PATENT DOCUMENT

Publication Number: US-10148546-B2
Application Number: US-201615178514-A
Country: US
Kind Code: B2

Title: Scheduling processing tasks based on predicted context

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

Claims:
What is claimed is: 
     
       1. A method comprising:
 storing, by a computing device, historical context data related to user contexts and device contexts over a period of time, the user contexts related to activities of a user of multiple user devices and the device contexts related to operational states of the multiple user devices; 
 storing, by the computing device, current context data indicating user contexts and devices contexts at a current time, the user contexts related to activities of the user of the multiple user computing devices and the device contexts related to operational states of the multiple user devices; 
 receiving, by the computing device, an activity request for determining when to perform an activity on the computing device, the activity request including one or more context parameters and a time window having a start time and an end time; 
 predicting, by the computing device, at least one future context comprising either: at least one future user context related to the user of the computing device or at least one device context related to a future operational state of the computing device, wherein the at least one future context is predicted based on historical context data associated with the one or more context parameters; 
 predicting, by the computing device for each of a plurality of times within the time window, a context score associated with the at least one future context based on the one or more context parameters and the historical context data; 
 selecting, by the computing device, a highest context score from the predicted context scores as an optimal score for the activity request, wherein the time corresponding to the context score is predicted to be an optimal time within the time window for performing the activity; 
 determining, by the computing device, a threshold value for determining whether to perform the activity, the threshold value being determined based on the optimal score; 
 determining, by the computing device, a current context score based on the current context data associated with the one or more context parameters; 
 determining, by the computing device, that the current context score is greater than the threshold value; and 
 in response to determining that the current context score is greater than the threshold value, performing, by the computing device, the activity corresponding to the activity request. 
 
     
     
       2. The method of  claim 1 , wherein the threshold value corresponds to a percentage of the optimal score. 
     
     
       3. The method of  claim 1 , wherein determining the current context score includes determining that a current time is within the time window specified by the activity request, and
 wherein the current context score is determined based on the current context data stored by the computing device at the current time. 
 
     
     
       4. The method of  claim 1 , wherein the current context data includes remote context data received from a remote device. 
     
     
       5. The method of  claim 1 , further comprising:
 while a current time is within the time window specified in the activity request, receiving a current context update; and 
 in response to receiving the current context update, the current context score for the activity request is determined based on the current context data stored by the computing device at the current time. 
 
     
     
       6. The method of  claim 1 , further comprising:
 adjusting the threshold value based on a priority specified in the activity request. 
 
     
     
       7. A non-transitory computer-readable medium including one or more sequences of instructions that, when executed by one or more processors, cause:
 receiving, by a computing device, an activity request corresponding to an activity including one or more context parameters and a time window having a start time and an end time; 
 predicting, by the computing device, at least one future context comprising either: at least one future user context related to the user of the computing device or at least one device context related to a future operational state of the computing device, wherein the at least one future context is predicted based on historical context data associated with the one or more context parameters; 
 predicting, by the computing device for each of a plurality of times within the time window, a context score associated with the at least one future context based on the one or more context parameters and the historical context data; 
 selecting, by the computing device, a highest context score from the predicted context scores as an optimal score for the activity request, wherein the time corresponding to the context score is predicted to be an optimal time within the time window for performing the activity; 
 determining, by the computing device, a threshold value for determining whether to perform the activity, the threshold value being determined based on the optimal score; 
 determining, by the computing device, a current context score based on current context data stored on the computing device and the one or more context parameters; 
 determining, by the computing device, that the current context score is greater than the threshold value; and 
 in response to determining that the current context score is greater than the threshold value, performing the activity corresponding to the activity request. 
 
     
     
       8. The non-transitory computer-readable medium of  claim 7 , wherein the threshold value corresponds to a percentage of the optimal score. 
     
     
       9. The non-transitory computer-readable medium of  claim 7 , wherein the instructions that cause determining the current context score include instructions that cause determining that a current time is within the time window specified by the activity request, and
 wherein the current context score is determined based on the current context data stored by the computing device at the current time. 
 
     
     
       10. The non-transitory computer-readable medium of  claim 7 , wherein the current context data includes remote context data received from a remote device. 
     
     
       11. The non-transitory computer-readable medium of  claim 7 , wherein the instructions cause:
 while a current time is within the time window specified in the activity request, receiving a current context update; and 
 in response to receiving the current context update, the current context score for the activity request is determined based on the current context data stored by the computing device at the current time. 
 
     
     
       12. The non-transitory computer-readable medium of  claim 7 , wherein the instructions cause:
 adjusting the threshold value based on a priority specified in the activity request. 
 
     
     
       13. A system comprising:
 one or more processors; and 
 a non-transitory computer-readable medium including one or more sequences of instructions that, when executed by the one or more processors, cause:
 receiving, by a computing device, an activity request corresponding to an activity including one or more context parameters and a time window having a start time and an end time; 
 predicting, by the computing device, at least one future context comprising either: at least one future user context related to the user of the computing device or at least one device context related to a future operational state of the computing device, wherein the at least one future context is predicted based on historical context data associated with the one or more context parameters; 
 predicting, by the computing device for each of a plurality of times within the time window, a context score associated with the at least one future context based on the one or more context parameters and the historical context data; 
 selecting, by the computing device, a highest context score from the predicted context scores as an optimal score for the activity request, wherein the time corresponding to the context score is predicted to be an optimal time within the time window for performing the activity; 
 determining, by the computing device, a threshold value for determining whether to perform the activity, the threshold value being determined based on the optimal score; 
 determining, by the computing device, a current context score based on current context data stored on the computing device and the one or more context parameters; 
 determining, by the computing device, that the current context score is greater than the threshold value; and 
 in response to determining that the current context score is greater than the threshold value, performing the activity corresponding to the activity request. 
 
 
     
     
       14. The system of  claim 13 , wherein the threshold value corresponds to a percentage of the optimal score. 
     
     
       15. The system of  claim 13 , wherein the instructions that cause determining the current context score include instructions that cause determining that a current time is within the time window specified by the activity request, and
 wherein the current context score is determined based on the current context data stored by the computing device at the current time. 
 
     
     
       16. The system of  claim 13 , wherein the current context data includes remote context data received from a remote device. 
     
     
       17. The system of  claim 13 , wherein the instructions cause:
 while a current time is within the time window specified in the activity request, receiving a current context update; and 
 in response to receiving the current context update, the current context score for the activity request is determined based on the current context data stored by the computing device at the current time. 
 
     
     
       18. The system of  claim 13 , wherein the instructions cause:
 adjusting the threshold value based on a priority specified in the activity request.

Description:
TECHNICAL FIELD 
     The disclosure generally relates to determining user context. 
     The disclosure generally relates to scheduling tasks based on user context. 
     BACKGROUND 
     Many modern computing devices collect information about user activities in order to predict or anticipate the user&#39;s behaviors and/or computing needs. Most of these devices attempt to predict or anticipate a user&#39;s behavior based on the data collected by a single device. However, most users have multiple devices. Thus, the data collected by a single device often provides an incomplete picture of the user&#39;s behavior and/or computing needs. 
     SUMMARY 
     In some implementations, a user device can maintain a multi-device context store. For example, the user device can receive device and/or user context information from multiple devices and store the context information in a local data store. The user device can collect local device context information and/or user context information and store the context information in the local context store. The user device can receive context queries from client processes requesting device context and/or user context information and send the client processes context information from multiple devices in response to the queries. 
     In some implementations, a user device can schedule tasks based on user behavior. For example, the user device can receive a task request that includes a time window and user/device context parameters for performing the task. The user device can predict a time when the user/device context is optimal for performing the task during the time window based on historical context data. For example, the user device can generate an optimal context score for the task based on the context parameters and the historical context data. The user device can execute the requested task at a current time within the time window when a context score for the current context exceeds a threshold determined based on the optimal context score. 
     Particular implementations provide at least the following advantages. By collecting and storing context information from multiple user devices, a clearer picture of the user&#39;s context, rather than individual device contexts, can be generated. By scheduling execution of tasks based on the actual current context relative to a predicted future context, rather than exclusively current context, the user device can execute tasks when the conditions or context of the user or device are optimal. 
     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  is a block diagram of an example system for providing a multi device context store. 
         FIG. 2  illustrates example context databases. 
         FIG. 3  is a block diagram of an example system for processing context client queries submitted to a context store. 
         FIG. 4  is a block diagram of an example system for scheduling activities based on context. 
         FIG. 5  is a block diagram of an example system for determining when to perform a requested activity on a user device. 
         FIG. 6  is a graph illustrating an example optimal score determination for an activity request. 
         FIG. 7  is a graph illustrating example how to determine when to run an activity based on an optimal activity score and a current activity score. 
         FIG. 8  is flow diagram of an example process for determining a context based on a multi-device context store. 
         FIG. 9  is a flow diagram of an example process for scheduling activities based on a current context. 
         FIG. 10  is a block diagram of an example computing device that can implement the features and processes of  FIGS. 1-9 . 
     
