US20210097624A1

US20210097624A1 - Method and apparatus for increasing the density of data surrounding an event

Info

Publication number: US20210097624A1
Application number: US17/071,722
Authority: US
Inventors: David Vavrasek
Original assignee: Sensormatic Electronics LLC
Current assignee: Johnson Controls Inc; Johnson Controls Tyco IP Holdings LLP; Johnson Controls US Holdings LLC
Priority date: 2016-06-06
Filing date: 2020-10-15
Publication date: 2021-04-01
Also published as: US10810676B2; US20170352102A1; WO2017213918A1

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.

Description

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 15/173,795, filed on Jun. 6, 2016, the entirety of which is incorporated by reference herein.

BACKGROUND

This description relates to operation of sensor networks such as those used for security, intrusion and alarm systems installed on industrial or commercial or residential premises.
It is common for businesses to have various types of systems such as intrusion detection, fire detection and surveillance systems for detecting various alarm conditions at their premises and signaling the conditions to a monitoring station or authorized users. These systems use various types of sensors such as motion detectors, cameras, and proximity sensors, thermal, optical, vibration sensors and so forth. Often such sensors, especially digital video recorders, typically record sensor data, e.g., video either continually or begin recording based on a trigger event. For many purposes, the video is typically manually reviewed to determine events leading up to and immediately following a specific occurrence.

SUMMARY

Described are techniques that capture sensor data, especially video data, from corresponding sensors, just prior to and following an identified incident. The techniques automatically evaluate such data by analyzing such data in conjunction with additional data feeds from other sensors and other external data sources for various purposes. One such purpose is for a tool to automatically initiate an insurance claim that can be validated and paid.
Described herein is a system that mines accumulated data and geographically-related data from systems deployed in a premises, and which produces predictions with respect to a risk level that either equipment or a user's actions relative to the equipment pose to the premises and/or the equipment and that sends appropriate control signals to sensors for enhanced monitoring of the premises.
According to an aspect, a computer program product tangibly stored on a computer readable hardware storage device for controlling operation of sensors at a physical premises includes instructions to cause a processor to receive a message corresponding to a prediction of an impending insurable event at the physical premises, process the received message according to an algorithm that is selected in accordance with the predicted insurable event, the algorithm producing one or more commands to modify operation of one or more specific sensors of a plurality of sensor devices that collect sensor information 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.
Aspects also include systems and methods.
Additional features of the computer program product, systems and methods may include to these and other features.
The message includes a calculated indication produced by instructions to continually analyze the collected sensor information by one or more unsupervised learning models to determine normal sets of states and drift states for the premises to produce the prediction of an occurrence of the insurable event. The algorithm to process the received message includes instructions to determine modifications of the operation of the one or more specific sensor devices at the identified premises according to an occurrence of a drift state and produce the messages including the commands that modify the operation of the one or more specific sensor devices from the determined modifications that are based on the drift state. The instructions to determine modifications further comprise instructions to analyze the prediction of the event, determine sensor devices that are in proximity to a location of the predicted event, determine modifications to sensor devices in proximity to the location, which modifications are based on the predicted event, specific locations of the sensor devices and specific types of the sensor devices, determine the commands based on the determined modifications and send the commands to the one or more specific sensor devices. The message is received from an external service. The computer program product also includes instructions to generate an insurance claim form by automatically populating a template insurance claim form with information required by the template insurance claim form.
The computer program product further includes instructions to detect an actual occurrence of the insurable event based on actual sensor data received from the plurality of sensor devices to provide a trigger to generate an insurance claim form and generate an insurance claim form subsequent to the actual occurrence of the event by automatically populating a template insurance claim form with information required by the template insurance claim form. The computer program product further includes instructions to retrieve from a database, operational data for specified equipment that are insured by the insurance carrier, the operational data comprising service records, raw sensor data, and/or alerts generated for the specified equipment and augment the insurance claim form with a report that includes the operational data for the specified equipment at a time period prior to the event. The algorithm being of a weather-related event, and the computer program product further includes instructions to receive the indication from an external service, the indication being of the weather-related event, parse the received indication to produce a representation of the indication that identifies a type of weather-related event, analyze the parsed indication according to the location of the physical premises to produce a likely prediction of damage to the physical premises, and produce the commands to modify the operation of the one or more specific sensors of the plurality of sensors, according to the likely prediction of damage to the physical premises. The computer program product further includes instructions to produce a request to upload service and usage data for one or more monitored units within the premises to an external database and send to the system at the physical premises the request to upload to the external database, the service and usage data.
The one or more specific sensors include one or more video cameras, and the algorithm, comprises instructions to receive current positioning information for each of the one or more video cameras, calculate based at least in part on the received indication repositioning information for the one or more video cameras, and send the repositioning information to at least some of the one or more video cameras to modify operation of the one or more video cameras by repositioning the at least some of the one or more video cameras. The one or more specific sensors include one or more video cameras, and the algorithm, comprises instructions to receive current frame rate information for each of the one or more video cameras, frame rate information being information of the frequency at which images are taken and sent by the one or more video cameras, calculate based at least in part on the received indication modified frame rate information for the one or more video cameras and send the modified frame rate information to at least some of the one or more video cameras to modify the frame rate operation of the one or more video cameras.
The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention is apparent from the description and drawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic diagram of an exemplary networked security system.

FIG. 2 is a block diagram of a sensor.

FIG. 3 is a block diagram of a sensor based state prediction system.

FIG. 3A is a diagram of a logical view of the sensor based state prediction system of FIG. 3.

FIG. 4 is a flow diagram of a state representation engine.

FIG. 5 is a flow diagram of sensor based state prediction system processing.

FIG. 5A is a flow diagram of training process for a Next state predictor engine that is part of the sensor based state prediction system.

FIG. 5B is a flow diagram of a Next state predictor engine model building process.

FIG. 6 is a flow diagram of operation processing by the sensor based state prediction system.

FIG. 7 is a flow diagram of an example of sensor based risk profiling.

FIG. 8 is a block diagram of a system architecture.

FIG. 9 is a flow diagram of overview of indication based sensor control.

FIG. 10 is a flow diagram of an example of indication based sensor control.

FIG. 11 is a flow diagram of an example of sensor based augmented claim filing.

FIG. 11A is a block diagram of an exemplary format of supplemental data to augment a claim form.

