PATENT DOCUMENT

Publication Number: US-10383568-B2
Application Number: US-201514871887-A
Country: US
Kind Code: B2

Title: Confirming sleep based on secondary indicia of user activity

Abstract:
In some implementations, a computing device can confirm a sleep determination for a user based on secondary indicia of user activity. For example, the computing device can be a user&#39;s primary computing device. The primary device can predict and/or determine when the user is sleeping based on the user&#39;s use (e.g., primary indicia), or lack of use, of the primary device. After the primary device determines that the user is sleeping, the primary device can confirm that the user is asleep based on secondary indicia of user activity. In some implementations, the secondary indicia can include user activity reported to the primary computing device by other secondary computing devices (e.g., a second user device, a household appliance, etc.). In some implementations, the secondary indicia can include user activity detected by sensors of the primary computing device (e.g., sound, light, movement, etc.).

Claims:
What is claimed is: 
     
       1. A method for providing a sleep determination for a user with a first computing device programmed to perform the method, comprising:
 provisionally determining, by the first computing device, that the user of the first computing device is sleeping based on data indicating that the user is not currently using the first computing device to provide a provisional determination that the user is sleeping; 
 receiving, by the first computing device, a communication from a second device indicating that conscious human activity is being performed with respect to the second device that is unrelated to the first computing device, wherein the communication includes a second identifier that identifies a user account corresponding to an operator of the second device; 
 comparing, by the first computing device, the second identifier received from the second computing device to a first identifier that identifies a user account corresponding to the user of the first computing device; 
 determining, by the first computing device, whether the conscious human activity is being performed by the user of the first computing device based on the comparison of the first identifier and the second identifier; and 
 revising, by the first computing device, the provisional determination that the user is sleeping to indicate that the user is awake in response to a determination that the conscious human activity is being performed by the user of the first computing device in order to provide a more accurate sleep determination for the user. 
 
     
     
       2. The method of  claim 1 , further comprising:
 confirming, by the first computing device, that the user is sleeping in response to a determination that the conscious human activity is being performed by someone other than the user of the first computing device. 
 
     
     
       3. The method of  claim 1 , further comprising:
 detecting, by the first computing device, sounds generated by second conscious human activity; 
 determining, by the first computing device, that the detected sounds correspond to the user of the first computing device; and 
 determining, by the first computing device, that the second conscious human activity is being performed by the user of the first computing device in response to a determination that the detected sounds correspond to the user of the first computing device. 
 
     
     
       4. The method of  claim 1 , further comprising:
 detecting, by the first computing device, sounds generated by second conscious human activity; 
 determining, by the first computing device, that the detected sounds are inconsistent with sounds associated with the user of the first computing device; and 
 determining, by the first computing device, that the second conscious human activity is being performed by someone other than the user of the first computing device in response to a determination that the detected sounds are inconsistent with sounds associated with the user of the first computing device. 
 
     
     
       5. The method of  claim 1 , further comprising:
 detecting, by the first computing device, sleep sounds generated by unconscious human activity; 
 determining, by the first computing device, whether the sleep sounds are being performed by the user of the first computing device; and 
 maintaining, by the first computing device, the provisional determination that the user is sleeping in response to a determination that the sleep sounds are being performed by the user of the first computing device. 
 
     
     
       6. The method of  claim 1 , further comprising determining that the second device is a personal device of the user of the first computing device, and
 wherein the determining whether the conscious human activity is being performed by the user of the first computing device further comprises determining, by the first computing device, that the conscious human activity is being performed by the user of the first computing device in response to the second device being the personal device of the user. 
 
     
     
       7. The method of  claim 1 , wherein the communication from the second device further indicates that the conscious human activity is being performed with respect to a third device. 
     
     
       8. The method of  claim 7 , wherein the conscious human activity is positioning of the second device within communications range of the third device. 
     
     
       9. A non-transitory computer-readable medium including one or more sequences of instructions that, when executed by one or more processors, causes:
 provisionally determining, by a first computing device, that a user of the first computing device is sleeping based on data indicating that the user is not currently using the first computing device to provide a provisional determination that the user is sleeping; 
 receiving, by the first computing device, a communication from a second device indicating that conscious human activity is being performed with respect to the second device that is unrelated to the first computing device, wherein the communication includes a second identifier that identifies a user account corresponding to an operator of the second device; 
 comparing, by the first computing device, the second identifier received from the second computing device to a first identifier that identifies a user account corresponding to the user of the first computing device; 
 determining, by the first computing device, whether the conscious human activity is being performed by the user of the first computing device based on the comparison of the first identifier and the second identifier; and 
 revising, by the first computing device, the provisional determination that the user is sleeping to indicate that the user is awake in response to a determination that the conscious human activity is being performed by the user of the first computing device in order to provide a more accurate sleep determination for the user. 
 
     
     
       10. The non-transitory computer-readable medium of  claim 9 , wherein the instructions cause:
 confirming, by the first computing device, that the user is sleeping in response to a determination that the conscious human activity is being performed by someone other than the user of the first computing device. 
 
     
     
       11. The non-transitory computer-readable medium of  claim 9 , wherein the instructions cause:
 detecting, by the first computing device, sounds generated by second conscious human activity; 
 determining, by the first computing device, that the detected sounds correspond to the user of the first computing device; and 
 determining, by the first computing device, that the second conscious human activity is being performed by the user of the first computing device in response to a determination that the detected sounds correspond to the user of the first computing device. 
 
     
     
       12. The non-transitory computer-readable medium of  claim 9 , wherein the instructions cause:
 detecting, by the first computing device, sounds generated by second conscious human activity; 
 determining, by the first computing device, that the detected sounds are inconsistent with sounds associated with the user of the first computing device; and 
 determining, by the first computing device, that the second conscious human activity is being performed by someone other than the user of the first computing device in response to a determination that the detected sounds are inconsistent with sounds associated with the user of the first computing device. 
 
     
     
       13. The non-transitory computer-readable medium of  claim 9 , wherein the instructions cause:
 detecting, by the first computing device, sleep sounds generated by unconscious human activity; 
 determining, by the first computing device, whether the sleep sounds are being performed by the user of the first computing device; and 
 maintaining, by the first computing device, the provisional determination that the user is sleeping in response to a determination that the sleep sounds are being performed by the user of the first computing device. 
 
     
     
       14. The non-transitory computer-readable medium of  claim 9 , wherein the instructions cause:
 determining that the second device is a personal device of the user of the first computing device, and 
 wherein the determining whether the conscious human activity is being performed by the user of the first computing device further comprises determining, by the first computing device, that the conscious human activity is being performed by the user of the first computing device in response to the second device being the personal device of the user. 
 
