US 12,076,108 B1
Automatic in-home senior care system augmented with internet of things technologies
Geoffrey Nudd, San Francisco, CA (US); David Cristman, Walnut Creek, CA (US); Jonathan J. Hull, San Carlos, CA (US); and Bala Krishna Nakshatrala, Los Angeles, CA (US)
Assigned to CLEARCARE, INC., San Francisco, CA (US)
Filed by ClearCare, Inc., San Francisco, CA (US)
Filed on Apr. 21, 2023, as Appl. No. 18/304,669.
Application 18/304,669 is a continuation of application No. 16/536,588, filed on Aug. 9, 2019, granted, now 11,633,103.
Application 16/536,588 is a continuation in part of application No. 16/386,002, filed on Apr. 16, 2019, abandoned.
Application 16/536,588 is a continuation in part of application No. 16/272,037, filed on Feb. 11, 2019, granted, now 11,120,226, issued on Sep. 14, 2021.
Claims priority of provisional application 62/769,220, filed on Nov. 19, 2018.
Claims priority of provisional application 62/726,883, filed on Sep. 4, 2018.
Claims priority of provisional application 62/717,650, filed on Aug. 10, 2018.
This patent is subject to a terminal disclaimer.
Int. Cl. G10L 15/00 (2013.01); A61B 5/00 (2006.01); A61B 5/16 (2006.01); G06F 9/54 (2006.01); G06F 17/18 (2006.01); G06N 20/10 (2019.01); G08B 21/04 (2006.01); G10L 15/16 (2006.01); G10L 25/30 (2013.01); G10L 19/00 (2013.01)
CPC A61B 5/0022 (2013.01) [A61B 5/165 (2013.01); G06F 9/542 (2013.01); G06F 17/18 (2013.01); G06N 20/10 (2019.01); G08B 21/0423 (2013.01); G10L 15/16 (2013.01); G10L 25/30 (2013.01); G10L 19/00 (2013.01)] 8 Claims
OG exemplary drawing
 
1. A system for providing in-home care for seniors, comprising:
a machine learning subsystem including a processor configured to:
generate a normalized feature vector from a set of sensor outputs from sensors disposed within a living area of a senior according to a sensor floorplan, including identifying a first relationship between symptoms of depression and events, wherein the events are measurable physical or mental features associated with at least one of the symptoms of depression, identifying a second relationship between the events and the set of sensor outputs based at least in part on the locations of each sensor and a sensor type of each sensor; and identifying a third relationship of sensor transformations required to transform sensor data into a format indicative of events, wherein the first relationship, the second relationship, and the third relationship is used to generate the normalized feature vector;
input the normalized feature vector to a logistic regression classifier of a machine learning model, wherein the logistic regression classifier is trained to determine thresholds for identifying depression based on a training data set of a set of seniors; and
determine a likelihood that the senior has depression.