U.S. patent application number 14/288177 was filed with the patent office on 2014-12-04 for sensors for usage-based property insurance.
The applicant listed for this patent is OneEvent Technologies, LLC. Invention is credited to Daniel Ralph PARENT, Kurt Joseph WEDIG.
Application Number | 20140358592 14/288177 |
Document ID | / |
Family ID | 51986138 |
Filed Date | 2014-12-04 |
United States Patent
Application |
20140358592 |
Kind Code |
A1 |
WEDIG; Kurt Joseph ; et
al. |
December 4, 2014 |
SENSORS FOR USAGE-BASED PROPERTY INSURANCE
Abstract
A method includes receiving, at an insurance provider server and
from at least one sensor located at an insured property, sensor
data indicative of an insurance risk associated with the insured
property. The method also includes determining, based at least in
part on the received sensor data, a risk-adjusted insurance premium
for an insurance account associated with the insured property. The
risk-adjusted insurance premium compensates for the insurance risk
associated with the insured property.
Inventors: |
WEDIG; Kurt Joseph; (Mount
Horeb, WI) ; PARENT; Daniel Ralph; (Mount Horeb,
WI) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
OneEvent Technologies, LLC |
Mount Horeb |
WI |
US |
|
|
Family ID: |
51986138 |
Appl. No.: |
14/288177 |
Filed: |
May 27, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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61829399 |
May 31, 2013 |
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Current U.S.
Class: |
705/4 |
Current CPC
Class: |
G06Q 40/08 20130101 |
Class at
Publication: |
705/4 |
International
Class: |
G06Q 40/08 20120101
G06Q040/08 |
Claims
1. A method comprising: receiving, at an insurance provider server
and from at least one sensor located at an insured property, sensor
data indicative of an insurance risk associated with the insured
property; and determining, based at least in part on the received
sensor data, a risk-adjusted insurance premium for an insurance
account associated with the insured property to compensate for the
insurance risk associated with the insured property.
2. The method of claim 1, further comprising determining an amount
of the insurance risk based on the sensor data, wherein the
risk-adjusted insurance premium is determined based at least in
part on the amount of the insurance risk.
3. The method of claim 2, wherein the amount of the insurance risk
is determined locally at the insured property and provided to the
insurance provider server.
4. The method of claim 2, further comprising determining whether
the determined amount of the insurance risk exceeds an insurance
risk threshold.
5. The method of claim 4, wherein determining the risk-adjusted
insurance premium is done only if the determined amount of the
insurance risk exceeds the insurance risk threshold.
6. The method of claim 1, wherein the at least one sensor comprises
a stress sensor positioned on a load bearing structural component
of the insured property, and wherein the insurance risk comprises a
structural integrity risk of the insured property.
7. The method of claim 1, wherein the at least one sensor comprises
a humidity sensor, and wherein the insurance risk comprises a mold
growth risk in the insured property.
8. The method of claim 1, wherein the at least one sensor comprises
an occupancy sensor, and wherein the insurance risk is based on a
lack of occupancy in one or more areas of the insured property.
9. An insurance provider server comprising: a memory configured to
store sensor data received from at least one sensor located at an
insured property, wherein the sensor data is indicative of an
insurance risk associated with the insured property; and a
processor operatively coupled to the memory and configured to
determine, based at least in part on the received sensor data, a
risk-adjusted insurance premium for an insurance account associated
with the insured property, wherein the risk-adjusted insurance
premium compensates for the insurance risk associated with the
insured property.
10. The insurance provider server of claim 9, wherein the processor
is configured to determine an amount of the insurance risk based on
the sensor data, and wherein the risk-adjusted insurance premium is
determined based at least in part on the amount of the insurance
risk.
11. The insurance provider server of claim 10, wherein the
processor is further configured to determine whether the determined
amount of the insurance risk exceeds an insurance risk
threshold.
12. The insurance provider server of claim 11, wherein the
processor determines the risk-adjusted insurance premium only if
the determined amount of the insurance risk exceeds the insurance
risk threshold.
13. The insurance provider server of claim 9, wherein the memory is
further configured to store an amount of the insurance risk based
on the sensor data, and wherein the amount of the insurance risk is
determined locally at the insured property and provided to the
insurance provider server.
14. The insurance provider server of claim 9, wherein the at least
one sensor comprises a stress sensor positioned on a load bearing
structural component of the insured property, and wherein the
insurance risk comprises a structural integrity risk of the insured
property.
15. The insurance provider server of claim 9, wherein the at least
one sensor comprises a humidity sensor, and wherein the insurance
risk comprises a mold growth risk in the insured property.
16. The insurance provider server of claim 9, wherein the at least
one sensor comprises an occupancy sensor, and wherein the insurance
risk is based on a lack of occupancy in one or more areas of the
insured property.
17. The insurance provider server of claim 9, wherein the
risk-adjusted insurance premium comprises a reduction in an
insurance premium associated with the insured property.
18. A non-transitory computer-readable medium having instructions
stored thereon for execution by a computing device, wherein the
instructions comprise: instructions to receive sensor data from at
least one sensor located at an insured property, wherein the sensor
data is indicative of an insurance risk associated with the insured
property; and instructions to determine, based at least in part on
the received sensor data, a risk-adjusted insurance premium for an
insurance account associated with the insured property, wherein the
risk-adjusted insurance premium compensates for the insurance risk
associated with the insured property.
19. The non-transitory computer-readable medium of claim 18,
further comprising instructions to determine an amount of the
insurance risk based on the sensor data, and wherein the
risk-adjusted insurance premium is determined based at least in
part on the amount of the insurance risk.
20. The non-transitory computer-readable medium of claim 19,
further comprising instructions to determine whether the amount of
the insurance risk exceeds an insurance risk threshold, wherein the
risk-adjusted insurance premium is determined only if the
determined amount of the insurance risk exceeds the insurance risk
threshold.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application claims priority to U.S. Provisional
Application No. 61/829,399 filed on May 31, 2013, the entire
disclosure of which is incorporated herein by reference.
BACKGROUND
[0002] When an insurance provider offers insurance for a home or
business, they are taking on the risk that any damage or liability
associated with that property can be offset by premium payments
made by property owners. In order to create a good balance between
offering competitive prices and managing risk, an insurance
provider may wish to assess the relative risk of each potential
insurable property. Then, properties with lower risk may be offered
lower premiums while higher-risk properties are offered higher
premiums to compensate for the higher risk. Unfortunately, the
information that is available to a provider may not be adequate in
assessing the actual risk associated with a particular property.
For example, a provider may assess all young homeowners as higher
risk than middle-aged homeowners because the provider lacks the
information to assess which young homeowners are more diligent in
maintenance and safety behaviors than their older counterparts. As
another example, a provider may assess two house insurance plans as
having equal risk despite the fact that one house is left vacant
for long periods of time, increasing its risk for damage from
burglary, natural disaster, and liability than the continuously
occupied house.
SUMMARY
[0003] An illustrative method involves an insurance provider
receiving information about an insured property from a sensory node
located in an area of the property, where the information is
indicative of risk associated with the property. Based on the
received information, the insurance provider determines a
risk-adjusted insurance premium for the property to adjust for the
indicated risk.
[0004] An illustrative node includes a transceiver and a processor
operatively coupled to the transceiver. The transceiver is
configured to receive readings from one or more sensors around an
area of a structure. The processor is configured to determine risk
information from the received readings and to cause the transceiver
to provide the determined risk information to an insurance
server.
[0005] An illustrative insurance server includes a communication
interface and a processor. The interface is configured to receive
sensor readings from a node at an insured property, where the
sensor readings are indicative of risk information about the
property. The processor is programmed to determine a risk-adjusted
insurance premium amount for an insurance plan associated with the
property based on the indicated risk information.
[0006] Another illustrative method involves receiving, at a node in
an area of a property, a signal that is indicative of conditions at
the property. The signal is processed to determine a risk value
associated with the conditions at the property indicated by the
signal. If the determined risk value has a particular relation to a
predetermined risk value, then the node transmits an indication of
the determined risk value to an insurance provider.
[0007] An illustrative non-transitory computer-readable medium
having computer-readable instructions stored thereon is also
provided. If executed by a processor of a node, the computer
readable instructions cause the node to receive sensor readings
indicative of conditions at a property and to determine a risk
value associated with the conditions of the property based on the
sensor readings. Also, when executed, the computer readable
instructions cause the node to transmit an indication of the
determined risk value to an insurance provider.
[0008] Another illustrative non-transitory computer-readable medium
has computer-readable instructions stored thereon. If executed by
an insurance server, the computer readable instructions cause the
server to receive risk information associated with a property from
a sensory node in the area of the property and to determine a
risk-adjusted insurance premium for an insurance plan associated
with the property.
[0009] Another illustrative method includes receiving, at an
insurance provider server and from at least one sensor located at
an insured property, sensor data indicative of an insurance risk
associated with the insured property. The method also includes
determining, based at least in part on the received sensor data, a
risk-adjusted insurance premium for an insurance account associated
with the insured property. The risk-adjusted insurance premium
compensates for the insurance risk associated with the insured
property.
[0010] An illustrative insurance provider server includes a memory
and a processor operatively coupled to the memory. The memory is
configured to store sensor data received from at least one sensor
located at an insured property. The sensor data is indicative of an
insurance risk associated with the insured property. The processor
is configured to determine, based at least in part on the received
sensor data, a risk-adjusted insurance premium for an insurance
account associated with the insured property. The risk-adjusted
insurance premium compensates for the insurance risk associated
with the insured property.
[0011] An illustrative non-transitory computer-readable medium has
instructions stored thereon for execution by a computing device.
The instructions include instructions to receive sensor data from
at least one sensor located at an insured property. The sensor data
is indicative of an insurance risk associated with the insured
property. The instructions also include instructions to determine,
based at least in part on the received sensor data, a risk-adjusted
insurance premium for an insurance account associated with the
insured property. The risk-adjusted insurance premium compensates
for the insurance risk associated with the insured property.
[0012] Other principal features and advantages will become apparent
to those skilled in the art upon review of the following drawings,
the detailed description, and the appended claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] Illustrative embodiments will hereafter be described with
reference to the accompanying drawings.
[0014] FIG. 1 is a block diagram illustrating a node system in
accordance with an illustrative embodiment.
[0015] FIG. 2 is a block diagram illustrating a sensory node in
accordance with an illustrative embodiment.
[0016] FIG. 3 is a block diagram illustrating an insurance server
in accordance with an illustrative embodiment.
[0017] FIG. 4 is a block diagram illustrating a sensory unit in
accordance with an illustrative embodiment.