     
     
       15. The non-transitory computer-readable medium of  claim 9 , wherein the communication from the second device further indicates that the conscious human activity is being performed with respect to a third device. 
     
     
       16. The non-transitory computer-readable medium of  claim 15 , wherein the conscious human activity is positioning of the second device within communications range of the third device. 
     
     
       17. 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, causes:
 provisionally determining, by a first computing device, that a user of the first computing device is sleeping based on data indicating that the user is not currently using the first computing device to provide a provisional determination that the user is sleeping; 
 receiving, by the first computing device, a communication from a second device indicating that conscious human activity is being performed with respect to the second device that is unrelated to the first computing device, wherein the communication includes a second identifier that identifies a user account corresponding to an operator of the second device; 
 comparing, by the first computing device, the second identifier received from the second computing device to a first identifier that identifies a user account corresponding to the user of the first computing device; 
 determining, by the first computing device, whether the conscious human activity is being performed by the user of the first computing device based on the comparison of the first identifier and the second identifier; and 
 revising, by the first computing device, the provisional determination that the user is sleeping to indicate that the user is awake in response to a determination that the conscious human activity is being performed by the user of the first computing device in order to provide a more accurate sleep determination for the user. 
 
 
     
     
       18. The system of  claim 17 , wherein the instructions cause:
 confirming, by the first computing device, that the user is sleeping in response to a determination that the conscious human activity is being performed by someone other than the user of the first computing device. 
 
     
     
       19. The system of  claim 17 , wherein the instructions cause:
 detecting, by the first computing device, sounds generated by second conscious human activity; 
 determining, by the first computing device, that the detected sounds correspond to the user of the first computing device; and 
 determining, by the first computing device, that the second conscious human activity is being performed by the user of the first computing device in response to a determination that the detected sounds correspond to the user of the first computing device. 
 
     
     
       20. The system of  claim 17 , wherein the instructions cause:
 detecting, by the first computing device, sounds generated by second conscious human activity; 
 determining, by the first computing device, that the detected sounds are inconsistent with sounds associated with the user of the first computing device; and 
 determining, by the first computing device, that the second conscious human activity is being performed by someone other than the user of the first computing device in response to a determination that the detected sounds are inconsistent with sounds associated with the user of the first computing device. 
 
     
     
       21. The system of  claim 17 , wherein the instructions cause:
 detecting, by the first computing device, sleep sounds generated by unconscious human activity; 
 determining, by the first computing device, whether the sleep sounds are being performed by the user of the first computing device; and 
 maintaining, by the first computing device, the provisional determination that the user is sleeping in response to a determination that the sleep sounds are being performed by the user of the first computing device. 
 
     
     
       22. The system of  claim 17 , wherein the instructions cause:
 determining that the second device is a personal device of the user of the first computing device, and 
 wherein the determining whether the conscious human activity is being performed by the user of the first computing device further comprises determining, by the first computing device, that the conscious human activity is being performed by the user of the first computing device in response to the second device being the personal device of the user. 
 
     
     
       23. The system of  claim 17 , wherein the communication from the second device further indicates that the conscious human activity is being performed with respect to a third device. 
     
     
       24. The system of  claim 23 , wherein the conscious human activity is positioning of the second device within communications range of the third device.

Description:
TECHNICAL FIELD 
     The disclosure generally relates to determining when a user is sleeping. 
     BACKGROUND 
     Mobile computing devices are ubiquitous in the modern world. People are constantly using their smartphones, tablet computers, and/or other portable devices. These devices can track when the user is using the device, the location where the user is using the device, environmental conditions around the device, and/or other indicia of use. The computing devices can analyze this usage data (e.g., indicia of use) and determine various behaviors or patterns of activity associated with the user of the device. For example, a user&#39;s pattern of use of a device (e.g., a smartphone) can be analyzed to predict the user&#39;s sleep period patterns and/or detect when a user is sleeping. However, if a user uses multiple computing devices, the sleep prediction and/or sleep detection performed by the user&#39;s primary computing device may be erroneous. 
     SUMMARY 
     In some implementations, a computing device can confirm a sleep determination for a user based on secondary indicia of user activity. For example, the computing device can be a user&#39;s primary computing device. The primary device can predict and/or determine when the user is sleeping based on the user&#39;s use (e.g., primary indicia), or lack of use, of the primary device. After the primary device initially determines that the user is sleeping, the primary device can confirm that the user is asleep based on secondary indicia of user activity. In some implementations, the secondary indicia can include user activity reported to the primary computing device by other secondary computing devices (e.g., a second user device, a household appliance, etc.). In some implementations, the secondary indicia can include user activity detected by sensors of the primary computing device (e.g., sound, light, movement, etc.). 
     Particular implementations provide at least the following advantages: sleep predictions can become more accurate by including secondary indicia in the sleep predication algorithm; sleep detection can become more accurate by accounting for user activity not associated with the primary device. 
     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 confirming sleep based on secondary indicia of user activity. 
         FIG. 2  is an illustration of examples of secondary indicia of user activity. 
         FIG. 3  is flow diagram of an example process for confirming a user of a primary device is asleep. 
         FIG. 4  is a flow diagram of an example process for confirming that a user of a primary device is sleeping based on sleep status data associated with users of other devices. 
         FIG. 5  is a flow diagram of an example process for confirming that a user is sleeping based on the sounds generated by conscious human activity. 
         FIG. 6  is a flow diagram of an example process for determining that a user is awake based on sleep sounds generated by other people. 
         FIG. 7  is a block diagram of an example computing device  700  that can implement the features and processes of  FIGS. 1-6 . 
     
    
    
