Patents by Inventor Thomas M. Robertson
Thomas M. Robertson has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11119141Abstract: Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. Disclosed is an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power tines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. Machine learning techniques are used to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. The system has been tested to evaluate system performance over time and in different types of houses. Results indicate that various electrical events can be learned and classified with accuracies ranging from 85-90%.Type: GrantFiled: February 27, 2019Date of Patent: September 14, 2021Assignee: GEORGIA TECH RESEARCH CORPORATIONInventors: Shwetak N. Patel, Thomas M. Robertson, Gregory D. Abowd, Matthew S. Reynolds
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Publication number: 20190195929Abstract: Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. Disclosed is an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power tines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. Machine learning techniques are used to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. The system has been tested to evaluate system performance over time and in different types of houses. Results indicate that various electrical events can be learned and classified with accuracies ranging from 85-90%.Type: ApplicationFiled: February 27, 2019Publication date: June 27, 2019Applicant: Georgia Tech Research CorporationInventors: Shwetak N. Patel, Thomas M. Robertson, Gregory D. Abowd, Matthew S. Reynolds
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Patent number: 10247765Abstract: Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. Disclosed is an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power tines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. Machine learning techniques are used to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. The system has been tested to evaluate system performance over time and in different types of houses. Results indicate that various electrical events can be learned and classified with accuracies ranging from 85-90%.Type: GrantFiled: February 1, 2016Date of Patent: April 2, 2019Assignee: GEORGIA TECH RESEARCH CORPORATIONInventors: Shwetak N. Patel, Thomas M. Robertson, Gregory D. Abowd, Matthew S. Reynolds
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Patent number: 9385783Abstract: Disclosed is a system that uses existing power line infrastructure in a building as a distributed reception antenna capable of receiving signals from very low-power wireless sensors, thus allowing these sensors to be detected at ranges that are otherwise impractical with over-the-air reception. Also disclosed is a wireless sensor platform that is able to be sensed throughout a building with very low current draw. The disclosed technique may also be utilized to extend the range of mid-frequency consumer electronic devices by leveraging the power line as a reception antenna.Type: GrantFiled: June 6, 2014Date of Patent: July 5, 2016Assignees: Georgia Tech Research Corporation, University of WashingtonInventors: Erich P. Stuntebeck, Thomas M. Robertson, Gregory D. Abowd, Shwetak N. Patel
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Publication number: 20160154043Abstract: Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. Disclosed is an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power tines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. Machine learning techniques are used to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. The system has been tested to evaluate system performance over time and in different types of houses. Results indicate that various electrical events can be learned and classified with accuracies ranging from 85-90%.Type: ApplicationFiled: February 1, 2016Publication date: June 2, 2016Applicant: Georgia Tech Research CorporationInventors: Shwetak N. Patel, Thomas M. Robertson, Gregory D. Abowd, Matthew S. Reynolds
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Patent number: 9250275Abstract: Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. Disclosed is an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power lines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. Machine learning techniques are used to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. The system has been tested to evaluate system performance over time and in different types of houses. Results indicate that various electrical events can be learned and classified with accuracies ranging from 85-90%.Type: GrantFiled: December 17, 2012Date of Patent: February 2, 2016Assignee: Georgia Tech Research CorporationInventors: Shwetak N. Patel, Thomas M. Robertson, Gregory D. Abowd, Matthew S. Reynolds
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Publication number: 20150174445Abstract: An adjustable exercise device includes a removable handle, a extendible stacking pole and a base. The handle can be any of a number of shapes for ergonomic control. The stacking pole is designed to hold typical round weightlifting discs in a stack on the base, which is attached to the bottom of the stacking pole to both restrain the weights and serve as a platform base for the floor. The stacking pole is adjustable in length. The handle can be removed from the stacking pole by pulling a pin, thereby releasing the handle.Type: ApplicationFiled: December 17, 2014Publication date: June 25, 2015Inventor: Thomas M. Robertson, JR.
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Publication number: 20150099463Abstract: Disclosed is a system that uses existing power line infrastructure in a building as a distributed reception antenna capable of receiving signals from very low-power wireless sensors, thus allowing these sensors to be detected at ranges that are otherwise impractical with over-the-air reception. Also disclosed is a wireless sensor platform that is able to be sensed throughout a building with very low current draw. The disclosed technique may also be utilized to extend the range of mid-frequency consumer electronic devices by leveraging the power line as a reception antenna.Type: ApplicationFiled: June 6, 2014Publication date: April 9, 2015Applicants: UNIVERSITY OF WASHINGTON, GEORGIA TECH RESEARCH CORPORATIONInventors: Erich P. Stuntebeck, Thomas M. Robertson, Gregory D. Abowd, Shwetak N. Patel
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Patent number: 8334784Abstract: Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. Disclosed is an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power lines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. Machine learning techniques are used to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. The system has been tested to evaluate system performance over time and in different types of houses. Results indicate that various electrical events can be learned and classified with accuracies ranging from 85-90%.Type: GrantFiled: December 22, 2011Date of Patent: December 18, 2012Assignee: Belkin International Inc.Inventors: Shwetak N. Patel, Thomas M. Robertson, Gregory D. Abowd, Matthew S. Reynolds
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Publication number: 20120092142Abstract: Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. Disclosed is an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power lines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. Machine learning techniques are used to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. The system has been tested to evaluate system performance over time and in different types of houses. Results indicate that various electrical events can be learned and classified with accuracies ranging from 85-90%.Type: ApplicationFiled: December 22, 2011Publication date: April 19, 2012Applicant: GEORGIA TECH RESEARCH CORPORATIONInventors: Shwetak N. Patel, Thomas M. Robertson, Gregory D. Abowd, Matthew S. Reynolds
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Patent number: 8094034Abstract: Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. Disclosed is an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power lines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. Machine learning techniques are used to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. The system has been tested to evaluate system performance over time and in different types of houses. Results indicate that various electrical events can be learned and classified with accuracies ranging from 85-90%.Type: GrantFiled: September 16, 2008Date of Patent: January 10, 2012Assignee: Georgia Tech Research CorporationInventors: Shwetak N. Patel, Thomas M. Robertson, Gregory D. Abowd, Matthew S. Reynolds
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Publication number: 20090072985Abstract: Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. Disclosed is an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power lines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. Machine learning techniques are used to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. The system has been tested to evaluate system performance over time and in different types of houses. Results indicate that various electrical events can be learned and classified with accuracies ranging from 85-90%.Type: ApplicationFiled: September 16, 2008Publication date: March 19, 2009Inventors: Shwetak N. Patel, Thomas M. Robertson, Gregory D. Abowd, Matthew S. Reynolds