    
    
     Like reference symbols in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
     Multi-Device Context Store 
       FIG. 1  is a block diagram of an example system  100  for providing a multi device context store. For example, system  100  can exchange context data (e.g., device context, user context, etc.) between devices and allow context clients to query the multi-device context store so that the context clients can determine a user context based on the multi-device context data. 
     In some implementations, system  100  can include user device  110 . For example, user device  110  can be a computing device such as a smartphone, tablet computer, laptop computer, or wearable device (e.g., smart watch, smart glasses, etc.). User device  110  can be one of many devices (e.g., user device  130 , wearable device  150 ) associated with a user and/or user account. For example, the user may have a user account with a vendor or service provider that provides various services to the user or to the user&#39;s devices. For example, the user can have an account with a cloud service provider that provides systems (e.g., cloud server  172 ) and devices (e.g., server device  170 ) that synchronize data between multiple user devices. 
     In some implementations, user device  110  can include context store  112 . For example, context store  112  can be a process (e.g., daemon or background process) that collects context data from other processes running on user device  110 . For example, context client  118  can submit context data (e.g., device state data, application state data, sensor data, location data, etc.) to context store  112 . For example, the context data can include user context, such as a location of the user (e.g., at home, at work, at school, etc.) and/or an activity performed by the user (e.g., running, reading, sleeping, using a device, etc.), The context data can include device context, such as device state, application state, device sensor data, etc. Examples of device context can include the device&#39;s display being lit, the device is connected to Wi-Fi, the device is connected to a cellular data connection, the device&#39;s location, accelerometer data indicating the device is moving, etc. The context data can be submitted in the form of key-value (e.g., attribute-value) pairs. When context data is received by context store  112  from local context clients (e.g., local processes, applications, etc.), context store  112  can store the context data in local context database  114 . 
     In some implementations, user device  110  can exchange context data with other user devices. For example, system  100  can include user device  130 . User device  130  can be configured (e.g., hardware and/or software) similarly to user device  110 . For example, user device  110  can be a tablet computer and user device  130  can be a smartphone. User device  130  can include context store  132  and local context database  134 , for example. As described above with reference to user device  110 , context store  132  can receive context data (e.g., key-value pairs) from context client  138  running locally on user device  130  and store the context data in local context database  134 . To get a more complete picture of the user&#39;s context, user device  110  and user device  130  can exchange context data. For example, context store  112  on user device  110  can send context data from local context database  114  to context store  132  on user device  130 . Upon receiving the context data from user device  110 , context store  132  can store the context data in remote context database  136 . Similarly, context store  132  on user device  130  can send context data from local context database  134  to context store  112  on user device  110 . Upon receiving the context data from user device  130 , context store  112  can store the context data in remote context database  116 . 
     To establish communication between user device  110  and user device  130 , each user device can obtain user device information from cloud server  172  that identifies each of the devices associated with the same user account. For example, user device  110  and user device  130  can be associated with the same user account with cloud server  172 . Cloud server  172  can, for example, maintain a database  174  of devices registered to the user&#39;s account with cloud server  172 . Registered devices database  174  can include, for example, records for each registered device that includes a user account identifier and a device identifier. The registered device information can be shared with the various devices associated with the user&#39;s account so that the devices can communicate with each other and exchange context data. 
     In some implementations, user device  110  and user device  130  can exchange context data through network  190 . For example, network  190  can be a wide area network, local area network, cellular network, Wi-Fi network, or adhoc network between devices. For example, network  190  can be implemented through the internet using standard internet protocols. Network  190  can be a direct device to device network using peer-to-peer communications technologies like near field communication signals, Bluetooth, Bluetooth LE, or peer-to-peer Wi-Fi, for example. 
     In some implementations, context data can be exchanged between user device  110  and user device  130  using a broadcast mechanism. For example, establishing a communication channel with user device  130  and sending the context data directly to user device  130 , user device  110  can broadcast context data using a Bluetooth advertising packet. Each user device within range of the Bluetooth broadcast can receive the context data and update its remote context database accordingly. For example, user device  110  can broadcast a Bluetooth LE advertisement packet that identifies user device  110  and has a payload encoded with context data from local context database  114 . User device  130  can listen for and receive the advertisement packet, determine that the advertisement is from a user device associated with the same user account as user device  130  (e.g., as determined based on the registered device data received from cloud server  172 ), and decode the context data for user device  110  from the advertisement payload. User device  130  can then store the context data from user device  110  in corresponding remote context database  136  on user device  130 . 
     Similarly, context data can be broadcast through cloud server  172  on server device  170 . For example, user device  130  can broadcast context data through cloud server  170  instead of directing the context data to user device  110 . User device  130  can send a context update message to cloud server  172  that includes context data updates from local context database  134 . The context update message can, for example, identify the sending device and include a list of key-value pairs representing the context updates. Upon receipt of the context update message, cloud server  172  can update device context database  176  with the updated context data and forward the updated context data received from user device  130  to the other user devices (e.g., user device  110 ) associated with the same user account. Thus, user device  130  does not need to know which devices are associated with the user account or how to reach each user device associated with the user account. Additionally, since the communication with server device  170  can be performed through the internet, user device  130  does not have to be near user device  110  in order to share context updates with user device  110  as is the case with the near field communication (NFC) or Bluetooth communication mechanisms. 
     In some implementations, user device  110  and user device  130  can exchange context data according to various timing mechanisms or in response to various triggers. For example, context store  112  on user device  110  can be configured to send context data updates from local context database  114  to user context store  132  on user device  130  periodically (e.g., according to a configured time interval). Context store  112  on user device  110  can be configured to send context data updates from local context database  114  in response to detecting an change or update in local context database  114 . For example, in response to receiving an updated or changed value for a corresponding attribute, context store  112  can broadcast the attribute value change to other user devices, as described above. In some implementations, user device  110  can send context data updates for specific context data that has been requested by other devices, as described further below. 
     In some implementations, a user device can serve as a context reporting proxy for other user devices. For example, user device  130  can serve as a context reporting proxy for wearable device  150 . Wearable device  150  can, for example, be a smart watch that is connected to user device  130  (e.g., a smartphone). In some implementations, wearable device  150  can depend on user device  130  for some functions. For example, user device  130  may receive some communications (e.g., telephone calls, instant messages, text messages, etc.) and send notifications to wearable device  150  so that wearable device  150  can present the notifications to the user. Wearable device  150  may not have a cellular data connection and may rely on the cellular data or network data connection provided by user device  130 . 
     In some implementations, wearable device  150  can have the same or similar features as user device  110  and/or user device  130 . On the other hand, wearable device  150  might provide some features, such as sensor  152  (e.g., a heart rate sensor), that user device  130  does not have. Wearable device  150  can provide sensor data from sensor  152  and/or other device context information specific to wearable device  150  to user device  130  so that user device  130  can use the sensor data and/or context data on user device  130 . When user device  130  receives the sensor data and/or context data from wearable device  150 , user device  130  can store the sensor data and/or context data (e.g., sensor data is context data) in local context database  134  as if the context data for wearable device  150  is context data for user device  130 . For example, user device  130  can store context data in local context database  134  that indicates that user device  130  is connected to wearable device  150  (e.g., key:value=wearableConnected:true) and context data that indicates a value or state associated with sensor  152  (e.g., key:value=heartrate: 152 ), as described further with reference to  FIG. 2 . Since the context data for wearable device  150  is stored in local context database  134 , user device  130  can share the context data in local context database  134  with other user devices (e.g., user device  110 ) as described above. Thus, user device  110  can use the context data associated with wearable device  150  to make decisions or provide various features even though user device  110  is not directly connected to (e.g., paired with) wearable device  150 . 
       FIG. 2  illustrates example context databases  200 . For example, local context database  114  and remote context database  116  can store context data as attribute-value pairs (e.g., key-value pairs) of data. Local context database  114  can store context data reported or submitted by local processes (e.g., applications, operating system, software utilities, hardware drivers, sensors, etc.) on user device  110 . For example, a power monitoring process can submit an “80%” value for a “BatteryLevel” attribute to context store  112  on user device  110  and context store  112  can save the attribute-value pair as entry  202  in local context database  114 . Similarly, the power monitoring process can determine that user device  110  is not connected to an external power source (e.g., the device is not charging and running on battery power) and send a “false” value for the “ExtPower” attribute indicating that user device  110  is not connected to an external power source and context store  112  can store the attribute-value pair as entry  204  in local context database  114 . 