     Like reference symbols in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
       FIG. 1  is a block diagram of an example system  100  for confirming sleep based on secondary indicia of user activity. System  100  can include primary device  110 . For example, primary device  110  can be a user&#39;s primary computing device (e.g., the computing device that the user uses most often). Primary device  110  can be a smartphone, a tablet computer, a smart watch, or any other computing device. 
     In some implementations, primary device  110  can include usage monitor  112 . For example, usage monitor  112  can monitor the user&#39;s use of primary device  110 . Usage monitor  112  can detect when the user turns on primary device  110 , for example. Usage monitor  112  can detect when the display of primary device  110  is illuminated. Usage monitor  112  can detect when the user provides input to primary device  110 , and/or uses an application installed on primary device  110 . Usage monitor  112  can detect when the user stops using primary device  110 . For example, usage monitor  112  can detect when the user puts primary device  110  into a sleep mode, low power mode, or turns primary device  110  off. Usage monitor can record the user&#39;s use of primary device  110  over time in use log  114  (e.g., database or log file) stored on primary device  110 , for example. 
     In some implementations, primary device  110  can include sleep logic  116 . For example, sleep logic  116  can be a software process (e.g., application, operating system utility, operating system daemon, etc.) running on primary device  110 . In some implementations, sleep logic  116  can analyze the historical data in use log  114  to predict when a user will sleep. For example, sleep logic  116  can determine when a user was sleeping in the past by identifying periods of time (e.g., greater than 5 hours, greater than 6 hours, etc.) when the user was not using primary device  110 . The user&#39;s intentional (e.g., deliberate, active, not passive) use of primary device  110  can be the primary indicia of user activity, for example. Sleep logic  116  can, for example, determine a pattern of sleep periods over a number of days or weeks. For example, based on the historical data in use log  114 , sleep logic  116  can determine that the user typically sleeps between 11 pm and 6 am. Sleep logic  116  can predict future sleep periods based on the sleep period patterns derived from the historical data in use log  114 . For example, if the user historically sleeps between 11 pm and 6 am, sleep logic  116  can predict that the user will go to sleep at 11 pm and wake at 6 am in the future. 
     In some implementations, sleep logic  116  can determine sleep period patterns based on a single usage statistic. For example, sleep logic  116  can analyze display illumination statistics to determine when primary device  110  is being used by the user and when the user is asleep or awake. For example, if the display is illuminated, primary device  110  is in use, and the user is awake. If the display is dark (not illuminated), then primary device  110  is not being used. If primary device  110  has not been used for a period of time (e.g., at least 5 hours), then sleep logic  116  can determine that the user is asleep. 
     In some implementations, sleep logic  116  can determine sleep period patterns based on multiple usage statistics. For example, in addition display illumination statistics, sleep logic  116  can determine when the user is sleeping based on use of a headphone jack. For example, the display may be dark but the user might be listening to music through headphones connected to primary device  110 . Thus, sleep logic  116  can determine that the user is awake even though the display is dark. 
     In some implementations, sleep logic  116  can confirm that the user is sleeping (or awake) based on secondary indicia of user activity. For example, when sleep logic  116  determines that a user is sleeping using the primary indicia of user activity described above, sleep logic  116  can monitor secondary indicia of user activity to confirm that the user is sleeping. For example, sleep logic  116  can provisionally determine that the user is sleeping based on primary indicia of user activity and confirm (or disprove) the user is sleeping based on secondary indicia of user activity. The secondary indicia of user activity can correspond to conscious human activity that indicates the user is awake, for example. 
     In some implementations, the secondary indicia of user activity can be passively detected by primary device  110  using sensors built in to or connected to primary device  110 . For example, sleep logic  116  can activate or turn on various sensors of primary device  110  when sleep logic  116  has determined (e.g., provisionally determined) that the user is sleeping in order to detect the secondary indicia of user activity. In some implementations, primary device  110  can include light sensor  118  that can detect ambient light around primary device  110 . For example, when the light sensor detects a high light level (e.g., above a threshold level) that indicates a bright room, sleep logic  116  can determine that the user is awake because most people do not sleep in well-lit rooms. 
     In some implementations, primary device  110  can include microphone  118  (e.g., sound sensor). Sleep logic  116  can use microphone  118  to detect sound associated with user activity. Sleep logic  116  can analyze patterns in the detected sound and determine particular user activities based on the sound patterns. For example, primary device  110  can obtain sound samples (e.g., fingerprints) from a sound database that are mapped to (correspond to) various human activities (e.g., walking, brushing teeth, closing blinds, etc.). The sound database can be stored on primary device  110  or stored on a remote server (not shown). Microphone  118  can detect sounds generated by a user (e.g., when the user is walking, brushing their teeth, closing the blinds, etc.) and compare the sounds to the sound samples obtained from the sound database to identify the human activity corresponding to the detected sound. If the detected sound can be matched to a human activity in the sound database, then sleep logic  116  can determine that the user is awake. 
     In some implementations, the secondary indicia of user activity can be received from secondary devices. For example, the secondary devices can be a user device  130  that the user uses less frequently than primary device  110 . User device  130  can be, for example, a smartphone, tablet computer, laptop computer, or other computing device. The secondary device can be wearable device  150 , such as a smartwatch or smart eye glasses. The secondary device can be smart appliance  140  (e.g., a smart refrigerator, a smart door lock, smart blinds, smart power grid, etc.). Each of these secondary devices can detect when the user is using the secondary device, or performing some other conscious human activity, and can report the activity to primary device  110 . For example, the secondary devices (e.g., user device  130 , smart appliance  140 , wearable device  150 , etc.) can report the user activity to primary device  110  through network  160  (e.g., a local area network, wide area network, Internet, home network, Wi-Fi network, Bluetooth network, etc.). 
     In some implementations, sleep logic  116  of primary device  110  can confirm that the user is asleep, or awake, based on the use statistics received from the secondary devices. For example, sleep logic  116  may initially determine that the user is asleep based on primary indicia of user activity, as described above. However, upon receiving secondary indicia of user activity from other devices, sleep logic  116  can determine that the user is actually awake. On the other hand, if sleep logic  116  does not receive any secondary indicia of user activity (e.g., from sensors or from other devices), sleep logic  116  can confirm that the user is asleep as initially determined. 
       FIG. 2  is an illustration  200  of examples of secondary indicia of user activity. For example, secondary indicia of user activity can be generated from conscious human activity. Since sleeping usually occurs in the user&#39;s home, illustration  200  depicts the home environment and various sources of secondary indicia of user activity that can be generated in the home environment. Moreover, when users share the home environment with other people (e.g., family, roommates, etc.), the primary device will need to determine whether the conscious human activity is attributable to the user or other people in the home environment. For example, the primary device can eliminate other people as the source of the conscious human activity by determining that the other people in the house are asleep. The primary device can determine that the user is the source of the conscious human activity when the devices being used receive data (e.g., log in credentials, physical characteristics, media preferences, etc.) specific to the user. 