[0018] FIG. 5 is a flow diagram illustrating operations performed
by a sensory node in accordance with an illustrative
embodiment.
[0019] FIG. 6 is a flow diagram illustrating operations performed
by an insurance server in accordance with an illustrative
embodiment.
DETAILED DESCRIPTION
[0020] Described herein are illustrative systems and methods for
facilitating usage-based property insurance associated with a
physical structure. An illustrative system can include one or more
sensory nodes configured to detect and/or monitor conditions in and
around the insured physical structure. An illustrative system can
also include communication interfaces for connecting with systems
associated with an insurance provider. As illustrative system can
further include data processing and storage components for
analyzing the detected/monitored condition data and determining
risk information that can be used to adjust insurance premiums and
payouts.
[0021] FIG. 1 is a block diagram of a detection system 100 in
accordance with an illustrative embodiment. In alternative
embodiments, detection system 100 may include additional, fewer,
and/or different components. Detection system 100 includes a
sensory node 104, a sensory node 110, a sensory node 114, and a
sensory node 120. In alternative embodiments, additional or fewer
sensory nodes may be included. Detection system 100 also includes a
decision node 124 and a decision node 130. Alternatively,
additional or fewer decision nodes may be included.
[0022] In an illustrative embodiment, sensory nodes 104, 110, 114,
and 120 can be configured to detect any number of risk conditions.
For example, the risk conditions can be a fire risk, a liability
risk, an unoccupancy risk, an over-occupancy risk, a flood risk, a
wind damage risk, a poison gas can risk, a structural integrity
risk, an intrusion risk, an alarm tampering risk, and/or an
occupancy demographic risk, among other examples. Sensory nodes
104, 110, 114, and 120 can be distributed throughout a structure.
The structure can be a home, an office building, a commercial
space, a store, a factory, or any other building or structure. As
an example, a single story office building can have one or more
sensory nodes in each office, each bathroom, each common area, etc.
An illustrative sensory node is described in more detail with
reference to FIG. 2.
[0023] Sensory nodes 104, 110, 114, and 120 can communicate with
decision nodes 124 and 130 through a network 134. Network 134 can
include a short-range communication network such as a Bluetooth
network, a Zigbee network, etc. Network 134 can also include a
local area network (LAN), a wide area network (WAN), a
telecommunications network, the Internet, a public switched
telephone network (PSTN), and/or any other type of communication
network known to those of skill in the art. Network 134 can be a
distributed intelligent network such that detection system 100 can
make decisions based on sensory input from any nodes in the
population of nodes. In an illustrative embodiment, decision nodes
124 and 130 can communicate with sensory nodes 104, 110, 114, and
120 through a short-range communication network. Decision nodes 124
and 130 can also communicate with an insurance server 140 through a
telecommunications network, the Internet, a PSTN, etc. As such,
when they risk condition is detected, insurance server 140 can be
automatically notified. Insurance server 140 can be any type of
computing, processing, or storage device, such as a cloud server,
server system, computer, etc.
[0024] To communicate information associated with any risk
conditions, a sensory node can provide an indication of the
detection condition to decision node 124 and/or decision node 130.
The indication can include an identification and/or location of the
sensory node, a type of the detection condition, and/or a magnitude
of the detection condition. The magnitude of the detection
condition can include an estimated damage from the risk, a
probability of the risk occurring, and a timing in which the risk
is likely to occur. The indication of the detection condition can
be used by decision node 124 and/or decision node 130 to determine
insurance-premium adjustments described in more detail with
reference to FIG. 5.
[0025] In an illustrative embodiment, sensory nodes 104, 110, 114,
and 120 can also periodically provide status information to
decision node 124 and/or decision node 130. The status information
can include an identification of the sensory node, location
information corresponding to the sensory node, information
regarding battery life, and/or information regarding whether the
sensory node is functioning properly. As such, decision nodes 124
and 130 can be used as a diagnostic tool to alert a system
administrator or other user of any problems with sensory nodes 104,
110, 114, and 120. Decision nodes 124 and 130 can also communicate
status information to one another for diagnostic purposes. The
system administrator, the insurer, or other computing devices can
also be alerted if any of the nodes of detection system 100 fail to
timely provide status information according to a periodic schedule.
In one embodiment, a detected failure or problem within detection
system 100 can be communicated to the system administrator or other
user via a text message or an e-mail.
[0026] In one embodiment, network 134 can include a redundant (or
self-healing) mesh network centered around sensory nodes 104, 110,
114, and 120 and decision nodes 124 and 130. As such, sensory nodes
104, 110, 114, and 120 can communicate directly with decision nodes
124 and 130, or indirectly through other sensory nodes. As an
example, sensory node 104 can provide status information directly
to decision node 124. Alternatively, sensory node 104 can provide
the status information to sensory node 114, sensory node 114 can
provide the status information (relative to sensory node 104) to
sensory node 120, and sensory node 120 can provide the status
information (relative to sensory node 104) to decision node 124.
The redundant mesh network can be dynamic such that communication
routes can be determined on the fly in the event of a
malfunctioning node. As such, in the example above, if sensory node
120 is down, sensory node 114 can automatically provide the status
information (relative to sensory node 104) directly to decision
node 124 or to sensory node 110 for provision to decision node 124.
Similarly, if decision node 124 is down, sensory nodes 104, 110,
114, and 120 can be configured to convey status information
directly or indirectly to decision node 130. The redundant mesh
network can also be static such that communication routes are
predetermined in the event of one or more malfunctioning nodes.
Network 134 can receive/transmit messages over a large range as
compared to the actual wireless range of individual nodes. Network
134 can also receive/transmit messages through various wireless
obstacles by utilizing the mesh network capability of detection
system 100. As an example, a message destined from an origin of
node A to a distant destination of node Z (i.e., where node A and
node Z are not in direct range of one another) may use any of the
nodes between node A and node Z to convey the information. In one
embodiment, the mesh network can operate within the 2.4 GHz range.
Alternatively, any other range(s) may be used.
[0027] In an illustrative embodiment, each of sensory nodes 104,
110, 114, and 120 and/or each of decision nodes 124 and 130 can
know its location. The location can be global positioning system
(GPS) coordinates. In one embodiment, a computing device 144 can be
used to upload the location to sensory nodes 104, 110, 114, and 120
and/or decision nodes 124 and 130. Computing device 144 can be a
portable GPS system, a cellular device, a laptop computer, or any
other type of communication device configured to convey the
location. As an example, computing device 144 can be a GPS-enabled
laptop computer. During setup and installation of detection system
100, a technician can place the GPS-enabled laptop computer
proximate to sensory node 104. The GPS-enabled laptop computer can
determine its current GPS coordinates, and the GPS coordinates can
be uploaded to sensory node 104. The GPS coordinates can be
uploaded to sensory node 104 wirelessly through network 134 or
through a wired connection. Alternatively, the GPS coordinates can
be manually entered through a user interface of sensory node 104.
The GPS coordinates can similarly be uploaded to sensory nodes 110,
114, and 120 and decision nodes 124 and 130. In one embodiment,
sensory nodes 104, 110, 114, and 120 and/or decision nodes 124 and
130 may be GPS-enabled for determining their respective locations.
In one embodiment, each node can have a unique identification
number or tag, which may be programmed during the manufacturing of
the node. The identification can be used to match the GPS
coordinates to the node during installation. Computing device 144
can use the identification information to obtain a one-to-one
connection with the node to correctly program the GPS coordinates
over network 134. In an alternative embodiment, GPS coordinates may
not be used, and the location can be in terms of position with
respect to a particular structure. For example, sensory node 104
may be located in room five on the third floor of a hotel, and this
information can be the location information for sensory node 104.
Regardless of how the locations are represented, detection system
100 can determine the detection route(s) based at least in part on
the locations and a known layout of the structure.
[0028] In one embodiment, a zeroing and calibration method may be
employed to improve the accuracy of the indoor GPS positioning
information programmed into the nodes during installation.
Inaccuracies in GPS coordinates can occur due to changes in the
atmosphere, signal delay, the number of viewable satellites, etc.,
and the expected accuracy of GPS is usually about 6 meters. To
calibrate the nodes and improve location accuracy, a relative
coordinated distance between nodes can be recorded as opposed to a
direct GPS coordinate. Further improvements can be made by
averaging multiple GPS location coordinates at each perspective
node over a given period (i.e., 4 minutes, etc.) during detection
system 100 configuration. At least one node can be designated as a
zeroing coordinate location. All other measurements can be made
with respect to the zeroing coordinate location. In one embodiment,
the accuracy of GPS coordinates can further be improved by using an
enhanced GPS location band such as the military P(Y) GPS location
band. Alternatively, any other GPS location band may be used.
[0029] FIG. 2 is a block diagram illustrating a sensory node 200 in
accordance with an illustrative embodiment. In alternative
embodiments, sensory node 200 may include additional, fewer, and/or
different components. Sensory node 200 includes sensor(s) 204, a
power source 210, a memory 214, a user interface 220, an occupancy
unit 224, a transceiver 230, a warning unit 234, and a processor
240. Sensor(s) 205 can include a smoke detector, a heat sensor, a
chemical sensor, humidity sensor, wind sensor, vibration sensor,
load sensor, and/or any other type of hazardous condition sensor
known to those of skill in the art. In an illustrative embodiment,
power source 210 can be a battery. Sensory node 200 can also be
hard-wired to the structure such that power is received from the
power supply of the structure (i.e., utility grid, generator, solar
cell, fuel cell, etc.). In such an embodiment, power source 210 can
also include a battery for backup during power outages.
[0030] Memory 215 can be configured to store identification
information corresponding to sensory node 200. The identification
information can be any indication through which other sensory nodes
and decision nodes are able to identify sensory node 200. Memory
215 can also be used to store location information corresponding to
sensory node 200. The location information can include global
positioning system (GPS) coordinates, position within a structure,
or any other information which can be used by other sensory nodes
and/or decision nodes to determine the location of sensory node
200. In one embodiment, the location information may be used as the
identification information. The location information can be
received from computing device 145 described with reference to FIG.
1, or from any other source. Memory 215 can further be used to
store communication information, such as routing information for a
mesh network in which sensory node 200 is located such that sensory
node 200 is able to forward information to appropriate nodes during
normal operation and in the event of one or more malfunctioning
nodes. Memory 215 can also be used to store historic risk
assessment information and/or one or more detection messages
generated by the detection of risk conditions. Memory 215 can
further be used for storing adaptive pattern recognition algorithms
and for storing compiled patterns.