     User device  110  can include other processes that report context information to context store  112 . For example, user device  110  can include a process that monitors and reports Wi-Fi connection state information (e.g., entry  206 ). User device  110  can include a process that monitors and reports the current location of user device  110  (e.g., entry  208 ). User device  110  can include a process that monitors and reports whether the device&#39;s screen is lit (e.g., entry  210 ). User device  110  can include a process that monitors and reports a timestamp corresponding to when the last user input was received (e.g., entry  212 ). The context data in local context database  114  can be reported by one or multiple processes. For example, the “LastUserInput” attribute of entry  212  can be reported by a single process that monitors all user input to user device  110  or by multiple different processes (e.g., applications) used by the user as the user interacts with user device  110 . When context store  112  receives the attribute-value pairs of context data from local processes, context store  112  can store the attribute value pairs in local context database  114 , as described above. 
     In some implementations, user device  110  can include remote context database  116 . For example, remote context database  116  can be structured similarly to local context database  114  in that remote context database  116  stores context data as attribute-value pairs of data. However, the context data in remote context database  116  reflects the device state and/or context data of a user device (e.g., user device  130 ) other than user device  110 . 
     In some implementations, remote context database  116  can include entries for some of the same attributes as local context database  114 . For example, even though the context data stored in remote context database  116  on user device  110  is received from user device  130 , there may be some standard or core attributes that are reported on all user devices (e.g., user device  110 , user device  130 ). These core attributes can include a battery level attribute (e.g., entry  202 , entry  230 ), an external power attribute (e.g., entry  204 , entry  232 ), and/or a last user input attribute (e.g., entry  212 , entry  240 ). However, while some of the reported attributes may be similar between devices, the values will be different depending on the current state or context of the respective devices. For example, the reported values for battery level, external power connection, and/or last user input attributes may be different between devices based on the device&#39;s current configuration or status. 
     In some implementations, remote context data database  116  can include attributes that are different than those in local context database  116 . For example, even though remote context database  116  is stored on user device  110 , remote context database  116  reflects the context of user device  130 . In some implementations, user device  130  may have a different configuration (e.g., different applications, different hardware, different sensors, etc.) than user device  110 . Thus, the context data reported to context store  132 , shared with context store  112  and stored in remote context database  116  on user device  110  may be different than the context data (e.g., attribute-value pairs) stored in local context database  114 . For example, user device  130  may be configured to pair with a wearable device (e.g., wearable device  150 ) and user device  110  may not. Thus, context store  132  on user device  130  may collect context data associated with wearable device  150 . The wearable device context data can then be shared with context store  112  on user device  110  and stored in remote context database  116  as entry  242  (e.g., wearable connection status) and entry  244  (e.g., wearable sensor data). 
     In some implementations, user device  110  can determine a user context based on data from local context database  114  and remote context database  116 . For example, individually, local context database  114  and remote context database  116  provide information reflecting the state or context of individual devices; however by analyzing the context data from both databases (e.g., context data from multiple devices), user device  110  can more accurately determine the current context of the user. For example, the user context can include a location of the user (e.g., at home, at work, at school, etc.) and/or an activity performed by the user (e.g., running, reading, sleeping, using a device, etc.), The user context can be determined and/or verified using device context data (e.g., device state data, sensor data, etc.) from multiple devices. For example, if user device  110  relied solely upon local context database  114  (e.g., local context data) to determine the location of the user, user device  110  would determine that the user is at home (e.g., entry  208 ). However, by analyzing the location (e.g., entries  208  and  236 ) and last user input (e.g., entries  212  and  240 ) data from both local context database  114  and remote context database  116 , user device  110  can determine that the user is actually located at the office since the latest user input was received from user device  130  that is located at the user&#39;s office. 
     In some implementations, the context data in remote context database  116  can allow user device  110  to provide features or services that user device  110  cannot provide on its own. For example, remote context database  116  can include entry  244  that includes heartrate sensor data from wearable device  150 . User device  110  may not have a heartrate sensor, but because user device  110  has received heartrate data from wearable device  150  through user device  130 , user device  110  can now provide heartrate based services and/or information to the user. For example, user device  110  can track the user&#39;s heartrate, determine the user&#39;s fitness level, and/or make recommendations regarding the user&#39;s fitness routine. As another example, because user device  110  is receiving battery level (e.g., entry  230 ) and charging status (e.g., entry  232 ) data for user device  130 , user device  110  can prompt (e.g., present a graphical element, audio warning, etc.) the user to plug in user device  130  when the battery level of user device  130  (as indicated in remote context database  116 ) drops below a threshold level (e.g., 20%) and user device  130  is not plugged in to an external power source (e.g., ExtPower=false). Thus, by sharing context data between devices, user device  110  can make a better determination of the user&#39;s context and/or provide features or services that would otherwise be unavailable on user device  110 . 
       FIG. 3  is a block diagram of an example system  300  for processing context client queries submitted to a context store. In some implementations, context client  118  can submit context queries to context store  112  on user device  110 . For example, context client  118  can be a user application installed on user device  110 . Context client  118  can be a process, function, utility, daemon, etc., of the operating system of user device  110 . Context client  118  can be configured to submit to and/or request from context store  112  context information managed by context store  112 . 
     In some implementations, context store  112  can receive a context query from context client  118 . For example, context client  118  can be a process configured to remind a user to plug in and/or charge various user devices when the battery level of a user device drops below a threshold level. Context client  118  can submit a context query (e.g., request) to context store  112  requesting the current value of the “BatteryLevel” attribute. For example, the context query can include a list of attributes for which context client  118  is requesting values. Upon receipt of the query, contest store  112  can obtain the current values for the attributes specified in the query from local context database  114  and/or remote context database  116  and return a mapping of device identifier to attribute-value pairs for the requested attributes that reflect the current values associated with the specified attributes on each device. 
     In some implementations, a context query can specify the scope of the query. For example, context client  118  may request context information for only the local device (e.g., user device  110 ). In response to receiving a context query that specifies a local device only scope, context store  112  can search local context database  114  for attributes corresponding to the attributes specified in the context query. Similarly, context client  118  may request context information for only a specified remote device (e.g., user device  130 ). In response to receiving a context query that specifies or identifies a remote device, context store  112  can search the remote context database (e.g., remote context database  116 ) corresponding to the identified remote device. In some situations, context client  118  can request context information for all devices. In response to receiving a context query that specifies all devices (e.g., the request can include a listing of device identifiers or just indicate all devices), context store  112  can search local context database  114  and all remote context databases (e.g., there can be one remote context database for each remote user device) for context data corresponding to the attributes specified in the context query. After the context data is obtained from local context database  114  and/or remote context database  116  according to the scope specified in the context query, context store  112  can send the context information (e.g., device ID—attribute-value mappings) to context client  118 . Context client  118  can then use the context information to provide services and/or information to the user (e.g., inform the user about the battery charge state of various user devices). 
     In some implementations, context client  118  can register interest in receiving updates to context data. For example, context client  118  can submit a context query to context store  112  that indicates that context client  118  is interested in receiving status or context updates when context changes occur. To register interest in context updates, context client  118  can submit a context query that identifies attributes that the context client is interested in, specifies the scope of the query (e.g., local device, specified device, all devices, etc.), and indicates that the client is interested being called back when the values of the specified attributes change. 
     When context store  112  receives a context query that indicates that context client  118  is requesting context updates, context store  112  can obtain context data corresponding to the context query and send the context data to context client  118 , as described above. However, when the context query indicates that context client  118  is requesting context updates (e.g., requesting a callback when an attribute value changes), context store  112  can store the context query in callback database  302  so that context store  112  can send context client  118  context updates when the attributes identified in the context query change values in the future. 