DETAILED DESCRIPTION

Described herein are surveillance/intrusion/fire/access systems that are wirelessly connected to a variety of sensors. In some instances those systems may be wired to sensors. Examples of detectors/sensors 28 (sensor detectors used interchangeably) include motion detectors, glass break detectors, noxious gas sensors, smoke/fire detectors, contact/proximity switches, video sensors, such as camera, audio sensors such as microphones, directional microphones, temperature sensors such as infrared sensors, vibration sensors, air movement/pressure sensors, chemical/electro-chemical sensors, e.g., VOC (volatile organic compound) detectors. In some instances, those systems sensors may include weight sensors, LIDAR (technology that measures distance by illuminating a target with a laser and analyzing the reflected light), GPS (global positioning system) receivers, optical, biometric sensors, e.g., retina scan sensors, EGG/Heartbeat sensors in wearable computing garments, network hotspots and other network devices, and others.
The surveillance/intrusion/fire/access systems employ wireless sensor networks and wireless devices, with remote, cloud-based server monitoring and report generation. As described in more detail below, the wireless sensor networks wireless links between sensors and servers, with the wireless links usually used for the lowest level connections (e.g., sensor node device to hub/gateway).
In the network, the edge (wirelessly-connected) tier of the network is comprised sensor devices that provide specific sensor functions. These sensor devices have a processor and memory, and may be battery operated and include a wireless network card. The edge devices generally form a single wireless network in which each end-node communicates directly with its parent node in a hub-and-spoke-style architecture. The parent node may be, e.g., a network access point (not to be confused with an access control device or system) on a gateway or a sub-coordinator which is, in turn is connected to the access point or another sub-coordinator.
Referring now to FIG. 1, an exemplary (global) distributed network topology for a wireless sensor network 10 is shown. In FIG. 1 the wireless sensor network 10 is a distributed network that is logically divided into a set of tiers or hierarchical levels 12 a-12 c. In an upper tier or hierarchical level 12 a of the network are disposed servers and/or virtual servers 14 running a “cloud computing” paradigm that are networked together using well-established networking technology such as Internet protocols or which can be private networks that use none or part of the Internet. Applications that run on those servers 14 communicate using various protocols such as for Web Internet networks XML/SOAP, RESTful web service, and other application layer technologies such as HTTP and ATOM. The distributed network 10 has direct links between devices (nodes) as shown and discussed below.
In one implementation hierarchical level 12 a includes a central monitoring station 49 comprised of one or more of the server computers 14 and which includes or receives information from a sensor based state prediction system 50 as will be described below.
The distributed network 10 includes a second logically divided tier or hierarchical level 12 b, referred to here as a middle tier that involves gateways 16 located at central, convenient places inside individual buildings and structures. These gateways 16 communicate with servers 14 in the upper tier whether the servers are stand-alone dedicated servers and/or cloud based servers running cloud applications using web programming techniques. The middle tier gateways 16 are also shown with both local area network 17 a (e.g., Ethernet or 802.11) and cellular network interfaces 17 b.
The distributed network topology also includes a lower tier (edge layer) 12 c set of devices that involve fully-functional sensor nodes 18 (e.g., sensor nodes that include wireless devices, e.g., transceivers or at least transmitters, which in FIG. 1 are marked in with an “F”), as well as wireless sensor nodes or sensor end-nodes 20 (marked in the FIG. 1 with “C”). In some embodiments wired sensors (not shown) can be included in aspects of the distributed network 10.
In a typical network, the edge (wirelessly-connected) tier of the network is largely comprised of devices with specific functions. These devices have a small-to-moderate amount of processing power and memory, and often are battery powered, thus requiring that they conserve energy by spending much of their time in sleep mode. A typical model is one where the edge devices generally form a single wireless network in which each end-node communicates directly with its parent node in a hub-and-spoke-style architecture. The parent node may be, e.g., an access point on a gateway or a sub-coordinator which is, in turn, connected to the access point or another sub-coordinator.
Each gateway is equipped with an access point (fully functional sensor node or “F” sensor node) that is physically attached to that access point and that provides a wireless connection point to other nodes in the wireless network. The links (illustrated by lines not numbered) shown in FIG. 1 represent direct (single-hop MAC layer) connections between devices. A formal networking layer (that functions in each of the three tiers shown in FIG. 1) uses a series of these direct links together with routing devices to send messages (fragmented or non-fragmented) from one device to another over the network.
In some instances the sensors 20 are sensor packs (not shown) that are configured for a particular types of business applications, whereas in other implementations the sensors are found in installed systems such as the example security systems discussed below.
Referring to FIG. 2, a sensor device 20 is shown. Sensor device 20 includes a processor device 21 a, e.g., a CPU and or other type of controller device that executes under an operating system, generally with 8-bit or 16-bit logic, rather than the 32 and 64-bit logic used by high-end computers and microprocessors. The device 20 has a relatively small flash/persistent store 21 b and volatile memory 21 c in comparison with other the computing devices on the network. Generally the persistent store 21 b is about a megabyte of storage or less and volatile memory 21 c is about several kilobytes of RAM memory or less. The device 20 has a network interface card 21 d that interfaces the device 20 to the network 10. Typically a wireless interface card is used, but in some instances a wired interface could be used. Alternatively, a transceiver chip driven by a wireless network protocol stack (e.g., 802.15.4/6LoWPAN) can be used as the (wireless) network interface. These components are coupled together via a bus structure. The device 20 also includes a sensor element 22 and a sensor interface 22 a that interfaces to the processor 21 a. Sensor 22 can be any type of sensor types mentioned above.
Also shown in FIG. 2 is a panel 38. Panel 38 may be part of an intrusion detection system (not shown). The panel 38, i.e., intrusion detection panel is coupled to plural sensors/detectors 20 (FIG. 1) disbursed throughout the physical premises. The intrusion detection system is typically in communication with a central monitoring station (also referred to as central monitoring center not shown) via one or more data or communication networks (not shown). Sensor/detectors may be hard wired or communicate with the panel 38 wirelessly. In general, detectors sense glass breakage, motion, gas leaks, fire, and/or breach of an entry point, and send the sensed information to the panel 38. Based on the information received from the detectors 20, the panel 38, e.g., intrusion detection panel determines whether to trigger alarms and/or sending alarm messages to the monitoring station 20. A user may access the intrusion detection panel to control the intrusion detection system, e.g., disarm, arm, enter predetermined settings, etc. Other systems can also be deployed such as access control systems, etc.
Referring now to FIG. 3, a sensor based state prediction system 50 is shown. The prediction system 50 executes on one or more of the cloud-based server computers and accesses database(s) 51 that store sensor data and store state data in a state transition matrix. In some implementations, dedicated server computers could be used as an alternative.
The sensor based state prediction system 50 includes a State Representation Engine 52. The State Representation Engine 52 executes on one or more of the servers described above and interfaces on the servers receive sensor signals from a large plurality of sensors deployed in various premises throughout an area. These sensor signals have sensor values and together with other monitoring data represent a data instance for a particular area of a particular premises in a single point in time. The data represent granular information collected continuously from the particular premises. The State Representation Engine takes these granular values and converts the values into a semantic representation. For example, a set of sensor values and monitoring data for particular time duration are assigned a label, e.g., “State-1.” As the data is collected continuously, this Engine 52 works in an unsupervised manner, as discussed below, to determine various states that may exist in the premises.
As the different states are captured, this Engine 52 also determines state transition metrics that are stored in the form a state transition matrix. A simple state transition matrix has all the states in its rows and columns, with cell entries being many times did the premises move from a state in cell i to a state in cell j are over a period of time and/or events. This matrix captures the operating behavior of the system. State transitions can happen either over time or due to events. Hence, the state transition metrics are captured using both time and events. A state is a representation of a group of sensors grouped according to a clustering algorithm.
The State transition matrix is a data structure that stores how many times the environment changed from State_i to State_j. The State transition matrix thus stores “knowledge” that the sensor based state prediction system 50 captures and which is used to determine predictions of the behavior of the premises. The State transition matrix is accessed by the Next prediction engine to make decisions and trigger actions by the sensor based state prediction system 50.
Unsupervised learning e.g., clustering is used to group sensor readings into states and conditions over a period of time that form a time trigger state and over events to form an event trigger state. Used to populate the state transition matrix per premises.
An exemplary simplified depiction for explanatory purposes of a State transition matrix is set out below:


