US 11,889,994 B2
Menstrual cycle tracking
Belen Lafon, San Francisco, CA (US); Chris Hanrahan Sarantos, San Francisco, CA (US); Conor Joseph Heneghan, Campbell, CA (US); Logan Niehaus, Alameda, CA (US); Jaclyn Leverett Wasson, Alameda, CA (US); Peter Colin Dess, San Francisco, CA (US); Amir Bahador Farjadian, Boston, MA (US); Zachary Todd Beattie, Pleasant Hill, CA (US); Atiyeh Ghoreyshi, San Francisco, CA (US); and Allison Shih Wu, San Francisco, CA (US)
Assigned to FITBIT, INC., San Francisco, CA (US)
Filed by Fitbit, Inc., San Francisco, CA (US)
Filed on Sep. 4, 2020, as Appl. No. 17/013,337.
Application 17/013,337 is a continuation of application No. 16/441,536, filed on Jun. 14, 2019, granted, now 10,765,409.
Claims priority of provisional application 62/691,355, filed on Jun. 28, 2018.
Prior Publication US 2021/0145415 A1, May 20, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. A61B 5/01 (2006.01); A61B 10/00 (2006.01); A61B 5/0205 (2006.01); A61B 5/11 (2006.01); A61B 5/145 (2006.01); A61B 5/1455 (2006.01); A61B 5/00 (2006.01); A61B 5/024 (2006.01)
CPC A61B 10/0012 (2013.01) [A61B 5/01 (2013.01); A61B 5/02055 (2013.01); A61B 5/1118 (2013.01); A61B 5/14546 (2013.01); A61B 5/14551 (2013.01); A61B 5/4815 (2013.01); A61B 5/681 (2013.01); A61B 5/742 (2013.01); A61B 5/02405 (2013.01); A61B 5/02433 (2013.01); A61B 2010/0019 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
a display device;
a monitoring device including a non-invasive measurement sub-system;
at least one processor; and
memory including instructions that, when executed by the at least one processor, cause the monitoring device to:
obtain historical user information for one or more events related to a menstrual cycle of a user;
generate a prediction for a future event related to the menstrual cycle, the prediction based at least in part on the historical user information as input into a predictive model;
obtain, using the non-invasive measurement sub-system, heart rate-derived data for the user over a period of time;
correlate, through the predictive model, patterns in the heart rate-derived data with events in the menstrual cycle;
provide the heart rate-derived data as additional input to the predictive model;
generate, using the predictive model and the additional input, an updated prediction for the future event related to the menstrual cycle, the predictive model using machine learning to analyze the heart-rate derived data, and the historical user information being used to refine or retrain the predictive model; and
display, on the display device, information associated with the updated prediction for the future event.