[0031] User interface 220 can be used by a system administrator or
other user to program and/or test sensory node 200. User interface
220 can include one or more controls, a liquid crystal display
(LCD) or other display for conveying information, one or more
speakers for conveying information, etc. User interface 220 can be
used to upload location information to sensory node 200, to test
sensory node 200 to ensure that sensory node 200 is functional, to
adjust a volume level of sensory node 200, to silence sensory node
200, etc. User interface 220 can also be used to alert a user of a
problem with sensory node 200 such as low battery power or a
malfunction. User interface 220 can further include a button such
that a user can report a risk condition and activate the detection
system.
[0032] Occupancy unit 225 can be used to detect and/or monitor
occupancy of a structure. As an example, occupancy unit 225 can
detect whether one or more individuals are in a given room or area
of a structure. A decision node can monitor this occupancy
information and analyze occupancy patterns to determine potential
risk conditions for the property. For example, if the system
detects that one room in a house has not been occupied for a given
period of time (e.g., 8 months), decision node may determine that
this unoccupied room is at greater risk damage from slow-acting
causes (e.g., mold, infestation, leaks, etc.) since the residents
might not notice and resolve problems in that room promptly. As
another example, occupancy unit 225 can determine that there are
generally no individuals in a property between the hours of 8:00 am
and 6:00 pm on Mondays through Fridays, and, based on that
occupancy pattern, determine that the property has a decreased
liability because the reduced occupancy leaves less time for
occupants to damage the property.
[0033] Occupancy unit 225 can detect/monitor the occupancy using
one or more motion detectors to detect movement. Occupancy unit 224
can also use a video or still camera and video/image analysis to
determine the occupancy. Occupancy unit 225 can also use
respiration detection by detecting carbon dioxide gas emitted as a
result of breathing. An example high sensitivity carbon dioxide
detector for use in respiration detection can be the MG-811 CO2
sensor manufactured by Henan Hanwei Electronics Co., Ltd. based in
Zhengzhou, China. Alternatively, any other high sensitivity carbon
dioxide sensor may be used. Occupancy unit 225 can also be
configured to detect methane, or any other gas which may be
associated with human presence.
[0034] Occupancy unit 225 can also use infrared sensors to detect
heat emitted by individuals. In one embodiment, a plurality of
infrared sensors can be used to provide multidirectional
monitoring. Alternatively, a single infrared sensor can be used to
scan an entire area. The infrared sensor(s) can be combined with a
thermal imaging unit to identify thermal patterns and to determine
whether detected occupants are human, feline, canine, rodent, etc.
The infrared sensors can also be used to determine if occupants are
moving or still, to track the direction of occupant traffic, to
track the speed of occupant traffic, to track the volume of
occupant traffic, etc. This information can be used to alert
emergency responders to a panic situation, or to a large captive
body of individuals. Activities occurring prior to a risk condition
can be sensed by the infrared sensors and recorded by the detection
system. As such, suspicious behavioral movements occurring prior to
a risk condition can be sensed and recorded. For example, if the
detection condition was maliciously caused, the recorded
information from the infrared sensors can be used to determine how
quickly the area was vacated immediately prior to the detection
condition. Infrared sensor based occupancy detection is described
in more detail in an article titled "Development of Infrared Human
Sensor" in the Matsushita Electric Works (MEW) Sustainability
Report 2004, the entire disclosure of which is incorporated herein
by reference.
[0035] Occupancy unit 225 can also use audio detection to identify
noises associated with occupants such as snoring, respiration,
heartbeat, voices, etc. The audio detection can be implemented
using a high sensitivity microphone which is capable of detecting a
heartbeat, respiration, etc. from across a room. Any high
sensitivity microphone known to those of skill in the art may be
used. Upon detection of a sound, occupancy unit 225 can utilize
pattern recognition to identify the sound as speech, a heartbeat,
respiration, snoring, etc. Occupancy unit 225 can similarly utilize
voice recognition and/or pitch tone recognition to distinguish
human and non-human occupants and/or to distinguish between
different human occupants. As such, occupancy unit 225 can
determine whether an occupant is a baby, a small child, an adult, a
dog, etc. Occupancy unit 225 can also detect occupants using scent
detection. An example sensor for detecting scent is described in an
article by Jacqueline Mitchell titled "Picking Up the Scent" and
appearing in the August 2008 Tufts Journal, the entire disclosure
of which is incorporated herein by reference.
[0036] Transceiver 230 can include a transmitter for transmitting
information and/or a receiver for receiving information. As an
example, transceiver 230 of sensory node 200 can receive status
information, occupancy information, detection condition
information, historical detection data, etc. from a first sensory
node and forward the information to a second sensory node, to a
decision node, or to an external server. Transceiver 230 can also
be used to transmit information corresponding to sensory node 200
to another sensory node or a decision node. For example,
transceiver 230 can periodically transmit occupancy information to
a decision node such that the decision node has the occupancy
information at all times. Alternatively, transceiver 230 can be
used to transmit the occupancy information to the decision node
along with an indication of the detection condition. Transceiver
230 can also be used to receive instructions regarding appropriate
detection routes and/or the detection routes from a decision node.
Alternatively, the detection routes can be stored in memory 215 and
transceiver 230 may only receive an indication of which detection
route to convey.
[0037] Processor 240 can be operatively coupled to each of the
components of sensory node 200, and can be configured to control
interaction between the components. For example, if a risk
condition is detected by sensor(s) 205, processor 240 can cause
transceiver 230 to transmit an indication of the risk condition to
a decision node. In response, transceiver 230 can receive other
risk information from the decision node regarding conditions at
other sensors or nodes. Processor 240 can interpret the received
information, determine the full risk conditions at the property,
and cause transceiver 230 to convey the detected risk conditions to
insurance server. Processor 240 can also receive inputs from user
interface 220 and take appropriate action. Processor 240 can
further be coupled to power source 210 and used to detect and
indicate a power failure or low battery condition. In one
embodiment, processor 240 can also receive manually generated alarm
inputs from a user through user interface 220. As an example, if a
fire is accidently started in a room of a structure, a user may
press an alarm activation button on user interface 220, thereby
signaling a risk condition and activating warning systems. In such
an embodiment, in the case of accidental alarm activation, sensory
node 200 may inform the user that he/she can press the alarm
activation button a second time to disable the alarm. After a
predetermined period of time (i.e., 5 seconds, 10 seconds, 30
seconds, etc.), the detection condition may be conveyed to other
nodes and/or an emergency response center through the network.
[0038] FIG. 3 is a block diagram illustrating an insurance server
300 in accordance with an illustrative embodiment. In alternative
embodiments, insurance server 300 may include additional, fewer,
and/or different components. Insurance server 300 includes a
processor 305, a memory 310, communication interfaces 320, and a
user-interface 330.
[0039] Memory 310 can be configured to store historical sensor
data, risk data, detection and monitoring algorithms, insurance
account information, and/or program instructions for execution by
processor 305. Communication interfaces 320, may include, for
example, wireless chipsets, antennas, wired ports, signal
converters, communication protocols, and other hardware and
software for interfacing with external systems via wired or
wireless networks over public or private communication links.
Devices in the example system may receive user-input and
user-commands via user-interface 330, which may include, for
instance, remote controllers, touch-screen input, actuation of
buttons/switches, voice input, and other user-interface elements.
Processor 305 can be operatively coupled to each of the components
of insurance server 300, and can be configured to control
interaction between the components.
[0040] FIG. 4 is a block diagram illustrating a sensor unit 400 in
accordance with an illustrative embodiment. In one embodiment, the
system herein can be implemented using a remote server that is in
communication with a plurality of sensory nodes that are located in
a dwelling. The remote server can be used to process information
reported by the sensory nodes and to control the sensory nodes. In
one embodiment, the remote server can replace the decision node(s)
such that a given dwelling is only equipped with the sensory nodes.
In such an embodiment, the system can be implemented using cloud
computing techniques as known to those of skill in the art.
[0041] Sensor unit 400 includes a gas detector 402, a microphone
detector 404, an infrared detector 406, a scent detector 408, an
ultrasonic detection system 410, a processor 412, a memory 414, a
user interface 416, an output interface 418, a power source 420, a
transceiver 422, and a global positioning system (GPS) unit 424. In
alternative embodiments, sensor unit 400 may include fewer,
additional, and/or different components. In one embodiment, sensor
unit 400 can be made from fire retardant materials and/or other
materials with a high melting point or heat tolerance in the event
that sensor unit 400 is used at the site of a fire. Alternatively,
any other materials may be used to construct sensor unit 400. Gas
detector 402, microphone detector 404, infrared detector 406, and
scent detector 408 can be used to detect occupancy as described
above with reference to occupancy unit 224 of FIG. 2.
[0042] Ultrasonic detection system 410 can be configured to detect
human, animal, or object presence using ultrasonic wave detection.
In one embodiment, ultrasonic detection system 410 can include a
wave generator and a wave detector. The wave generator can emit
ultrasonic waves into a room or other structure. The ultrasonic
waves can reflect off of the walls of the room or other structure.
The wave detector can receive and examine the reflected ultrasonic
waves to determine whether there is a frequency shift in the
reflected ultrasonic waves with respect to the originally generated
ultrasonic waves. Any frequency shift in the reflected ultrasonic
waves can be caused by movement of a person or object within the
structure. As such, an identified frequency shift can be used to
determine whether the structure is occupied. Alternatively,
processor 412 may be used to identify frequency shifts in the
reflected ultrasonic waves. In one embodiment, occupancy unit 224
described with reference to FIG. 2 can also include an ultrasonic
detection system.
[0043] Processor 412 can be used to process detected signals
received from gas detector 402, microphone detector 404, infrared
detector 406, scent detector 408, and/or ultrasonic detection
system 410. In an illustrative embodiment, processor 412 can
utilize one or more signal acquisition circuits (not shown) and/or
one or more algorithms to process the detected signals and
determine occupancy data. In one embodiment, processor 412 can
utilize the one or more algorithms to determine a likelihood that
an occupant is present in a structure. For example, if the detected
signals are low, weak, or contain noise, processor 412 may
determine that there is a low likelihood that an occupant is
present. The likelihood can be conveyed to a user of sensor unit
400 as a percentage, a description (i.e., low, medium, high), etc.
Alternatively, processor 412 can determine the likelihood that an
occupant is present and compare the likelihood to a predetermined
threshold. If the likelihood exceeds the threshold, sensor unit 400
can alert the user to the potential presence of an occupant. If the
determined likelihood does not exceed the threshold, sensor unit
400 may not alert the user.