State	State	State	State	State	State
tran-	tran-	tran-	tran-	tran-	tran-
sition	sition	sition	sition	sition	sition

Instance	x, y	x, y	x, y	x, y	x, y	x, y
	x, y	x, y	x, y	x, y	x, y	x, y
	x, y	x, y	x, y	x, y	x, y	x, y

Where columns in the State transition matrix is are “state transitions” expressed as a listing by instance with pointer to the state time and event trigger tables.
Entries x,y in cells of the State transition matrix are pointers that corresponds to the trigger tables that store the number of time periods and events respectively for each particular cell of the State transition matrix.
The State time trigger is depicted below. The State time trigger tracks the time periods t1 . . . t8 for each state transition corresponding to the number x in each particular cell.


t1	t2	t3
State	State	State	***
transition 1	transition 2	transition 3	***

Instance	1	1	1	***
	1	1	1	***
	t1 t5	t2 t3	t4 t7 t8	***

State event trigger tracks the event E1 . . . E2 for each state transition corresponding to the number y in each particular cell (if any).


e1	e2	e3
State	State	State	***
transition 1	transition 2	transition 3	***

Instance			E2	***
			E2	***
	E1	E1	E3	***

The State Representation Engine 52 in addition to populating the State transition matrix, also populates a State time trigger that is a data structure to store, the time value spent in each state and a distribution of the time duration for each state. Similar to the State transition matrix, the State time trigger also encapsulates the behavior knowledge of the environment. State transitions can be triggered using these values.
The State Representation Engine 52 also populates a State event trigger. The State event trigger is a data structure to store, event information. An example of an event can be sensor on a door sensing that a door was opened. There are many other types of events. This data structure captures how many times such captured events caused a state transition.
The State Representation Engine 52 populates the State Transition matrix and the State Time and State triggers, which together capture metrics, which provide a Knowledge Layer of the operational characteristics of the premises.
The sensor based state prediction system 50 also includes a Next State Prediction Engine 54. The Next State Prediction Engine 54 predicts an immediate Next state of the premises based the state transition matrix. The Next State Prediction Engine 54 predicts if the premises will be in either a safe state or a drift state over a time period in the future. The term “future” as used herein refers to a defined window of time in the future, which is defined so that a response team has sufficient time to address a condition that is predicted by the Next State Prediction Engine 54 that may occur in the premises to restore the state of the premises to a normal state. The Next State Prediction Engine operates as a Decision Layer in the sensor.
The sensor based state prediction system 50 also includes a State Representation graphical user interface generator 56. State Representation graphical user interface generator 56 provides a graphical user interface that is used by the response team to continuously monitor the state of the premises. The State Representation graphical user interface generator 56 receives data from the Next State Prediction Engine 54 to graphically display whether the premises is either in the safe state or the drifting state. The State Representation graphical user interface generator 56 operates as an Action Layer, where an action is performed based on input from Knowledge and Decision Layers.
The sensor based state prediction system 50 applies unsupervised algorithm learning models to analyze historical and current sensor data records from one or more customer premises and generates a model that can predict Next patterns, anomalies, conditions and events over a time frame that can be expected for a customer site. The sensor based state prediction system 50 produces a list of one or more predictions that may result in on or more alerts being sent to one more user devices as well as other computing system, as will be described. The prediction system 50 uses various types of unsupervised machine learning models including Linear/Non-Linear Models, Ensemble methods etc.
Referring now to FIG. 3A, a logical view 50′ of the sensor based state prediction system 50 is shown. In this view at the bottom is the raw events layer that is the sensors values and monitoring data from the environment under surveillance. The middle layer is an abstraction layer that abstracts these raw events as state (represented in FIG. 5A by the blocks “States” (State Representation Engine 52), STM (State Transition Matrix), STT (State Time Trigger) and SET (State Event Trigger) that produce a state as a concise semantic representation of the underlying behavior information of the environment described by time and various sensor values at that point in time. With the upper blocks being a Decisions block (Next State Prediction Engine 54) and Actions block (State Representation graphical user interface generator 56.)
Referring now to FIG. 4, the processing 60 for the State Representation Engine 52 is shown. The State Representation Engine 55 collects 62 (e.g., from the databases 51 or directly from interfaces on the servers) received sensor signals from a large plurality of sensors deployed in various premises throughout an area that is being monitored by the sensor based state prediction system 50. The sensor data collected from the premises, includes collected sensor values and monitoring data values.
An example of the sensor values is shown below (using fictitious data):
Site no.: 448192
Kitchen thermostat: 69,
Stove thermostat: 72,
Outdoor security panel: Active,
Kitchen Lights: On,
Delivery Door: Shutdown
As these sensor signals have sensor values that represent a data instance for a particular area of a particular premises in a single point in time, the State Representation Engine 52 converts 64 this sensor data into semantic representations of the state of the premises at instances in time. The State Representation Engine 52 uses 66 the converted sensor semantic representation of the sensor data collected from the premises to determine the empirical characteristics of the premises. The State Representation Engine 52 assigns 67 an identifier to the state.
For example, the kitchen in a restaurant example for a premises identified in the system as “Site no.: 448192” uses the sensor values to produce a first state that is identified here as “State 1.” Any labelling can be used and is typically consecutive identified and this state is semantically described as follows:

