U.S. patent application number 17/466722 was filed with the patent office on 2021-12-23 for real property monitoring systems and methods for risk determination.
This patent application is currently assigned to STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY. The applicant listed for this patent is STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY. Invention is credited to Nicholas U. Christopulos, Erik Donahue, Meghan Sims Goldfarb, Gregory L. Hayward.
Application Number | 20210398227 17/466722 |
Document ID | / |
Family ID | 1000005822591 |
Filed Date | 2021-12-23 |
United States Patent
Application |
20210398227 |
Kind Code |
A1 |
Hayward; Gregory L. ; et
al. |
December 23, 2021 |
REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR RISK
DETERMINATION
Abstract
Machine learning techniques for determining a risk level of a
target building or other type of real property include receiving
data indicative of various historical characteristics of and/or
associated with real property, and/or receiving data included in
historical, electronic claims pertaining to buildings/real
properties, and utilizing the received data to train a machine
learning or other model that identifies or discovers risk factors
associated with buildings/real properties. The machine learning or
other model may be applied to characteristic data associated with
the target building/real property to generate risk factors and/or
risk indicators of the target building/real property. The
techniques may include analyzing the generated risk factors and/or
risk indicators to determine a risk level of the target
building/real property. The risk factors, risk indicators, and/or
risk level may be used for many purposes, such as pricing, quoting,
underwriting, or re-underwriting of insurance policies.
Inventors: |
Hayward; Gregory L.;
(Bloomington, US) ; Goldfarb; Meghan Sims;
(Bloomington, US) ; Christopulos; Nicholas U.;
(Bloomington, US) ; Donahue; Erik; (Normal,
US) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY |
Bloomington |
IL |
US |
|
|
Assignee: |
STATE FARM MUTUAL AUTOMOBILE
INSURANCE COMPANY
BLOOMINGTON
IL
|
Family ID: |
1000005822591 |
Appl. No.: |
17/466722 |
Filed: |
September 3, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16136519 |
Sep 20, 2018 |
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17466722 |
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62652121 |
Apr 3, 2018 |
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62646729 |
Mar 22, 2018 |
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62617851 |
Jan 16, 2018 |
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62610599 |
Dec 27, 2017 |
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62580655 |
Nov 2, 2017 |
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62580713 |
Nov 2, 2017 |
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62564055 |
Sep 27, 2017 |
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62646735 |
Mar 22, 2018 |
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62646740 |
Mar 22, 2018 |
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62632884 |
Feb 20, 2018 |
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62625140 |
Feb 1, 2018 |
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62622542 |
Jan 26, 2018 |
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62621797 |
Jan 25, 2018 |
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62621218 |
Jan 24, 2018 |
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62618192 |
Jan 17, 2018 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06N 3/08 20130101; G06Q
40/08 20130101; G06N 20/00 20190101 |
International
Class: |
G06Q 40/08 20060101
G06Q040/08; G06N 3/08 20060101 G06N003/08; G06N 20/00 20060101
G06N020/00 |
Claims
1. A method implemented on a computing system comprising one or
more processors and one or more memories, the method comprising:
training, by the one or more processors, a neural network to
identify a plurality of risk factors based upon a set of historical
insurance claims corresponding to real properties, the neural
network including a plurality of input layers, each input layer of
the plurality of input layers including a respective plurality of
input parameters, each input parameter of a respective plurality of
input parameters corresponding to a respective characteristic of
real properties; receiving information corresponding to a target
real property, the received information including respective
indications of one or more characteristics of the target real
property, at least one characteristic of the one or more
characteristics of the target real property corresponding to an
input parameter of a respective plurality of input parameters of an
input layer; analyzing, by the one or more processors, the received
information using the trained neural network to determine one or
more risk factors of the plurality of risk factors associated with
the target real property; determining, by the one or more
processors, the risk level of the target real property based upon
the one or more risk factors associated with the target real
property; and generating, by the one or more processors, an
indication of the risk level of the target real property.
2. The method of claim 1, further comprising: pre-processing, by
the one or more processors, the set of historical insurance claims
corresponding to real properties to generate one or more labels for
each historical insurance claim of the set of historical insurance
claims; generating a set of labeled insurance claims by
incorporating respective one or more labels with the set of
insurance claims; and training, by the one or more processors, the
neural network based upon the set of labeled historical insurance
claims.
3. The method of claim 2, further comprising: selecting a subset of
the set of labeled historical insurance claims based upon at least
one label; and training, by the one or more processors, the neural
network based upon the subset of the set of labeled historical
insurance claims.
4. The method of claim 3, wherein the at least one label is
associated with at least one selected from a group of a
geographical area, a location type, a real property type, a
percentage of an outstanding mortgage balance verse a property
value, a hazard type, an age of a real property, and a type of
insurance.
5. The method of claim 1, wherein: the neural network is trained
based upon at least one of a group consisting of one or more static
characteristics of the real properties corresponding to the set of
historical insurance claims and one or more dynamic characteristics
of the real properties corresponding to the set of historical
insurance claims, each dynamic characteristic of the one or more
dynamic characteristics being associated with a change over time;
and the received information corresponding to the target real
property includes respective indications of at least one of a group
consisting of one or more static characteristics of the target real
property and one or more dynamic characteristics of the target real
property.
6. The method of claim 1, wherein: the respective plurality of
input parameters of the plurality of input layers of the neural
network includes one or more characteristics of applicants of the
set of historical insurance claims; at least a portion of the
received information of the target real property is obtained from
an application for insurance for the target real property; and the
at least a portion of the received information includes respective
indications of one or more characteristics of an applicant of the
insurance application.
7. The method of claim 1, further comprising: training the neural
network based upon additional insurance claims corresponding to
real properties.
8. A computer system comprising one or more processors, the
computer system configured to: train a neural network to identify a
plurality of risk factors based upon a set of historical insurance
claims corresponding to real properties, the neural network
including a plurality of input layers, each input layer of the
plurality of input layers including a respective plurality of input
parameters, each input parameter of a respective plurality of input
parameters corresponding to a respective characteristic of real
properties; receive information corresponding to a target real
property, the received information including respective indications
of one or more characteristics of the target real property, at
least one characteristic of the one or more characteristics of the
target real property corresponding to an input parameter of a
respective plurality of input parameters of an input layer; analyze
the received information using the trained neural network to
determine one or more risk factors of the plurality of risk factors
associated with the target real property; determine the risk level
of the target real property based upon the one or more risk factors
associated with the target real property; and generate an
indication of the risk level of the target real property.
9. The computer system of claim 8, the computer system further
configured to: pre-process the set of historical insurance claims
corresponding to real properties to generate one or more labels for
each historical insurance claim of the set of historical insurance
claims; generate a set of labeled insurance claims by incorporating
respective one or more labels with the set of insurance claims; and
train the neural network based upon the set of labeled historical
insurance claims.
10. The computer system of claim 9, the computer system further
configured to: select a subset of the set of labeled historical
insurance claims based upon at least one label; and train the
neural network based upon the subset of the set of labeled
historical insurance claims.
11. The computer system of claim 10, wherein the at least one label
is associated with at least one selected from a group of a
geographical area, a location type, a real property type, a
percentage of an outstanding mortgage balance verse a property
value, a hazard type, an age of a real property, and a type of
insurance.
12. The computer system of claim 8, wherein: the neural network is
trained based upon at least one of a group consisting of one or
more static characteristics of the real properties corresponding to
the set of historical insurance claims and one or more dynamic
characteristics of the real properties corresponding to the set of
historical insurance claims, each dynamic characteristic of the one
or more dynamic characteristics being associated with a change over
time; and the received information corresponding to the target real
property includes respective indications of at least one of a group
consisting of one or more static characteristics of the target real
property and one or more dynamic characteristics of the target real
property.
13. The computer system of claim 8, wherein: the respective
plurality of input parameters of the plurality of input layers of
the neural network includes one or more characteristics of
applicants of the set of historical insurance claims; at least a
portion of the received information corresponding to the target
real property is obtained from an application for insurance for the
target real property; and the at least the portion of the received
information includes respective indications of one or more
characteristics of an applicant of the insurance application.