     In some implementations, primary device  202  can determine that user  204  is sleeping. For example, primary device  202  can correspond to primary device  110  of  FIG. 1 . In some implementations, primary device  202  can determine user  204  is sleeping based on primary indicia of user activity (e.g., user activity with respect to primary device  202 ), as described above. However, in some instances, user  204  may have just left primary device  202  on a table or nightstand while user  204  performs some other activity away from primary device  202 . Primary device  202  may interpret the inactivity with respect to primary device  202  while primary device  202  is on the table as user  204  sleeping when user  204  is actually awake and active elsewhere. 
     Detecting Conscious Human Activity 
     In some implementations, primary device  202  can detect environmental conditions indicating that a human is awake. For example, the detected environmental conditions (e.g., sound, light, etc.) can be secondary indicia of user activity. In some implementations, primary device  202  can detect noises (e.g., with a microphone) that indicate that a conscious activity is being performed by a human. For example, when blinds  210  are opened or closed, blinds  210  can make a distinctive noise that can be detected and identified by primary device  202 . Once the noise of blinds  210  opening or closing is identified (e.g., using the sound samples described above), primary device  202  can determine that a conscious human activity has been performed with respect to blinds  210 . Similarly, primary device  202  can detect and identify as conscious human activity sounds associated with opening and closing doors, opening and closing drawers, and/or opening and closing windows. 
     In some implementations, primary device  202  can detect sounds attributable to conscious human activity in bathroom  212 . For example, when a human brushes her teeth, turns on a water faucet, takes a shower, or flushes the toilet, these activities create distinctive noises that are attributable to a human (e.g., while a dog can be trained to flush a toilet, this is an unusual activity for a dog). These bathroom noises can be detected and identified by primary device  202 , as described above. Once the noise of these bathroom activities are identified, primary device  202  can determine that a conscious human activity has been performed. Similarly, primary device  202  can detect and identify the sound of human footsteps in hallway  214 . Once the footsteps are detected and identified, primary device  202  can determine that a conscious human activity has been performed. 
     In some implementations, primary device  202  can receive indications from secondary devices that a human is awake. For example, the secondary devices can include smart television  220 , smart refrigerator  222 , smart scale  224 , smart oven  226 , smart coffee maker  228 , and/or a smart personal hygiene device  230  (e.g., electric toothbrush, electric shaver, hairdryer, etc.). The secondary devices can include a computing device  232 , such as a laptop computer, smartphone, tablet computer, and/or wearable device. Each of these secondary devices  220 - 232  can be connected through a network (e.g., Wi-Fi, Bluetooth, etc.) to primary device  202 . When used, devices  202 - 232  can send a message to primary device  202  indicating that the device has been turned on or has received some other conscious interaction from a human. Primary device  202  can interpret a message from the devices  220 - 232  as evidence of human activity with respect to the corresponding device. 
     Attributing the Activity to the User 
     In some implementations, primary device  202  can determine that a detected conscious human activity is attributable to the user of primary device  202 . For example, if the user lives alone, all conscious human activity can be attributed to the user. If the user lives with other humans, primary device  202  can determine if the detected conscious human activity should be attributed to the user by identifying the human performing the conscious human activity. 
     In some implementations, primary device  202  can determine whether the user lives alone by monitoring environmental conditions surrounding primary device  202 . For example, primary device  202  can detect noises generated by humans (e.g., breathing, talking, walking, etc.) and determine sounds associated with the user and/or other humans. Each human may have a unique way of walking, talking, breathing, etc., that can be used to distinguish one human from another human. When primary device  202  detects multiple distinct sound patterns associated with different humans, primary device  202  can determine that there are multiple humans present in the house. For example, when primary device  202  detects sleeping sounds and conscious user activity, then primary device  202  can determine that user  204  lives with other humans. When primary device  202  detects sounds (e.g., breathing pattern or other activity) associated with only a single human, then primary device  202  can determine that the user lives alone. If user  204  lives alone, primary device  202  can attribute all conscious human activity to user  204 , as described above. If user  204  lives with other humans, primary device  202  can determine whether the detected conscious human activity should be attributed to the user of primary device  202 . 
     Self-Identification 
     In some implementations, primary device  202  can determine whether a detected conscious human activity should be attributed to the user based on user identification information included in messages received from secondary devices. For example, the user identification information can be a user account identifier (e.g., user name). The user identification information can be a device identifier for a single user device owned by the user. For example, since the single user device can only be operated by a single user who has the log in credentials for the single user account on the device, primary device  202  can attribute each use of the single user device to the owner of the device. Examples of single user devices can include a smartphone, smart watch, smart eye glasses, and the like that operate under a single user account. 
     In some implementations, primary device  202  can determine whether a detected conscious human activity should be attributed to the user based on user account information. For example, when a human uses one of smart devices  202 - 230  or computing device  232  (e.g., television, set top box, streaming device, computer, etc.), the human may be required to log into the device. In order to log into the device, the human may provide log in credentials (e.g., account identifier, user identifier, and/or password) that can be used by the device to identify the human using the device. In some implementations, the log in credentials can be received by the device as user input from the human. After the log in credentials are received and the human using the device is identified, the device (e.g., one of smart devices  202 - 230 , or computing device  232 ) can send the human identification to primary device  202  in a message indicating that a conscious human activity has occurred with respect to the device. Thus, if user  204  logged into the secondary device, primary device  202  can attribute the conscious human activity with respect to the secondary device to user  204 . 
     In some implementations, a secondary device can receive the log in credentials from a single user device, such as a wearable device (e.g., smartwatch  150 ). For example, a human interacting with smart refrigerator  222  can passively (e.g., without user input) log into smart refrigerator  222  when smart refrigerator  222  detects smart watch  150  associated with a human that is near the smart refrigerator  222 . For example, smart watch  150  can automatically transmit information (e.g., over Bluetooth, near field communication “NFC”, etc.) to smart refrigerator  222  that can be used by smart refrigerator  222  to identify the human wearing smartwatch  150 . After smart refrigerator  222  receives the information identifying the human from smartwatch  150 , smart refrigerator  222  can send the identification information to primary device  202  in the message indicating that a conscious human activity has occurred with respect to the smart refrigerator  222 . Thus, if user  204  is the owner of the single user device, primary device  202  can attribute the conscious human activity with respect to the secondary device to user  204 . 
     In some implementations, the single user device can notify primary device  202  of conscious user activity with respect to a smart device. For example, since wearable devices are typically single user devices, messages sent through or activities reported by a wearable device can be attributed to the owner of the wearable device. For example, smart watch  150  can receive a signal from smart scale  224  when the wearable device is near smart scale  244 . The signal can be received using a short range communication mechanism, such as Bluetooth or NFC, for example. The signal can include information identifying the smart scale  244  and/or a current status of smart scale  244 . After smart watch  150  receives the information status information from smart scale  244 , smart watch  150  can send the identification information to primary device  202  in a message indicating that a conscious human activity has occurred with respect to the smart scale  244 . Smart watch  150  can send the status and identification information received from the smart scale  224  to primary device  202  along with an identifier of the human wearing smart watch  150 , for example. Thus, if user  204  is the owner of the single user device (e.g., smart watch  150 ), primary device  202  can attribute the conscious human activity with respect to the secondary device to user  204 . 