- State 1: Kitchen thermostat: 69, Stove thermostat: 72, Outdoor security panel: Active, Kitchen Lights: On, Delivery Door: Shutdown, current time: Monday 5:00 AM PST, start time: Sunday 10:00 PM PST

The semantic description includes the identifier “State 1” as well as semantic descriptions of the various sensors, their values and dates and times.
The State Representation Engine 52 determines an abstraction of a collection of “events” i.e., the sensor signals as state. The state thus is a concise representation of the underlying behavior information of the premises being monitored, described by time and data and various sensor values at that point in time and at that date.
The semantic representation of the state is stored 68 by the State Representation Engine 52 as state transition metrics in the State Representation matrix. Over time and days, as the sensors produce different sensor values, the State Representation Engine 55 determines different states and converts these states into semantic representations that are stored the state transition metrics in the matrix, e.g., as in a continuous loop 70.
The kitchen example is further set out below:
The State Representation Engine 52 collects the following data (fictitious data) from these three sensors at a particular points in time,


Obstruction	Room	Stove
Detector	Thermostat	Thermostat

0	71.1755732	78.95655605
0	68.27180645	79.97821825
0	71.80483918	79.428149
0	70.46354628	81.90901291
0	69.83508114	81.12026772
0	71.46074066	81.613552
1	70.14174204	80.12242015
1	70.98180652	78.03049081

The state representation engine 52, converts these raw values into state definitions and assigns (labels) each with a unique identifier for each state, as discussed above. As the premises is operated over a period of time, the Next transition matrix, the state time trigger matrix and the state event trigger matrix are filled.
Continuing with the concrete example, the state representation engine 52 produces the following two states (State 1 is repeated here for clarity in explanation).
State 1: Kitchen thermostat: 69, Stove thermostat: 72, Outdoor security panel: Active, Kitchen Lights: On, Delivery Door: Shutdown, current time: Sunday 10:00 PM.
State 2: Kitchen thermostat: 69, Stove thermostat: 80, Outdoor security panel: Active, Kitchen Lights: On, Delivery Door: Shutdown, current time: Sunday 10:15 PM
State 3: Kitchen thermostat: 69, Stove thermostat: 60, Outdoor security panel: Active, Kitchen Lights: On, Delivery Door: Shutdown, current time: Monday 1:00 AM.
Between State 1 and State 2 there is a transition in which over a 15 minute span the Stove thermostat value increased from 72 to 80 and from State 2 to State 3 the Stove thermostat value decreased from 80 to 72 over a 2 hr. and 45 min. period, which can likely be attributed to something being cooked between State 1 and State 2 and by State 3 the order was filled, item removed from stove and the stove thermostat shows a lower value.
The state representation engine 52, adds to the state transition matrix an entry that corresponds to this transition, that the premises moved from state 1 to state 2. The state representation engine 52, also adds to the state transition matrix in that entry, an indicator that the transition was “time trigger,” causing the movement, and thus the state representation engine 52 adds an entry in state time trigger matrix. The state representation engine 52, thus co-ordinates various activities inside the premises under monitoring and captures/determines various operating characteristics of the premises.
Referring now to FIG. 5 processing 80 for the Next State Prediction Engine 54 is shown. This processing 80 includes training processing 80 a (FIG. 5A) and model building processing 80 b (FIG. 5B), which are used in operation of the sensor based state prediction system 50.
Referring now to FIG. 5A, the training processing 80 a that is part of the processing 80 for the Next State Prediction Engine 54 is shown. In FIG. 5A, training processing 80′ trains the Next State Prediction Engine 54. The Next State Prediction Engine 54 accesses 82 the state transition matrix and retrieves a set of states from the state transition matrix. From the retrieved set of states the Next State Prediction Engine 54 generates 84 a list of most probable state transitions for a given time period, the time period can be measured in minutes, hours, days, weeks, months, etc. For example, consider the time period as a day. After a certain time period of active usage, the sensor based state prediction system 50, through the state representation engine 52, has acquired knowledge states s1 to s5.
From the state transition matrix the system uses the so called “Markov property” to generate state transitions. As known, the phrase “Markov property” is used in probability and statistics and refers to the “memoryless” property of a stochastic process.
From the state transition matrix using the so called “Markov property” the system generates state transition sequences, as the most probable state sequences for a given day.
An exemplary sequence uses the above fictitious examples is shown below:

- s1 s2 s4 s5
- s2 s2 s4 s5

The Next State Prediction Engine 54 determines 86 if a current sequence is different than an observed sequence in the list above. When there is a difference, the Next State Prediction Engine 54 determines 88 whether something unusual has happened in the premises being monitored or whether the state sequence is a normal condition of the premises being monitored.
With this information the Next State Prediction Engine 54 90 these state transitions as “safe” or “drift state” transitions. Either the Next State Prediction Engine 54 or manual intervention is used to label either at the state transition level or the underlying sensor value levels (fictitious) for those state transitions producing the follow:


Obstruction	Room	Stove	Safety State
Detector	Thermostat	Thermostat	(label)

0	71.1755732	78.95655605	G
0	68.27180645	79.97821825	G
0	71.80483918	79.428149	G
0	70.46354628	81.90901291	G
0	69.83508114	81.12026772	G
0	71.46074066	81.613552	G
1	70.14174204	80.12242015	G
1	70.98180652	78.03049081	G
0	68.58285177	79.981358	G
0	69.91571802	79.4885171	G
1	69.89799953	79.3838372	G
0	70.42668373	80.20397118	G
1	70.23391637	81.80212485	Y
0	68.19244768	81.19203004	G