14. The computer system of claim 8, the computer system further
configured to train the neural network based upon additional
insurance claims corresponding to real properties.
15. One or more non-transitory computer readable media having
instructions stored thereon that, when executed by one or more
processors, cause the one or more processors to: train a neural
network to identify a plurality of risk factors based upon a set of
historical insurance claims corresponding to real properties, the
neural network including a plurality of input layers, each input
layer of the plurality of input layers including a respective
plurality of input parameters, each input parameter of a respective
plurality of input parameters corresponding to a respective
characteristic of real properties; receive information
corresponding to a target real property, the received information
including respective indications of one or more characteristics of
the target real property, at least one characteristic of the one or
more characteristics of the target real property corresponding to
an input parameter of a respective plurality of input parameters of
an input layer; analyze the received information using the trained
neural network to determine one or more risk factors of the
plurality of risk factors associated with the target real property;
determine the risk level of the target real property based upon the
one or more risk factors associated with the target real property;
and generate an indication of the risk level of the target real
property.
16. The one or more non-transitory computer readable media of claim
15 further including instructions that, when executed by one or
more processors, cause the one or more processors to: pre-process
the set of historical insurance claims corresponding to real
properties to generate one or more labels for each historical
insurance claim of the set of historical insurance claims; generate
a set of labeled insurance claims by incorporating respective one
or more labels with the set of insurance claims; and train the
neural network based upon the set of labeled historical insurance
claims.
17. The one or more non-transitory computer readable media of claim
16 further including instructions that, when executed by one or
more processors, cause the one or more processors to: select a
subset of the set of labeled historical insurance claims based upon
at least one label; and train the neural network based upon the
subset of the set of labeled historical insurance claims.
18. The one or more non-transitory computer readable media of claim
17, wherein the at least one label is associated with at least one
selected from a group of a geographical area, a location type, a
real property type, a percentage of an outstanding mortgage balance
verse a property value, a hazard type, an age of a real property,
and a type of insurance.
19. The one or more non-transitory computer readable media of claim
15, wherein: the neural network is trained based upon at least one
of a group consisting of one or more static characteristics of the
real properties corresponding to the set of historical insurance
claims and one or more dynamic characteristics of the real
properties corresponding to the set of historical insurance claims,
each dynamic characteristic of the one or more dynamic
characteristics being associated with a change over time; and the
received information corresponding to the target real property
includes respective indications of at least one of a group
consisting of one or more static characteristics of the target real
property and one or more dynamic characteristics of the target real
property.
20. The one or more non-transitory computer readable media of claim
15, wherein: the respective plurality of input parameters of the
plurality of input layers of the neural network includes one or
more characteristics of applicants of the set of historical
insurance claims; at least a portion of the received information
corresponding to the target real property is obtained from an
application for insurance for the target real property; and the at
least the portion of the received information includes respective
indications of one or more characteristics of an applicant of the
insurance application.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 16/136,519 filed Sep. 20, 2018 and entitled "REAL PROPERTY
MONITORING SYSTEMS AND METHODS FOR RISK DETERMINATION," which
claims priority to and the benefit of:
[0002] U.S. Prov. App. 62/564,055 filed Sep. 27, 2017 and entitled
"REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR DETECTING DAMAGE
AND OTHER CONDITIONS;"
[0003] U.S. Prov. App. 62/580,655 filed Nov. 2, 2017 and entitled
"AUTOMOBILE MONITORING SYSTEMS AND METHODS FOR DETECTING DAMAGE AND
OTHER CONDITIONS;"
[0004] U.S. Prov. App. 62/610,599 filed Dec. 27, 2017 and entitled
"AUTOMOBILE MONITORING SYSTEMS AND METHODS FOR DETECTING DAMAGE AND
OTHER CONDITIONS;"
[0005] U.S. Prov. App. 62/621,218 filed Jan. 24, 2018 and entitled
"AUTOMOBILE MONITORING SYSTEMS AND METHODS FOR LOSS MITIGATION AND
CLAIMS HANDLING,"
[0006] U.S. Prov. App. 62/621,797 filed Jan. 25, 2018 and entitled
"AUTOMOBILE MONITORING SYSTEMS AND METHODS FOR LOSS RESERVING AND
FINANCIAL REPORTING;"
[0007] U.S. Prov. App. 62/580,713 filed Nov. 2, 2017 and entitled
"REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR DETECTING DAMAGE
AND OTHER CONDITIONS;"
[0008] U.S. Prov. App. 62/618,192 filed Jan. 17, 2018 and entitled
"REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR DETECTING DAMAGE
AND OTHER CONDITIONS;"
[0009] U.S. Prov. App. 62/625,140 filed Feb. 1, 2018 and entitled
"SYSTEMS AND METHODS FOR ESTABLISHING LOSS RESERVES FOR
BUILDING/REAL PROPERTY INSURANCE;"
[0010] U.S. Prov. App. 62/646,729 filed Mar. 22, 2018 and entitled
"REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR LOSS MITIGATION
AND CLAIMS HANDLING;"
[0011] U.S. Prov. App. 62/646,735 filed Mar. 22, 2018 and entitled
"REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR RISK
DETERMINATION;"
[0012] U.S. Prov. App. 62/646,740 filed Mar. 22, 2018 and entitled
"SYSTEMS AND METHODS FOR ESTABLISHING LOSS RESERVES FOR
BUILDING/REAL PROPERTY INSURANCE;"
[0013] U.S. Prov. App. 62/617,851 filed Jan. 16, 2018 and entitled
"IMPLEMENTING MACHINE LEARNING FOR LIFE AND HEALTH INSURANCE
PRICING AND UNDERWRITING;"
[0014] U.S. Prov. App. 62/622,542 filed Jan. 26, 2018 and entitled
"IMPLEMENTING MACHINE LEARNING FOR LIFE AND HEALTH INSURANCE LOSS
MITIGATION AND CLAIMS HANDLING;"
[0015] U.S. Prov. App. 62/632,884 filed Feb. 20, 2018 and entitled
"IMPLEMENTING MACHINE LEARNING FOR LIFE AND HEALTH INSURANCE LOSS
RESERVING AND FINANCIAL REPORTING;" and,
[0016] U.S. Prov. App. 62/652,121 filed Apr. 3, 2018 and entitled
"IMPLEMENTING MACHINE LEARNING FOR LIFE AND HEALTH INSURANCE CLAIMS
HANDLING,"
[0017] the entire disclosures of which are hereby incorporated by
reference herein in their entireties.
FIELD OF INVENTION
[0018] This disclosure generally relates to detecting damage, loss,
and/or other conditions associated with a real property using a
property monitoring system. Also, machine learning methods
facilitate determining real property risk levels, as well as real
property insurance pricing and underwriting.
BACKGROUND
[0019] As computer and computer networking technology has become
less expensive and more widespread, more and more devices have
started to incorporate digital "smart" functionalities. For
example, controls and sensors capable of interfacing with a network
may now be incorporated into devices such as appliances, security
systems, light switches, and water valves, and other portions of
building monitoring systems. Furthermore, it is possible for one or
more central controllers to interface with the smart devices to
facilitate monitoring, automation, and security applications for a
building.
[0020] However, such systems may not be able to automatically
detect and characterize various conditions associated with a
building. For example, when sensors detect water in a basement of a
building, such systems may not be able to automatically determine
whether the water in the basement is due to an outside water main
breaking and flooding the property, or whether a levee has been
breached and the entire neighborhood is flooded. In another
example, such monitoring systems may not be able to detect or
sufficiently identify and describe damage that is hidden from human
view, and that typically has to be characterized by explicit human
physical exploration, such as damage between walls or in
foundations, extent and range of electrical malfunctions, etc.
Conventional systems further may not be able to formulate precise
characterizations of loss without including unconscious biases, and
may not be able to equally weight all historical data in
determining risk levels.