[0044] In an illustrative embodiment, processor 412 can determine
room conditions based on the combined input from each of gas
detector 402, microphone detector 404, infrared detector 406, scent
detector 408, and/or ultrasonic detection system 410. In an
illustrative embodiment, the one or more algorithms used by
processor 412 to determine conditions at the property can be
weighted based on the type of sensor(s) that identify a condition
at the property, the number of sensors that identify the condition,
and/or the likelihood of the condition corresponding to each of the
sensor(s) that identified the condition. As an example, detection
by ultrasonic detection system 410 (or any of the other detectors)
may be given more weight than detection by scent detector 408 (or
any of the other detectors). As another example, processor 412 may
increase the likelihood of a condition as the number of detectors
that detected any sign of the condition increases. Processor 412
can also determine the likelihood of a particular condition based
on the likelihood corresponding to each individual sensor. For
example, if all of the detectors detect conditions with a low
likelihood of accuracy, the overall likelihood of a present
occupant may be low. In one embodiment, any sign of a particular
condition by any of the sensors can cause processor 412 to alert
the insurance provider. Similarly, processor 412 can provide the
user with information such as the overall likelihood that a
particular risk factor exists, the likelihood of the risk factor
associated with each sensor, the number of sensors that detected
the factor, the type of sensors that detected the risk factor, etc.
such that the user can make an informed decision.
[0045] In addition to the sensors shown in FIG. 4, a sensory node
according to an illustrative embodiment may include or be capable
of connecting to various other sensors, such as a climate control
unit, and includes a water flow sensor, flood sensor, a wind
sensor, and a hail/rain sensor. Instructions and/or data can also
be provided to climate control unit, water flow sensor, flood
sensor, wind sensor, and hail/rain sensor from decision node 124,
sensory node 104, and/or computing device 144 via network 134. In
an alternative embodiment, climate control unit, water flow sensor,
flood sensor, wind sensor, and hail/rain sensor may communicate
directly with decision node 124, sensory node 104, and computing
device 144 through a wired or wireless connection outside of
network 134.
[0046] A climate control unit can be a thermostat or other unit
that is used to control the temperature within a building by
controlling heating units and air conditioning units for the
building. In one embodiment, decision node 124 and/or sensory node
104 of a sensor system can include a thermometer or other known
apparatus for determining temperature. The decision node 124 and/or
sensory node 104 can also include data regarding the usual or
normal temperature for one or more different rooms of the building
in which the system is installed. The data can be based on sensed
temperature data that is accumulated over time. The data can also
be received from a user through the user interface of the system as
threshold temperatures for various rooms of the building. For
example, the user may indicate that the minimum temperature for a
bedroom of the building is 68 degrees Fahrenheit (F) and that the
minimum temperature for the basement of the building is 60 degrees
F. As another example, the user may indicate that the maximum
temperature for the bedroom of the building is 72 degrees F., the
maximum temperature for a kitchen of the building is 76 degrees F.,
and the maximum temperature for a bathroom of the building is 74
degrees F.
[0047] In an illustrative embodiment, the temperature data is used
by decision node 124 and/or sensory node 104 to control climate
control unit such that the desired temperature or normal
temperature is maintained throughout the various rooms of the
building. As a result, there can be numerous locations throughout
the building at which decision/sensory nodes are installed, and the
temperature can be controlled through each of these locations. This
is in contrast to many traditional systems in which a single, a
centrally located thermostat is used to control the temperature for
an entire building. In one embodiment, the user can also manually
control a climate control unit by sending instructions via the user
interface of a sensor system. For example, the user may leave on
vacation during the winter and forget to turn the heat down prior
to departure. With the present system, the user can log in to the
user interface and provide an instruction to lower the heat from 72
degrees F. to 60 degrees F. for the entire building. The
instruction can be received by decision node 124 and/or sensory
node 104 via network 134. Responsive to receiving the instruction,
decision node 124 and/or sensory node 104 can control the climate
control unit to implement the temperature change in the
building.
[0048] In one embodiment, the user can be provided a notification
if the temperature in a given room of the building exceeds a set
temperature or an expected temperature by a threshold amount. For
example, if the temperature in a bedroom exceeds the expected
temperature by 10 degrees, the user may be provided a notification.
The notification can be a visual and/or audio notification from the
decision/sensory node, or the notification may be in the form of an
e-mail, text message, telephone call, etc. to a computing device of
the user. In one embodiment, one or more neighbors of the user may
also be provided with such a notification. The threshold amount and
form of notification can be specified by the user during setup of
the detection system. In an alternative embodiment, decision node
124 and/or sensory node 104 may include the functionality of a
thermostat such that decision node 124 and/or sensory node 104
controls the heating and air conditioning units directly. In such
an embodiment, the building may not include a centrally located
climate control unit.
[0049] A water flow sensor can be used to determine if continuous
water flow is occurring within a dwelling. Such detection is
beneficial in both an environmental sense and also as a method of
predicting a home flooding catastrophe. In an illustrative
embodiment, a sensor system can learn normal water flow patterns of
the building based on sensor data received from the water flow
sensor and/or based on data received from the user. The
learned/received data can include an identification of times of day
when it is generally expected that there will be little or no water
flow, times of day when it is generally expected that there will be
heavy water flow, an identification of days of the week on which
water flow is expected to light or heavy, areas of the house where
it is generally expected that there will be light or heavy water
flow, etc. Abnormal water flow or excessive water flow can occur if
a water pipe breaks, a garden hose is left on, a toilet runs
continuously, a water faucet is left on, etc. In one embodiment,
abnormal water flow can be detected if the water runs longer than a
predetermined threshold amount of time such as a number of minutes
or a number of hours. The threshold can be set by the user via the
user interface, or established by the system, depending on the
embodiment. In the event of detection of abnormal water flow, the
user can be provided with a notification. The notification can be a
visual and/or audio notification from the decision/sensory node, or
the notification may be in the form of an e-mail, text message,
telephone call, etc. to a computing device of the user. In one
embodiment, one or more neighbors of the user may also be provided
with such a notification.
[0050] In an illustrative embodiment, the water flow sensor can be
an acoustic sensor mounted on or near a water pipe. In one
embodiment, the water flow sensor can include a microphone, a
processor, a memory, and a transmitter. The microphone can be
mounted on, near, or around a water pipe to detect the sound of
running water within the pipe. In an illustrative embodiment, the
microphone is part of a sleeve that wraps around the water pipe.
The microphone can be acoustically isolated from environmental
noises via insulation, noise cancellation techniques, or any other
techniques known to those of skill in the art. The processor of the
water flow sensor can receive volume and frequency characteristics
of sounds received through the microphone. The memory can store the
data, and the transmitter, which can be wired or wireless, can
transmit the measured values to a decision node, a sensory node, or
a local/remote server, which in turn can determine whether there is
water flow, the amount of water flow, and whether the water flow is
normal or abnormal. Alternatively, the processor of the water flow
sensor can make such determinations. If the water flow is abnormal,
a notification is provided as discussed above. The water pipe that
is monitored can be the main water line coming into the
home/building, or any other water pipe in the building, including
the water supply to a sprinkler system designed to combat fire. In
one embodiment, the water flow sensor can be installed on each
water pipe in the building.
[0051] In one embodiment, the water flow sensor may also include a
thermistor or other temperature detection device to monitor a
temperature of the water pipe. The temperature of the water pipe
can also be used to detect water flow and determine whether the
water flow is normal or abnormal. For example, if the hot water
faucet is left on, the thermistor may sense that the temperature of
the water pipe is high for an extended period of time, which is an
indication that hot water is running. The thermistor may similarly
detect that cold water is running if the temperature of the water
pipe is low for an extended period of time. In an illustrative
embodiment, the thermistor can be used in conjunction with the
microphone to help prevent false alarms. For example, if the
microphone data is inconclusive, the system may relay on the
thermistor data to help determine whether water is flowing through
a pipe. Alternatively, the thermistor may be used independent of
the microphone.
[0052] The flood sensor can be used to detect a flood in accordance
with an illustrative embodiment. As an example, one or more flood
sensors can be placed in areas on a lowest level of a building
where flooding may occur, such as a basement generally, near a sump
pump in a basement, in a bathroom within the basement, near a
washing machine, etc. The flood sensor may also be placed in upper
levels of the building in or near bathrooms, laundry rooms,
kitchens, and/or other areas that are potentially at risk of
flooding. The flood sensor can detect flooding that occurs as a
result of internal water leaks or water from outside that flows
into a building. In one embodiment, the flood sensor can measure
the electrical conductivity between two or more sensors or probes
of the flood sensor that are placed at or near floor level to
detect the presence of water. Any water detecting probes or sensing
components known to those of skill in the art can be used.
[0053] In addition to the sensors, the flood sensor can include a
processor, a transmitter, and a memory. In an illustrative
embodiment, upon detection of water by the flood sensor, the
processor of the flood sensor can receive an indication that water
has been detected, store the information in memory, and cause the
transmitter to transmit data to a decision node, sensory node, or
local/remote server via wireless and/or wired communication. In
response to detection of water and a potential flood, the user can
be provided with a notification. The notification can be a visual
and/or audio notification from the decision/sensory node, or the
notification may be in the form of an e-mail, text message,
telephone call, etc. to a computing device of the user. In one
embodiment, one or more neighbors of the user may also be provided
with such a notification.
[0054] The wind sensor can be used to detect wind proximate to a
building in accordance with an illustrative embodiment. As an
example one or more wind sensors can be placed in areas on or near
an exterior of a building, such as a fence post, a roof, a
dedicated post, etc. The wind sensor can be used to detect high
winds that may potentially damage an exterior of a building, such
as siding, roofing, etc. In one embodiment, the wind sensor can be
implemented in part as a hot wire anemometer. A hot wire anemometer
uses a very fine wire (generally on the order of several
micrometers) electrically heated up to some temperature above the
ambient temperature. Air flowing past the wire has a cooling effect
on the wire. As the electrical resistance of metals such as
tungsten, for example, is dependent upon the temperature of the
metal, a relationship can be obtained between the resistance of the
wire and the flow speed such that the flow speed of the wind can be
determined.
[0055] Alternatively, the wind sensing components may be
ultrasonic. Both wind speed and direction can be measured using an
ultrasonic sensor. The ultrasonic sensor uses ultrasound to
determine horizontal wind speed and direction. In one embodiment,
an array of three equally spaced ultrasonic transducers on a
horizontal plane can be used to ensure accurate wind measurement
from all wind directions, without blind angles or corrupted
readings. The ultrasonic wind sensor has no moving parts, which
makes it maintenance free.