     In some implementations, primary device  202  can determine that user  204  is awake based on location information received from a single user device. For example, the location information can indicate a location relative to an object (e.g., smart device). For example, even if user  204  does not use (e.g., open the door) smart refrigerator  222 , the user&#39;s proximity to smart refrigerator  222  can indicate that the user is awake since user  204  is not likely to be sleeping when in the kitchen near smart refrigerator  222 . When the single user device (e.g., wearable device, smart watch, etc.) reports to primary device  202  that the single user device detected smart refrigerator  222  (e.g., received a NFC or Bluetooth signal from smart refrigerator  222 ), primary device  202  can determine that user  204  is located in the kitchen and is therefore awake. 
     Similarly, primary device  202  can receive multiple smart device detection reports from the wearable device over a short period of time. For example, as user  204  moves through the house, the wearable device worn by user  204  can detect smart scale  224 , smart refrigerator  222 , smart stove  226  and/or smart coffee maker  228  and report these detections to primary device  202 . When primary device  202  receives multiple smart device detection reports within a short period of time from the wearable device, primary device  202  can determine that user  204  is moving throughout the house and is not asleep. 
     Identification Based on Use of Personal Device 
     Similarly, smart personal hygiene devices (e.g., electric shavers, electric toothbrushes, blow dryers, etc.) can be associated with a single human. For example, while these devices typically do not require a user to log into the devices, most people do not share toothbrushes or electric shavers with other people. When these devices are first used, these smart personal hygiene devices can be paired (e.g., by Bluetooth, NFC, Wi-Fi, etc.) to a computing device (e.g., smartphone, wearable device, other personal computing device, etc.) associated with a single human. When these personal hygiene devices are turned on and used, the personal hygiene device can connect to the paired computing device. If the paired computing device is primary device  202 , primary device  202  can determine that user  204  associated with primary device  202  is performing a conscious human activity with respect to the smart personal hygiene device. 
     Identification Based on Context 
     In some implementations, primary device  202  can determine whether a detected conscious human activity should be attributed to the user based on context information included in messages received from other devices. For example, when the other devices do not have information identifying the human using the other devices, the other devices can provide context information that can be used by primary device  202  to determine the identity of the human using the device. For example, when a human steps on smart scale  224 , smart scale  224  can determine the weight of the human. Smart scale  224  can send the weight measurement to primary device  202 . When primary device  202  receives the weight measurement, primary device  202  can compare the new weight measurement to historical weight measurements for the user of primary device  202 . If the new weight measurement is similar to prior weight measurements (e.g., the immediately previous weight measurement), then primary device  202  can determine that the user of primary device  202  used smart scale  224 . If the new weight measurement is not similar to prior weight measurements, then primary device  202  can determine that the user of primary device  202  is not the human the used smart scale  224 . 
     Similarly, when a human uses a smart media device (e.g., smart television, set top box, streaming media player, etc.), the smart media device can determine the type and/or characteristics of the media that the human is consuming. The smart media device can send the media type (e.g., music, movie, television show, etc.) and/or characteristics (e.g., genre, actors, director, etc.) information to primary device  202 . When primary device  202  receives the media information, primary device  202  can compare the media information to historical media preferences data collected for the user of primary device  202 . If the media information for the media item currently playing on the smart media device corresponds to the user&#39;s historical media preferences, primary device  202  can determine that user  204  is using the smart media device. If the media information for the media item currently playing on the smart media device does not match the user&#39;s historical media preferences, primary device  202  can determine that user  204  is not using the smart media device. 
     Identification Based on Sensor Data 
     In some implementations, primary device  202  can determine whether a detected human activity should be attributed to the user based on environmental sensor data. For example, a microphone (e.g., sound sensor) can detect sounds associated with human footsteps and/or breathing. Primary device  202  can store historical sound information that includes samples of the user&#39;s footsteps while walking on various surfaces (e.g., carpeting, wood floors, concrete, etc.). Primary device  202  can store historical sound information describing the user&#39;s walking cadence, for example. Primary device  202  can store historical sound information that includes samples of the user&#39;s breathing in various contexts (e.g., while sleeping, walking, sitting, running, etc.). Primary device  202  can compare the detected sounds to the historical sound samples to determine whether the detected sounds should be attributed to the user of primary device  202 . For example, when primary device  202  detects footstep sounds and the detected sounds match historical footstep sounds for the user of primary device  202 , then primary device  202  can determine that the user is performing a conscious human activity (e.g., walking, running, etc.). When primary device  202  detects breathing sounds associated with an active human activity and the detected breathing sounds match historical breathing sounds for the user of primary device  202 , then primary device  202  can determine that the user is performing a conscious human activity. When primary device  202  detects breathing sounds that match historical breathing sounds associated with the user sleeping, then primary device  202  can determine that the user is sleeping. When primary device  202  detects breathing sounds that do not match historical breathing sounds associated with the user sleeping, then primary device  202  can determine that the user is not sleeping. 
     In some implementations, primary device  202  can receive sensor data from other devices associated with user  204  that indicate user  204  is awake. For example, user  204  can wear a wearable device (e.g., a smartwatch, smart eye glasses, smart contacts, etc.) that includes sensors that can detect heartrate, eye movement, body movement, arm swing, blood pressure, breathing, footsteps, and/or other biometric data about user  204 . The wearable device can transmit the sensor data to primary device  202 . Primary device  202  can analyze the sensor data to determine whether user  204  is awake or asleep. For example, when the sensor data indicates a lot of body movement (e.g., arm swing, footsteps, fast breathing, etc.), then primary device  202  can determine that user  204  is awake. When the sensor data indicates little body movement, slow deep breathing, rapid eye movement, slow heartrate, etc., then primary device  202  can determine that user  204  is sleeping. 
     Identification Based on Sleep Status from Other Devices 
     In some implementations, primary device  202  can determine whether user  204  is performing a conscious human activity based on sleep information received from personal devices of other humans. For example, primary device  202  can determine that the user  204  lives with human  240  and/or human  250  based on detected environmental signals (e.g., breathing sounds, walking sounds, voice sounds, etc.). For example, each person in the house can be associated with unique breathing patterns, walking patterns, and/or voice patterns. Primary device  202  can use these human-specific sounds to distinguish different people living in the house and/or identify the number of people living in the house. 