The last column in the above table is the label, wherein in this example “G” is used to indicate green, e.g., a normal operating state, e.g., “a safe state” and “Y” is used to indicate yellow, e.g., an abnormal or drift state, e.g., an “unsafe state” and “R” (not shown above) would be used to represent red or a known unsafe state. This data and states can be stored in the database 51 and serves as training data for a machine learning model that is part of the Next State Recommendation Engine 54.
Referring now to FIG. 5B, the model building processing 80 b of the Next State Recommendation Engine 54 is shown. The model building processing 80 b uses the above training data to build a model that classify a system's state into either a safe state or an unsafe state. Other states can be classified. For example, three states can be defined, as above, “G Y R states” or green (safe state) yellow (drifting state) and red (unsafe state). For ease of explanation two states “safe” (also referred to as normal) and “unsafe” (also referred to as drift) are used. The model building processing 80 b accesses 102 the training data and applies 104 one or more machine learning algorithms to the training data to produce the model that will execute in the Next State Recommendation Engine 54 during monitoring of systems. Machine learning algorithms such as Linear models and Non-Linear Models, Decision tree learning, etc., which are supplemented with Ensemble methods (where two or more models votes are tabulated to form a prediction) and so forth can be used. From this training data and the algorithms, the model is constructed 106.
Below is table representation of a fictitious Decision Tree using the above fictitious data (again where “G” is used to indicate green, “a safe state” e.g., a normal operating state, and “Y” is used to indicate yellow, e.g., drifting state, and “R” (shown below) to represent red or a known unsafe state. This data and states can be stored in the database 51 and serves as training data for a machine learning model that is part of the Next State Recommendation Engine 54.
stoveThermoStat=‘(−inf-81.064396]’
|obstructionDetector=0: G
|obstructionDetector=1: G
stoveThermoStat=‘(81.064396-84.098301]’
|obstructionDetector=0. G
|obstructionDetector=1: Y
stove ThermoStat=‘(84.098301-87.132207]’: R
stoveThermoStat=‘(87.132207-90.166112]’
|obstructionDetector=0: R
|obstructionDetector=1: R
stoveThermoStat=‘(90.166112-inf)’
|obstructionDetector=0: R
|obstructionDetector=1: R
Empirical characteristics can be a model based and human based are determined 106 for various states of the premises in terms of, e.g., safety of the occupants and operational conditions of the various systems within the premises. Examples of such systems include intrusion detection systems, fire alarm systems, public annunciation systems, burglar alarm systems, the sensors deployed at the premises, as well as other types of equipment, such as refrigeration equipment, stoves, and ovens that may be employed in the kitchen example that will be discussed below. Other instances of particular premises will have other types of systems that are monitored. Based on the empirical determined states of the various systems within the premises being monitored, the sensor based state prediction system 50 will determine the overall state of the premises as well as individual states of the various systems within the premises being monitored, as will be discussed below.
Referring now to FIG. 6, operational processing 100 of the sensor based state prediction system 50 is shown. The sensor based prediction system 50 receives 102 (by the State Representation Engine 52) sensor signals from a large plurality of sensors deployed in various premises throughout an area being monitored. The State Representation Engine 52 converts 104 the sensor values from these sensor signals into a semantic representation that is identified, as discussed above. As the data is collected continuously, this Engine 52 works in an unsupervised manner to determine various states that may exist in sensor data being received from the premises. As the different states are captured, the State Representation Engine 52 also determines 106 state transition metrics that are stored in the state transition matrix using both time and events populating the State time trigger and the State event trigger, as discussed above. The State transition matrix is accessed by the Next prediction engine 54 to make decisions and trigger actions by the sensor based state prediction system 50.
The Next State Prediction Engine 54 receives the various states (either from the database and/or from the State Representation Engine 52 and forms 108 predictions of an immediate Next state of the premises/systems based the state data stored in the state transition matrix. For such states the Next State Prediction Engine 54 predicts if the premises will be in either a safe state or a drift state over a time period in the Next as discussed above.
The sensor based state prediction system 50 also sends 110 the predictions to the State Representation engine 56 that generates a graphical user interface to provide a graphical user interface representation of predictions and states of various premises/systems. The state is tagged 112 and stored 114 in the state transition matrix.
The sensor based state prediction system 50 using the State Representation Engine 52 that operates in a continuous loop to generate new states and the Next State Prediction Engine 54 that produces predictions together continually monitor the premises/systems looking for transition instances that result in drift in states that indicate potential problem conditions. As the sensors in the premises being monitored operate over a period of time, the state transition matrix, the state time trigger matrix and the state event trigger matrix are filled by the state representation engine 52 and the Next State Prediction Engine 54 processing 80 improves on predictions.
The sensor based state prediction system 50 thus determines the overall state of the premises and the systems by classifying the premises and these systems into a normal or “safe” state and the drift or unsafe state. Over a period of time, the sensor based state prediction system 50 collects information about the premises and the sensor based state prediction system 50 uses this information to construct a mathematical model that includes a state representation, state transitions and state triggers. The state triggers can be time based triggers and event based triggers, as shown in the data structures above.
Referring now to FIG. 7, processing 120 of sensor information using the architecture above is shown. The sensor-based state prediction system 50 receives 122 sensor data from sensors monitoring each physical object or physical quantity from the sensors (FIG. 2) deployed in a premises. The sensor-based state prediction system 50 is configured 124 with an identity of the premises and the physical objects being monitored by the sensors in the identified premises. The sensor based state machine 50 processes 126 the received sensor data to produce states as set out above using the unsupervised learning models. Using these models the sensor-based state prediction system 50 monitors various physical elements to detect drift states.
For example, one of the sensors can be a vibration sensor that sends the sensor-based state prediction system 50 a signal indicating a level of detected vibration from the vibration sensor. This signal indicates both magnitude and frequency of vibration. The sensor-based state prediction system 50 determines over time normal operational levels for that sensor based on what system that sensor is monitoring and together with other sensors produces 128 series of states for the object and/or premises. These states are associated 130 with either a state status of “safe” or “unsafe” (also referred to herein as “normal” or “drift,” respectively). Part of this process of associating is provided by the learning process and this associating can be empirically determined based on human input. This processing thus develops more than a mere envelope or range of normal vibration amplitude and vibration frequency indications for normal operation for that particular vibration sensor, but rather produces a complex indication of a premises or object state status by combining these indications for that sensor with other indications from other sensors to produce the state transition sequences mentioned above.
States are produced from the unsupervised learning algorithms (discussed above in FIGS. 5-5B) based on that vibration sensor and states from other sensors, which are monitoring that object/premises. The unsupervised learning algorithms continually analyze that collected vibration data and producing state sequences and analyze state sequences that include that sensor. Overtime, as the analysis determines 134 that states including that sensor have entered into a drift state that corresponds to an unsafe condition, the sensor-based state prediction system 50 determines 136 a suitable action alert (in the Action layer) to indicate to a user that there may be something wrong with the physical object being monitored by that sensor. The analysis provided by the prediction system sends the alert to indicate that there is something going wrong with object being monitored. The sensor-based state prediction system 50 produces suggested actions 138 that the premises' owner should be taking with respect to the object being monitored.
Referring now to FIG. 8, an architecture 139 that combines the sensor-based state prediction system 50 (FIG. 5) in a cooperative relationship with business application servers 139 a in the cloud is shown. In FIG. 8, the sensor-based state prediction system 50 receives sensor data from the sensor network 11 (or storage 51) for a particular premises, processes that data to produce states and state sequences, and uses this information in conjunction with event indications that can be calculated, premises event driven, and/or external from system 125 as well as business application servers to process augmented claims submissions for insured events under an insurance policy using an augmented claim filing module 220.
Referring now to FIG. 9, an event prediction module 140 is shown. The event prediction module 140 (which can be part of the sensor-based state prediction system 50 or a separate computer system) receives 142 external inputs/notifications and inputs from the sensor-based state prediction system 50. The event prediction module 140 analyzes 144 these external inputs and predictions from the sensor-based state prediction system 50. Based on the received external input/notification and inputs from the state-based prediction engine, the event prediction module selects one or more algorithms for processing of data. The event prediction module 140 produces 146 control signals to control specific sensors in the premises to modify the manner in which the specific sensors sense conditions external to and within the premises.