SUMMARY
[0021] The present disclosure generally relates to systems and
methods for detecting damage, loss, and/or other conditions
associated with a building, land, structure, or other real property
using a property monitoring system. Machine learning techniques may
facilitate determining real property risk levels, as well as real
property insurance pricing and underwriting. Embodiments of
exemplary systems and computer-implemented methods are summarized
below. The methods and systems summarized below may include
additional, less, or alternate components, functionality, and/or
actions, including those discussed elsewhere herein.
[0022] In one aspect, a real property monitoring system may include
a plurality of sensors fixedly disposed at respective locations at
a building. Each sensor may monitor a respective dynamic, physical
characteristic associated with the building, and at least some of
the plurality of sensors may be fixedly attached to the building.
The real property monitoring system may also include one or more
user interfaces via which the real property monitoring system and
end-users (e.g., residents, tenants, property owners, property
managers, etc.) of the real property monitoring system communicate;
one or more processors; and a data storage entity communicatively
connected to the one or more processors, and storing dynamic
characteristic data that is indicative of respective dynamic,
physical characteristics detected by the plurality of sensors. The
dynamic characteristic data may be generated based upon signals
transmitted by the plurality of sensors, for example. Additionally,
the real property monitoring system may include one or more network
interfaces via which third-party input is received at the real
property monitoring system. The third-party input may include
digitized information that is descriptive of an event impacting the
building, such as digital text, notes, images, etc. Typically, the
third-party that or who has generated the contents of the
third-party input is not an end-user of the real-property
monitoring system.
[0023] Further, the real property monitoring system may include a
damage detection module including a set of computer-executable
instructions stored on one or more memories. The set of
computer-executable instructions, when executed by the one or more
processors, may cause the system to train, by utilizing the
third-party input and the dynamic characteristic data corresponding
to the building, an analytics model that is predictive of one or
more conditions associated with the building. The system may apply
the trained-analytics model to at least one of the dynamic
characteristic data corresponding to the building or additional
characteristic data corresponding to the building to thereby
discover or predict at least one of the one or more conditions
associated with the building. The one or more discovered conditions
may include particular damage to the building that is associated
with the event, e.g., particular damage to the building that is
caused at least in part by the occurrence of the event, and
optionally other conditions. An indication of the particular damage
to the building (and any other discovered conditions corresponding
to the building) may be transmitted by the real property monitoring
system to at least one of a remote computing device or a user
interface.
[0024] In another aspect, a computer-implemented method of
detecting damage and other conditions at a building may include
monitoring, using a plurality of sensors included in a real
property monitoring system, a plurality of dynamic, physical
characteristics associated with the building. The plurality of
sensors may be fixedly disposed at respective locations at the
building, and at least some of the plurality of sensors may be
fixedly attached to the building. The method may include storing
dynamic characteristic data that is indicative of the plurality of
dynamic, physical characteristics associated with the building and
monitored by the plurality of sensors. The dynamic characteristic
data may be generated based upon signals transmitted by the
plurality of sensors, and stored in a data storage entity included
in the real property monitoring system, for example. Additionally,
the method may include obtaining input whose content is generated
by a third-party. The third-party input may include digitized or
digital data that is descriptive of an event impacting the
building, and may include note, text, images, and other types of
digital data, and the third-party input may be obtained via a
network interface of the real property monitoring system that is
different than, or excluded from, a set of user interfaces via
which end-users of the real property monitoring system (e.g.,
residents, tenants, property owners, property managers, etc.)
communicate with the real-property monitoring system. Typically,
the third-party that or who generates the content included in
third-party input is not an end-user of the real-property
monitoring system.
[0025] The computer-implemented method may further include
training, by using the third-party input, the dynamic
characteristic data of the building, and optionally other data, an
analytics model (such as a machine learning program, algorithm,
model, or module, or other artificial intelligence program,
algorithm, model, or module) that is predictive of one or more
conditions associated with the building. The training may be
performed, for example, by an information processor included in the
real property monitoring system. The method may also include
applying, e.g., by the information processor, the trained,
analytics model to at least one of the dynamic characteristic data
corresponding to the building or additional characteristic data
corresponding to the building, thereby discovering or predicting at
least one of the one or more conditions associated with the
building, one of which may be particular damage to the building
that is associated with the event. For instance, the occurrence of
the event may have at least in part caused the particular damage to
the building that has been discovered via the use of the trained
analytics model. Other conditions associated with the building
which may be discovered include, for example, a cause of loss
corresponding to the event and/or to the particular damage, an
adjustment to one or more terms of an insurance policy providing
insurance coverage for the building, an adjustment to the pricing
of a group of insurance policies, one of which provides insurance
coverage for the building, and the like. The method may further
include transmitting an indication of the particular damage to the
building and/or or other discovered conditions to at least one of a
remote computing device or a user interface.
[0026] In yet another aspect, a computer-implemented method of
detecting and/or estimating damage may include receiving, e.g., via
one or more processors and/or associated transceivers (such as via
wired communication or data transmission, and/or wireless
communication or data transmission over one or more radio links or
communication channels), free form text or voice/speech associated
with a submitted insurance claim for a damaged insured asset, where
the damaged insured asset comprises a building. The method may also
include identifying, e.g., via one or more processors, one or more
key words within the free form text or voice/speech; and/or based
upon the one or more keywords, determining, e.g., via one or more
processors, a cause of loss and/or peril that caused damage to the
damaged insured asset to facilitate handling an insurance claim and
enhancing the customer experience, as well as loss mitigation.
[0027] In still another aspect, a computer-implemented method of
determining damage to property may include inputting, e.g., via one
or more processors, historical claim data into a machine learning
algorithm to train the algorithm to identify one or more insured
assets, respective types of the one or more insured assets,
respective insured asset features or characteristics, one or more
perils associated with the one or more insured assets, and/or
respective repair or replacement costs of at least a portion of the
one or more insured assets, wherein the one or more insured assets
comprise a building or type of real property, such as a house or a
home. The method may further include receiving, e.g., via the one
or more processors and/or one or more transceivers (such as via
wireless communication or data transmission over one or more radio
links or communication channels), one or more images, such as
digital images, of a damaged insured asset (such as digital or
electronic images submitted by the insured via a webpage, website,
and/or mobile device); and/or inputting, via one or more
processors, the images of the damaged insured asset into a
processor having the trained machine learning algorithm installed
in a memory unit, where the trained machine learning algorithm
identifies, based upon the input image(s), a type of the damaged
insured asset, one or more features or characteristics of the
damaged insured asset, a peril associated with the damaged insured
asset, and/or a repair or replacement cost of at least a portion of
the damaged insured asset to facilitate handling an insurance claim
associated with the damaged insured asset, as well as enhancing the
customer experience and loss mitigation.
[0028] In another aspect, a computer system configured to detect
and/or estimate damage may include one or more processors, sensors,
transceivers, and/or servers configured to receive (such as via
wired communication or data transmission, and/or wireless
communication or data transmission over one or more radio links or
communication channels) free form text associated with a submitted
insurance claim for a damaged insured asset, where the damaged
insured asset comprises a building or another type of real
property. The one or more processors, sensors, transceivers, and/or
servers may be further configured to identify one or more key words
included in the free form text; and/or based upon the one or more
keywords, determine a cause of loss and/or peril that caused damage
to the damaged insured asset to facilitate handling an insurance
claim and enhancing the customer experience, as well as loss
mitigation.
[0029] In yet another aspect, a computer system configured to
determine damage to real property comprises one or more processors,
servers, sensors, and/or transceivers configured to input
historical claim data into a machine learning algorithm to train
the algorithm to identify an asset (or type thereof), at least one
feature or characteristic of the asset, a peril, and/or a repair or
replacement cost of at least a portion of the asset, where the
asset comprises real property. Additionally, the one or more
processors, servers, sensors, and/or transceivers may be further
configured to receive (such as via wired communication, and/or via
wireless communication or data transmission over one or more radio
links or communication channels), one or more images, such as
digital images, of a damaged insured asset (such as one or more
images submitted by the insured via a webpage, website, or mobile
device); and/or input the one or more images of the damaged insured
asset into a processor having the trained machine learning
algorithm installed in a memory unit, where the trained machine
learning algorithm identifies, e.g., based upon the one or more
images, a type of the damaged insured asset, one or more features
or characteristics of the damaged insured asset, a peril associated
with the damaged insured asset, and/or a repair or replacement cost
of at least a portion of the damaged insured asset to facilitate
handling an insurance claim associated with the damaged insured
asset, as well as the customer experience and loss mitigation.