[0056] In addition to the sensors, the wind sensor can include a
processor, a transmitter, and a memory. In an illustrative
embodiment, upon detection of wind with a speed in excess of a
threshold by the wind sensor, the processor of the wind sensor can
receive an indication that high speed wind has been detected, store
the data in memory, and can cause the transmitter to transmit the
data to a decision node, sensory node, or local/remote server via
wireless and/or wired communication. The wind speed threshold can
be set by the user, or set by the system depending on the
embodiment. In response to detection of the high speed wind, the
user can be provided with a notification. The notification can be a
visual and/or audio notification from the decision/sensory node, or
the notification may be in the form of an e-mail, text message,
telephone call, etc. to a computing device of the user. In one
embodiment, one or more neighbors of the user may also be provided
with such a notification.
[0057] The hail/rain sensor can be used to detect hail and/or heavy
rain in accordance with an illustrative embodiment. As an example,
one or more hail/rain sensors can be placed in areas on or near an
exterior of a building, such as a fence post, a roof, a dedicated
post, etc. In one embodiment, the hail/rain sensor can be a
piezoelectric sensor that includes a round stainless steel cover
mounted to a rigid frame. A piezoelectric detector is located
beneath the cove, and the electronics of the system can be mounted
beneath the detector. Hail and raindrops hit the sensor at their
terminal velocity, which is a function of the hail/raindrop
diameter. Measurement is based on the acoustic detection of each
individual rain drop or piece of hail as it impacts the sensor
cover. Larger raindrops or pieces of hail create a larger acoustic
signal than smaller drops or pieces of hail. The piezoelectric
detector converts the acoustic signals into voltages. Total
rain/hail fall is calculated from the sum of the individual voltage
signals per unit time and the known surface area of the sensor.
This information is also used to calculate intensity and duration
of rain or hail. In one embodiment, the sensor can also distinguish
between hail and raindrops based on the acoustic differences when
rain vs. hail contacts the sensor. Alternatively, the hail/rain
sensor can be a fully shielded, low mass, thin, large surface
sensor that includes a sensing element constructed of elastic
electret film and a plurality of layers of polyester with aluminum
electrodes. Crimped connectors can be used for connecting the
electrodes to an electronic measuring device as known to those of
skill in the art. Alternatively, any other hail/rain sensor known
to those of skill in the art may be used.
[0058] In an alternative embodiment, the hail/rain sensor can be
implemented in whole or in part as a tipping bucket sensor that is
configured to detect precipitation. The tipping bucket sensor can
be implemented as a rain/hail gauge that includes a funnel that
collects and channels the precipitation into a small seesaw-like
container. After a pre-set amount of precipitation falls, the lever
tips, dumping the precipitation and sending an electrical signal
via the processor and transmitter, as discussed below.
[0059] In addition to the sensors, the hail/rain sensor can include
a processor, a transmitter, and a memory. In an illustrative
embodiment, upon detection of hail/rain by the hail sensor, the
processor of the hail sensor can receive an indication that
hail/rain has been detected, store the data in memory, and cause
the transmitter to transmit data to a decision node, sensory node,
or local/remote server via wireless and/or wired communication. In
response to detection of hail and/or rain that exceeds a hail/rain
threshold, the user or an interested party such as the home insurer
can be provided with a notification. The notification can be a
visual and/or audio notification from the decision/sensory node, or
the notification may be in the form of an e-mail, text message,
telephone call, etc. to a computing device of the user. The
hail/rain threshold can be set by the user or by the system, and
can be based on the duration of hail/rain, the size of the
hail/rain, and/or the amount of hail/rain.
[0060] In addition to the sensors discussed above, a detection
system may also include indoor and/or outdoor temperature sensors,
indoor and/or outdoor humidity sensors, lightning detection
sensors, lightening range detection sensors, sun intensity sensors,
freeze sensors, earthquake sensors, etc. that operate in a similar
fashion to the sensors discussed above. As one example, the system
may include a combined temperature and humidity sensor that detects
relative humidity and temperature outputs. A lightning detector can
function by detecting the electromagnetic pulse emitted by a
lightning strike. By measuring the strength of the detected
electromagnetic pulse, the lightning sensor can then estimate how
far away the detected strike was. When exposed to multiple detected
strikes, the lightning detector can be configured to calculate and
extrapolate the direction of the storm's movement relative to its
position (i.e., approaching, departing, or stationary). Sun
intensity can be measured using optical sensors as known to those
of skill in the art. An earthquake sensor can be implemented using
an accelerometer as known to those of skill in the art.
[0061] Any of these additional sensors can include a processor, a
transmitter, and a memory. In an illustrative embodiment, upon
detection of a detected condition or a detected condition in excess
of a threshold, the processor of the sensor can receive an
indication that a condition has been detected, store the data in
memory, and cause the transmitter to transmit data to a decision
node, sensory node, or local/remote server via wireless and/or
wired communication. In response to detection of the condition or a
condition that exceeds a threshold, the user or other interested
party can be provided with a notification. The notification can be
a visual and/or audio notification from the decision/sensory node,
or the notification may be in the form of an e-mail, text message,
telephone call, etc. to a computing device of the user. In one
embodiment, one or more neighbors of the user may also be provided
with such a notification. The threshold, if used, can be set by the
user or by the system.
[0062] In addition, any of the sensors described herein can be used
in part for multi-parameter detection of a risk condition. In an
illustrative embodiment, multi-parameter detection can refer to use
of multiple environmental conditions as detected by differing types
of sensors to determine when a risk condition occurs, and to
prevent false alarms. In one embodiment, the detected environmental
conditions can be compared against one other or compared against
themselves over time to determine the presence or absence of flame,
smoke, or other physical conditions that embody or are precursors
to a fire or other detection condition. As such, the system can be
configured to store and organize data collected by the various
sensors of the system. That data can then be used to further refine
the algorithms described herein in a manner that creates a more
sensitive and more accurate detection condition detection
algorithm.
[0063] In one embodiment, the collected data and the algorithm can
be normalized for geographic differences, location of the sensor in
specific places in a structure (such as a room with regularly
elevated or diminished levels of a particular parameter--e.g.,
greater humidity in a bathroom or kitchen), etc. For example, the
system may take geographic location and elevation into
consideration when interpreting sensed humidity levels and
temperatures. A building in a desert climate is more likely to have
high temperature and low humidity than a building located in a
mountainous region. The system can also utilize historical weather
data to help evaluate sensor readings and determine whether a
reading indicates a risk condition or a false alarm. For example,
the system may know to expect elevated humidity levels during what
is traditionally a rainy season for a given region. The system can
also access a weather database to obtain upcoming forecast
information such that the system can know whether a storm,
temperature increase, temperature decrease, etc. is to be
expected.
[0064] In an illustrative embodiment, any of the decision nodes or
sensor nodes disclosed herein can include a silence switch, button,
or other control such that the user can terminate an alarm/warning
in the event of a false positive. The detection system can use
activation of the silence switch to identify trends of when false
positives occur, and to adjust system sensitivity based on the
trends. As an example, a user may cook a frozen pizza at 6:00 pm in
a kitchen of a house. The oven used to cook the pizza may generate
smoke and cause a sensory node in the kitchen to identify a risk
condition. In response, the user may press the silence button
because there is not really a fire in the kitchen. The same
occurrence may occur numerous times over the course of several
months (i.e., a false positive may occur at around 6:00 pm due to
smoke sensed by the kitchen sensory node, and the user may use the
silence switch). As a result, the system can automatically adjust
the sensitivity of the sensory node in the kitchen such that a
small amount of smoke does not set off the alarm if the small
amount of smoke is detected between 4:30-6:30 pm on weekdays, for
example. The times during which the sensitivity is adjusted, the
days on which sensitivity is adjusted, and the amount by which the
sensitivity is adjusted can vary based on the specific
implementation. In one embodiment, the system may require
permission from the user prior to adjusting the sensitivity to
ensure that the user is comfortable with the sensitivity
adjustment. The sensitivity adjustment is not limited to the
kitchen. A similar sensitivity adjustment based on use of the
silence switch may occur in a bathroom due to humidity/temperature
increases responsive to the user taking a shower at a certain time
of day, or in any other room of the house where false alarms
routinely occur.
[0065] The detection systems described herein can also include
microphones within the nodes to monitor noises within a building.
As one example, the system can be used to monitor and detect
potential problems with elderly individuals based on sounds. For
example, a loud noise (e.g., bang, crash, etc.) in the middle of
the night may be an indication that an elderly individual has
fallen out of bed, fallen down on the way to the restroom, etc. As
a result of such a noise, the system can send a notification to an
individual responsible for caring for the elderly individual, such
as a relative, a nursing home custodian, etc. The occupancy
detection functionality of the detection system can also be used to
detect if an elderly individual unexpectedly leaves his/her room
and send a notification to one or more individuals caring for the
elderly individual.
[0066] In an embodiment in which the detection system includes
video capabilities, the system may also use biometric monitoring in
conjunction with occupancy detection to identify what individuals
enter and leave the building. The biometric monitoring can be
implemented through retinal detection as known to those of skill in
the art. Retinal scans can be taken of individuals that live at,
work in, or otherwise regularly enter the building. As such, in
addition to identifying a number of occupants in the building or in
a portion of the building, the system can also identify which
individuals are in the building. The system can also identify
individuals who are not regularly in the building if their retinal
scan does not match any stored retinal scan information. In one
embodiment, a notification can be sent to a user if an individual
with an unknown retinal pattern enters the building. This may be an
indication of a burglar or of unwanted individuals in the
building.
[0067] The detection system can further be configured to tie into
existing systems of the building such that lights can be remotely
controlled, doors can be locked/unlocked, a garage door can be
opened/closed, etc. For example, the system can be configured to
send wireless signals to a garage door opener such that a user can
remotely open/close the garage door. The system can also be
integrated into the building's electrical system to control lights,
electronic door locks, and/or any other electronic components of
the building.
[0068] Processor 412 can also be used to monitor and track the use
of sensor unit 400 such that a report can be created, stored,
and/or conveyed to a provider. As an example, the report can
include a time, location, and likelihood of a risk factor for each
potential risk factor that is identified by sensor unit 400. The
report can also include any commands received from the user of
sensor unit 400, any information received from outside sources and
conveyed to the user through sensor unit 400, etc. The report can
be stored in memory 414. The report can also be conveyed to the
insurance provider server.
[0069] In addition to determining whether a risk condition is
detected and/or a likelihood that the detection is accurate, sensor
unit 400 can also determine whether a detected occupant is a human
or an animal (i.e., dog, cat, rat, etc.) using infrared pattern
analysis based on information received from infrared detector 406
and/or audible sound analysis based on information received from
microphone detector 404. Sensor unit 400 can also use detected
information and pattern analysis to determine and convey a number
of persons or animals detected and/or whether detected persons are
moving, stationary, sleeping, etc. In one embodiment, sensor unit
400 can also use temperature detection through infrared detector
406 and/or any of the other detection methods to help determine and
convey whether a detected occupant is dead or alive.
