Abstract: An occupancy tracking device configured to receive sound samples, to identify voices within the sound samples, and to determine a first occupancy level based on the identified voices. The device is further configured to identify user devices connected to an access point and to determine a second occupancy level based on the user devices that are connected to the access point. The device is further configured to measure a signal strength of a network connection with the access point and to determine a third occupancy level based on the signal strength of the network connection with the access point. The device is further configured to determine a predicted occupancy level based on the first occupancy level, the second occupancy level, and the third occupancy level and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.

Abstract: An occupancy tracking device configured to receive sound samples, to identify voices within the sound samples, and to determine a first occupancy level based on the identified voices. The device is further configured to identify user devices connected to an access point and to determine a second occupancy level based on the user devices that are connected to the access point. The device is further configured to measure a signal strength of a network connection with the access point and to determine a third occupancy level based on the signal strength of the network connection with the access point. The device is further configured to determine a predicted occupancy level based on the first occupancy level, the second occupancy level, and the third occupancy level and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.

Abstract: An occupancy tracking device configured to receive a plurality of sound samples over a predetermined time period. The device is further configured to compute an audio signature for each sound sample. The device is further configured to populate entries in the voice data log for the sound samples, to identify one or more clusters based on an audio signature that is associated with the populated entries, and to determine a number of clusters that are identified. The device is further configured to determine a predicted occupancy level based on the number of clusters that are identified and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.

Abstract: An occupancy tracking device configured to identify devices connected to an access point over a predetermined time period. The device is further configured to populate entries in a device log for the identified devices. The device is further configured to determine a predicted occupancy level and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.

Abstract: An occupancy tracking device configured to identify devices connected to an access point over a predetermined time period. The device is further configured to populate entries in a device log for the identified devices. The device is further configured to determine a predicted occupancy level and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.

Abstract: An occupancy tracking device configured to receive a plurality of sound samples over a predetermined time period. The device is further configured to compute an audio signature for each sound sample. The device is further configured to populate entries in the voice data log for the sound samples, to identify one or more clusters based on an audio signature that is associated with the populated entries, and to determine a number of clusters that are identified. The device is further configured to determine a predicted occupancy level based on the number of clusters that are identified and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.

Abstract: An occupancy tracking device configured to receive a plurality of sound samples over a predetermined time period. The device is further configured to compute an audio signature for each sound sample. The audio signature includes a numerical value that uniquely identifies characteristics of an audio signal. The device is further configured to populate entries in the voice data log for the sound samples, to identify one or more clusters based on an audio signature that is associated with the populated entries, and to determine a number of clusters that are identified. The device is further configured to determine a predicted occupancy level based on the number of clusters that are identified and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.

Abstract: An occupancy tracking device configured to establish a network connection with an access point and to capture wireless signal distortion information for the network connection. The device is further configured to generate statistical metadata for the wireless signal distortion information. The device is further configured to input the wireless signal distortion information and the statistical metadata for the wireless signal distortion information into a machine learning model. The machine learning model is configured to determine a predicted occupancy level based on the wireless signal distortion information and the statistical metadata for the wireless signal distortion information. The predicted occupancy level indicates a number of people that are present within with the space. The device is further configured to obtain the predicted occupancy level from the machine learning model and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.

Abstract: An occupancy tracking device configured to identify devices connected to an access point over a predetermined time period. The device is further configured to populate entries in a device log for the identified devices. The device is further configured to determine a predicted occupancy level and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.

Abstract: An occupancy tracking device configured to identify devices connected to an access point over a predetermined time period. The device is further configured to populate entries in a device log for the identified devices. The device is further configured to determine a presence value for each device that indicates an amount of time that a device was present during the predetermined time period. The device is further configured to identify entries that are associated with a presence value that is less than a presence threshold value and to associate the entries with a user device classification. The device is further configured to identify clusters for the entries that are associated with a user device classification, to determine a predicted occupancy level based on the number of clusters that are identified, and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.