     In some implementations, a context query can specify attribute values that should trigger a callback to a context client. For example, context client  118  can generate a context query, as described above, that specifies that context client  118  is requesting to be called back when an update or change to an attribute value is detected. For example, context client  118  can submit a context query indicating that context store should notify (e.g., call back) context client  118  when the “BatteryLevel” attribute value for any user device drops below 20%. The context query in this case can include the battery level attribute (e.g., “BatteryLevel”), a relational operator (e.g., greater than, less than, equal to, etc.), the attribute value (e.g., 20%), the scope of the query (e.g., all devices), and a value indicating that context client  118  should receive context updates (e.g., callbacks) for future context changes for any user device where the battery level is less than 20%. 
     When context store  112  receives context data changes or updates (e.g., locally and/or from other user devices), context store  112  can search callback database  302  for callback queries that relate to the changed context data (e.g., attributes and/or attribute values). When context store  112  identifies a callback query that is related to a changed attribute or attribute value, context store  112  can notify the context client that submitted the context query (e.g., context client  118 ) that the current context corresponds to the criteria specified in the context query. Thus, context client  118  can be notified by context store  112  when any change to a specified attribute occurs or when a change occurs that meets the criteria specified in the context query. 
     In some implementations, a context query can cause a context data exchange between user devices. As described above, user device  110  and/or user device  130  can broadcast context data (e.g., attribute-value pairs) on a periodic basis. However, in some implementations, a user device (e.g., user device  130 ) can be selective about which attributes and attribute values are broadcast to other user devices. For example, user device  130  can be configured to periodically broadcast a configurable (e.g., core) set of attributes and attribute values (e.g., battery level, external power, location, etc.). For other attributes, user device  130  may broadcast attribute-value pairs when another device (e.g., user device  110 ) has expressed interest in those attribute-value pairs. 
     As described above, context store  112  can receive a context query from context client  118  indicating that context client  118  is requesting heartrate data from user device  130 . In response to receiving the query, context store  112  can send a message  304  to context store  132  on user device  130  requesting context data corresponding to the heartrate attribute. Context store  132  can obtain the heartrate attribute value and send the heartrate attribute-value pair to context store  112  in message  306 . Context store  112  can then update remote context database  116  with the heartrate attribute-value data and send the heartrate attribute-value pair to context client  118 . 
     Similarly, context store  112  can receive a context query from context client  118  indicating that context client  118  is interested in receiving notifications when the heartrate attribute on user device  130  (e.g., and/or other devices) changes or meets a specified condition (e.g., heartrate is greater than a value, heartrate is less than a value. In response to receiving the query, context store  112  can send a message  304  to context store  132  on user device  130  indicating that context store  112  is requesting context data updates corresponding to the heartrate attribute. In response to receiving message  304 , context store  132  can obtain the current heartrate attribute value and send the heartrate attribute-value pair to context store  112  in message  306 . When context store  132  is notified (e.g., in message  304 ) that context store  112  is interested in receiving updates regarding the heartrate attribute, context store  132  can monitor for updates to the heartrate attribute and broadcast the heartrate updates when the heartrate attribute value changes or periodically, as described above. Upon receipt of the heartrate context data, context store  112  can update remote context database  116  with the heartrate attribute-value data and send the heartrate attribute-value pair to context client  118  or notify context client  118  when the heartrate attribute value meets the criteria or conditions specified in the context query. Thus, a user device can selectively broadcast a context data attribute (and not broadcast other attributes) based on whether another user device has expressed interest in the attribute. 
     In some implementations, the context data can be broadcast with other data that is regularly broadcast by the user device. For example, the context data can be added to other regularly broadcast state data such that the context data is “piggy-backed” on messages sent by other services on the user device. Thus, in some implementations, broadcasting the context data does not require the additional overhead and/or resources required to generate and broadcast a completely new message. 
     Scheduling Activities Based on Context 
       FIG. 4  is a block diagram of an example system  400  for scheduling activities based on context. For example, system  400  can determine when to initiate an activity (e.g., task, process, operation, etc.) on user device based on a comparison between a predicted optimal context and a current context as determined based on context data collected by context store  112 . For example, the activity or task can be performed by the operating system of the user device and/or an application running on the user device. 
     In some implementations, system  400  can include activity scheduler  402 . For example, activity scheduler  402  can be a process (e.g., operating system utility, daemon, background process, etc.) executing on user device  110  and configured to determine a time when the user context or device context is optimal for running a requested activity. 
     In some implementations, system  400  can include scheduler client  404 . For example, scheduler client  404  can be a process (e.g., application, operating system utility, daemon, etc.) running on user device  110 . Scheduler client  404  can submit an activity request to activity scheduler to perform an activity (e.g., task, operation, etc.). For example, scheduler client  404  may need to perform a networking task (e.g., download data, upload data, communicate with other devices, etc.) that requires turning on Wi-Fi, Bluetooth, or other communications subsystems (e.g., radios). Scheduler client  404  can submit an activity request that specifies the need to access the network. In response to receiving the request, activity scheduler  402  can determine the best time for scheduler client  404  to perform the networking task and notify scheduler client  404  when scheduler client  404  can perform the networking task. Similarly, activity scheduler  402  can determine the optimum time to perform CPU intensive tasks, prefetch application data in anticipation of a user invocation of an application, and/or other tasks based on the parameters specified in the activity request. 
     In some implementations, an activity request can include parameters that specify a time window for performing the requested activity. For example, the activity may be performed by scheduler client  404  (or some other process), however, scheduler client  404  can submit an activity request that specifies parameters that activity scheduler  402  can use to determine the optimal time for scheduler client  404  to perform the corresponding task. For example, scheduler client  404  can generate an activity request that specifies a time window for performing a task. In some implementations, the activity request can include an indication that the task repeats over some fixed or variable time interval and activity scheduler  402  can determine (e.g., infer) current and future time windows based on the specified time window and the specified interval for the repeating task. The time window can be defined by a start time and an end time specified by scheduler client  404  in the activity request. The time window is a defined period of time during which the requested activity should be performed. The time window can be defined by a recurrence period (e.g., time interval). For example, if the recurrence period is seven days, then the task window defined by start and end times, can be repeated every seven days. When the activity request is received by activity scheduler  402 , activity scheduler  402  will determine the best time within the specified time window for performing the requested activity, as described further below. 
     In some implementations, an activity request can specify a priority for the requested activity. For example, the priority (e.g., high, medium, low, maintenance) can be defined by scheduler client  404  in the activity request. Alternatively, the priority can be automatically determined by activity scheduler based on the identity or type of the scheduler client  404 . For example, when activity scheduler  402  receives an activity request from an operating system process (e.g., background process, daemon, etc.), activity scheduler  402  can assign a maintenance priority level to the activity request. When activity scheduler  402  receives an activity request from a frequently invoked user application (e.g., to prefetch application data from a network resource), activity scheduler  402  can assign a high priority to the activity request. 
     In some implementations, an activity request can specify the amount of work that will be performed when the activity is performed. For example, the amount of work can be specified as a duration of time (e.g., the activity will last 1 minute, 5 minutes, etc.). The amount of work can be specified as an amount of data (e.g., 2 GB of data to transmit/receive). The amount of work can be specified as a percentage of CPU cycles required for the activity and/or an amount of memory required for the activity. 
     In some implementations, an activity request can specify a context for performing the requested activity. For example, scheduler client  404  may be a frequently invoked software application that is scheduling a prefetch activity to prefetch application data in advance of the user invoking scheduler client  404 . Scheduler client  404  can specify attributes and corresponding attribute values that define a user or device context during which the prefetch activity should be run. 
     The activity request can specify a network context. For example, if the prefetch activity will require a network connection, the activity request can specify a required network context. For example, the activity request can specify attributes and/or attribute values corresponding to a required network connection type (e.g., Wi-Fi, cellular, etc.), a required bandwidth (e.g., 10 megabits download, 4 megabits upload), and/or a required connection quality (e.g., high, medium, low, etc.). 
     The activity request can specify a user activity context. For example, the activity request can specify attributes and attribute values indicating that user device  110  not be in use by the user (e.g., “inUse” attribute=false), or that the user be asleep (e.g., “userSleeping”=true), or that the user is performing some other activity (e.g., driving, walking, running, etc.). The user activity context can include attributes and attribute values indicating that the device&#39;s display screen is lit and/or that the device is connected to a peripheral device, such as headphones, Bluetooth speakers, a wearable device, or an in-dash entertainment system in an automobile, for example. 
     The activity request can specify a power context. For example, the activity request can specify attributes and attribute values corresponding to a battery charge level of the device (e.g., a percent charge), a charging state (e.g., plugged in, charging, not plugged in, a particular stage of charging, etc.) 