     In some implementations, primary device  202  can determine user  204  lives with human  240  and human  250  based on signals received from the personal electronic devices  242  and  252  of human  240  and human  250 , respectively. For example, devices  242  and  252  can be configured to determine when their respective users are sleeping in a similar manner to the mechanisms described herein for primary device  202 . 
     In some implementations, primary device  202  can determine which devices belong to user  204  based on information obtained from a service provider account associated with user  204 . For example, the service provider can be a media service provider that provides access to applications, movies, music, and/or other media or services. User  204  can register an account with the service provider to gain access to the services provided by the service provider. User  204  can associated primary device  202  and other devices (e.g., a tablet computer, smart watch, wearable device, etc.) belonging to user  204  with the service provider account. Primary device  202  can access the service provider account associated with user  204  and obtain information identifying the devices associated with user  204 . Thus, primary device  202  can determine which devices belong to user  204  and which devices belong to other users (e.g., human  240  and/or human  250 ). 
     In some implementations, primary device  202  can receive signals from device  242  and/or device  252  indicating the sleep status of human  240  and/or human  252 , respectively. For example, primary device  202 , personal device  242 , and/or personal device  252  can be configured to share sleep status data when the devices are near (e.g., within a threshold distance of) each other. For example, primary device  202 , personal device  242 , and/or personal device  252  can be configured to transmit sleep status data to other devices that are connected to the same Wi-Fi access point. Primary device  202 , personal device  242 , and/or personal device  252  can be configured to transmit sleep status data to other devices that connected through a Bluetooth connection or some other near field communication mechanism. Primary device  202 , personal device  242 , and/or personal device  252  can be configured to transmit sleep status data to other devices that are within a threshold distance of each other (e.g., as determined by a location determining technology, such as a global navigational satellite system, cellular data location system, or Wi-Fi location system). 
     In some implementations, primary device  202  can determine whether humans other than user  204  are sleeping based on the received sleep status information. For example, primary device  202  can receive sleep status information from personal device  242  and/or personal device  252  that describes the sleep status of human  240  and/or human  250 , respectively. When the sleep status data is received, primary device  202  can determine whether human  240  and/or human  250  are sleeping. For example, the sleep status data can indicate whether the human associated with the device is sleeping. Based on the sleep information received from devices  242  and  252 , primary device  202  can determine whether user  204  is sleeping. For example, if primary device  202  has determined that three humans are in the house and receives an indication of conscious human activity in the house, primary device  202  can determine that the source of the conscious human activity in the house is user  204  when primary device  202  receives sleep status data from device  242  and device  252  indicating that the other two humans in the house (e.g., human  240  and human  250 ) are sleeping. If primary device  202  has determined that three humans are in the house and receives an indication of conscious human activity in the house, primary device  202  can determine that the source of the conscious human activity in the house is not user  204  when primary device  202  receives sleep status data from device  242  and/or device  252  indicating that one of the other two humans in the house (e.g., human  240  and human  250 ) is awake. 
     In some implementations, primary device  202  can determine that user  204  is awake when the detected conscious human activity can be attributed to user  204 . For example, after primary device  202  has detected the conscious human activity, as described above, and has identified user  204  as the source of the conscious human activity, primary device  202  can determine that user  204  is awake. However, when primary device  202  has detected the conscious human activity, as described above, and has identified another human (e.g., other than user  204 ) as the source of the conscious human activity, primary device  202  can confirm that user  204  is asleep. 
     Example Process 
       FIG. 3  is flow diagram of an example process  300  for confirming a user of primary device  202  is asleep. At step  302 , primary device  202  can provisionally determine that user  204  is sleeping. For example, primary device  202  can determine that user  204  is sleeping based on primary indicia of user activity. Primary indicia of user activity can be, for example, any active use or input with respect to primary device  202 , as described above. 
     At step  304 , primary device  202  can receive a message from a secondary device indicating human interaction with the secondary device. For example, the secondary device can be a household appliance (e.g., smart television, set top box, streaming media player, smart refrigerator, etc.). The secondary device can be a computing device (e.g., a smartphone, tablet computer, laptop computer, etc.). The secondary device can be a wearable device (e.g., smart watch, smart eye glasses, smart contacts, etc.). The message can include state information describing the human interaction with or conscious human use of (e.g., operating context) the secondary device, as described above. For example, the state information can describe whether the secondary device is powered on, which user has logged in to the secondary device, what media is being viewed on the secondary device, or any other information describing the use of the secondary device. 
     At step  306 , primary device  202  can determine whether the human interaction with the secondary device is attributable to the user of primary device  202 . For example, primary device  202  can determine whether user  204  is operating the secondary device based on user identification information (e.g., log in information, personal user device information, etc.) received in the message from the secondary device. Primary device  202  can determine whether user  204  is operating the secondary device based on context information received in the message from the secondary device. For example, the context information can describe how the secondary device is being used, what media is being played, sensor data collected by the secondary device, etc. Primary device  202  can compare the context information to historical data related to user  204  to determine whether user  204  is operating the secondary device. For example, when the context information matches the user&#39;s music preferences, movie preferences, weight, etc., then primary device  202  can determine that user  204  is operating the secondary device, as described above. 
     At step  308 , primary device  202  can determine that the user is awake when the human interaction with the secondary device is attributable to the user. For example, when primary device  202  can match the identification information or context information to user  204 , primary device  202  can determine that the user of primary device  202  is awake. 
     At step  310 , primary device  202  can confirm that the user is asleep when the human interaction with the secondary device is attributable to another human. For example, when primary device  202  is unable to match the identification information or context information to user  204 , primary device  202  can determine that another human is using the secondary device and that the user of primary device  202  is asleep. 
       FIG. 4  is a flow diagram of an example process  400  for confirming that a user of primary device  202  is sleeping based on sleep status data associated with users of other devices. For example, when user  204  of primary device  202  shares a house with other people, primary device  202  will need to determine whether the detected conscious human activity detected by primary device  202  is attributable to user  204  or some other person in the house. 
     At step  402 , primary device  202  can provisionally determine that user  204  is sleeping. For example, primary device  202  can determine that user  204  is sleeping based on primary indicia of user activity. Primary indicia of user activity can be, for example, any active use or input with respect to primary device  202 , as described above. 
     At step  404 , primary device  202  can detect conscious human activity. For example, primary device  202  can detect conscious human activity based on sensor data generated by the sensors (e.g., sound sensors, light sensors, motion sensors, etc.) of primary device  202 . Primary device  202  can detect conscious human activity based on signals or messages received from other devices, as described above. 
