Abstract: Techniques for detecting physical conditions at a physical premises from collection of sensor information from plural sensors execute one or more unsupervised learning models to continually analyze the collected sensor information to produce operational states of sensor information, produce sequences of state transitions, detect during the continual analysis of sensor data that one or more of the sequences of state transitions is a drift sequence, correlate determined drift state sequence to a stored determined condition at the premises, and generate an alert based on the determined condition. Various uses are described for these techniques.

Abstract: A non-transitory computer-readable storage medium having instructions stored thereon that cause one or more processors to collect first sensor data from a set of sensors deployed in a first location, generate a model using second sensor data collected during operation of a second location, evaluate the first sensor data from the first location using the model to detect a drift state associated with the first location, wherein the drift state represents an abnormal or unsafe event, and in response to detecting the drift state associated with the first location, transmit a notification.

Abstract: Described are techniques for determining a quotation score that would be applicable across lines of insurance and/or carriers, and which involves the collection in real time of sensor information from plural groups of sensors deployed in a specific premises and associated with intrusion detection, access control, burglar, fire alarm systems and surveillance systems and/or other systems that monitor for physical/chemical/biological conditions. The techniques execute unsupervised learning models to continually analyze the collected sensor information to produce sequences of state transitions that are assign scores and from which a quotation score is produced.

Abstract: Techniques for detecting physical conditions at a physical premises from collection of sensor information from plural sensors execute one or more unsupervised learning models to continually analyze the collected sensor information to produce operational states of sensor information, produce sequences of state transitions, detect during the continual analysis of sensor data that one or more of the sequences of state transitions is a drift sequence, correlate determined drift state sequence to a stored determined condition at the premises, and generate an alert based on the determined condition. Various uses are described for these techniques.

Abstract: Techniques for detecting physical conditions at a physical premises from collection of sensor information from plural sensors execute one or more unsupervised learning models to continually analyze the collected sensor information to produce operational states of sensor information, produce sequences of state transitions, detect during the continual analysis of sensor data that one or more of the sequences of state transitions is a drift sequence, correlate determined drift state sequence to a stored determined condition at the premises, and generate an alert based on the determined condition. Various uses are described for these techniques.

Abstract: Techniques for detecting physical conditions at a physical premises from collection of sensor information from plural sensors execute one or more unsupervised learning models to continually analyze the collected sensor information to produce operational states of sensor information, produce sequences of state transitions, detect during the continual analysis of sensor data that one or more of the sequences of state transitions is a drift sequence, correlate determined drift state sequence to a stored determined condition at the premises, and generate an alert based on the determined condition. Various uses are described for these techniques.

Abstract: Techniques for controlling operation of sensors at a physical premises are described. The techniques process received messages corresponding to a prediction of an impending event and produce commands that modify operation of one or more specific sensors at the physical premises, send the commands that modify the operation of the one or more sensor devices at the physical premises at a period of time prior to a likely occurrence of the predicted insurable event, collect sensor information from the plurality of sensor devices deployed at the premises, and store the sensor information in a remote persistent storage system.

Abstract: Techniques for detecting physical conditions at a physical premises from collection of sensor information from plural sensors execute one or more unsupervised learning models to continually analyze the collected sensor information to produce operational states of sensor information, produce sequences of state transitions, detect during the continual analysis of sensor data that one or more of the sequences of state transitions is a drift sequence, correlate determined drift state sequence to a stored determined condition at the premises, and generate an alert based on the determined condition. Various uses are described for these techniques.

Abstract: Techniques for controlling operation of sensors at a physical premises are described. The techniques process received messages corresponding to a prediction of an impending event and produce commands that modify operation of one or more specific sensors at the physical premises, send the commands that modify the operation of the one or more sensor devices at the physical premises at a period of time prior to a likely occurrence of the predicted insurable event, collect sensor information from the plurality of sensor devices deployed at the premises, and store the sensor information in a remote persistent storage system.

Abstract: Described are techniques for determining a quotation score that would be applicable across lines of insurance and/or carriers, and which involves the collection in real time of sensor information from plural groups of sensors deployed in a specific premises and associated with intrusion detection, access control, burglar, fire alarm systems and surveillance systems and/or other systems that monitor for physical/chemical/biological conditions. The techniques execute unsupervised learning models to continually analyze the collected sensor information to produce sequences of state transitions that are assign scores and from which a quotation score is produced.