     The activity request can specify an application context. For example, a user may install the same application on different devices or complementary applications (e.g., one application provides data to another application) on the same or different user devices. Scheduler client  404  may be configured to perform an activity in response to a state change in another application (e.g., another instance of the same application or a different application) or a reported state of another application on the same device or different user device. Thus, scheduler client  404  can submit an activity request that specifies an attribute or attribute value that is specific to another application and reported by the other application to context store  112 . 
     In some implementations, activity scheduler  402  can determine when to perform an activity (e.g., run a task) based on the activity request and the current context. For example, activity scheduler  402  can determine, based on the parameters of the activity request, when during the specified time window to run the requested activity. Activity scheduler  402  can determine when to run the requested activity based on historical context data and current context data. For example, activity scheduler can determine or predict based on historical data when the best or optimal conditions will exist within the specified time window for performing the activity. For example, if an activity request specifies attributes and attribute values that require the device to be plugged in and connected to Wi-Fi, activity scheduler  402  can determine when during the specified window those conditions or context will exist. For example, activity scheduler  402  can generate scores for different times during the specified time window representing probabilities that the specified conditions or context will exist at the corresponding time and select the highest probability as optimal score. 
     In some implementations, even if the activity does not explicitly require the device to be plugged in or that its data transfer occur over Wi-Fi rather than a cellular data connection, the scheduler may choose a time that where the device is plugged in and/or connected to Wi-Fi because these conditions yield the best combination of power efficiency and performance in the given window. 
     Alternatively, the scores can be indicative of a quality of the specified conditions or context. For example, an activity request may require a Wi-Fi connection and the score for the activity can be determined based on the quality of the Wi-Fi connection (e.g., 0-1 score indicating quality, where 0.95 is high quality, 0.2 is low quality, and 0 is no connection) rather than a score based solely on whether the Wi-Fi connection exists (e.g., a 0 or 1 score indicating that there is or is not a connection). Activity scheduler  402  can then determine a threshold value based on the optimal score (e.g., threshold can be 80% of optimal score). When a score generated for a current context within the specified time window exceeds the determined threshold value, activity scheduler  402  can notify scheduler client  404  that scheduler client  404  can perform the requested activity (e.g., run the requested task), as described further below. 
     In some implementations, user device  110  can include historical context database  406 . For example, historical context database  406  can store historical context data corresponding to the context data in local context database  114  and/or remote context database  116 . As context store  112  receives context data (e.g., local context data, remote context data, etc.), context store  112  can store the context data in local context database  114  and/or remote context database  116 , as described above. Local context database  114  and/or remote context database  116  can represent the current context, for example. Context store  112  can also store the received context data in historical context database  406 , for example. Context entries in historical context database  406  can include the attribute-value pairs of data stored in local context database  114  and/or remote context database  116 , as described with reference to  FIG. 2 . Context entries in historical context database  406  can include device identifiers that identify from which device the context entry data came. Context entries in historical context database  406  can include a timestamp indicating the date and/or time when the context data was reported to the respective context store. For example, if a local context client reports an attribute-value pair of data to a local context store, the timestamp stored with the context entry can represent the time when the local context client reported the attribute-value pair to the local context store. When a remote context store sends context data to a local context store, the timestamps for context entries in historical context database  406  will represent the time when the remote context store received the context data, not the time when the context data was transmitted between context stores. Thus, context store  112  can determine a timeline of context changes based on the historical context data and predict, based on historical context patterns, future contexts of user device  110  and/or the user of user device  110 . 
       FIG. 5  is a block diagram of an example system  500  for determining when to perform a requested activity on a user device. For example, activity scheduler  402  can receive activity request  502 . Activity request  502  can include information describing parameters (e.g., a time window, priority, size, context, etc.) for performing an activity, as described above. When activity scheduler  402  receives activity request  502 , activity manager  504  can store activity request  502  in activity request database  506 . For example, activity request database  506  can store multiple activity requests received by activity scheduler  402  from multiple different scheduler clients. Activity scheduler  402  can keep track of each activity request in activity request database and monitor context data to determine the best time to run each requested activity, as described further below. 
     In some implementations, activity manager  504  can send a message (e.g., API invocation) to activity scorer  508  requesting that activity scorer  508  determine a score for activity request  502 . For example, activity manager  504  can divide the time window (e.g., 1:00 am-1:05 am) into blocks of time (e.g., 1 minute, 30 seconds, etc.). For each block of time within the time window (e.g., 1:00 am, 1:01 am, 1:02 am, etc.) activity manager  504  can send a request to activity scorer  508  to score activity request  502  based on the context parameters specified in activity request  502 . For example, activity manager  504  can send activity scorer  508  the context parameters (e.g., attribute-value data pairs) and a time (e.g., 1:01 am) corresponding to a block of time. For example, the context parameters can specify that the device should be connected to Wi-Fi (e.g., “Wi-Fi”=“true”), that the device should be connected to external power (e.g., “ExtPower”=“true”), and that the user should be asleep (e.g., “UserSleeping”=“true”). 
     In some implementations, activity scorer  508  can obtain individual scores for each context parameter (e.g., attribute-value pair) within a specified block of time. For example, when activity scorer  508  receives context parameters and a time from activity manager  504 , activity scorer  508  can request that an activity policy (e.g., activity policy  510 , activity policy  512 , etc.) determine a score for the attribute corresponding to the specified time. For example, activity policy  510  can be a power policy. Activity scorer  508  can send the context parameters and the time specified by activity manager  504  (e.g., 1:01 am) to activity policy  510 . Activity policy  510  can obtain historical context data  514  (e.g., from historical context database  406  and through context store  112 ) and determine based on historical context data  514  a score that represents whether the predicted power conditions at 1:01 am on the current day are sufficient to satisfy the conditions of the activity request  502 . For example, activity policy  510  can return a score the represents the probability that user device  110  will be connected to external power at 1:01 am. Activity policy  510  can then return the score to activity scorer  508 . 
     Similarly, activity scorer  508  can send the context parameters and the specified time to activity policy  512 . For example, activity policy  512  can be a networking policy that can generate a score representing the predicted quality of network conditions at the specified time. As described above, activity request  502  can specify an amount of work associated with activity request  502 . For example, activity request  502  can specify that activity request corresponds to a networking task that requires a Wi-Fi connection and will upload or download 2 GB of data. Activity policy  512  can analyze historical context data to predict network conditions around the requested time (e.g., 1:01 am) and generate a score that represents whether the network connection that normally exists around 1:01 am is suitable for transmitting the 2 GB of data. Alternatively, activity policy  512  can determine, based on historical context data  514 , a probability that the value of the “Wi-Fi” attribute will be “true” at 1:01 am on the current day. After determining the score, activity policy  512  can send the score to activity scorer  508 . 
     As another example, activity scorer  508  can send the activity request context parameters to activity policy  513 . For example, activity policy can be a user sleep policy that predicts the likelihood that the user is sleeping at the specified time. In this case, activity policy  513  must determine or predict a user context, not just a device context. In some implementations, the user context can be determined by another process running on user device  110  that submits the “UserSleeping” attribute and determined value representing whether the user is sleeping to context store  112  for entry into the local context database  114  and/or historical context database  406 . In this case, activity policy  513  can search historical context data  514  for “UserSleeping” attribute entries and corresponding values to generate a score representing the probability that the user will be sleeping at “1:01 am.” 
     In some implementations, activity policy  513  can determine a score based on a combination of device contexts. For example, activity policy  513  can obtain historical context data  514  that indicates values for location (e.g., “CurrentLocation”), screen lit (e.g., “ScreenLit”), and last user input (e.g., “LastUserInput” attributes for multiple (e.g., all) user devices. The user&#39;s location, screen lit, and last user input values combined with the time of day can be a good indication of the user&#39;s sleep context. For example, if the historical context data for each device indicates that at the requested time of day (e.g., 1:01 am) the user is typically at home, the device&#39;s screen is not lit, the last user input was more than 10 minutes ago, and the time corresponds to an empirically determined normal sleep time (e.g., lam), then activity policy  513  can determine that the user is likely to be asleep. Activity policy  312  can collect these attributes and corresponding values from historical context data  514  corresponding to each user device and determine a score representing the probability that the user will be sleeping at the requested time (e.g., 1:01 am). Activity policy  513  can then send the determined score to activity scorer  508 . 