Referring now to FIG. 10, an augmented claim process 160 for supporting an insurance claim upon an occurrence of an insurable event at a physical premises is shown. This process 160 can be executed on a computer system (not shown) or by the sensor-based state prediction system 50. The process 160 supports an insurance claim filing upon an occurrence of an insurable event at a physical premises.
The process 160 receives 162 an indication of an impending insurable event that may affect the physical premises. The received indication is processed 164 according to an algorithm that is selected in accordance with the insurable event. The algorithm produces 166 one or more messages that are sent 168 to various sensors (determined from the algorithm as discussed below). These messages in the form of data packets including address information and a payload are parsed 170 at the sensors and provide commands 172 that modify operation of one or more specific sensors of the sensors of, e.g., FIG. 1.
These commands cause the specific sensors to collect sensor information in a different manner that prior to execution of the command by the sensor. For example, messages can include the following format:
Command <address of device> <command> <data>
Examples for a video camera
Reposition command: <IP address of camera> <reposition> <position data>
Frame rate command: <IP address of camera> <rate> <rate data>
Other commands can include turning on a sensor that is normally in a sleep mode or requesting a reading from a sensor that is in a periodic schedule mode, where a reading from the sensor is required more frequently than scheduled. Many other commands can be provided.
The process sends the commands that modify the operation of one or more sensors at the physical premises at a period of time prior to a likely occurrence of the insurable event.
The process can receive an indication that is a calculated indication produced by the sensor-based state prediction system 50. In this scenario, the sensor-based state prediction system 50 collects sensor information from the plurality of sensors deployed at the premises and continually analyzes the collected sensor information by one or more unsupervised learning models to determine normal sets of states and drift states for the premises. Upon detection of a prediction of a likely occurrence of one or more drift states, as correlated to normal operation at the premises, this calculated indication of the occurrence of a drift state is used to determine which of the sensors will have a modified operation and what the modified operation will be.
For example, returning to the example of the kitchen, in the event that stove and room temperature sensors sense conditions that cause a drift state “State N” below, the computer can cause other sensors in the vicinity of the Stove in the Kitchen to wake up, and possible reposition video cameras to that area.
State N: Kitchen thermostat: 90, Stove thermostat: 120, Outdoor security panel: Active, Kitchen Lights: On, Delivery Door: Shutdown, current time: Monday 2:15 AM
In order to accomplish this the process 160 will access a stored computer representation (e.g., as a graph or the like) that provides a mapping of all sensors in a premises such that the computer determines what sensors to modify operation of and what the modifications would be.
Another example, would be an intrusion detection. The sensor-based state prediction system 50 produces a drift state
State Y: Room thermostat: 68, Outdoor security panel: Active, Lights: Off, Delivery Door: Opened, current time: Monday 2:15 AM
Upon detection of an intrusion into the premises, video cameras can be repositioned by the computer producing corresponding commands.
Another example is where the indication is a premises-related event. Such algorithms would include instructions to cause the computer to analyze the indication according to sensor data received from sensors at the physical premises to produce a likely prediction of the event at the physical premises, produce the commands to modify the operation of the one or more specific sensors of the plurality of sensors according to the likely prediction of the event at the physical premises, and send the commands to the one or more specific sensors of the plurality of sensors.
Another example is where the indication is a received indication from an external service or source such as a weather service. The indication is an input to the computer and/or the sensor-based state prediction system 50. The computer and/or the sensor-based state prediction system collects sensor information from the plurality of sensors deployed at the premises and analyzes the indication received from the external service by applying one or more unsupervised learning models to predict one or more drift states that result from collected sensor information, normal sets of states for the premises, and the received indication. The sensor-based state prediction system 50 correlates the prediction of the occurrence of a drift state to determine which of the sensors at the premises to modify the operation of. The sensor-based state prediction system 50 produces the commands to modify the operation of specific sensors according to the indication, the drift state and the representation of the premises.
The computer system can generate an insurance claim form subsequent to occurrence of the event by automatically populating a template insurance claim form with information required of the insurance claim form according to the template. Upon detect from the analysis by the drift states of an actual occurrence of one or more of the predicted drift states, actual sensor data received from the plurality of sensors, and/or an external notification any of these can trigger generation of an insurance claim.
The computer system retrieves from a database, operational data for specified equipment that are insured by the insurance carrier, the operational data comprising service records, raw sensor data, and alerts generated for the specified equipment and augments the insurance claim form with a report that includes the operational data for the specified equipment at a time period prior to the event.
For example, the computer executes an algorithm that processes a weather-related event, when the indication is a received indication from an external service that indicates a weather-related event. Being a weather related event can be determined either by the computer receiving the indication from particular sources and/or by parse the received indication to produce a representation of the indication that identifies the type of weather-related event. The computer analyzes the parsed indication according to the location of the physical premises to produce a likely prediction of damage to the physical premises and produces the commands to modify the operation of specific sensors according to the likely prediction of damage to the physical premises. As an example, if the prediction predicts damage to a specific portion of the premises the sensors in that portion can have their operation modified.
With any of the indications, but especially those that involve potential for catastrophic damage, the computer produces a request to upload service and usage data from specific monitored units within the premises to an external cloud based database, by sending to the system at the physical premises the request to upload to the external database, the service and usage data.
When modifying operation of sensors, the computer can for video cameras, execute an algorithm that receives (or accesses from a database) current positioning information for each of the one or more video cameras and calculates based at least in part on the received indication repositioning information for the one or more video cameras. The computer sends the repositioning information to at least some of the one or more video cameras to modify operation of the one or more video cameras by repositioning the at least some of the one or more video cameras.
Similarly, the computer can modify frame (or resolution or other parameters) by receiving (or accessing) current frame rate information for the video cameras. The frame rate information the frequency at which images are taken and sent by the video cameras. The computer calculates based at least in part on the received indication, modified frame rate information for the video cameras, and sends the modified frame rate information to the corresponding video cameras to modify the frame rate operation of such video cameras.
The above techniques allow the intrusion detection system, or the like, for example, to capture video data prior to and after a potential identified incident, and combine the captured video with additional data feeds such that an insurance claim can be generated, validated and paid. The techniques involve increasing the “density” of sensor data surrounding the occurrence of an event such that the analysis of that data can be used by an insurance company to automate the evaluation of an insurance claim. This approach provides a minimal increase in overall processing and network traffic because the increase in sensor data is controlled by the system to occur over a time period prior to and after a potential identified incident.
Often, insurance companies makes decision about certain types of claims without a significant amount of actual evidence of what happened leading up to the insurable incident. Using the proposed system, an insurance company can use an automated process that combines video data (such as automatically generated video primitives) with other available data streams such as weather data, seismic data, crime statistics, maintenance records, sensor data (intrusion, fire, vibration, temperature, humidity etc.), and other data that is specifically stored for a pre-set time before the incident and for a pre-set time after the incident. All of the data that is recorded is reduced to quantifiable values that can easily be evaluated with respect to ranges and/or coverages set out in an insurance policy. When the policy is set up, criteria for acceptable ranges of sensor data are also set up such that upon a system validating that the sensor data exceeded the established ranges, an immediate payment can be made.
Referring now to FIG. 11, the augmented claim filing module 220 (FIG. 8) executes processing 220′ as shown. The sensor-based state prediction system 50 can be used in conjunction with an insurance claim module to populate and submit an insurance claim or at least supporting documentation upon an occurrence of an insured event. The insurance claim module in the sensor-based state prediction system 50 receives sensor data 222 for each physical object or physical quantity being monitored based on one or more sets of data from sensors (FIG. 2) or sensor packs (FIG. 16). Upon the occurrence 224 of an event that results in an insurance claim, the insurance claim module 226 prepares an electronic report that can be used to supplement or provide the insurance claim.