[0070] In one embodiment, a separate signal acquisition circuit can
be used to detect/receive signals for each of gas detector 402,
microphone detector 404, infrared detector 405, scent detector 408,
and ultrasonic detection system 410. Alternatively, one or more
combined signal acquisition circuits may be used. Similarly, a
separate algorithm can be used to process signals detected from
each of gas detector 402, microphone detector 404, infrared
detector 406, scent detector 408, and ultrasonic detection system
410. Alternatively, one or more combined algorithms may be
used.
[0071] The one or more algorithms used by processor 412 can include
computer-readable instructions and can be stored in memory 414.
Memory 414 can also be used to store present occupancy information,
a layout or map of a structure, occupancy pattern information, etc.
User interface 416 can be used to receive inputs from a user for
programming and use of sensor unit 400. In one embodiment, user
interface 416 can include voice recognition capability for
receiving audible commands from the user. Output interface 418 can
include a display, one or more speakers, and/or any other
components through which sensor unit 400 can convey an output
regarding whether a risk condition is detected, etc. Power source
420 can be a battery and/or any other source for powering sensor
unit 400.
[0072] Transceiver 422 can be used to communicate with sensors
and/or servers. As such, sensor unit 400 can receive present
information and/or pattern information from the sensors. Sensor
unit 400 can use the present sensor information and/or occupancy
pattern information to help determine a likelihood that they risk
condition exists in an area of the structure. For example, the
pattern information may indicate that there is generally a large
number of people in a given area at a given time. If used in the
given area at or near the given time, the risk-event detection
algorithms used by sensor unit 400 may be adjusted such that any
indication of risk is more likely to be attributed to an actual
risk condition. The present risk information can be similarly
utilized. Transceiver 422 can also include short range
communication capability such as Bluetooth, Zigbee, etc. for
conveying information to insurance servers through such wireless
paths.
[0073] Global positioning system unit 424 can be used to determine
a current location of sensor unit 400 so that sensor unit 400 can
determine the location of received sensor readings. Then, when a
risk condition is determined from the sensor readings, sensor unit
400 may determine and/or communicate the location of each
determined risk condition.
[0074] In some cases, the system may alert property owners,
residents, or others with suggestions to better maintain the
property. For example, the detection of hail could also generate
automated messages to home inspectors, providing a rapid customer
interaction. Hail detection in an area or neighborhood could also
prompt the system to send text warning messages alerting insurance
customers to move their vehicles indoors. The outdoor wind speed
and direction sensor could also be used to improve conditions
during the heating season. Under high wind conditions, homes tend
to cool much quicker than on calm, sunny days. As such, the user
may be provided with a suggestion to open/close windows to improve
heating/cooling of the building. Further, by collecting and
analyzing internal and external environmental conditions including
wind speed, sunlight intensity, humidity, and external temperature,
the home temperature could be regulated much more efficiently to
save energy. Further, detecting high levels of humidity over long
period of times may be indicative of broken water pipes within a
building's walls, leading to mold development. Sensing persistent,
elevated levels of humidity could warn the homeowner prior to the
onset of mold. An indoor freeze sensor can also be used to warn a
homeowner that the heating system is not working and that water
pipes may be at risk of freezing and bursting.
[0075] In one embodiment, the system determines a severity of a
sensed condition. The severity may be based at least in part on a
rate of change (or spread rate) of the sensed condition. As an
example, a condition may be detected at a first sensory node. The
rate of change can be based on the amount of time it takes for
other sensory nodes to sense the same condition or a related
condition. If the other sensory nodes rapidly sense the condition
after the initial sensing by the first sensory node, the system can
determine that the condition is severe and rapidly spreading. As
such, the severity of a sensed condition can be based at least in
part on the rate at which the sensed condition is spreading.
Detected occupancy can also be used to determine the severity of a
sensed condition. As an example, a sensed condition may be
determined to be more severe if there are any occupants present in
the structure where the condition was sensed.
[0076] The type of sensed condition may also be used to determine
the severity of a sensed condition. As an example, sensed smoke or
heat indicative of a fire may be determined to be more severe than
a sensed gas such as carbon monoxide, or vice versa. The amount of
dispersion of a sensed condition may also be used to determine the
severity of the sensed condition. In one embodiment, known GPS
locations associated with each of the sensory nodes that have
sensed a condition can be used to determine the dispersion of the
condition. As an example, if numerous sensory nodes spread out over
a large area detect the sensed condition, the system can determine
that the severity is high based on the large amount of dispersion
of the sensed condition. In one embodiment, the GPS locations
associated with each of the nodes can be fine-tuned using wireless
triangulation as known to those of skill in the art. As an example,
a first node may be considered to be at location zero, and
locations of all of the other nodes in the building/structure can
be relative to location zero. Using wireless triangulation
techniques, the relative signal strength of the nodes can be used
to determine the locations of the nodes relative to location zero,
and the determined locations can be used to replace and improve the
accuracy of the GPS locations originally assigned to the nodes
during installation.
[0077] The magnitude of the sensed condition can further be used to
determine the severity of the sensed condition. As an example, a
high temperature or large amount of smoke can indicate a fire of
large magnitude, and the system can determine that the severity is
high based on the large magnitude. As another example, a large
amount of detected carbon dioxide can indicate a high risk to
occupants and be designated a risk condition of high severity.
[0078] In an illustrative embodiment, the determination of whether
a sensed condition has high severity can be based on whether any of
the factors taken into consideration for determining severity
exceed a predetermined threshold. As an example, a determination of
high severity may be made based on the spread rate if a second
sensory node detects the sensed condition (that was originally
detected by a first sensory node) within 4 seconds of detection of
the sensed condition by the first sensory node. Alternatively, the
spread rate threshold may be 0.4 seconds, 1 second, 3 seconds, 10
seconds, etc. As another example, the high severity threshold for
occupancy may be if one person or pet is detected in the building,
if one person or pet is detected within a predetermined distance of
the sensory node that sensed the condition, etc. With respect to
magnitude, the high severity threshold may be if the temperature is
greater than 140 degrees Fahrenheit (F), greater than 200 degrees
F., greater than 300 degrees F., etc. The magnitude threshold may
also be based on an amount of smoke detected, an amount of gas
detected, etc. The high severity threshold with respect to
dispersion can be if the sensed condition is detected by two or
more sensory nodes, three or more sensory nodes, four or more
sensory nodes, etc. The high severity threshold with respect to
dispersion may also be in terms of a predetermined geographical
area. As an example, the system may determine that the severity is
high if the detection condition has dispersed an area larger than
100 square feet, 200 square feet, etc. The system may also
determine that the severity is high if the detection condition has
dispersed through at least two rooms of a structure, at least three
rooms of the structure, etc.
[0079] In some embodiments, the sensitivity of one or more sensory
nodes may be adjusted. Sensitivity can refer to the rate at which a
sensory node scans its environment for smoke, gas such as carbon
monoxide, temperature, occupancy, battery power, ambient light,
etc. Examples of sensitivity can be scanning twice a second, once a
second, once every 4 seconds, once every 30 seconds, once a minute,
once an hour, etc. As indicated above, in one embodiment, the
system may adjust the sensitivity of one or more sensory nodes
based on the severity of a sensed condition. As also described
above, severity can be determined based on factors such as the rate
of change of the sensed condition, detected occupancy, the type of
sensed condition, the amount of dispersion of the sensed condition,
the magnitude of the sensed condition, etc. As an example, smoke
may be detected at a sensory node X, and sensory node X can
transmit an indication that smoke was detected to a decision node
and/or a remote server. If the decision node and/or remote server
determine that the sensed condition has high severity, the system
can increase the sensitivity of the sensory node X and/or sensory
nodes Y and Z in the vicinity of sensory node X such that the scan
rate for these nodes increases. The increased sensitivity can also
result in a higher communication rate such that the decision node
and/or remote server receive more frequent communications from
sensory nodes X, Y, and Z regarding sensor readings. The increased
sensitivity may also result in a reduction in one or more
predetermined thresholds that the system uses to determine if a
sensed condition has high severity, to determine if the sensed
condition triggers a notification, etc.
[0080] The sensitivity of sensory nodes can also be adjusted if any
sensory node detects a condition, regardless of the severity of the
condition. As an example, the system may automatically increase the
sensitivity of sensory nodes Y and Z (which are in the vicinity of
sensory node X) if sensory node X detects a condition. The system
may also increase the sensitivity of all sensory nodes in a
building/structure if any one of the sensory nodes in that
building/structure senses a condition. In one embodiment, in the
event of an alternating current (AC) power failure, the sensitivity
of sensory nodes may be decreased to conserve battery power within
the sensory nodes. Similarly, in embodiments where AC power is not
present, the system may decrease the sensitivity of any nodes that
have low battery power.
[0081] The sensitivity of sensory nodes may also be controlled
based on a location of the sensory node and/or a learned condition
relative to the sensory node. For example, a sensory node in a
kitchen or in a specific location within a kitchen (such as near
the oven/stovetop) may have higher sensitivity than sensory nodes
located in other portions of the structure. The sensitivity may
also be higher in any sensory node where a condition has been
previously detected, or in sensory nodes where a condition has been
previously detected within a predetermined amount of time (e.g.,
within the last day, within the last week, within the last month,
within the last year, etc.). The sensitivity may also be based on
occupancy patterns. For example, the sensitivity of a given sensory
node may be lower during times of the day when occupants are
generally not in the vicinity of the node and rose during times of
the day when occupants are generally in the vicinity of the node.
The sensitivity may also be raised automatically any time that an
occupant is detected within the vicinity of a given sensory
node.
[0082] The sensitivity of a sensory node may also be increased in
response to the failure of another sensory node. As an example, if
a sensory node X is no longer functional due to loss of power or
malfunction, the system can automatically increase the sensitivity
of nodes Y and Z (which are in the vicinity of node X). In one
embodiment, the system may increase the sensitivity of all nodes in
a building/structure when any one of the sensory nodes in that
building/structure fails. In another embodiment, the system may
automatically increase the sensitivity of one or more nodes in a
building/structure randomly or as part of a predetermined schedule.
The one or more nodes selected to have higher sensitivity can be
changed periodically according to a predetermined or random time
schedule. In such an embodiment, the other nodes in the
building/structure (e.g., the nodes not selected to have the higher
sensitivity) may have their sensitivity lowered or maintained at a
normal sensitivity level, depending on the embodiment.