[0030] In another aspect, a computer system configured to determine
damage to real property comprises one or more processors, servers,
sensors, and/or transceivers configured to input historical claim
data into a machine learning algorithm to train the algorithm to
develop a risk profile for an insurable asset based upon a type of
the insurable asset and at least one feature or characteristic of
the insurable asset, where the insurable asset comprises real
property. The one or more processors, servers, sensors, and/or
transceivers may also be configured to receive (such as via wired
communication or data transmission, and/or wireless communication
or data transmission over one or more radio links or communication
channels), one or more images, such as digital image acquired via a
mobile device or smart home controller, of an undamaged insurable
asset (such as one or more images submitted by an insured party via
a webpage, website, and/or mobile device); and/or input the one or
more images of the undamaged insurable asset into a processor
having the trained machine learning algorithm installed in a memory
unit. Based upon the one or more images, the trained machine
learning algorithm may identify or determine a risk profile for the
undamaged insurable asset to facilitate generating an insurance
quote for the undamaged insurable asset and the customer
experience, as well as loss mitigation and prevention.
[0031] In still another aspect, a computer-implemented method for
determining damage to real property may comprise, e.g., via one or
more processors, servers, sensors, and/or transceivers, inputting,
via the one or more processors, historical claim data into a
machine learning algorithm to train the algorithm to develop
respective risk profiles for at least one insurable asset based
upon a type of the at least one insurable asset and at least one
feature or characteristic of the at least one insurable asset. The
at least one insurable asset may comprise real property such as a
building, house, or home. The method may also include receiving,
e.g., via the one or more processors and/or transceivers (such as
via wired communication or data transmission, and/or via wireless
communication or data transmission over one or more radio links or
communication channels) one or more images, such as digital image
acquired via a mobile device or smart home controller, of an
undamaged insurable asset (such as one or more images submitted by
an insured party via a webpage, website, and/or mobile device);
and/or inputting, e.g., via the one or more processors, the one or
more images of the undamaged insurable asset into a processor
having the trained machine learning algorithm installed in a memory
unit, where the trained machine learning algorithm, identifies or
determines a risk profile for the undamaged insurable asset based
upon the one or more images to facilitate generating an insurance
quote for the undamaged insurable asset and the customer
experience, as well as loss mitigation and prevention.
[0032] Advantages will become more apparent to those skilled in the
art from the following description of the preferred embodiments
which have been shown and described by way of illustration. As will
be realized, the present embodiments may be capable of other and
different embodiments, and their details are capable of
modification in various respects. Accordingly, the drawings and
description are to be regarded as illustrative in nature and not as
restrictive.
BRIEF DESCRIPTION OF THE DRAWINGS
[0033] The Figures described below depict various aspects of the
system and methods disclosed therein. It should be understood that
each Figure depicts one embodiment of a particular aspect of the
disclosed system and methods, and that each of the Figures is
intended to accord with a possible embodiment thereof. Further,
wherever possible, the following description refers to the
reference numerals included in the following Figures, in which
features depicted in multiple Figures are designated with
consistent reference numerals.
[0034] There are shown in the drawings arrangements which are
presently discussed, it being understood, however, that the present
embodiments are not limited to the precise arrangements and
instrumentalities shown, wherein:
[0035] FIG. 1 illustrates a block diagram of an exemplary real
property monitoring system for detecting damage and/or loss
associated with a building, structure, land, and/or other real
property that may operate in accordance with the described
embodiments;
[0036] FIG. 2 illustrates a block diagram of an exemplary real
property monitoring system controller which may be included in the
system of FIG. 1;
[0037] FIG. 3 illustrates a flow diagram of an exemplary
computer-implemented method for detecting damage using a real
property monitoring system that may operate in accordance with the
described embodiments;
[0038] FIG. 4 depicts an exemplary computing environment in which
techniques for training a neural network to identify a risk level
of a building or other real property may be implemented, according
to one embodiment;
[0039] FIG. 5 depicts an exemplary computing environment in which
techniques for collecting and processing user input, and training a
neural network to identify a risk level of a real property may be
implemented, according to one embodiment;
[0040] FIG. 6 depicts an exemplary artificial neural network which
may be trained by the neural network unit of FIG. 4 or the neural
network training application of FIG. 5, according to one embodiment
and scenario;
[0041] FIG. 7 depicts an exemplary neuron, which may be included in
the artificial neural network of FIG. 6, according to one
embodiment and scenario;
[0042] FIG. 8 depicts text-based content of an exemplary electronic
claim record that may be processed by an artificial neural network,
in one embodiment;
[0043] FIG. 9 depicts a flow diagram of an exemplary
computer-implemented method of determining a risk level posed by a
particular real property, according to one embodiment;
[0044] FIG. 10 depicts a flow diagram of an exemplary
computer-implemented method of identifying risk indicators from
real property information, according to one embodiment;
[0045] FIG. 11 depicts a flow diagram of an exemplary
computer-implemented method of detecting and/or estimating damage
to real property, according to one embodiment;
[0046] FIG. 12 illustrates a flow diagram of an exemplary
computer-implemented method of determining damage to property that
may operate in accordance with the described embodiments;
[0047] FIG. 13 illustrates a flow diagram of an exemplary
computer-implemented method to detect and/or estimate damage to
real property, where the computer system may be included in the
system of FIG. 1; and
[0048] FIG. 14 illustrates a flow diagram of an exemplary
computer-implemented method to detect and/or estimate damage to
real property, where the computer system may be included in the
system of FIG. 1.
[0049] The Figures depict preferred embodiments for purposes of
illustration only. One skilled in the art will readily recognize
from the following discussion that alternative embodiments of the
systems and methods illustrated herein may be employed without
departing from the principles of the invention described
herein.
DETAILED DESCRIPTION
Artificial Intelligence System for Homeowners Insurance
[0050] The present embodiments are directed to, inter alia, machine
learning and/or training a model using historical home/property
insurance claim data to discover risk levels and price home/real
property insurance accordingly. Systems and methods may include
natural language processing of free-form notes/text, or free-form
speech/audio, recorded by call center and/or claim adjustor,
photos, and/or other evidence. The free-form text and/or free-form
speech may also be received from a customer who is inputting the
text or speech into a mobile device app or into a smart home
controller, and/or into a chat bot or robo-advisor.
[0051] Other inputs to a machine learning/training model may be
harvested from historical claims may, and may include make, model,
year of appliances in the house (e.g., water heater, toilet,
dishwasher, etc.), type of home, materials used in building the
home, claim paid or not paid, liability (e.g., types of injuries,
where treated, how treated, etc.), disbursements related to claim
such as hotel costs and other payouts, etc. Additional inputs to
the machine learning/training model may include home telematics
data received from a smart home controller, such as how long and
when are the doors unlocked, how often is the security system
armed, how long is the stove on and during which times of the day,
etc.
[0052] The present embodiments may facilitate discovering new
causes of loss that may be utilized to set pricing of insurance.
Causes of loss for homeowners may include wind, hail, fire, mold,
etc. The present embodiments may dynamically characterize insurance
claims, and/or dynamically determine causes of loss associated with
insurance claims, which may vary geographically. The present
embodiments may dynamically update pricing models to facilitate
better matching insurance premium price to actual risk.
Exemplary Real Property Monitoring System for Detecting Damage
[0053] FIG. 1 illustrates a block diagram of an exemplary real
property monitoring system 100. The high-level architecture
includes both hardware and software applications, as well as
various data communications channels for communicating data between
the various hardware and software components. Generally, the real
property monitoring system 100 may automatically monitor conditions
and/or characteristics (which may be dynamically occurring) of a
building, structure, land, and/or other type of real property,
e.g., any designated portion of land and/or anything permanently
placed on or under the designated portion of land.
[0054] The real property monitoring system 100 may be roughly
divided into front-end components 102 and back-end components 104.