The insurance claim module receives 228 a triggering message that triggers the insurance claim module to prepare an insurance claim(s) on for a business that suffered an insured loss. The insurance module is triggered by the sensor-based state prediction system 50 detecting a state indicative of a loss or by an owner or owner system starting an insurance claim process. Upon receipt of the triggering message, the insurance claim module parses 230 the triggering message to extract information regarding the insured loss to extract identifying information regarding the premises that were insured, the nature of the loss, the insurance carrier, etc., as well as other generally conventional information.
From this extracted generally conventional information the insurance claim module constructs 232 a request to the sensor-based state prediction system 50 to extract 236 service and usage data for one or more monitored units within the premises, and sends 234 the request to the sensor-based state prediction system 50. In particular, the sensor-based state prediction system 50 extracts service record data for each system within the premises, as well as states of the system/premises prior to the incident and/or actual sensor data of sensors monitoring the system/premises prior to the incident.
The insurance claim module generates 238 an insurance claim form from a template form used by the insurance carrier for the particular premises. The insurance claim module 50 fills in the template with the conventional information such as the policy number, address, policyholder, etc. The insurance claim module however also provides 240 either in the template form or as a supplemental form, the extracted operational data for each specific piece of equipment based upon service and usage records retrieved from the database 51 and sensor states prior to and subsequent to the insured event. The format of this supplemental form can take various configurations. One such configuration is shown in FIG. 11A.
Referring now to FIG. 11A, the populated claim form (or the populated supplemental form, i.e., supporting documentation for the insurance claim form) is populated with the premises and system/equipment ID's and the extracted operational data that shows operational performance of the system/equipment ID before the event and after the event. The populated claim form or the populated supplemental form, also will show whether damaged, monitored systems were running properly, properly serviced etc., based on actual sensor data and historical service record data, as information provided are the actual conditions of the premises as measured by the sensor data and the calculated states as determined by the sensor based prediction system 50 showing the events before the insured event happened and possibly during the insured event. This could benefit customer by yielding more accurate reimbursement of insurance funds depending on the type of insurance coverage. Thus in FIG. 11A a set of records are provided for historical state transitions (several before and during and after event, if any), sensor semantic records, and service records all pertaining to the specific ID equipment/system.
Various combinations of the above described processes are used to implement the features described.
Servers interface to the sensor based state prediction system 50 via a cloud computing configuration and parts of some networks can be run as sub-nets. In some embodiments, the sensors provide in addition to sensor data, detailed additional information that can be used in processing of sensor data evaluate. For example, a motion detector could be configured to analyze the heat signature of a warm body moving in a room to determine if the body is that of a human or a pet. Results of that analysis would be a message or data that conveys information about the body detected. Various sensors thus are used to sense sound, motion, vibration, pressure, heat, images, and so forth, in an appropriate combination to detect a true or verified alarm condition at the intrusion detection panel.
Recognition software can be used to discriminate between objects that area human and objects that are an animal; further facial recognition software can be built into video cameras and used to verify that the perimeter intrusion was the result of a recognized, authorized individual. Such video cameras would comprise a processor and memory and the recognition software to process inputs (captured images) by the camera and produce the metadata to convey information regarding recognition or lack of recognition of an individual captured by the video camera. The processing could also alternatively or in addition include information regarding characteristic of the individual in the area captured/monitored by the video camera. Thus, depending on the circumstances, the information would be either metadata received from enhanced motion detectors and video cameras that performed enhanced analysis on inputs to the sensor that gives characteristics of the perimeter intrusion or a metadata resulting from very complex processing that seeks to establish recognition of the object.
Sensor devices can integrate multiple sensors to generate more complex outputs so that the intrusion detection panel can utilize its processing capabilities to execute algorithms that analyze the environment by building virtual images or signatures of the environment to make an intelligent decision about the validity of a breach.
Memory stores program instructions and data used by the processor of the intrusion detection panel. The memory may be a suitable combination of random access memory and read-only memory, and may host suitable program instructions (e.g. firmware or operating software), and configuration and operating data and may be organized as a file system or otherwise. The stored program instruction may include one or more authentication processes for authenticating one or more users. The program instructions stored in the memory of the panel may further store software components allowing network communications and establishment of connections to the data network. The software components may, for example, include an internet protocol (IP) stack, as well as driver components for the various interfaces. Other software components suitable for establishing a connection and communicating across network will be apparent to those of ordinary skill.
Program instructions stored in the memory, along with configuration data may control overall operation of the system. Servers include one or more processing devices (e.g., microprocessors), a network interface and a memory (all not illustrated). Servers may physically take the form of a rack mounted card and may be in communication with one or more operator terminals (not shown). An example monitoring server is a SURGARD™ SG-System III Virtual, or similar system.
The processor of each monitoring server acts as a controller for each monitoring server, and is in communication with, and controls overall operation, of each server. The processor may include, or be in communication with, the memory that stores processor executable instructions controlling the overall operation of the monitoring server. Suitable software enable each monitoring server to receive alarms and cause appropriate actions to occur. Software may include a suitable Internet protocol (IP) stack and applications/clients.
Each monitoring server of the central monitoring station may be associated with an IP address and port(s) by which it communicates with the control panels and/or the user devices to handle alarm events, etc. The monitoring server address may be static, and thus always identify a particular one of monitoring server to the intrusion detection panels. Alternatively, dynamic addresses could be used, and associated with static domain names, resolved through a domain name service.
The network interface card interfaces with the network to receive incoming signals, and may for example take the form of an Ethernet network interface card (NIC). The servers may be computers, thin-clients, or the like, to which received data representative of an alarm event is passed for handling by human operators. The monitoring station may further include, or have access to, a subscriber database that includes a database under control of a database engine. The database may contain entries corresponding to the various subscriber devices/processes to panels like the panel that are serviced by the monitoring station.
All or part of the processes described herein and their various modifications (hereinafter referred to as “the processes”) can be implemented, at least in part, via a computer program product, i.e., a computer program tangibly embodied in one or more tangible, physical hardware storage devices that are computer and/or machine-readable storage devices for execution by, or to control the operation of, data processing apparatus, e.g., a programmable processor, a computer, or multiple computers. A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a network.
Actions associated with implementing the processes can be performed by one or more programmable processors executing one or more computer programs to perform the functions of the calibration process. All or part of the processes can be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) and/or an ASIC (application-specific integrated circuit).
Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only storage area or a random access storage area or both. Elements of a computer (including a server) include one or more processors for executing instructions and one or more storage area devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from, or transfer data to, or both, one or more machine-readable storage media, such as mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks.
Tangible, physical hardware storage devices that are suitable for embodying computer program instructions and data include all forms of non-volatile storage, including by way of example, semiconductor storage area devices, e.g., EPROM, EEPROM, and flash storage area devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks and volatile computer memory, e.g., RAM such as static and dynamic RAM, as well as erasable memory, e.g., flash memory.
In addition, the logic flows depicted in the figures do not require the particular order shown, or sequential order, to achieve desirable results. In addition, other actions may be provided, or actions may be eliminated, from the described flows, and other components may be added to, or removed from, the described systems. Likewise, actions depicted in the figures may be performed by different entities or consolidated.
Elements of different embodiments described herein may be combined to form other embodiments not specifically set forth above. Elements may be left out of the processes, computer programs, Web pages, etc. described herein without adversely affecting their operation. Furthermore, various separate elements may be combined into one or more individual elements to perform the functions described herein.
Other implementations not specifically described herein are also within the scope of the following claims.