     In some implementations, activity scorer  508  can determine an activity score for the requested activity based on the activity policy scores. For example, activity scorer  508  can average the attribute scores received from each activity policy to determine a score for the activity. Alternatively, activity scorer  508  can multiply the attributes scores to generate an activity score. Thus, if one of the required attribute values has a zero probability of occurring, the score (e.g., probability) for the requested activity will be zero at the requested time. After the score for the requested activity is determined for the requested time block (e.g., 1:01 am), activity scorer  508  can send the activity score to activity manager  504 . 
     In some implementations, activity manager  504  can determine the optimum score and corresponding time for the requested activity. For example, activity manager  504  can receive an activity score from activity scorer  508  for each time block within the specified time window. Activity manager  504  can determine the best score (e.g., optimal score) and corresponding time received from activity scorer  508 . The optimal score can be a predictor of the best or optimal time during the specified time window for performing the requested activity, for example. Activity manger  504  can store the optimal score and corresponding time in association with activity request  502  in activity request database  506 . 
     In some implementations, activity scheduler  402  can monitor context data to determine when to run the activity corresponding to activity request  502 . For example, activity scheduler  402  can receive current context data  516  from context store  112 . Activity scheduler  502  can register interest in context data (e.g., attributes, attribute values) specified in activity request  502  and receive a callback from context store  112  when context data items change state. Activity scheduler  502  can register interest in all context updates received by context store  112  and receive a callback from context store  112  when context data items change state. Activity scheduler  502  can request specific context data, as needed. 
     In some implementations, in response to receiving current context data  518 , activity manager  504  can identify activity requests in activity request database  506  that have time windows corresponding to the current time. Activity manager  504  can then determine which of the identified activity requests are ready to run based on the current context. For example, activity manager  504  can request a current score for activity request  502  from activity scorer  508 . 
     In some implementations, activity scorer  508  can determine a current score for activity request  502  based on current context data  516 . For example, activity scorer  508  can receive from activity manager  504  a request to score an activity request (e.g., activity request  502 ) having a time window that corresponds to the current time. The request can include the context parameters for the activity request and the current time. Activity scorer  508  can then generate an activity score as described above for the current time based on current context data  516  and/or historical context data  514 . For example, activity policies  510 ,  512  and/or  514  can generate a score based on current context data  516  and/or historical context data  514  as described above. Activity policies  510 ,  512 , and/or  514  can deliver the scores to activity scorer  508  so that activity scorer  508  can generate a current score for activity request  502  based on current context data  516 . After computing the current score, activity scorer  508  can send the current activity score for activity request  502  to activity manager  504 . 
     In some implementations, activity manager  504  can determine when to run a task based the predicted optimal score and the current task score. For example, activity manager  504  can determine a threshold value as a portion (e.g., 80%, 75%, etc.) of the optimal score. As time progresses through the time window specified in activity request  502 , activity manager  504  can receive a current activity score for each block of time (e.g., every 1 minute) and compare the current activity score to the threshold value. When the current activity score exceeds the threshold value, the activity manager  504  can send activity notification  520  to the requesting scheduler client (e.g., scheduler client  404 ). Activity notification  520  can, for example, indicate that scheduler client  404  should run the activity corresponding to activity request  502  at the current time. 
     In some implementations, activity scheduler  402  can preempt or interrupt a running activity in response to receiving current context data  516 . For example, to free up computing resources (e.g., CPU cycles, battery charge, network bandwidth, etc.) in anticipation of the execution of new activities, activity preemption logic  518  can identify running activities (e.g., tasks, jobs, etc.) on user device  110  that should be terminated or suspended. For example, scheduler clients (e.g., scheduler client  404 ) can report the status of executing activities to activity scheduler  402  in activity status report  522 . Activity status report  522  can include information describing the amount of work done (e.g., amount of data processed, elapsed time, etc.) and/or the current state of the activity (e.g., running, completed, suspended, terminated, etc.). When activity scheduler  402  receives activity status report  522 , activity manager  504  can store the status information in activity request database  506  in association with the corresponding activity request. Activity preemption logic  518  can then use the activity status information to determine which activities to suspend or terminate. For example, activity preemption logic  518  can suspend or terminate any activities that have not finished within a period of time (e.g., as specified in the corresponding activity request). Activity preemption logic  518  can suspend or terminate running activities by sending activity preemption message  524  to the corresponding scheduler client indicating that the scheduler client should suspend or terminate the running activity. 
       FIG. 6  is a graph  600  illustrating an example optimal score determination for an activity request. For example, axis  602  (e.g., y-axis) can correspond to a range of scores (e.g., 0-100, 0-1, etc.) generated by an activity policy (e.g., activity policy  510 ,  512 , and/or  513 ). Axis  604  (e.g., x-axis) can correspond to a range of time. Line  610  can correspond to scores generated for an activity request over time by activity policy  510 , for example. Similarly, line  612  and line  614  can correspond to scores generated for an activity request over time by activity policies  512  and  514 , respectively. For example, each policy can be configured to generate a score for an activity request based on the parameters of the activity request (e.g., context parameters) and historical context data. 
     In some implementations, the score can be predictive of how good the context conditions will be for executing the activity associated with (e.g., defined by) the activity request at a corresponding future time. The score does not just account for the predicted network conditions, but also how the predicted network conditions relate or satisfy the parameters and/or context criteria of the corresponding activity request. For example, the scores along line  610  can be predictive of how good power conditions will be for performing the requested activity at corresponding times. Thus, predicted poor power conditions can still produce a high score if the corresponding requested activity does not require or use a lot of power (e.g., as determined by the amount of work to be done by the activity). 
     Similarly, scores along line  612  can be predictive of how good networking conditions will be for performing the requested activity at corresponding times. Thus, predicted poor network conditions can still produce a high score if the corresponding requested activity does not require a network connection or a lot of bandwidth. Scores along line  612  can be predictive of how good the user context will be for performing the requested activity at corresponding times relative to a user context specified in the activity request, as described above. 
     In some implementations, activity manager  504  can predict an optimal score and corresponding time within a time window specified by an activity request. For example, activity request  502  can specify a time window for performing a corresponding activity that starts at time “T 1 ” indicated by line  616  and ends at time “T 2 ” indicated by line  618 . For each time block (e.g., 30 seconds, 1 minute, etc.) within the time window, activity scorer  508  can calculate an activity score by combining the policy scores illustrated by lines  610 ,  612  and  614  and deliver the activity score to activity manager  504 . For example, activity scorer  508  can combine (e.g., average) the policy scores at time “T 3 ” indicated by line  620  to generate an activity score represented by dot  602 . Activity manager  504  can determine the highest activity score within the activity window (e.g., T 1 -T 2 ) to determine the optimal activity score (e.g., dot  622 ) and corresponding time (e.g., “T 3 ”) for the activity request. Activity manager  504  can determine a current activity score using a similar process. For example, instead of predicting a future activity score based on historical context data, the activity policies can generate policy scores based on current context data received from context store  112 . 
       FIG. 7  is a graph  700  illustrating example how to determine when to run an activity based on an optimal activity score and a current activity score. For example, activity manager  504  can determine when to execute an activity based on a current activity score exceeding a threshold value determine based on the optimal activity score. The threshold value can be a percentage (e.g., 90%, 80%, 75%, etc.) of the optimal score, for example. 
     In some implementations, the threshold value can change over time. For example, the threshold value can change based on the priority of the activity. As described above, an activity can have a high, medium, low, or maintenance priority. For example, the threshold value for each priority can be a constant value (e.g., 80% of optimal score) from the beginning of the activity time window at T 1  to the time corresponding to the optimal score T 3 . This approach allows an opportunity for the best predicted context or conditions to occur at T 3  before executing the requested activity. 
     After the optimal time at T 3 , the threshold value can be adjusted based on priority. For example, the threshold value for a high priority activity can be quickly reduced as time approaches the end of the activity time window at T 2 , as illustrated by line  702 . The threshold value for a medium priority activity can be reduced as time approaches the end of the activity time window but not as quickly as a high priority activity, as illustrated by line  704 . The threshold value for a low priority activity can be reduced as time approaches the end of the activity time window but not as quickly as a medium priority activity, as illustrated by line  706 . The threshold value for a maintenance activity may or may not be reduced as time approaches the end of the activity time window but not as quickly as a high priority task, as illustrated by line  704 . 
     As described above, when the current time corresponds to the beginning of the time window for a requested activity, activity manager  504  can begin requesting current activity scores for the requested activity. For example, as time progresses through the time window (e.g., T 1 -T 2 ), activity manger  504  can request and receive current activity scores from activity scorer  508 . Line  710  represents the current activity scores received by activity manager  504  over time. 
     When activity manager  504  receives a current activity score, activity manager  504  can compare the current activity score to the threshold value for the requested activity, as determined based on priority and optimal score. For example, if at time T 4  the current activity score is greater (or equal to) the threshold value, activity manager  504  can notify the corresponding scheduler client to run the requested activity even though the current time (e.g., T 4 ) is before the predicted optimal time at T 3 . 