The front-end components 102 may be disposed within, on, or at a
physical real property, such as within, on, or at a residential or
non-residential building 130. For example, the exemplary real
property monitoring system 100 may be installed in, or at, a
single-family house, an apartment building, or a condominium, or
even in or at a non-residential location, such as business,
warehouse, school, government building, museum, etc. For ease of
reading and illustration herein, the system 100 is described as
monitoring a building 130, however, it is understood that the
system 100 and/or any of the techniques, methods, apparatuses,
and/or devices described herein may be easily applied to other
types of real property.
[0055] Further, while some of the exemplary front-end components
102 are described as being disposed within or inside the building
130, it is understood that some or all of the front-end components
102 may be installed outside of or nearby the building 130. For
example, one or more front-end components 102 may be fixedly
attached to the interior and/or the exterior of the building 130,
and/or fixedly attached to respective supports or fixtures that are
located on the particular portion of land or real estate on which
the building 130 is situated. Additionally or alternatively, one or
more front-end components 102 may be removably attached to the
interior and/or the exterior of the building 130, and/or removably
attached to respective supports or fixtures that are located on the
particular portion of land or real estate on which the building 130
is situated.
[0056] Generally speaking, as used herein, one or more front-end
components 102 that are installed "at" a building 130 may be
disposed inside, outside, around, and/or nearby the building 130.
Further still, in one embodiment, one or more of the front-end
components 102 may be disposed at a location that is remote from
the building 130. For example, the remote intelligent monitoring
system controller 106R may be located remotely from the building
130 and communicatively connected with other front-end components
102, e.g., via the network 132. Generally, though, the front-end
components 102 are positioned and/or located so that the system 100
is able to monitor conditions at the building 130.
[0057] The real property monitoring system 100 may include an
intelligent monitoring system controller 106, one or more control
devices 110, one or more sensors 112, one or more appliances 114,
one or more displays 116, and/or one or more user input devices or
user interfaces 118, which are collectively referred to herein as
"intelligent building products." Typically, but not necessarily,
the real property monitoring system 100 may include multiples of
the intelligent building products 110, 112, 114, 116, and/or 118.
For example, the real property monitoring system 100 may include a
plurality of control devices 110, a plurality of sensors 112, a
plurality appliances 114, a plurality of displays 116, and/or a
plurality of user interfaces 118. In some arrangements (not shown),
the front-end components 102 may also include a back-up power
supply (e.g., battery, uninterruptable power supply, generator,
etc.).
[0058] The front-end components 102 may be connected to each other
via one or more links 120 and/or may be connected to a monitoring
system network 108 by the link(s) 120. The one or more links 120
may include at least one of a wired connection, a wireless
connection (e.g., one of the IEEE 802.11 standards), an optical
connection, etc. In certain embodiments in which the real property
monitoring system 100 may include a remote intelligent monitoring
system controller 106R, the remote intelligent monitoring system
controller 106R may be communicatively connected to the monitoring
system network 108 via another network 132 and the data and/or
communication links 122, 128, as is described in more detail in a
later section below.
Exemplary Block Diagram of Real Property Monitoring System
[0059] FIG. 2 illustrates a more detailed block diagram of the
exemplary intelligent monitoring system controller 106 of FIG. 1.
The intelligent monitoring system controller 106 may include a
controller 202 that is operatively connected to a database 210 via
a link 218. It should be noted that, while not shown, additional
databases may be linked to the controller 202 in a known manner.
Additionally, the controller 202 may include a program memory 204,
a processor 206 (may be called a microcontroller or a
microprocessor), a random-access memory (RAM) 208, and an
input/output (I/O) circuit 214, all of which may be interconnected
via an address/data bus 216. It should be appreciated that although
only one microprocessor 206 is shown, the controller 202 may
include multiple microprocessors 206. Similarly, the memory of the
controller 202 may include multiple RAMs 208 and multiple program
memories 204. Further, although the I/O circuit 214 is shown as a
single block, it should be appreciated that the I/O circuit 214 may
include a number of different types of I/O circuits. The program
memory 204 and/or the RAM 208 may include or store a graphical user
interface 220 and an intelligent monitoring system application 222,
for example.
[0060] The graphical user interface 220 may include a set of
computer-readable or computer-executable instructions that, when
executed by the processor 206, cause the display(s) 116/116R and
the user input device(s) or user interface(s) 118/118R to display
information, e.g., to an end-user, and/or to receive input from the
end-user. As used herein, the term "end-user" refers to a user or
operator of the real property monitoring system 100 who uses the
building 130 and/or is responsible, at least in part, for the
condition and/or safety of and associated with the building 130.
There may be more than one user or operator of the real property
monitoring system 100 (e.g., a family, a staff of people, etc.).
Further, the set of end-users of the real property monitoring
system 100 associated with the building 130 may include a primary
user (e.g., the owner of the building 130, a tenant of the building
130, a property manager of the building 130, or the person under
whose name the monitoring account is held for the building 130) and
one or more authorized secondary users (e.g., a personal assistant
of the primary user, a dependent child of the primary user,
employees of a tenant the building, etc.).
[0061] End-users may communicate with the real property monitoring
system 100 via a local user interface that is disposed at the
building 130 (e.g., devices 116, 118). For example, the local user
interfaces 116, 118 may include panels, touchscreens, etc. that are
fixedly attached at various locations inside of the building and/or
at various proximate locations external to the building, such as on
the parcel of land or real estate on which the building is located.
Additionally or alternatively, end-users may communicate with the
real property monitoring system 100 via a remote user interface
(e.g., devices 116R, 118R), such as a mobile or smart device,
laptop, tablet, or the like, which may be physically disposed
(e.g., when ported by the end-user) inside the building or at some
other remote location.
[0062] It is noted that, in some implementations, a local display
116 and a local user interface 118 may be an integral device,
and/or a remote display 116R and a remote user input device 118R
may be another integral device. For example, the monitoring system
100 may include one or more intelligent building control panels
that are fixedly disposed within or at the building 130, such as a
downstairs building control panel and an upstairs intelligent
building control panel, and/or may include one or more control
panels that are implemented on one or more mobile devices via which
end-users may utilize to communicate with the system 100.
[0063] Such local and/or remote control panels may respectively
include, for example, a display and/or input product (e.g., a
touchscreen) and may perform the functions of an intelligent
monitoring system controller 106 as described above. For example,
such an intelligent building control panels may be used to receive
user input to the real property monitoring system 100 as described
above, and/or to display statuses, alerts, and/or alarms to the
end-user.
[0064] The intelligent monitoring system application 222 may
include a set of computer-executable or computer-readable
instructions that, when executed by the processor 206, cause the
intelligent monitoring system controller 106 to carry out one or
more of the functions associated with the real property monitoring
system 100 described herein. Various functions of the real property
monitoring system 100 may be implemented by one or more respective
operating modules included in the intelligent monitoring system
application 222, which may be implemented as one or more software
applications and/or one or more software routines (e.g.,
computer-executable instructions that are stored on the memory 204
and that are executable by the processor 206). For example, a
monitoring module or local monitor 224 may implement functionality
for monitoring one or more dynamic, physical characteristics and/or
conditions of the building 130, and a damage detection module or
damage detector 226 may implement functionality for determining
and/or detecting damage, loss, and/or other conditions associated
with the building 130. More detailed descriptions of the local
monitor 224 and of the damage detector 226 are provided in other
sections of this disclosure.
[0065] Of course, the intelligent monitoring system application 222
may not be limited to including only the local monitor 224 and the
damage detector 226, and may include one or more other modules 228
to implement desired functionality. Similarly, the program memory
204 may store one or more applications 230 other than the graphical
user interface 220 and the intelligent monitoring system
application 222 as desired. Further, in some implementations, some
or all of the operating modules, applications, or portions thereof
may be stored in the back-end components 104 and implemented by the
back-end components 104, or on another instance of another
controller 106 associated with the building 130, which may be the
remote controller 106R or another controller (not shown). In a
non-limiting example, the monitoring module 224 may be included in
the controller 106 at the building 130, while the damage detection
module 226 may be included in the controller 106R that is remotely
situated from the building 130.