Abstract: Described are techniques for determining a quotation score that would be applicable across lines of insurance and/or carriers, and which involves the collection in real time of sensor information from plural groups of sensors deployed in a specific premises and associated with intrusion detection, access control, burglar, fire alarm systems and surveillance systems and/or other systems that monitor for physical/chemical/biological conditions. The techniques execute unsupervised learning models to continually analyze the collected sensor information to produce sequences of state transitions that are assign scores and from which a quotation score is produced.

Abstract: Techniques for detecting physical conditions at a physical premises from collection of sensor information from plural sensors execute one or more unsupervised learning models to continually analyze the collected sensor information to produce operational states of sensor information, produce sequences of state transitions, detect during the continual analysis of sensor data that one or more of the sequences of state transitions is a drift sequence, correlate determined drift state sequence to a stored determined condition at the premises, and generate an alert based on the determined condition. Various uses are described for these techniques.

Abstract: Techniques for detecting physical conditions at a physical premises from collection of sensor information from plural sensors execute one or more unsupervised learning models to continually analyze the collected sensor information to produce operational states of sensor information, produce sequences of state transitions, detect during the continual analysis of sensor data that one or more of the sequences of state transitions is a drift sequence, correlate determined drift state sequence to a stored determined condition at the premises, and generate an alert based on the determined condition. Various uses are described for these techniques.

Abstract: Techniques for detecting changes in operational characteristics of a group of sensor devices are described. The techniques collect sensor information from plural sensor devices deployed in a system, with the collected sensor information including sensor data and sensor device metadata. The techniques continually analyze the collected sensor information to detect changes in the operational characteristics of a sensor device in the group of sensor devices. Upon detection of changes in the operational characteristics of the sensor, a database that stores maintenance organization contact information is accessed and a request is generated for maintenance on the sensor device, which request is sent to the maintenance organization.

Abstract: Techniques for detecting physical conditions at a physical premises from collection of sensor information from plural sensors execute one or more unsupervised learning models to continually analyze the collected sensor information to produce operational states of sensor information, produce sequences of state transitions, detect during the continual analysis of sensor data that one or more of the sequences of state transitions is a drift sequence, correlate determined drift state sequence to a stored determined condition at the premises, and generate an alert based on the determined condition. Various uses are described for these techniques.

Abstract: Techniques for detecting physical conditions at a physical premises from collection of sensor information from plural sensors execute one or more unsupervised learning models to continually analyze the collected sensor information to produce operational states of sensor information, produce sequences of state transitions, detect during the continual analysis of sensor data that one or more of the sequences of state transitions is a drift sequence, correlate determined drift state sequence to a stored determined condition at the premises, and generate an alert based on the determined condition. Various uses are described for these techniques.

Abstract: Techniques for detecting changes in operational characteristics of a group of sensor devices are described. The techniques collect sensor information from plural sensor devices deployed in a system, with the collected sensor information including sensor data and sensor device metadata. The techniques continually analyze the collected sensor information to detect changes in the operational characteristics of a sensor device in the group of sensor devices. Upon detection of changes in the operational characteristics of the sensor, a database that stores maintenance organization contact information is accessed and a request is generated for maintenance on the sensor device, which request is sent to the maintenance organization.

Abstract: Techniques for controlling operation of sensors at a physical premises are described. The techniques process received messages corresponding to a prediction of an impending event and produce commands that modify operation of one or more specific sensors at the physical premises, send the commands that modify the operation of the one or more sensor devices at the physical premises at a period of time prior to a likely occurrence of the predicted insurable event, collect sensor information from the plurality of sensor devices deployed at the premises, and store the sensor information in a remote persistent storage system.

Abstract: Described are techniques for determining a quotation score that would be applicable across lines of insurance and/or carriers, and which involves the collection in real time of sensor information from plural groups of sensors deployed in a specific premises and associated with intrusion detection, access control, burglar, fire alarm systems and surveillance systems and/or other systems that monitor for physical/chemical/biological conditions. The techniques execute unsupervised learning models to continually analyze the collected sensor information to produce sequences of state transitions that are assign scores and from which a quotation score is produced.

Abstract: Techniques for detecting physical conditions at a physical premises from collection of sensor information from plural sensors execute one or more unsupervised learning models to continually analyze the collected sensor information to produce operational states of sensor information, produce sequences of state transitions, detect during the continual analysis of sensor data that one or more of the sequences of state transitions is a drift sequence, correlate determined drift state sequence to a stored determined condition at the premises, and generate an alert based on the determined condition. Various uses are described for these techniques.