     At step  406 , primary device  202  can receive information from a second device another user is sleeping. For example, the second device can be a primary device of a second user who is not user  204 . The second device can be similar to primary device  202  and can be configured to detect and confirm the sleep state of the second user, as described herein with respect to primary device  202 . In some implementations, the second device can determine whether the second user is sleeping using the mechanisms described herein and report the second user&#39;s sleep state (e.g., sleeping, awake, etc.) to primary device  202 . 
     At step  408 , primary device  202  can determine whether the detected conscious human activity is attributable to user  204 . For example, if there are two people in the house (e.g., user  204  and the second user) and primary device  202  receives information indicating that the second user is asleep, then primary device  202  can attribute the conscious human activity to user  204 . If there are two people in the house (e.g., user  204  and the second user) and primary device  202  receives information indicating that the second user is awake, then primary device  202  can attribute the conscious human activity to the second user. 
     At step  410 , primary device  202  can determine that user  204  is awake. For example, primary device  202  can determine that user  204  is awake based on the determination that the detected conscious human activity is attributable to user  204  at step  408 . 
     At step  412 , primary device  202  can confirm that user  204  is sleeping. For example, primary device  202  can determine that user  204  is sleeping based on the determination that the detected conscious human activity is attributable to the second user at step  408 . 
       FIG. 5  is a flow diagram of an example process  500  for confirming that a user is sleeping based on the sounds generated by conscious human activity. For example, the sounds generated by certain conscious user activities can be uniquely tied to a particular person. For example, a person&#39;s voice, footsteps, walking cadence, etc., can be unique and can be used to identify a particular person and/or distinguish one person from another. 
     At step  502 , primary device  202  can provisionally determine that user  204  is sleeping. For example, primary device  202  can determine that user  204  is sleeping based on primary indicia of user activity. Primary indicia of user activity can be, for example, any active use or input with respect to primary device  202 , as described above. 
     At step  504 , primary device  202  can detect sounds corresponding to conscious human activity. For example, primary device  202  can be configured with a microphone that can detect sounds generated near primary device  202 . Some of the detected sounds can be generated by conscious human activity (e.g., walking, talking, brushing teeth, taking a shower, opening a door, etc.). 
     At step  506 , primary device  202  can determine whether the conscious human activity is attributable to user  204  based on the detected sound. For example, the detected sounds of conscious user activity can be analyzed to identify a person who is performing the activity. For example, a person&#39;s voice can be used to identify the person speaking. The pattern or cadence of foot falls when a person walks can be used to identify the person walking. A person may brush their teeth in a particular way (e.g., pattern) that can be used to identify the person brushing their teeth. Primary device  202  collect and can store these sound patterns associated with user  204  and use the sound patterns to later identify user  204  or distinguish activities performed by user  204  from activities performed by other people. For example, primary device  202  can compare a detected sound (or pattern of sounds) of conscious human activity to the stored sounds associated with user  204 . If the detected sounds match at least one of the stored sounds associated with user  204 , primary device  202  can determine that the conscious human activity is attributable to user  204 . If the detected sounds do not match at least one of the stored sounds associated with user  204 , primary device  202  can determine that the conscious human activity is attributable to some other person. 
     At step  508 , primary device  202  can determine that user  204  is awake when the conscious human activity is attributable to user  204 . For example, when primary device  202  determines that the detected sound of conscious human activity is attributable to user  204  at step  506 , primary device  202  can determine that user  204  is awake. 
     At step  510 , primary device  202  can determine that user  204  is awake when the conscious human activity is attributable to user  204 . For example, when primary device  202  determines that the detected sound of conscious human activity is attributable to another person at step  506 , primary device  202  can confirm that user  204  is sleeping. 
       FIG. 6  is a flow diagram of an example process  600  for determining that user  204  is awake based on sleep sounds generated by other people. For example, some computing devices can detect sleep sounds (e.g., slow breathing, deep breathing, snoring, etc.) generated by a person sleeping alone in a room and determine based on the sleep sounds that the person is sleeping. However, when multiple people share a room for sleeping, it may be difficult to determine who is sleeping and who is awake. The computing device may be able to determine based on the sleep noises that someone is sleeping but may not be able to identify who is sleeping. Thus, in some implementations, primary device  202  can be configured to distinguish between user  204  sleeping and other people sleeping based on detected sleep noises. The description of process  600  below describes a scenario where primary device  202  determines which of two people who share a room are sleeping, however process  600  can be applied to situations where more than two people share a room. 
     At step  602 , primary device  202  can provisionally determine that user  204  is sleeping. For example, primary device  202  can determine that user  204  is sleeping based on primary indicia of user activity. Primary indicia of user activity can be, for example, any active use or input with respect to primary device  202 , as described above. 
     At step  604 , primary device  202  can detect conscious human activity. For example, primary device  202  can detect conscious user activity based on sensor data and/or information obtained from other devices, as described above. However, when multiple people share a home, primary device  202  must determine whether user  204  or some other person is responsible for the conscious human activity. 
     At step  606 , primary device  202  can detect human sleep sounds. For example, when primary device  202  provisionally determines that user  204  is sleeping, primary device  202  can turn on the microphone of primary device  202  to monitor and/or detect noises near primary device  202 . For example, primary device  202  can detect the sounds of sleep made by people sleeping near primary device  202 . For example, user  204  may be married or have a roommate and primary device  202  may detect the sleep noises generated by user  204  and/or the other person. 
     At step  608 , primary device  202  can obtain sleep sound information corresponding to user  204 . For example, over time primary device  202  can record sleep sounds while user  204  is sleeping and generate samples (e.g., fingerprints) of the sounds of user  204  sleeping. Primary device  202  can store the sleep sound samples and use the sleep sounds samples to later identify user  204 , as described below. If user  204  typically shares a room with another person, primary device  202  can determine which sleep sounds correspond to user  204  based on frequency of detection and similarity of sounds. For example, since primary device  202  is the personal device of user  204 , primary device  202  will detect sleep sounds associated with user  204  more frequently than the sleep sounds of other people (e.g., sometimes user  204  will sleep alone). Thus, to determine which sleep sounds correspond to user  204  and to filter out sleep sounds generated by other people, primary device can generate groups of sleep sound samples based on similarity of sound and determine the frequency of occurrence of the sleep sounds in each group. The group with the highest frequency (or highest number of instances) of sleep sound samples will most likely correspond to user  204 . This group can be the sleep sound information (e.g., sample set) used to identify sleep sounds of user  204 . 
     At step  610 , primary device  202  can compare detected sleep sounds to the sleep sound information for user  204 . After primary device  202  obtains the sleep sound information (e.g., sleep sound samples) for user  204 , primary device  202  can compare the detected sleep sounds to the sleep sound information to determine whether the detected sleep sounds match (e.g., are similar to) the sleep sound samples recorded for user  204 . 
