recognition, voice replication, digital recording, and telephony functions. The audio subsystem  1626  can be configured to facilitate processing voice commands, voiceprinting and voice authentication, for example. 
     The I/O subsystem  1640  can include a touch-surface controller  1642  and/or other input controller(s)  1644 . The touch-surface controller  1642  can be coupled to a touch surface  1646 . The touch surface  1646  and touch-surface controller  1642  can, for example, detect contact and movement or break thereof using any of a plurality of touch sensitivity technologies, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with the touch surface  1646 . 
     The other input controller(s)  1644  can be coupled to other input/control devices  1648 , such as one or more buttons, rocker switches, thumb-wheel, infrared port, USB port, and/or a pointer device such as a stylus. The one or more buttons (not shown) can include an up/down button for volume control of the speaker  1628  and/or the microphone  1630 . 
     In one implementation, a pressing of the button for a first duration can disengage a lock of the touch surface  1646 ; and a pressing of the button for a second duration that is longer than the first duration can turn power to the computing device  1600  on or off. Pressing the button for a third duration can activate a voice control, or voice command, module that enables the user to speak commands into the microphone  1630  to cause the device to execute the spoken command. The user can customize a functionality of one or more of the buttons. The touch surface  1646  can, for example, also be used to implement virtual or soft buttons and/or a keyboard. 
     In some implementations, the computing device  1600  can present recorded audio and/or video files, such as MP3, AAC, and MPEG files. In some implementations, the computing device  1600  can include the functionality of an MP3 player, such as an iPod™. 
     The memory interface  1602  can be coupled to memory  1650 . The memory  1650  can include high-speed random access memory and/or non-volatile memory, such as one or more magnetic disk storage devices, one or more optical storage devices, and/or flash memory (e.g., NAND, NOR). The memory  1650  can store an operating system  1652 , such as Darwin, RTXC, LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such as VxWorks. 
     The operating system  1652  can include instructions for handling basic system services and for performing hardware dependent tasks. In some implementations, the operating system  1652  can be a kernel (e.g., UNIX kernel). In some implementations, the operating system  1652  can include instructions for performing dynamic adjustment of the mobile device based on user activity. For example, operating system  1652  can implement the dynamic adjustment features as described with reference to  FIGS. 1-15 . 
     The memory  1650  can also store communication instructions  1654  to facilitate communicating with one or more additional devices, one or more computers and/or one or more servers. The memory  1650  can include graphical user interface instructions  1656  to facilitate graphic user interface processing; sensor processing instructions  1658  to facilitate sensor-related processing and functions; phone instructions  1660  to facilitate phone-related processes and functions; electronic messaging instructions  1662  to facilitate electronic-messaging related processes and functions; web browsing instructions  1664  to facilitate web browsing-related processes and functions; media processing instructions  1666  to facilitate media processing-related processes and functions; GNSS/Navigation instructions  1668  to facilitate GNSS and navigation-related processes and instructions; and/or camera instructions  1670  to facilitate camera-related processes and functions. 
     The memory  1650  can store other software instructions  1672  to facilitate other processes and functions, such as the dynamic adjustment processes and functions as described with reference to  FIGS. 1-15 . 
     The memory  1650  can also store other software instructions  1674 , such as web video instructions to facilitate web video-related processes and functions; and/or web shopping instructions to facilitate web shopping-related processes and functions. In some implementations, the media processing instructions  1666  are divided into audio processing instructions and video processing instructions to facilitate audio processing-related processes and functions and video processing-related processes and functions, respectively. 
     Each of the above identified instructions and applications can correspond to a set of instructions for performing one or more functions described above. These instructions need not be implemented as separate software programs, procedures, or modules. The memory  1650  can include additional instructions or fewer instructions. Furthermore, various functions of the computing device  1600  can be implemented in hardware and/or in software, including in one or more signal processing and/or application specific integrated circuits.

Metadata:
Filing Date: 20140502
Publication Date: 20170321
Grant Date: 20170321
Priority Date: 20130609
Inventors: WOOD JUSTIN
VYAS AMIT K.
VYRROS ANDREW H.
SCHUCKER DANIEL DOUGLAS
POLLACK DANIEL B.
RUSSELL LEE
RAMADURAI ANAND
NALAM NAVEEN
ANDREWS JONATHAN J.
Assignee: APPLE INC
CPC Classifications: [{"code": "H04W12/08", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04W52/0258", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F1/3206", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04W52/0264", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06F1/3206", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04W52/0264", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04W52/0258", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04L67/26", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04W52/0264", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04M1/72569", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04L67/322", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04M1/72525", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F1/3206", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04W52/0258", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04W12/08", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04L67/2819", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04L67/2842", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04L67/61", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04L67/564", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04L67/568", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04L67/55", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04L67/568", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04L67/564", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04L67/55", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04L67/61", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04M1/72454", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04M1/72406", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04M1/72454", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04M1/72406", "inventive": true, "first": false, "tree": "[]"}, {"code": "Y02D30/70", "inventive": false, "first": false, "tree": "[]"}, {"code": "Y02D30/70", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04W12/08", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 52005402