[0083] FIG. 5 is a flow diagram illustrating operations performed
by a detection system in accordance with an illustrative
embodiment. In alternative embodiments, additional, fewer, and/or
different operations may be performed. Further, the use of a flow
diagram is not meant to be limiting with respect to the order of
operations performed. Any of the operations described with
reference to FIG. 5 can be performed by one or more sensory nodes
and/or by one or more decision nodes.
[0084] In an operation 500, sensor readings are received from
sensors at the insured property. The sensor readings may be
received at sensory/decision nodes, such as nodes 105-130 and 200,
or it may be received by insurance servers and systems, such as
server 300. The sensor readings may be received over network
interfaces, either wired or wireless, and may be in the form of
digital or analog electronic signals. The sensor readings may be
raw/unprocessed data and sensor signals, or it may be processed
prior to being received. For example, a signal may be processed, so
that only a significant portion of the signal (e.g., signal above a
threshold value, signal changing significantly, etc.) is
transmitted. As another example, a node may include an indication
of a typical or non-risk sensor reading for comparison with the new
sensor readings for the property. In an illustrative example, the
sensor readings may also contain indications of the type of sensor
from which the readings are received, the location of the sensor,
and time/date associated with the sensor readings, among other
example, information.
[0085] In some implementations, sensor readings may be received
periodically. In such a case, the frequency of received readings
may be high enough to provide substantially real-time readings or
low enough to provide very low data transfer while keeping a server
up to date. In other implementations, readings may be received only
in response to particular circumstances. For example,
sensors/sensory nodes may be programmed to only send readings when
the readings reached a certain threshold level (e.g., a certain
temperature, a concentration of the gas, a raised humidity for
particular amount of time, etc.). As another example,
sensor/sensory nodes may be programmed to send readings only
response to receiving a request for the readings. In some cases in
which readings are not sent periodically, sensors may send all the
readings collected since a last transmission occurred. In other
cases, sensors may only send the most recent readings.
[0086] In an operation 505, an insurance risk is determined from
the sensor readings. Insurance risk may be determined by a sensory
node/decision node at or near the insured property and/or it may be
determined by systems are servers of the insurance company. If the
risk is determined at least in part by a local node, then a
determined risk may be sent to the insurance company rather than
sensor readings/information. Sending risk determinations rather
than sensor readings may help in reducing resources needed for the
transmission and may provide an extra layer of privacy for
occupants. On the other hand, receiving sensor readings in full at
the insurance company may help the insurer to better assess the
risk of conditions at the property.
[0087] In some cases, insurance risks may be associated with the
occupancy or lack of occupancy of certain rooms/areas of a
property. For example, if certain areas of the property are not
occupied for long periods of time, then in the damage occurring in
those areas will be greatly increased due to the lack of human
intervention. In some instances, the lack of occupancy may also
lead to reduced climate control usage, which can cause damage to
the property during seasons of extreme temperature. As another
example, areas that are heavily trafficked in use may be at greater
risk for damage from the many occupants of the areas. In some
cases, an insurer may have received intended occupancy information
at the start of the insurance plan, and therefore may compare the
intended occupancy with the actual occupancy. For instance, if the
property owner discloses that two people and no animals would be
living in a residence, then detecting an actual occupancy of four
people and two dogs for an extended period may indicate an
increased risk than previously quoted. As another example, if all
of the residents of the property are away from home for more than
half of the day, then the property may be at reduced risk because
the residents have less time to cause damage to the property.
[0088] In some cases, insurance risks may be associated with gases
or other chemical compounds detected in the ambient air by a gas
detector 402, scent detector 408, or a humidity detector. For
example, an overabundance of toxic gases such as carbon monoxide,
carbon dioxide, methane may pose a serious problem to occupants of
property, leaving an insurer open to liability risk. Additionally,
a humidity sensor reading indicating an extended period of time
with high water content in the air may leave the property open to
growth of mold, which can both produce disease-inducing spores and
damage wooden/plaster structural components. Further, readings from
scent detector 408 that indicate the presence of incendiary or
controlled narcotic substances at the property may likewise
indicate increased risk.
[0089] In some cases, insurance risks may be associated with user
actions related to sensor equipment. For example, if occupants of
the property frequently leave smoke detectors or other sensors
unplugged/turned off, then the property and occupants are at
increased risk. As another example, if sensor systems are turned to
a very low sensitivity setting, then the usefulness of these
sensors may be reduced and, therefore, risk increased. As still
another example, the sensor node may connect to other systems in
the property, such as burglar alarms, security devices, or panic
alarms, and detect the increased risk associated with these systems
being deactivated.
[0090] Some major damage events, such as fire, flood, and storm
damage may also be detected as insurance risks. For example, smoke
detectors may sense a fire, or a near fire, occurring in a
property, regardless of whether the property owner reports the
incident to the insurance company. As another example, although
flood damage is not normally covered by private insurance,
detection of flooding may be indicative of other large insurance
risks to the property. As yet another example, a hail sensor, or
wind sensor may detect how often severe storm events occur at the
residence and the damage done by such storms.
[0091] In addition to simply detecting the occurrence of major
damage events as insurance risks, a decision node or insurance
server may determine that a property is at increased risk of damage
because of interesting weather patterns in the area of the
property. For example, pattern recognition software may be used to
determine property locations that receives the highest incidences
of, for example, lightning strikes, severe wind events,
earthquakes, wildfires, hail storms, severe thunderstorms,
flooding, snow damage, etc. Such pattern recognition may also be
useful in predicting property locations that are at higher risk for
other insurance damage claims as well, such as burglary, animal
infestations, arson, vandalism, rapid depreciation, issues with
utilities, or higher incidence of lawsuits, among other
examples.
[0092] Further still, sensors and property may detect structural
issues with the property as they occur. For example, stress/strain
sensors may be placed on load bearing structural components to
report when damaging tension, torsion, or compression is placed on
these important components of the property. Additionally, movement
sensors may also detect if structural components move out of their
original positions, indicating weakening and breaking of the
building. In addition to such sensors providing the insurance
provider or property owner with an early warning to correct such
problems, the factors leading to such structural problems may also
be analyzed to yield a predictive model for risk factors. For
example, if many structural defects are found in constructions by a
certain company, constructions using certain materials, buildings
in a particular area, buildings constructed at or before a certain
time, or buildings that have a particular design feature (e.g.,
unique layout, open floorplan, pentagonal rooms, etc.), then an
insurance provider can charge a higher premium for those properties
which fit the pattern. Correspondingly insurance company may lower
premiums for buildings that do not fit the pattern or that fit a
pattern for low structural defects.
[0093] In some cases, risk factors may include particular actions
of occupants in the property. For example, an insurance risk may be
that occupants often produce smoke alarm events (e.g. they burn a
pizza every Thursday night). As another example, if occupants leave
appliances running (e.g., oven, space heater, fans, stove, sink,
etc.) while they are unattended or not being used, this inattention
may represent an insurance risk. As a further example, in the case
that the sensor system provides recommendations to occupants, it
may represent an insurance risk. If occupants do not heed those
suggestions. For instance, if the property owner leaves their car
out of the garage after having received and read an alert warning
of approaching hail, then such an owner may be hazardous to the
property, increasing insurance risk.
[0094] Many possible insurance risks may be determined from the
sensors and systems discussed above and the foregoing set of
examples are not intended as comprehensive and full. Rather,
various other examples of insurance risks will be evident to those
of skill in the art.
[0095] In operation 510, an insurance premium adjustment is
determined for the determined risk. Premium adjustment may be made
with respect to the current insurance premium charged for the
property (e.g., reduction by a percentage of current premium
payment, reduction by a particular dollar value, etc.). Premium
adjustment may alternately be made with respect to a set premium
value (e.g., a typical value for a type of property, a median
premium value for all property plans, etc.). In still other
embodiments, a premium adjustment may simply be a new value of
premium for the property, rather than an adjustment to any other
level of premium payment.
[0096] In some cases, an adjustment to the insurance premium may
increase the premium amount. For example, if more frequent and/or
more severe risks are detected at the insured property than in
similar properties, then the insurance provider may raise the
insurance premiums for that plan to compensate for the increased
risk. In other cases, an adjustment to insurance premium may reduce
the premium amount. For example, if less frequent and/or much less
severe risk conditions are detected at a property than are
typically detected in a similar type of property, then the
insurance provider may offer a reduced insurance premium rate to
compensate for the detected safety of the insured property.
[0097] In some embodiments, the insurance provider may design the
premium adjustment system such that only reductions in premium
amount are allowed. For example, in such a system a property that
is detected to have a higher than normal risk may simply yield no
offers for lowered insurance premiums, while lower-risk plans may
yield lower premium offers.
[0098] Since the risk values determined from the sensor readings
may include many complex factors, and combinations of factors, the
discount algorithm may apply principal component analysis (PCA),
and other mathematical scoring and statistical analysis techniques
to produce a reasonable risk assessment. Such an assessment may
produce a property risk score indicating the relative risk
associated with the particular property, based on the analyzed
sensor readings, with respect to other properties in the system. In
cases where the node analyzes the data, and determines the risk
score before transmitting to the insurance servers, the PCA and
other scoring techniques may be applied at the node and the
composite property risk score may be transmitted directly to the
insurance server, without needing to transmit other data.
[0099] In some cases, an insurance premium adjustment is determined
based on the severity of detected risk conditions. In one
embodiment, the system can prioritize the sensed condition based at
least in part on the severity. A sensed condition with high
severity may be prioritized higher than a sensed condition with low
severity. In one embodiment, the priority can be provided to the
insurance provider as an indication of the urgency of the sensed
condition. The severity can also be used by the system to help
determine whether a sensed condition is a false alarm. A sensed
condition with a high severity can be determined to be an actual
detection condition and the system can trigger the appropriate
alarms, notifications, etc. In one embodiment, the severity of a
sensed condition may also be used to control the sensitivity of the
sensory node that sensed the condition and other sensory nodes in
the vicinity of the sensory node that sensed the condition.
[0100] After insurance risks and premium price adjustments are
determined, status information regarding the sensory nodes will
continue to be received from the sensory nodes. In an illustrative
embodiment, the sensory nodes periodically provide status
information to the decision node and/or remote server. The status
information can include an identification of the sensory node,
location information corresponding to the sensory node, information
regarding battery life of the sensory node, information regarding
whether the sensory node is functioning properly, information
regarding whether any specific sensors of the sensory node are not
functioning properly, information regarding whether the speaker(s)
of the sensory node are functioning properly, information regarding
the strength of the communication link used by the sensory node,
etc. In one embodiment, information regarding the communication
link of a sensory node may be detected/determined by the decision
node and/or remote server. The status information can be provided
by the sensory nodes on a predetermined periodic basis. In the
event of a problem with any sensory node, the system can alert a
system administrator (or user) of the problem. The system can also
increase the sensitivity of one or more nodes in the vicinity of a
sensory node that has a problem to help compensate for the
deficient node. The system may also determine that a node which
fails to timely provide status information according to a periodic
schedule is defective and take appropriate action to notify the
user and/or adjust the sensitivity of surrounding nodes.