Abstract: An occupancy tracking device configured to receive a plurality of sound samples over a predetermined time period. The device is further configured to compute an audio signature for each sound sample. The audio signature includes a numerical value that uniquely identifies characteristics of an audio signal. The device is further configured to populate entries in the voice data log for the sound samples, to identify one or more clusters based on an audio signature that is associated with the populated entries, and to determine a number of clusters that are identified. The device is further configured to determine a predicted occupancy level based on the number of clusters that are identified and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.

Abstract: An occupancy tracking device configured to receive a plurality of sound samples over a predetermine time period. The device is further configured to compute an audio signature for each sound sample. The audio signature includes a numerical value that uniquely identifies characteristics of an audio signal. The device is further configured to determine a direction of arrival for each sound sample. The device is further configured to populate entries in the voice data log for the sound samples, to identify one or more clusters based on an audio signature that is associated with the populated entries, and to determine a number of clusters that are identified. The device is further configured to determine a predicted occupancy level based on the number of clusters that are identified and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.

Abstract: An occupancy tracking device configured to establish a network connection with an access point and to capture wireless signal distortion information for the network connection. The device is further configured to generate statistical metadata for the wireless signal distortion information. The device is further configured to input the wireless signal distortion information and the statistical metadata for the wireless signal distortion information into a machine learning model. The machine learning model is configured to determine a predicted occupancy level based on the wireless signal distortion information and the statistical metadata for the wireless signal distortion information. The predicted occupancy level indicates a number of people that are present within with the space. The device is further configured to obtain the predicted occupancy level from the machine learning model and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.

Abstract: An occupancy tracking device configured to receive a plurality of sound samples over a predetermine time period. The device is further configured to compute an audio signature for each sound sample. The audio signature includes a numerical value that uniquely identifies characteristics of an audio signal. The device is further configured to determine a direction of arrival for each sound sample. The device is further configured to populate entries in the voice data log for the sound samples, to identify one or more clusters based on an audio signature that is associated with the populated entries, and to determine a number of clusters that are identified. The device is further configured to determine a predicted occupancy level based on the number of clusters that are identified and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.

Abstract: An occupancy tracking device configured to receive sound samples, to identify voices within the sound samples, and to determine a first occupancy level based on the identified voices. The device is further configured to identify user devices connected to an access point and to determine a second occupancy level based on the user devices that are connected to the access point. The device is further configured to measure a signal strength of a network connection with the access point and to determine a third occupancy level based on the signal strength of the network connection with the access point. The device is further configured to determine a predicted occupancy level based on the first occupancy level, the second occupancy level, and the third occupancy level and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.

Abstract: An occupancy tracking device configured to establish a network connection with an access point and to capture wireless signal distortion information for the network connection. The device is further configured to generate statistical metadata for the wireless signal distortion information. The device is further configured to input the wireless signal distortion information and the statistical metadata for the wireless signal distortion information into a machine learning model. The machine learning model is configured to determine a predicted occupancy level based on the wireless signal distortion information and the statistical metadata for the wireless signal distortion information. The predicted occupancy level indicates a number of people that are present within with the space. The device is further configured to obtain the predicted occupancy level from the machine learning model and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.

Abstract: An occupancy tracking device configured to identify devices connected to an access point over a predetermined time period. The device is further configured to populate entries in a device log for the identified devices. The device is further configured to determine a presence value for each device that indicates an amount of time that a device was present during the predetermined time period. The device is further configured to identify entries that are associated with a presence value that is less than a presence threshold value and to associate the entries with a user device classification. The device is further configured to identify clusters for the entries that are associated with a user device classification, to determine a predicted occupancy level based on the number of clusters that are identified, and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.

Abstract: An occupancy tracking device configured to establish a network connection with an access point and to capture wireless signal distortion information for the network connection. The device is further configured to generate statistical metadata for the wireless signal distortion information. The device is further configured to input the wireless signal distortion information and the statistical metadata for the wireless signal distortion information into a machine learning model. The machine learning model is configured to determine a predicted occupancy level based on the wireless signal distortion information and the statistical metadata for the wireless signal distortion information. The predicted occupancy level indicates a number of people that are present within with the space. The device is further configured to obtain the predicted occupancy level from the machine learning model and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.