     At step  612 , primary device  202  can determine that the detected sleep sounds are attributable to another human. For example, when the detected sleep sounds do not match the sleep sound information for user  204 , primary device  202  can determine that the sleep sounds were generated by another person (e.g., spouse, roommate, etc.) sleeping near primary device  202 . 
     At step  614 , primary device  202  can determine that the conscious human activity is attributable to user  204 . For example, when there are only two people sharing a room or a home, primary device  202 , through a process of elimination, can determine the conscious human activity is attributable to user  204  because the other person in the house is asleep. 
     At step  616 , primary device  202  can determine that user  204  is awake when the conscious human activity is attributable to the user. For example, user  204  cannot performing the conscious human activity when user  204  is asleep. 
     Thus, primary device  202  can perform processes  300 - 600  to confirm (or disprove) the provisional sleep determination based on secondary indicia of user activity. Each of the processes described above may be performed individually or in combination to confirm whether the user of primary device  202  is actually asleep. For example, sensor data may be used in combination with secondary device data to determine whether user  204  is awake or asleep. While the steps of each process  300 - 600  are presented in a particular order for ease of explanation, the order in which the steps are performed may be changed or rearranged while still producing similar results. 
     Example System Architecture 
       FIG. 7  is a block diagram of an example computing device  700  that can implement the features and processes of  FIGS. 1-6 . The computing device  700  can include a memory interface  702 , one or more data processors, image processors and/or central processing units  704 , and a peripherals interface  706 . The memory interface  702 , the one or more processors  704  and/or the peripherals interface  706  can be separate components or can be integrated in one or more integrated circuits. The various components in the computing device  700  can be coupled by one or more communication buses or signal lines. 
     Sensors, devices, and subsystems can be coupled to the peripherals interface  706  to facilitate multiple functionalities. For example, a motion sensor  710 , a light sensor  712 , and a proximity sensor  714  can be coupled to the peripherals interface  706  to facilitate orientation, lighting, and proximity functions. Other sensors  716  can also be connected to the peripherals interface  706 , 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  720  and an optical sensor  722 , 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  720  and the optical sensor  722  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  724 , 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  724  can depend on the communication network(s) over which the computing device  700  is intended to operate. For example, the computing device  700  can include communication subsystems  724  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  724  can include hosting protocols such that the device  100  can be configured as a base station for other wireless devices. 
     An audio subsystem  726  can be coupled to a speaker  728  and a microphone  730  to facilitate voice-enabled functions, such as speaker recognition, voice replication, digital recording, and telephony functions. The audio subsystem  726  can be configured to facilitate processing voice commands, voiceprinting and voice authentication, for example. 
     The I/O subsystem  740  can include a touch-surface controller  742  and/or other input controller(s)  744 . The touch-surface controller  742  can be coupled to a touch surface  746 . The touch surface  746  and touch-surface controller  742  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  746 . 
     The other input controller(s)  744  can be coupled to other input/control devices  748 , 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  728  and/or the microphone  730 . 
     In one implementation, a pressing of the button for a first duration can disengage a lock of the touch surface  746 ; and a pressing of the button for a second duration that is longer than the first duration can turn power to the computing device  700  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  730  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  746  can, for example, also be used to implement virtual or soft buttons and/or a keyboard. 
     In some implementations, the computing device  700  can present recorded audio and/or video files, such as MP3, AAC, and MPEG files. In some implementations, the computing device  700  can include the functionality of an MP3 player, such as an iPod™. The computing device  700  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  702  can be coupled to memory  750 . The memory  750  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  750  can store an operating system  752 , such as Darwin, RTXC, LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such as VxWorks. 
     The operating system  752  can include instructions for handling basic system services and for performing hardware dependent tasks. In some implementations, the operating system  752  can be a kernel (e.g., UNIX kernel). For example, operating system  752  can implement the sleep confirmation features as described with reference to  FIGS. 1-6 . 
     The memory  750  can also store communication instructions  754  to facilitate communicating with one or more additional devices, one or more computers and/or one or more servers. The memory  750  can include graphical user interface instructions  756  to facilitate graphic user interface processing; sensor processing instructions  758  to facilitate sensor-related processing and functions; phone instructions  760  to facilitate phone-related processes and functions; electronic messaging instructions  762  to facilitate electronic-messaging related processes and functions; web browsing instructions  764  to facilitate web browsing-related processes and functions; media processing instructions  766  to facilitate media processing-related processes and functions; GNSS/Navigation instructions  768  to facilitate GNSS and navigation-related processes and instructions; and/or camera instructions  770  to facilitate camera-related processes and functions. 
     The memory  750  can store other software instructions  772  to facilitate other processes and functions, such as the sleep confirmation processes and functions as described with reference to  FIGS. 1-6 . 
     The memory  750  can also store other software instructions  774 , 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  766  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  750  can include additional instructions or fewer instructions. The instructions can be executed by processor(s)  704 , for example, to perform the various processes and functions described above. Furthermore, various functions of the computing device  700  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: 20150930
Publication Date: 20190820
Grant Date: 20190820
Priority Date: 20150930
Inventors: TRIBBLE, GUY L.
RAYMANN, ROY J.
DOUGHERTY, WREN N.
NAG, DIVYA
LAMBERT, DEBORAH M.
GREER, STEPHANIE
GRUBER, THOMAS R.
Assignee: APPLE INC
CPC Classifications: [{"code": "A61B5/4809", "inventive": true, "first": false, "tree": "[]"}, {"code": "A61B5/6803", "inventive": false, "first": false, "tree": "[]"}, {"code": "A61B5/1118", "inventive": false, "first": false, "tree": "[]"}, {"code": "A61B5/002", "inventive": false, "first": false, "tree": "[]"}, {"code": "A61B5/681", "inventive": false, "first": false, "tree": "[]"}, {"code": "A61B5/0022", "inventive": false, "first": false, "tree": "[]"}, {"code": "A61B5/746", "inventive": false, "first": false, "tree": "[]"}, {"code": "A61B5/4809", "inventive": true, "first": true, "tree": "[]"}, {"code": "A61B5/002", "inventive": true, "first": false, "tree": "[]"}, {"code": "A61B5/1118", "inventive": true, "first": false, "tree": "[]"}, {"code": "A61B5/746", "inventive": true, "first": false, "tree": "[]"}, {"code": "A61B5/6803", "inventive": true, "first": false, "tree": "[]"}, {"code": "A61B5/0022", "inventive": true, "first": false, "tree": "[]"}, {"code": "A61B5/681", "inventive": true, "first": false, "tree": "[]"}, {"code": "A61B5/002", "inventive": true, "first": false, "tree": "[]"}, {"code": "A61B5/0022", "inventive": true, "first": false, "tree": "[]"}, {"code": "A61B5/6803", "inventive": true, "first": false, "tree": "[]"}, {"code": "A61B5/1118", "inventive": true, "first": false, "tree": "[]"}, {"code": "A61B5/681", "inventive": true, "first": false, "tree": "[]"}, {"code": "A61B5/4809", "inventive": true, "first": true, "tree": "[]"}, {"code": "A61B5/746", "inventive": true, "first": false, "tree": "[]"}, {"code": "A61B5/6898", "inventive": true, "first": true, "tree": "[]"}, {"code": "A61B5/7221", "inventive": false, "first": false, "tree": "[]"}]
Family ID: 58408403