[0101] Additional information regarding a risk condition can also
include statistics regarding the condition. The statistics can
include a heat rise at the structure in terms of degrees per time
unit (e.g., 40 degrees F./second), a smoke rise at the structure in
terms of parts per million (ppm) per time unit (e.g., 2000
ppm/second), and/or a gas rise such as a carbon monoxide level
increase. The heat rise, smoke rise, and/or gas rise can be
provided textually and/or visually through the use of a graph or
chart. The statistics can also include a heat magnitude and/or
smoke magnitude. The statistics can also include one or more
locations of the dwelling where occupants were last detected,
whether there is still AC power at the location, whether
communication to/from the sensory nodes is still possible, whether
there is any ambient light at the location, etc. In an illustrative
embodiment, any of the statistics may be associated with a
timestamp indicative of a time of the measurements, etc. that the
statistic is based on.
[0102] The additional information regarding a risk condition can
also include maps. The maps may include a street map of the area
surrounding the location at which the detection condition was
sensed, a map that illustrates utility locations and fire hydrants
proximate to the location at which the detection condition was
sensed, an overhead satellite view showing the location at which
the detection condition was sensed, a map showing neighborhood
density, etc. The additional information may also include a weather
report and/or predicted weather for the location at which the
detection condition was sensed. The maps and/or weather information
can be obtained from mapping and weather databases as known to
those of skill in the art.
[0103] The additional information regarding a risk condition can
also include pictures of the interior and/or exterior of the
structure. The pictures can include one or more views of the home
exterior, illustrating windows, doors, and other possible exits
and/or one or more views of the lot on which the structure is
located. The pictures can also include one or more interior views
of the structure such as pictures of the kitchen, pictures of the
bathroom(s), pictures of the bedroom(s), pictures of the basement,
pictures of the family room(s), pictures of the dining room(s),
etc. The pictures can further include blueprints of the structure.
The blueprints can illustrate each floor/level of the structure,
dimensions of rooms of the structure, locations of windows and
doors, names of the rooms in the structure, etc. In one embodiment,
construction information may be included in conjunction with the
pictures. The construction information can include the
type/composition of the roof, the type of truss system used, the
type of walls in the structure, whether there is a basement,
whether the basement is finished, whether the basement is exposed,
whether the basement has egress windows, the type(s) of flooring in
the structure, the utilities utilized by the structure such as
water, electricity, natural gas, etc., the grade of the lot on
which the structure is located, etc.
[0104] In one embodiment, the system can also generate an
investigation page that illustrates statistics relevant to an event
investigation. The investigation page can include information
regarding what was detected by each of the sensory nodes based on
location of the sensory nodes. The detected information can be
associated with a timestamp indicating the time that the detection
was made. As an example, an entry for a first sensory node located
in a kitchen 7:00 pm can indicate a detected smoke level at 7:00
pm, a detected temperature at 7:00 pm, a detected carbon monoxide
level at 7:00 pm, a detected number of occupants at 7:00 pm, etc.
Additional entries can be included for the first sensory node at
subsequent times such as 7:01 pm, 7:02 pm, 7:03 pm, etc. until the
detection condition is resolved or until the first sensory node is
no longer functional. Similar entries can be included for each of
the other nodes in the structure. The entries can also indicate the
time at which the system determined that there is a risk condition,
the time at which the system sends an alert to emergency responders
and/or an emergency call center, the time at which emergency
responders arrive at the scene, etc.
[0105] The investigation page may also include textual and/or
visual indications of smoke levels, heat levels, carbon monoxide
levels, occupancy, ambient light levels, etc. as a function of
time. The investigation page can also include diagnostics
information regarding each of the sensory nodes at the structure.
The diagnostics information can include information regarding the
battery status of the node, the smoke detector status of the node,
the occupancy detector status of the node, the temperature sensor
status of the node, the carbon monoxide detector status of the
node, the ambient light detector status of the node, the
communication signal strength of the node, the speaker status of
the node, etc. The diagnostic information can also include an
installation date of the system at the structure, a most recent
date that maintenance was performed at the structure, a most recent
date that a system check was performed, etc. The investigation page
can also include a summary of the detection condition that may be
entered by an event investigator.
[0106] The investigation page may be used by the insurance provider
in the event that a claim for damage. For example, occupancy
information may indicate possible sources of the damage and may be
indicative of whether a particular party is at fault. Such a use
may reduce spurious claims from being filed to the provider or may
reduce the number of spurious claims that are honored by the
provider.
[0107] In one embodiment, one or more of the sensory nodes in a
structure can include a video camera that is configured to capture
video of at least a portion of the structure. Any type of video
camera known to those of skill in the art may be used. In one
embodiment, the video captured by the video camera can be sent to a
remote server and stored at the remote server. To reduce the memory
requirements at the remote server, the remote server may be
configured to automatically delete the stored video after a
predetermined period of time such as one hour, twelve hours,
twenty-four hours, one week, two weeks, etc. A user can log in to
the remote server and view the video captured by any one of the
sensory nodes. As such, when the user is away from home, the user
can check the video on the remote server to help determine whether
there is a risk condition. Also, when the user is on vacation or
otherwise away from home for an extended period of time, the user
can log in to the remote server to make sure that there are no
unexpected occupants in the structure, that there are no
unauthorized parties at the structure, etc. The stored video can
also be accessible to emergency responders, emergency call center
operators, event investigators, etc. In one embodiment, in the
event of a risk condition, the video can be streamed in real-time
and provided to emergency responders and/or emergency call center
operators when they log in to the system and view details of the
detection condition. As such, the emergency responders and/or
emergency call center operators can see a live video feed of the
detection condition. The live video feed can be used to help
determine the appropriate amount of resources to dispatch, the
locations of occupants, etc.
[0108] The user can also access system integrity and status
information through the user interface. The system integrity and
status information can include present battery levels, historic
battery levels, estimated battery life, estimated sensor life for
any of the sensors in any of the sensory nodes, current and
historic AC power levels, current and historic communication signal
strengths for the sensory nodes, current and historic sensitivity
levels of the sensory nodes, the date of system installation, the
dates when any system maintenance has been performed and/or the
type of maintenance performed, etc. The system information
accessible through the user interface can further include current
and historic levels of smoke, heat, carbon monoxide, ambient light,
occupancy, etc. detected by each of the sensory nodes.
[0109] The system can also provide the user with weekly, monthly,
yearly, etc. diagnostic reports regarding system status. The
reports may also be provided to emergency response departments such
as a fire department and an insurance provider that insure the
user's home. The system can also send reminders to the user to
perform periodic tests and/or simulations to help ensure that the
system is functional and that the user stays familiar with how the
system operates. In one embodiment, users may receive an insurance
discount from their insurance provider only if they run the
periodic tests and/or simulations of the system. The system can
also send periodic requests asking the user to provide any changes
to the information provided during installation. Examples of
information that may change can include an addition to the
structure, additional occupants living at the structure, a new pet,
the death of a pet, fewer occupants living at the structure, a
change in construction materials of the structure such as a new
type of roof, new flooring, etc.
[0110] FIG. 6 is a flow diagram illustrating operations performed
by a node in accordance with an illustrative embodiment. In
alternative embodiments, additional, fewer, and/or different
operations may be performed. Further, the use of a flow diagram is
not meant to be limiting with respect to the order of operations
performed. Any of the operations described with reference to FIG. 6
can be performed by one or more sensory nodes and/or by one or more
decision nodes.
[0111] In an operation 600, sensor readings are received at a
sensory or decision node. In some cases, the node may be local to
an insured property. In operation 605, the node determines a risk
value based on the sensor readings. For example, the risk value may
be determined in the same way as described above with regard to the
insurance risk determined by the insurance server in operation 505
of FIG. 5. The risk value may be associated with a particular
sensor reading or it may be associated with a particular type of
risk. If the risk value is associated with a type of sensor, then
multiple risk values may be determined if multiple sensor readings
have been received at the node. If the risk value is associated
with a type of insurance risk, then each individual sensor reading
may account for more than one assessed risk value and/or more than
one sensor reading may be combined to account for a single risk
value.
[0112] In operation 610, the node determines if the risk value is
significant enough to send on to the insurance provider and, if the
value is significant, the node transmits an indication of the risk
value to the insurance provider. In some cases, the indication of
the risk value may simply be the sensor readings received at the
node. In other cases, one or more determined values of risk may be
transmitted from the node the insurance provider. In such a case, a
node may receive instructions from the insurance provider
instructing the node on how to determining the risk value. In some
cases, the indication of the risk value may be further processed
prior to sending. For example, the risk value may be converted to a
hash value in order to preserve privacy. In such a case, the
insurance provider only receives the necessary risk assessment
indications rather than a full explanation of how those risks were
assessed or the sensor readings from which they were assessed. In
still other cases, the insurance provider may provide the
algorithms necessary for determining the insurance premium
adjustment directly at the node. In such cases, the node may
determine the insurance premium adjustment and transmit the
determined adjustment to an insurance server rather than requiring
the insurance server to analyze the transmitted risk values to
determine the adjustment. In this way, the local device may serve
to determine a premium discount and the insurance server merely
applies that discount to the associated insurance plan.
[0113] To determine whether the risk value is significant, the node
may store one or more pre-determined risk values with which to
compare the determined current risk values. In the case where
multiple risks are assessed and multiple risk values determined, a
different predefined threshold risk value may be stored at the node
for use in determining whether to transmit each type of risk value
to the insurance provider. When the risk value is determined, the
node may then compare the determined risk value to the stored
threshold value associated with that type of risk and, if the
comparison satisfies a particular comparison condition (e.g.,
greater than a threshold value, within 10% of the threshold value,
less than a threshold value, etc.), then the node transmits the
indication of the risk value to insurance provider systems.
[0114] In an illustrative embodiment, any of the operations
described herein can be implemented at least in part as
computer-readable instructions stored on a computer-readable
memory. Upon execution of the computer-readable instructions by a
processor, the computer-readable instructions can cause a node to
perform the operations.
[0115] The foregoing description of illustrative embodiments has
been presented for purposes of illustration and of description. It
is not intended to be exhaustive or limiting with respect to the
precise form disclosed, and modifications and variations are
possible in light of the above teachings or may be acquired from
practice of the disclosed embodiments. It is intended that the
scope of the invention be defined by the claims appended hereto and
their equivalents.
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