Claims

1-26. (canceled)

27. One or more non-transitory computer-readable storage media for monitoring a building having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to:

receive an indication corresponding to an insurable event associated with a space, the indication including a period of time the insurable event is likely to occur;

retrieve, from devices monitoring the space, operational data associated with the insurable event based on characteristics associated with the insurable event included in the indication;

determine, based on the operational data, using an unsupervised learning algorithm, a status associated with an attribute of the space; and

generate a claim corresponding to the insurable event including the status associated with the attribute of the space.

28. The one or more non-transitory computer-readable storage media of claim 27, wherein the insurable event is a predicted insurable event, and wherein the indication is received from an unsupervised learning model monitoring the space.

29. The one or more non-transitory computer-readable storage media of claim 27, wherein the attribute includes equipment, and wherein the operational data includes state transitions associated with the equipment.

30. The one or more non-transitory computer-readable storage media of claim 29, wherein the operational data further includes service records associated with the equipment, and wherein the status indicates a level of functionality associated with the equipment prior to the insurable event.

31. The one or more non-transitory computer-readable storage media of claim 27, wherein the characteristics associated with the insurable event include at least one of an event type, a location, or an insurance carrier.

32. The one or more non-transitory computer-readable storage media of claim 27, wherein the attribute includes an individual, wherein the devices include cameras, and wherein the instructions further cause the one or more processors to control a position of the cameras based on the status of the individual.

33. The one or more non-transitory computer-readable storage media of claim 27, wherein determining the status using the unsupervised learning algorithm includes:

generating a semantic representation of the attribute based on the operational data; and

comparing the semantic representation to a model associated with the attribute to determine a state associated with the attribute, wherein the state is one of a safe state or a drift state.

34. A method, comprising:

receiving an indication corresponding to an insurable event associated with a space, the indication including a period of time the insurable event is likely to occur;

retrieving, from devices monitoring the space, operational data associated with the insurable event based on characteristics associated with the insurable event included in the indication;

determining, based on the operational data, using an unsupervised learning algorithm, a status associated with an attribute of the space; and

generating a claim corresponding to the insurable event including the status associated with the attribute of the space.

35. The method of claim 34, wherein the insurable event is a predicted insurable event, and wherein the indication is received from an unsupervised learning model monitoring the space.

36. The method of claim 34, wherein the attribute includes equipment, and wherein the operational data includes state transitions associated with the equipment.

37. The method of claim 36, wherein the operational data further includes service records associated with the equipment, and wherein the status indicates a level of functionality associated with the equipment prior to the insurable event.

38. The method of claim 34, wherein the characteristics associated with the insurable event include at least one of an event type, a location, or an insurance carrier.

39. The method of claim 34, wherein the attribute includes an individual, wherein the devices include cameras, and wherein the instructions further cause the one or more processors to control a position of the cameras based on the status of the individual.

40. The method of claim 34, wherein determining the status using the unsupervised learning algorithm includes:

41. A system, comprising:

a server computer comprising a processor and memory, the server computer coupled to a network and wherein the memory has instructions stored thereon that, when executed by the processor, cause the server computer to:

42. The system of claim 41, wherein the insurable event is a predicted insurable event, and wherein the indication is received from an unsupervised learning model monitoring the space.

43. The system of claim 41, wherein the attribute includes equipment, and wherein the operational data includes state transitions associated with the equipment.

44. The system of claim 43, wherein the operational data further includes service records associated with the equipment, and wherein the status indicates a level of functionality associated with the equipment prior to the insurable event.

45. The system of claim 41, wherein the characteristics associated with the insurable event include at least one of an event type, a location, or an insurance carrier.

46. The system of claim 41, wherein the attribute includes an individual, wherein the devices include cameras, and wherein the instructions further cause the one or more processors to control a position of the cameras based on the status of the individual.