- Cupertino CA, US Wren N. Dougherty - San Francisco CA, US Divya Nag - Palo Alto CA, US Deborah M. Lambert - San Francisco CA, US Stephanie Greer - San Francisco CA, US Thomas R. Gruber - Santa Cruz CA, US
In some implementations, a mobile device can adjust an alarm siting based of the sleep onset latency duration detected for a user of the mobile device. For example, sleep onset latency can be the amount of time it takes for the user to fall asleep after the user attempts to go to sleep (e.g., goes to bed). The mobile device can determine when the user intends or attempts to go to sleep based on detected sleep ritual activities. Sleep ritual activities can include those activities user performs in preparation for sleep. The mobile device can determine when the user is asleep based on detected sleep signals (e.g., biometric data, sounds, etc.). In some implementations, the mobile device can determine recurring patterns of long or short sleep onset latency and present suggestions that might help the user sleep better or feel more rested.
- Cupertino CA, US Wren N. Dougherty - San Francisco CA, US Divya Nag - Palo Alto CA, US Deborah M. Lambert - San Francisco CA, US Stephanie Greer - San Francisco CA, US Thomas R. Gruber - Santa Cruz CA, US
In some implementations, a mobile device can adjust an alarm setting based on the sleep onset latency duration detected for a user of the mobile device. For example, sleep onset latency can be the amount of time it takes for the user to fall asleep after the user attempts to go to sleep (e.g., goes to bed). The mobile device can determine when the user intends or attempts to go to sleep based on detected sleep ritual activities. Sleep ritual activities can include those activities a user performs in preparation for sleep. The mobile device can determine when the user is asleep based on detected sleep signals (e.g., biometric data, sounds, etc.). In some implementations, the mobile device can determine recurring patterns of long or short sleep onset latency and present suggestions that might help the user sleep better or feel more rested.
- Cupertino CA, US Wren N. Dougherty - San Francisco CA, US Divya Nag - Palo Alto CA, US Deborah M. Lambert - San Francisco CA, US Stephanie M. Greer - San Francisco CA, US Thomas R. Gruber - Santa Cruz CA, US
Assignee:
Apple Inc. - Cupertino CA
International Classification:
G06F 9/54 G06F 3/01 G08B 5/22
Abstract:
In some implementations, a computing device may detect that a user of the computing device intends to sleep. The computing device may cause a reminder notification to be presented on a display of the computing device that prompts the user to prepare one or more secondary devices for sleep. The computing device may obtain, for each of the one or more secondary devices, a desired state for sleep specified by the user. The computing device may cause, for each of the one or more secondary devices, a current state to change to the desired state for sleep. In some implementations, the user activities may be detected by receiving sensor data from one or more sensor devices of the computing device and identifying the user activities based on the received sensor data. In some implementations, the computing device may automatically change the current state to the desired state for sleep.
- Cupertino CA, US Wren N. Dougherty - San Francisco CA, US Divya Nag - Palo Alto CA, US Deborah M. Lambert - San Francisco CA, US Stephanie Greer - San Francisco CA, US Thomas R. Gruber - Santa Cruz CA, US
In some implementations, a mobile device can adjust an alarm setting based on the sleep onset latency duration detected for a user of the mobile device. For example, sleep onset latency can be the amount of time it takes for the user to fall asleep after the user attempts to go to sleep (e.g., goes to bed). The mobile device can determine when the user intends or attempts to go to sleep based on detected sleep ritual activities. Sleep ritual activities can include those activities a user performs in preparation for sleep. The mobile device can determine when the user is asleep based on detected sleep signals (e.g., biometric data, sounds, etc.). In some implementations, the mobile device can determine recurring patterns of long or short sleep onset latency and present suggestions that might help the user sleep better or feel more rested.
- Cupertino CA, US Wren N. Dougherty - San Francisco CA, US Divya Nag - Palo Alto CA, US Deborah M. Lambert - San Francisco CA, US Stephanie Greer - San Francisco CA, US Thomas R. Gruber - Santa Cruz CA, US
In some implementations, a mobile device can adjust an alarm setting based on the sleep onset latency duration detected for a user of the mobile device. For example, sleep onset latency can be the amount of time it takes for the user to fall asleep after the user attempts to go to sleep (e.g., goes to bed). The mobile device can determine when the user intends or attempts to go to sleep based on detected sleep ritual activities. Sleep ritual activities can include those activities a user performs in preparation for sleep. The mobile device can determine when the user is asleep based on detected sleep signals (e.g., biometric data, sounds, etc.). In some implementations, the mobile device can determine recurring patterns of long or short sleep onset latency and present suggestions that might help the user sleep better or feel more rested.
- Cupertino CA, US Wren N. Dougherty - San Francisco CA, US Divya Nag - Palo Alto CA, US Deborah M. Lambert - San Francisco CA, US Stephanie Greer - San Francisco CA, US Thomas R. Gruber - Santa Cruz CA, US
In some implementations, a mobile device can adjust an alarm setting based on the sleep onset latency duration detected for a user of the mobile device. For example, sleep onset latency can be the amount of time it takes for the user to fall asleep after the user attempts to go to sleep (e.g., goes to bed). The mobile device can determine when the user intends or attempts to go to sleep based on detected sleep ritual activities. Sleep ritual activities can include those activities a user performs in preparation for sleep. The mobile device can determine when the user is asleep based on detected sleep signals (e.g., biometric data, sounds, etc.). In some implementations, the mobile device can determine recurring patterns of long or short sleep onset latency and present suggestions that might help the user sleep better or feel more rested.
- Cupertino CA, US Wren N. Dougherty - Cupertino CA, US Divya Nag - Cupertino CA, US Deborah M. Lambert - Cupertino CA, US Stephanie Greer - Cupertino CA, US Thomas R. Gruber - Cupertino CA, US
In some implementations, a computing device can remind a user to perform sleep ritual activities. The computing device can automatically determine the user's sleep ritual. The users sleep ritual can include various activities performed before going to sleep. The computing device can detect when the user performs the various sleep ritual activities. The computing device can remind the user about specific sleep ritual activities when the user forgets to perform the sleep ritual activities before going to sleep. In some implementation, the computing device can perform sleep ritual activities (e.g., turning off devices, locking doors, setting the air conditioning, etc.) on behalf of the user in response to user input. In some implementation, the computing device can perform sleep ritual activities on behalf of the user automatically and without user input.
Confirming Sleep Based On Secondary Indicia Of User Activity
- Cupertino CA, US Roy J. Raymann - Cupertino CA, US Wren N. Dougherty - Cupertino CA, US Divya Nag - Cupertino CA, US Deborah M. Lambert - Cupertino CA, US Stephanie Greer - Cupertino, US Thomas R. Gruber - Cupertino CA, US
International Classification:
A61B 5/00 A61B 5/11
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's primary computing device. The primary device can predict and/or determine when the user is sleeping based on the user'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.).