[0066] The RAM(s) 208 and program memories 204 of the controller
202 may be implemented as one or more non-transitory, tangible
computer-storage media, such as one or more semiconductor memories,
magnetically readable memories, biological memories, and/or
optically readable memories, for example. The controller 202 may
further include and/or may communicatively connect (e.g., via the
link 218) to one or more databases 210 or other data storage
mechanisms or entities 210 (e.g., one or more hard disk drives,
optical storage drives, solid state storage devices, etc.), which
may include one or more respective non-transitory, tangible
computer-storage media.
[0067] In one embodiment, at least one of the data storage entities
210 is local to the controller 202 and, in some implementations,
may be included with the controller 202 in an integral device. In
another embodiment, at least some of the data storage entities 210
may be located or disposed remotely from the controller 202, but
nonetheless may be communicatively connected to the controller 202,
e.g., via the network 108 and optionally the network 132. For
example, at least a portion of the data storage entities 210 may be
implemented as a remote data bank or data cloud storage. It is
noted that although more than one data storage entity 210 may be
included in the real property monitoring system 100, for ease of
reading, the data storage entities 210 are referred to herein using
the singular tense, e.g., the database 210 or the data storage
entity 210.
[0068] At any rate, the database 210 may be adapted to store data
related to the operation of the real property monitoring system
100. Such data might include, for example, telematics data
collected by the intelligent monitoring system controller 106 from
the intelligent building products 110, 112, 114, 116, 118
pertaining to the real property monitoring system 100 such as
sensor data, power usage data, control data, input data, other data
pertaining to the usage of the intelligent building products, user
profiles and preferences, and/or other types of data. Generally
speaking, the data stored in the database 210 may include
time-series data, where each time-series data value is associated
with a respective timestamp or other suitable indication of a
particular time at which the data value was collected and/or
stored. The intelligent monitoring system controller 106 may access
data stored in the database 210 when executing various functions
and tasks associated with the operation of the real property
monitoring system 100.
[0069] The intelligent monitoring system controller 106 may use the
monitoring application 222 to receive and process data that is
generated by the intelligent building products 110, 112, 114, 116,
118. For example, data indicative of sensed conditions may be
transmitted from the sensors 112 to the monitoring application 220,
which may then store the received data, process the received data
(e.g., in conjunction with other data received from other
intelligent building products), and take any resulting actions
based upon the processed data, such as activating alarm, notifying
an end-user, controlling another intelligent building product or
component of the building 130, etc.
[0070] The intelligent monitoring system controller may use the
graphical user interface 220 to provide, e.g., on the display 116
and/or on the remote display 116R, information based upon the data
received from the intelligent building products 110, 112, 114,
116/116R, 118/118R. For example, the intelligent monitoring system
controller 106 may be configured to provide with the display 116
and/or remote display 116R the state of one or more control devices
110 (e.g., whether a light is on or off), a reading from a sensor
112 (e.g., whether water has been detected in the basement), the
state of or a reading from an appliance 114 (e.g., whether the
stove is on), etc. Additionally, or alternatively, the intelligent
monitoring system controller 106 may use the graphical user
interface 220 to provide, e.g., on the display 116 and/or remote
display 116R, with alerts generated from the data received from the
intelligent building products 110, 112, 114, 116, 118 such as, for
example, a security system alert, a fire alert, a flooding alert,
power outage alert, etc.
[0071] The end-user may acknowledge the information provided,
disable alerts, forward an alert to a monitoring service and/or to
authorities, adjust the state of a control device 110, adjust the
state of an appliance 114, etc. using the display 116 and/or remote
display 116R in conjunction with an input device 118 and/or remote
input device 118R. For example, an end-user may receive an alert
that the security system in the building 130 has been activated on
the user's smartphone. Using his or her smartphone, the end-user
may disable the alert or forward the alert to a monitoring service
or to local authorities. In another example, an end-user may use
his or her tablet computer to check to see if s/he remembered to
turn off the stove. The tablet computer may access the intelligent
building controller 106 over the network 132 to query the current
state of the stove. If s/he sees that the stove is on, s/he may
input a command on the tablet computer to deactivate the stove. Of
course, it will be understood that the foregoing are but two
examples.
[0072] Alternatively or additionally, the intelligent monitoring
system controller 106 may send the information based upon the data
received from the intelligent building products 110, 112, 114, 116,
118 to the server 140 over the network 132, and the server 140 may
be configured to provide the information with the display 116
and/or remote display 116R. In such cases, the server 140 may act
as a middleman between the intelligent building controller 106 and
the display 116 and/or remote display 116R.
[0073] Referring again to FIG. 1, as an alternative to or in
addition to the intelligent monitoring system controller 106, a
remote intelligent monitoring system controller 106R may be used to
replace or augment the functions of the intelligent monitoring
system controller 106. The remote intelligent monitoring system
controller 106R may be a computer system or server connected to the
network 132 by one or more data and/or communications links 128,
and may generally have an architecture similar to that of the
intelligent monitoring system controller 106 shown in FIG. 2.
Further, in one embodiment, the remote intelligent monitoring
system controller 106R may be implemented using distributed
processing or "cloud computing" where the functions of the remote
intelligent monitoring system controller 106R are performed by
multiple computers or servers connected to the network 132. In one
embodiment, the remote intelligent monitoring system controller
106R may be implemented in one or more servers 140 included in the
back-end components 104 or in a similar server arrangement included
in the front-end components 102.
[0074] Again referring to FIG. 1, a control device 110 may be any
of a number of devices that allow automatic and/or remote control
of components or systems at the building 130. For example, the
control device 110 may be a thermostat that can be adjusted
according to inputs from the intelligent monitoring system
controller 106 to increase or decrease the temperature in the
building 130. Such a thermostat may control the temperature in a
room and/or the entire building 130. The control device 110 may
also be a light switch that can be adjusted according to inputs
from the intelligent monitoring system controller 106 to turn on,
turn off, brighten, and/or dim lights in the building. Such light
switches may be coupled to all the lights in a room and/or an
individual light fixture.
[0075] The control device 110 may be an automated power outlet that
can be adjusted according to inputs from the intelligent monitoring
system controller 106 to apply power and/or remove power from an
outlet. Such an automated power outlet may, for example, allow for
remote turning off of a television that was left on with a user
command, automatic turning off of an electric stove that was left
on after a threshold amount of time has elapsed since motion was
detected in the building 130, automatic turning on of a lamp when
motion is detected in the room, etc.
[0076] Similarly, the control device 110 may be an automated
circuit breaker that can be adjusted according to input from the
intelligent monitoring system controller 106 to automatically
and/or remotely apply or remove power to the entire building 130.
The control device 110 may be an automated water valve that can be
adjusted according to inputs from the intelligent monitoring system
controller 106 to adjust the flow of water in and around the
building 130 (e.g., turning on or turning off sprinklers, turning
on a pump to prevent the basement from flooding, etc.).
[0077] The control device 110 may be an automated gas valve that
can be adjusted according to input from the intelligent monitoring
system controller 106 to adjust the flow of gas in and around the
building 130. Such an automated gas valve may, for example, allow
for automatic and/or remote shutting off of gas during a fire or
earthquake, etc. Of course, other control devices 110 may be
included in the real property monitoring system 100.
[0078] The sensor 112 may be any of a number of sensors that may
gather information about conditions in or around the building 130
and/or activities in or around the building 130. That is, one or
more sensors 112 may monitor respective dynamic, physical
characteristics and/or conditions associated with the building 130
and/or its internal and/or external environment. For example, the
sensor 112 may be a smoke detector which may send an input to the
intelligent monitoring system controller 106 indicating the
presence of smoke in the building 130. The sensor 112 may also be a
part of the thermostat discussed above which may send input to the
intelligent monitoring system controller 106 indicating the
temperature in the building 130.
[0079] The sensor 112 may be a water sensor which may send input to
the intelligent monitoring system controller 106 indicating, for
example, the flow rate of a faucet, the presence of water in the
basement, a roof leak in the attic, whether the sprinkler system is
turned on, etc. The sensor 112 may be an energy monitor which may
measure the power usage of a light fixture, an appliance, an entire
room, the entire building 130, etc.