     However, when the current activity scores obtained between the beginning of the activity time window (T 1 ) and the optimal time (T 3 ) never exceed the threshold value, the threshold value for the requested activity can be adjusted thereafter based on priority, as described above. For example, if the requested activity is a high priority activity, activity manager  504  can cause the corresponding activity to be run when the current activity score is greater than or equal to the high priority threshold value at time T 5 . If the requested activity is a medium priority activity, activity manager  504  can cause the corresponding activity to be run when the current activity score is greater than or equal to the medium priority threshold value at time T 6 . If the requested activity is a low priority activity, activity manager  504  can cause the corresponding activity to be run when the current activity score is greater than or equal to the low priority threshold value at time T 7 . If the requested activity is a maintenance activity, activity manager  504  may never cause the corresponding activity to be run because the current activity score is never greater than or equal to the maintenance priority threshold value represented by line  708 . Thus, a high priority activity is almost guaranteed to be run by the end of the time window (e.g., at T 2 ) while a maintenance task may not be run at all. 
     Coalescing Compatible Activities 
     In some implementations, activity manager  504  can determine compatible requested activities to run when a requested activity exceeds the threshold value. For example, activity request database  506  can store many different activity requests (e.g., activity request  502 ) from many different scheduler clients (e.g., scheduler client  404 ). When activity manger  504  determines that an activity corresponding to an activity request should be run (e.g., based on the threshold value described above), activity manager  504  can determine whether other compatible activities should be run based on the corresponding activity requests stored in activity request database  506 . 
     In some implementations, activity manger  504  can determine that a compatible activity request is compatible with an activity request that is ready to be run. For example, compatibility can be determined based on activity requests that share similar or complementary context parameters. For example, activity manager  506  can determine that activity requests that share similar networking contexts should be run together. For example, activity manger  506  can determine that a networking activity associated with a first activity request should be run and then identify other compatible activity requests that specify time windows that correspond to the current time and that are also associated with networking activities so that the networking activities can be performed while the networking components of user device  110  are powered. Alternatively, activity manger  504  can identify activity requests with complementary context parameters. For example, an activity request that specifies a large amount of data to process may be run with an activity request that specifies a small amount of data to process. Thus, both activity requests can be run while the CPU is active without overwhelming user device  110 . 
     After identifying the compatible activity requests, activity manager  504  can send each compatible activity request to activity scorer  508  to be scored based on the current context, as described above. Activity manager  504  can receive current activity scores for the compatible activity requests and compare the current activity score to threshold values based on the optimal activity score previously determined for each of the respective compatible activity requests to determine whether to run the corresponding activities. Thus, activity manger  504  can coalesce or run activities together when the activities share common context parameters and/or require similar computing resources. 
     In some implementations, the threshold value for a compatible activity request can be adjusted to allow the compatible activity request to be run with the first activity request. For example, after determining that the first activity request should be run, activity manager  504  can identify compatible activity requests, as described above. While the compatible activity request may be analyzed independently with respect to a first threshold value (e.g., 80% of the optimal activity score), the threshold value can be reduced (e.g., 70% of the optimal score, −10 points, −0.01 points, etc.) when the compatible activity is going to be run with another activity. Thus, by reducing the threshold score compatible activities can be run together thereby improving the efficiency of resource usage on user device  110 . 
     In some implementations, activity manager  504  can enforce a limit on the number of activities that are run at a given time. For example, activity manager  504  can be configured to restrict the number of tasks that are run in parallel to a configured number of tasks. For example, an activity request from a scheduler client can specify a number of tasks that can be run in parallel with the task defined by the activity request. Alternatively, activity manager  504  can dynamically and/or automatically determine a limit on the number of tasks that can be run in parallel based on the performance of the device, the current load on the device&#39;s processor, available battery power, whether the device is connected to an external power source, the bandwidth available through the current network connection, and/or the type of the current network connection. Thus, activity manager  504  can determine a number of tasks to run that will have the least impact on device performance and/or network connectivity. 
     Example Processes 
       FIG. 8  is flow diagram of an example process  800  for determining a context based on a multi-device context store. For example, a computing device (e.g., user device  110 , user device  130 ) can collect context data (e.g., user context, device context, etc.) from multiple devices and provide context information to context clients so that the context clients can make decisions based on the collected context data. For example, rather than making decisions or determinations based solely on local context data or device state, the computing device can determine a user context based on context data or state data received from multiple devices and make more intelligent decisions based on the user context. 
     At step  802 , user device  110  can collect local device context data. For example, context store  112  can collect context data (e.g., attribute-value data pairs) from local context clients (e.g., context client  118 ) running on user device  110 . 
     At step  804 , user device  110  can send local context data to remote user devices. For example, user device  110  can broadcast local context data updates using a device-to-device network connection or a traditional Wi-Fi, LAN, WAN or internet network connection. User device  110  can broadcast local context data periodically, in response to a local context data update, in response to another user device registering interest in local context data, and/or in response to a request for context data from another user device, as described above. 
     At step  806 , user device  110  can receive context data from a remote user device. For example, user device  110  can receive context data from user device  130 . For example, user device  130  can broadcast local context data updates using a device-to-device network connection or a traditional Wi-Fi, LAN, WAN or internet network connection. User device  130  can broadcast local context data periodically, in response to a local context data update, in response to another user device (e.g., user device  110 ) registering interest in context data collected by user device  130 , and/or in response to a request for context data from another user device (e.g., user device  110 ), as described above. 
     At step  808 , user device  110  can store context data in a local data store. For example, user device  110  can store local context data collected by context store  112  in local context database  114  on user device  110 . User device can store remote context data received from user device  130  in remote context store database  116  on user device  110 . 
     At step  810 , user device  110  can receive a context query from a context client running on user device  110 . For example, context client  118  can submit a query to context store  112  for context data. The query can specify the scope of the context query (e.g., local device, specified remote device, all user devices, etc.). The query can specify whether context client  118  is interested in future context updates or is just requesting current context data. The query can request specify specific context data to be returned by context client  118 . For example, the context data can be specified by identifying attributes and/or attribute values for which context client  118  would like to receive context data and/or notifications. 
     At step  812 , user device  110  can determine the requested context based on the remote context data in the local data store. For example, context store  112  can obtain from local context database  114  and/or remote context database  116  context data (e.g., attribute-value data pairs) corresponding to the context query received from context client  118 . For example, context store  110  can determine the requested context once for each context query. Alternatively, context store  110  can determine the requested context on an ongoing basis (e.g., after a context client registers interest in context data) when a value for a requested attribute changes. 
     At step  814 , user device  110  can send a context response to the context client. For example, context store  112  can generate a context response that includes the requested context data (e.g., local context data and/or remote context data). Context store  112  can send the context response to context client  118 . Context client  118  can then perform an operation based on the local context and/or remote context data received from context store  112 . For example, context client  118  can determine a user context based on a combination of local context data and remote context data and then perform an operation (e.g., provide a service to the user, present a notification or other information, etc.) to the user based on the determined user context. 
       FIG. 9  is a flow diagram of an example process  900  for scheduling activities based on a current context. For example, user device  110  can initiate an activity when the current context data of the device and/or user is optimal for executing the activity. 
     At step  902 , user device  110  can receive an activity request including context parameters. For example, activity scheduler  402  can receive an activity request from a scheduler client  404  that specifies parameters for performing a corresponding activity. The activity request can, for example, define a time window (e.g., start time, end time) for performing a corresponding activity. The activity request can specify a priority (e.g., high, medium, low, maintenance) for the activity. The activity request can specify a context (e.g., user context, device context, etc.) for executing the corresponding activity. For example, the activity request can include attribute-value pairs of data that describe or defined the context needed to run the corresponding activity. 
