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: 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: A system for preventing a false alarm that occurs at a building, the system includes a processing circuit configured to receive, via a communications interface, building data including events for the building devices. The processing circuit is configured to determine, based on the events, whether a false alarm rule has triggered, where the false alarm rule indicates relationships between one or more of the events that is indicative of a situation at the building site that causes the false alarm, generate a parameter update for at least one of the plurality of building devices in response to determining that the false alarm rule has triggered, and implement the parameter update by providing, via the communications interface, the parameter update to the at least one of the building devices.

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 system for generating a false alarm rule for preventing a false alarm that occurs at a building includes a processing circuit configured to receive, via a communications interface, building data including events for the building devices. The building events include a first non-alarm event, a second non-alarm event different than the first non-alarm event, and a false alarm event. The processing circuit is configured to generate an event sequence based on the events, where the event sequence includes the first non-alarm event and the second non-alarm event and indicates a relationship between the first non-alarm event and the second non-alarm event that is indicative of a situation at the building that causes the false alarm event to occur. The processing circuit is configured to generate the false alarm rule based on the event sequence. The false alarm rule includes a recommendation for preventing the false alarm event from occurring.

Abstract: A system for preventing a false alarm that occurs at a building, the system includes a processing circuit configured to receive, via a communications interface, building data including events for the building devices. The processing circuit is configured to determine, based on the events, whether a false alarm rule has triggered, where the false alarm rule indicates relationships between one or more of the events that is indicative of a situation at the building site that causes the false alarm, generate a parameter update for at least one of the plurality of building devices in response to determining that the false alarm rule has triggered, and implement the parameter update by providing, via the communications interface, the parameter update to the at least one of the building devices.

Abstract: A system for determining and presenting false alarm reduction includes a processing circuit configured to determine addressable false alarms by determining, based on events of building devices of a building and event sequences of one or more false alarm rules, whether the one or more false alarm rules are triggered, where the one or more false alarm rules each include one of the event sequences indicating relationships between the events that are indicative of a situation at the building that causes an addressable false alarm. The processing circuit is configured to generate a plurality of recommendations based the triggered false alarm rules, wherein the recommendations prompt the user to rectify situations at the building that cause the addressable false alarms, generate a user interface comprising an indication of the addressable false alarms and the plurality of recommendations, and cause a display of a user device to display the user interface.

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: A system for determining and presenting false alarm reduction includes a processing circuit configured to determine addressable false alarms by determining, based on events of building devices of a building and event sequences of one or more false alarm rules, whether the one or more false alarm rules are triggered, where the one or more false alarm rules each include one of the event sequences indicating relationships between the events that are indicative of a situation at the building that causes an addressable false alarm. The processing circuit is configured to generate a plurality of recommendations based the triggered false alarm rules, wherein the recommendations prompt the user to rectify situations at the building that cause the addressable false alarms, generate a user interface comprising an indication of the addressable false alarms and the plurality of recommendations, and cause a display of a user device to display the user interface.

Abstract: A system for preventing a false alarm that occurs at a building, the system includes a processing circuit configured to receive, via a communications interface, building data including events for the building devices. The processing circuit is configured to determine, based on the events, whether a false alarm rule has triggered, where the false alarm rule indicates relationships between one or more of the events that is indicative of a situation at the building site that causes the false alarm, generate a parameter update for at least one of the plurality of building devices in response to determining that the false alarm rule has triggered, and implement the parameter update by providing, via the communications interface, the parameter update to the at least one of the building devices.

Abstract: A system for generating a false alarm rule for preventing a false alarm that occurs at a building includes a processing circuit configured to receive, via a communications interface, building data including events for the building devices. The building events include a first non-alarm event, a second non-alarm event different than the first non-alarm event, and a false alarm event. The processing circuit is configured to generate an event sequence based on the events, where the event sequence includes the first non-alarm event and the second non-alarm event and indicates a relationship between the first non-alarm event and the second non-alarm event that is indicative of a situation at the building that causes the false alarm event to occur. The processing circuit is configured to generate the false alarm rule based on the event sequence. The false alarm rule includes a recommendation for preventing the false alarm event from occurring.

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.