[0080] The sensor 112 may be any of a number of security sensors.
Such security sensors may include motion sensors, door sensors (to
detect the opening, closing, and/or breaking of a door), window
sensors (to detect the opening, closing, and/or break of a window),
etc. The sensor 112 may be a camera and/or a microphone which may
send visual and/or audible input to the intelligent monitoring
system controller 106.
[0081] The appliance 114 may be any of a number of appliances that
may be present in the building 130 and communicating with the
intelligent monitoring system controller 106. Each appliance 114
may be a "smart" appliance. For example, the appliance 114 may have
an integrated computer system that helps to optimize the operation
of the appliance 114. Such an integrated computer system may
assist, for example, with scheduling usage of the appliance (e.g.,
a smart dishwasher that will wait to run the dishwashing cycle
until off-peak hours), sending usage reports to the intelligent
monitoring system controller 106, sending sensor data to the
intelligent monitoring system controller 106, receiving commands
from the intelligent monitoring system controller 106, etc.
[0082] An appliance 114 may be a refrigerator, dishwasher, a
washing machine, a dryer, an oven, a stove, a microwave, a
coffeemaker, a blender, a stand mixer, a television, a video game
console, a cable box or digital video recorder, an air conditioning
unit or system, a dishwasher, etc. Additionally, an appliance 114
may also be a household robot (e.g., a robotic vacuum cleaner).
[0083] The display 116 may be any of a number of visual and/or
audible output devices that may be used to display output from the
intelligent monitoring system controller 106. Such output may
include sensor readings, alert messages, reports on the usage of
various system in the building (e.g., electricity, water, etc.), a
list of supplies to purchase (e.g., a smart refrigerator has
reported that the milk and eggs are running out and recommends to
purchase some of each), video or images from a camera, a user
interface operating in conjunction with the input device 118, etc.
The display 116 may also display data generated outside the
building 130, such as information about weather conditions, public
safety announcements, sports scores, advertisements, television
channels, videos, etc.
[0084] The display 116 may be a monitor (e.g., an LCD monitor, a
CRT monitor), a television, a screen integrated into a control
panel of the intelligent monitoring system controller 106, a screen
integrated into an appliance 114, etc. The display 116 may be used
to present a graphical user interface 220 with which the end-user
can interact with the intelligent monitoring system controller 106.
Additionally, the display 116 may also include or be connected to
speakers (not shown). Such speakers may be used to present
information from the intelligent monitoring system controller 106,
for example, in connection with the graphical user interface 220,
an audible alert, etc.
[0085] The display 116 may also be a display that is remote from
the building 130. The display 116 may be a remote display 116R
(e.g., a smartphone, tablet computer, or personal computer, etc.)
that sends and receives information over the network 132 over one
or more wireless connections or links 124 (e.g., a cellular network
connection, an 802.11 connection, and/or other type of data or
communications connection or link), and/or over one or more wired
data and/or communications connections or links 126.
[0086] The remote display 116R may include a user interface to
display information about the intelligent monitoring system to a
user via an application installed on the smartphone, tablet
computer, or laptop computer. The remote display 116R may receive
information from the intelligent monitoring system controller 106
and display information about one or more of the control device
110, sensor 112, appliance 114, display 116, or input device 118.
For example, a user may use the application on his smartphone to
receive an alert from the intelligent monitoring system controller
106 over the wireless connection(s) 124. Of course, it will be
understood that devices other than a smartphone, tablet computer,
or personal computer may be a remote display 116R.
[0087] The input device or user interface 118 may be any of a
number of input devices or user interfaces that may be used to
input data and/or commands to the intelligent monitoring system
controller 106. For example, the input device 118 may be a
keyboard, mouse, remote control, etc. The input device 118 may also
be integrated with the display 116, for example, as a touchscreen.
The input device 118 may also be a microphone which can receive
verbal commands from a user. The input device 118 may be used to
receive commands in connection with the graphical user interface
220, the intelligent monitoring system application 222, and/or any
other applications or routines associated with the exemplary real
property monitoring system 100.
[0088] The input device 118 may be a remote input device 118R
(e.g., a smartphone, tablet computer, or personal computer, etc.)
that sends and receives information over the network 132 over one
or more wireless connections 124 (e.g., a cellular network
connection, an 802.11 connection, and/or another type of wireless,
data and/or communications connection or link), and/or over one
more wired connections or links 126. The remote input device 118R
may receive user input via an application installed on the
smartphone, tablet computer, or laptop computer that may present a
user interface to display information about the intelligent
building system and receive user input. The remote input device
118R may send commands (e.g., activate, deactivate, toggle, etc.)
to the intelligent monitoring system controller 106 to affect one
or more of the control device 110, sensor 112, appliance 114,
display 116, or input device 118. For example, a user may use the
application on his smartphone to turn off his stove over the
wireless connection(s) 124. Of course, it will be understood that
devices other than a smartphone, tablet computer, or personal
computer may be a remote input device 118R.
[0089] The front-end components 102 may communicate with the
back-end components 104 via the network 132. For example, the
intelligent monitoring system products 106-118 situated at the
building 130 may be communicatively connected to the network 132
via the network 108 and one or more network interfaces 121
supporting one or more data and/or communication links 122. The one
or more links 122 may be include one or more wired communication or
data links and/or one or more wireless communication or data links,
and as such, the one or more network interfaces 121 may include one
or more physical ports and/or one or more wireless transceivers.
The remote products 106R, 116R, 118R may be similarly connected to
the network 132 over respective data and/or communication links
124, 126, and 128.
[0090] The network 132 may include one or more proprietary
networks, the public Internet, one or more virtual private
networks, or some other type of network, such as dedicated access
lines, plain ordinary telephone lines, satellite links, data links,
communications links, combinations of these, etc. Where the network
132 comprises an internet (either private and/or public), data
communications may take place over the network 132 via an Internet
communication protocol.
[0091] The back-end components 104 may include a server 140. The
server 140 may include one or more computer processors adapted and
configured to execute various software applications and components
of the real property monitoring system 100, in addition to other
software applications. Although the server 140 is depicted in FIG.
1 as being a single computing device, it is understood that the
server 140 may logically be implemented using multiple computing
devices, such as a server bank or a computing cloud.
[0092] Similarly to the intelligent monitoring system controller
106, the server 140 may have a controller 155 that is operatively
connected to a database 146 via a link 156. It should be noted
that, while not shown, additional databases may be linked to the
controller 155 in a known manner. The controller 155 may include a
program memory 160, a processor 162 (may be called a
microcontroller or a microprocessor), a random-access memory (RAM)
164, and an input/output (I/O) circuit 166, all of which may be
interconnected via an address/data bus 165.
[0093] It should be appreciated that although only one
microprocessor 162 is shown, the controller 155 may include
multiple microprocessors 162. Similarly, the memory of the
controller 155 may include multiple RAMs 164 and multiple program
memories 160. Although the I/O circuit 166 is shown as a single
block, it should be appreciated that the I/O circuit 166 may
include a number of different types of I/O circuits.
[0094] The RAM(s) 164 and program memories 160 may be implemented
as semiconductor memories, magnetically readable memories,
biologically readable memories, and/or optically readable memories,
for example. The controller 155 may also be operatively connected
to the network 132 via one or more network interfaces 134
supporting one or more data and/or communications links 135, which
may include any number of wireless and/or wired communication or
data links. As such, the one or more network interfaces 134 may
include one or more physical ports and/or one or more wireless
transceivers.
[0095] The server 140 may include and/or may be communicatively
connected to (e.g., via the link 156) to one or more databases 146
or other data storage mechanisms or entities 146 (e.g., one or more
hard disk drives, optical storage drives, solid state storage
devices, etc.), which may comprise one or more respective,
non-transitory, tangible computer-storage media. In one embodiment,
at least one of the data storage entities 146 is local to the
controller 155 and, in some implementations, may be included with
the controller 155 in an integral device.
