US 11,732,917 B2
Occupancy tracking using wireless signal distortion
Sunil Bondalapati, Frisco, TX (US); Prasad Mecheri Chandravihar, Richardson, TX (US); and FNU Kriti, Dallas, TX (US)
Assigned to Lennox Industries Inc., Richardson, TX (US)
Filed by Lennox Industries Inc., Richardson, TX (US)
Filed on Apr. 21, 2022, as Appl. No. 17/726,233.
Application 17/726,233 is a continuation of application No. 17/139,115, filed on Dec. 31, 2020, granted, now 11,353,230.
Prior Publication US 2022/0243945 A1, Aug. 4, 2022
Int. Cl. G05B 13/02 (2006.01); G05B 13/04 (2006.01); F24F 11/62 (2018.01); H04W 76/10 (2018.01); H04W 4/02 (2018.01); G10L 25/78 (2013.01); G10L 25/51 (2013.01); F24F 11/58 (2018.01); G10L 15/06 (2013.01); F24F 11/49 (2018.01); F24F 120/12 (2018.01); F24F 110/10 (2018.01)
CPC F24F 11/49 (2018.01) [F24F 11/58 (2018.01); F24F 11/62 (2018.01); G05B 13/0265 (2013.01); G05B 13/048 (2013.01); G10L 15/063 (2013.01); G10L 25/51 (2013.01); G10L 25/78 (2013.01); H04W 4/023 (2013.01); H04W 76/10 (2018.02); F24F 2110/10 (2018.01); F24F 2120/12 (2018.01)] 17 Claims
OG exemplary drawing
 
1. An occupancy tracking device, comprising:
a network interface operably coupled to a Heating, Ventilation, and Air Conditioning (HVAC) system, wherein the HVAC system is configured to control a temperature of a space; and
a processor operably coupled to the network interface, configured to:
establish a network connection with an access point;
capture wireless signal distortion information for the network connection over a first predetermined time period, wherein the wireless signal distortion information identifies a first plurality of signal strength measurements of the network connection with the access point;
input the wireless signal distortion information into a machine learning model, wherein:
the machine learning model is configured to determine a predicted occupancy level based at least in part upon the wireless signal distortion information; and
the predicted occupancy level indicates a number of people that are present within with the space;
obtain the predicted occupancy level from the machine learning model;
control the HVAC system based at least in part upon the predicted occupancy level;
capture training information for the network connection over a second predetermined time period, wherein:
the training information identifies a second plurality of signal strength measurements of the network connection with the access point; and
the second predetermined time period is different from the first predetermined time period;
when capturing the training information over the second predetermined time period:
determine a number of people that are present within the space; and
train the machine learning model using the training information and the number of people that are present within the space.