     At step  904 , user device  110  can determine an optimal score for the activity request based on the context parameters specified in the activity request. For example, activity scheduler  402  can determine an optimal score for the activity request based on the parameters defined by the activity request and historical context data, as described above. 
     At step  906 , user device  110  can detect a change in the current context. For example, activity scheduler  402  can receive current context data from context store  112  indicating that context data obtained by context store  112  has changed, as described above. 
     At step  908 , user device  110  can suspend activities running on user device  112 . For example, activity scheduler  402  can determine which running activities, if any, have been running for longer than the time specified in (or derived from) the corresponding activity request and suspend one or more of the activities to free up resources to run new activities based on pending activity requests. 
     At step  910 , user device  110  can determine a current score for a waiting activity based on the context parameters specified in the corresponding activity request. For example, activity scheduler  402  can determine when the current time corresponds to the start time of a time window specified in an activity request. While the current time moves through the time window (e.g., every 1 minute, every 30 seconds, etc.) of the activity request, activity scheduler  402  can determine a current activity score for the activity request based on the current context data received from context store  112 , as described above. 
     At step  912 , user device  110  can determine an activity execution threshold for the waiting activity request based on the optimal score. For example, activity scheduler  402  can determine an activity execution threshold as a percentage (e.g., 90%, 85%, etc.) of the optimal score. In some implementations, the activity execution threshold can be adjusted based on the predicted time (e.g., optimal time) of the optimal score, the priority of the activity request, how close the current time is to the end of the activity request time window, and/or whether the activity request is being coalesced with another activity request, as described above. 
     At step  914 , user device  110  can compare the current activity score to the activity execution threshold. For example, activity scheduler  402  can determine when the current activity score is greater than or equal to the activity execution threshold. 
     At step  916 , user device  110  can execute the waiting activity when the current activity score is greater than the activity execution threshold. For example, activity scheduler  402  can when the current activity score is greater than or equal to the activity execution threshold and send a message to the scheduler client (e.g., scheduler client  404 ) to cause the scheduler client to perform the activity corresponding to the activity request. 
     Privacy 
     The present disclosure recognizes that the use of such personal information data, in the present technology, can be used to the benefit of users. For example, the personal information data can be used to deliver targeted content that is of greater interest to the user. Accordingly, use of such personal information data enables calculated control of the delivered content. Further, other uses for personal information data that benefit the user are also contemplated by the present disclosure. 
     The present disclosure further contemplates that the entities responsible for the collection, analysis, disclosure, transfer, storage, or other use of such personal information data will comply with well-established privacy policies and/or privacy practices. In particular, such entities should implement and consistently use privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining personal information data private and secure. For example, personal information from users should be collected for legitimate and reasonable uses of the entity and not shared or sold outside of those legitimate uses. Further, such collection should occur only after receiving the informed consent of the users. Additionally, such entities would take any needed steps for safeguarding and securing access to such personal information data and ensuring that others with access to the personal information data adhere to their privacy policies and procedures. Further, such entities can subject themselves to evaluation by third parties to certify their adherence to widely accepted privacy policies and practices. 
     Despite the foregoing, the present disclosure also contemplates embodiments in which users selectively block the use of, or access to, personal information data. That is, the present disclosure contemplates that hardware and/or software elements can be provided to prevent or block access to such personal information data. For example, in the case of advertisement delivery services, the present technology can be configured to allow users to select to “opt in” or “opt out” of participation in the collection of personal information data during registration for services. In another example, users can select not to provide location information for targeted content delivery services. In yet another example, users can select to not provide precise location information, but permit the transfer of location zone information. 
     Example System Architecture 
       FIG. 10  is a block diagram of an example computing device  1000  that can implement the features and processes of  FIGS. 1-9 . The computing device  1000  can include a memory interface  1002 , one or more data processors, image processors and/or central processing units  1004 , and a peripherals interface  1006 . The memory interface  1002 , the one or more processors  1004  and/or the peripherals interface  1006  can be separate components or can be integrated in one or more integrated circuits. The various components in the computing device  1000  can be coupled by one or more communication buses or signal lines. 
     Sensors, devices, and subsystems can be coupled to the peripherals interface  1006  to facilitate multiple functionalities. For example, a motion sensor  1010 , a light sensor  1012 , and a proximity sensor  1014  can be coupled to the peripherals interface  1006  to facilitate orientation, lighting, and proximity functions. Other sensors  1016  can also be connected to the peripherals interface  1006 , 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  1020  and an optical sensor  1022 , 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  1020  and the optical sensor  1022  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  1024 , 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  1024  can depend on the communication network(s) over which the computing device  1000  is intended to operate. For example, the computing device  1000  can include communication subsystems  1024  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  1024  can include hosting protocols such that the device  100  can be configured as a base station for other wireless devices. 
     An audio subsystem  1026  can be coupled to a speaker  1028  and a microphone  1030  to facilitate voice-enabled functions, such as speaker recognition, voice replication, digital recording, and telephony functions. The audio subsystem  1026  can be configured to facilitate processing voice commands, voiceprinting and voice authentication, for example. 
     The I/O subsystem  1040  can include a touch-surface controller  1042  and/or other input controller(s)  1044 . The touch-surface controller  1042  can be coupled to a touch surface  1046 . The touch surface  1046  and touch-surface controller  1042  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  1046 . 
     The other input controller(s)  1044  can be coupled to other input/control devices  1048 , 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  1028  and/or the microphone  1030 . 
     In one implementation, a pressing of the button for a first duration can disengage a lock of the touch surface  1046 ; and a pressing of the button for a second duration that is longer than the first duration can turn power to the computing device  1000  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  1030  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  1046  can, for example, also be used to implement virtual or soft buttons and/or a keyboard. 
     In some implementations, the computing device  1000  can present recorded audio and/or video files, such as MP3, AAC, and MPEG files. In some implementations, the computing device  1000  can include the functionality of an MP3 player, such as an iPod™. The computing device  1000  can, therefore, include a 36-pin connector that is compatible with the iPod. Other input/output and control devices can also be used. 
     The memory interface  1002  can be coupled to memory  1050 . The memory  1050  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  1050  can store an operating system  1052 , such as Darwin, RTXC, LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such as VxWorks. 
     The operating system  1052  can include instructions for handling basic system services and for performing hardware dependent tasks. In some implementations, the operating system  1052  can be a kernel (e.g., UNIX kernel). In some implementations, the operating system  1052  can include instructions for performing voice authentication. For example, operating system  1052  can implement the multi-device context store and/or context store features as described with reference to  FIGS. 1-9 . 
     The memory  1050  can also store communication instructions  1054  to facilitate communicating with one or more additional devices, one or more computers and/or one or more servers. The memory  1050  can include graphical user interface instructions  1056  to facilitate graphic user interface processing; sensor processing instructions  1058  to facilitate sensor-related processing and functions; phone instructions  1060  to facilitate phone-related processes and functions; electronic messaging instructions  1062  to facilitate electronic-messaging related processes and functions; web browsing instructions  1064  to facilitate web browsing-related processes and functions; media processing instructions  1066  to facilitate media processing-related processes and functions; GNSS/Navigation instructions  1068  to facilitate GNSS and navigation-related processes and instructions; and/or camera instructions  1070  to facilitate camera-related processes and functions. 
     The memory  1050  can store software instructions  1072  and/or software instructions  1074  to facilitate other processes and functions, such as the multi-device context store and/or context store processes and functions as described with reference to  FIGS. 1-9 . 
     The memory  1050  can also store other software instructions (not shown), 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  1066  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  1050  can include additional instructions or fewer instructions. Furthermore, various functions of the computing device  1000  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: 20160609
Publication Date: 20181204
Grant Date: 20181204
Priority Date: 20160609
Inventors: VENKATRAMAN, Kartik
DE LA CROPTE DE CHANTERAC, Cyril
Mangade, Shardul
Assignee: APPLE INC
CPC Classifications: [{"code": "H04L67/325", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04L43/16", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04L67/22", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F9/4843", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04L67/535", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04L67/62", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04L67/535", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04L67/62", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F9/4843", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F9/4843", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04L43/16", "inventive": true, "first": true, "tree": "[]"}]
Family ID: 60572619