- 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.
Confirming Sleep Based On Secondary Indicia Of User Activity
- Cupertino CA, US Roy J. Raymann - Carlsbad 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:
A61B 5/00 A61B 5/11
Abstract:
In some implementations, a provisional determination that a user of a first device is awake may be based on data indicating that the first device is being used. Also, sleep sounds associated with a human sleeping may be detected, and sleep sound information corresponding to the user may be obtained. Next, the detected sleep sounds may be compared to the sleep sound information, and a determination may be made as to whether the detected sleep sounds are attributable to the user based on the comparison of the detected sleep sounds and the sleep sound information. In addition, the provisional determination that the user is awake may be revised to indicate that the user is sleeping in response to a determination that the detected sleep sounds are being performed by the user in order to provide a more accurate sleep determination for the user.
- 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.
Scheduling Device For Customizable Electronic Notifications
- Cupertino CA, US Jay Kriz Blahnik - San Francisco CA, US Stephanie M. Greer - San Francisco CA, US Aroon Pahwa - Palo Alto CA, US Jonathan T. Varbel - San Jose CA, US
Sleep alerts associated with an alarm can be scheduled using a first electronic device. Once scheduled, data can be collected from a second electronic device. This data can be used to determine an appropriate device, other than the first electronic device, at which the sleep alert can be presented. Once determined, the information can be sent to the appropriate device for presenting the sleep alert.
Scheduling Device For Customizable Electronic Notifications
- Cupertino CA, US Jay Kriz Blahnik - San Francisco CA, US Stephanie M. Greer - San Francisco CA, US Aroon Pahwa - Palo Alto CA, US Jonathan T. Varbel - San Jose CA, US
An adjustable alarm indicator of an alarm application is described. The adjustable alarm indicator may be presented in connection with an alarm setting sequence. The adjustable alarm indicator may include a variable element having a variable annular shape, a first element associated with a first end of the variable element, and a second element associated with a second end of the variable element. The first element may be independently moveable to adjust the size of the variable element. The second element also may be independently moveable to adjust the size of the variable element.
- 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.
Hopelab Sep 2017 - Jun 2019
Research and Product Lead
Waybetter Jun 2017 - Oct 2017
User Experience Researcher
Apple Jul 2015 - May 2017
Health Special Projects Pm
Smglabs Jul 2015 - May 2017
Founder
Tidepool Jun 2014 - Jul 2015
Head of Cognitive Science
Education:
General Assembly 2016 - 2016
University of California, Berkeley 2009 - 2014
Doctorates, Doctor of Philosophy, Philosophy, Neuroscience
Brown University 2003 - 2007
Bachelors, Bachelor of Arts, Bachelor of Science, Computer Science, Neuroscience
Holton - Arms School 1997 - 2003
Skills:
Fmri Statistics Machine Learning Research Matlab Cognitive Neuroscience Eeg Teaching Science Experimental Design Psychology R Computer Science Scientific Writing Programming Applied Behavior Analysis Behavioural Change Physiology Data Analysis Scientific Computing Research Design Design Thinking Human Centered Design Sleep User Experience Design Wireframing User Research Usability Testing Presentations Quantitative Research Qualitative Research
Law Offices of Leann G Bischoff Apr 2014 - Jan 2018
Office Manager Legal Assistant
Airbnb Oct 2012 - Nov 2017
Airbnb Host
San Francisco School Alliance Mar 2010 - Jun 2012
Office Manager
Adler & Colvin Jan 2008 - Apr 2009
Assistant Office Supervisor
Stoel Rives Llp 2000 - 2007
Assistant Administrator and Attorney Recruiting Coordinator
Education:
Berkeley High School
University of California, Berkeley
Bachelors, Bachelor of Arts, Humanities, History
Skills:
Full Life Cycle Recruiting Onboarding Offboarding Benefits Administration Event Planning Event Management Public Speaking Research Nonprofits Microsoft Office Microsoft Word Microsoft Excel Outlook Fundraising Non Profits Handmade Jewelry Human Resources Interviews Recruiting Social Media
DigiCircle - Fremont, CA since May 2013
QA Tester
Volt Workforce Solutions Jun 2012 - Apr 2013
3D QA and Touch Up worker for Volt providing technical services for Apple engineering
Zynga - San Francisco, CA Mar 2012 - May 2012
QA Engineer II
Namco Bandai Games America Jan 2011 - Mar 2012
Mobile QA Tester
Crystal Dynamics Feb 2010 - Oct 2010
PS3 Lead
Education:
University of Tennessee-Knoxville 2002 - 2003
No degree, Graphic Design
Skills:
Video Games Xbox 360 Ps3 Quality Assurance Jira Gameplay Ios Xbox Devtrack Seapine Test Track Pro Wii Multiplayer Social Games Psp Android Software Quality Assurance