[0096] In one embodiment, at least one of the data storage entities
146 may be located or disposed remotely from the controller 155,
but nonetheless may be communicatively connected to the controller
155, e.g., via the network 132. For example, at least a portion of
the data storage entities 146 may be implemented as a remote data
bank or data cloud storage. It is noted that although more than one
data storage entity 146 may be included in the intelligent
monitoring system 100, for ease of reading herein, the data storage
entities 146 are referred to herein using the singular tense, e.g.,
the database 146 of the data storage entity 146.
[0097] The database 146 may be adapted to store data related to the
operation of the real property monitoring system 100. Such data
might include, for example, telematics data collected by the
intelligent monitoring system controller 106 pertaining to the real
property monitoring system 100 and uploaded to the server 140, such
as data pertaining to the usage of the intelligent building
products, data pertaining to third-party input and its processing
(e.g., by the information processor 226), data pertaining to
detected damage associated with real property, user and/or customer
profiles, information about various intelligent building products
that are available for installation, web page templates and/or web
pages, or other kinds of data. The server 140 may access data
stored in the database 146 when executing various functions and
tasks associated with the operation of the real property monitoring
system 100.
[0098] As shown in FIG. 1, the program memory 160 and/or the RAM
164 may store various applications for execution by the
microprocessor 162. For example, a user-interface application 236
may provide a user interface to the server 140. The user interface
application 236 may, for example, allow a network administrator to
configure, troubleshoot, or test various aspects of the server's
operation, or otherwise to access information thereon.
[0099] A server application 238 operates to transmit and receive
information from one or more intelligent monitoring system
controllers 106 on the network 132. The server application 238 may
receive and aggregate alerts and usage data, and forward alerts to
a remote system monitor 142, e.g., via one or more data and/or
communication links 145. The server application 238 may be a single
module 238 or a plurality of modules 238A, 238B. While the server
application 238 is depicted in FIG. 1 as including two modules,
238A and 238B, the server application 238 may include any number of
modules accomplishing tasks related to implantation of the server
140.
[0100] By way of example, the module 238A may populate and transmit
the client application data and/or may receive and evaluate inputs
from the end-user to receive a data access request, while the
module 238B may communicate with one or more of the back-end
components 104 to fulfill a data access request or forward an alert
to a remote system monitor 142. In one embodiment, at least a
portion of or the entire monitoring module 224 of FIG. 2 may be
included in the server application 238 (not shown). Additionally or
alternatively, at least a portion of or the entire damage detection
module 226 of FIG. 2 may be included in the server application 238
(also not shown).
[0101] Additionally, the back-end components 104 may further
include the intelligent, remote monitoring system monitor 142. The
remote system monitor 142 may be a human monitor and/or a computer
monitor as shown in FIG. 1. The remote system monitor 142 may
receive data from the server 140 and/or the front-end components
102 over the network 132, e.g., via the link(s) 145, which may
comprise any number of wired and/or wireless data and/or
communications links. Such data may include information from and/or
about the intelligent building controller 106, control device 110,
sensor 112, appliance 114, display 116, and/or input device
118.
[0102] The remote system monitor 142 may also receive this
information indirectly (e.g., the server 140 may forward
information to the remote system monitor 142, the end-user may
forward alerts to the remote system monitor 142 with an input
device 118 or remote input device 118R). If the remote system
monitor 142 receives information indicating an event potentially
requiring an appropriate responder or authority (e.g., law
enforcement for a security alert, fire department for a fire alert,
paramedics for a medical alert, plumber for a leak alert, power
company for a power outage alert, etc.), the remote system monitor
142 may attempt to contact one of the authorized end-users (e.g.,
with a telephone call, text message, email, app alert, etc.) to
verify the event potentially requiring an appropriate responder
and/or notify the appropriate responder. For example, the remote
system monitor 142 may receive information from a smoke detector
(i.e., a sensor 112) indicating that the building 130 may be
ablaze.
[0103] The remote system monitor 142 may then attempt to contact
the end-user to ascertain the severity of the fire and ask if the
fire department should be called. If none of the end-users answer
or if an end-user requests that the fire department be notified,
the remote system monitor 142 may contact the fire department and
provide the fire dispatch with information about the building 130
(e.g., address, number of residents, configuration of building,
etc.) and/or information about the fire (e.g., smoke detected in
four rooms of the house).
[0104] In another example, the remote system monitor 142 may
receive information from water valve (i.e., a control 110)
indicating that the valve is open and may also receive information
from a water sensor (i.e., a sensor 112) indicating that the
basement has begun to flood. The remote system monitor 142 may
attempt to contact one of the authorized end-users to notify the
user and ask if remote closing of the water valve and/or calling a
plumber is requested. If none of the end-users answer, or if the
user responds in the affirmative, the remote system monitor 142 may
close the water valve and/or call a plumber to prevent further
flooding of the basement. It may be advantageous to call the
appropriate responder without first attempting to contact end-users
(e.g. if the user has indicated he or she will be out of the
country or in the wilderness).
[0105] Although the real property monitoring system 100 is shown to
include one server 140, one remote system monitor 142, one building
130, one intelligent monitoring system controller 106, one control
device 110, one sensor 112, one appliance 114, one display 116, and
one input device 118, it should be understood that different
numbers of servers 140, monitors 142, buildings 130, intelligent
monitoring system controllers 106, control devices 110, sensors
112, appliances 114, displays 116, and input devices 118 may be
utilized. For example, the system 100 may include a plurality of
servers 140 and hundreds of buildings 130, all of which may be
interconnected via the network 132.
[0106] Further, each building 130 may include more than one of each
of an intelligent monitoring system controller 106, a control
device 110, a sensor 112, an appliance 114, a display 116, and an
input device 118. For example, a large building 130 may include two
intelligent monitoring system controllers 106 that are connected to
multiple control devices 110, multiple sensors 112, multiple
appliances 114, multiple displays 116, and/or input devices
118.
[0107] Additionally several buildings 130 may be located, by way of
example rather than limitation, in separate geographic locations
from each other, including different areas of the same city,
different cities, or different states. Furthermore, the processing
performed by the one or more servers 140 may be distributed among a
plurality of servers in an arrangement known as "cloud computing."
According to the disclosed example, this configuration may provide
several advantages, such as, for example, enabling near real-time
uploads and downloads of information as well as periodic uploads
and downloads of information.
[0108] Turning now in particular to the local monitor 224 and the
damage detector 226, as previously discussed, at least a portion of
each of these components may be included in the front-end
components 102 (e.g., in the controller 106 and/or the controller
106R), and/or at least a portion of each of these components may be
included in the back-end components 104 (e.g., in the server 140).
In one embodiment, for example, a first portion of one of the
components 224, 226 may be included in the front-end components
102, while another portion of the one of the components 224, 226
may be included in the back-end components 104. In one embodiment,
for example, the entirety of one of the components 224, 226 (e.g.,
the local monitor 224) may be included in the front-end components
102, and the entirety of another one of the components 224, 226
(e.g., the damage detector 226) may be included in the back-end
components 104. Of course, other arrangements may be possible.
[0109] The local monitor 224 may implement functionality for
monitoring one or more dynamic, physical characteristics and/or
conditions associated with the building 130, e.g., of the building
130 and/or of its internal and/or external environment. As
illustrated in FIG. 1, the local monitor 224 may be communicatively
connected to one or more intelligent building products, e.g., one
or more control devices 110, one or more sensors 112, one or more
appliances 114, one or more displays 116, one or more user
interfaces 118, etc., and data generated by the intelligent
building products 110-118 may be transmitted to the local monitor
224.
[0110] Generally speaking, but not necessarily, data generated by
the intelligent building products 110-118 may be time-series data
where each data point includes a value and a corresponding
indication of time at which the value was collected, observed, or
generated by the respective intelligent building product. Control
devices 110 may generate data indicative of changes of state of
various devices at the building 130, such as on/off, opened/closed,
degree or amount (e.g., of temperature for thermostat, of amount of
light for a light dimmer, of airflow for a fan, etc.), and/or other
changes of state of the various devices. Additionally or
alternatively, control devices 110 may generate data indicative of
a control command that changed a device state, e.g., manual or
automatic adjustment of a thermostat, turning sprinklers on and
off, etc. Sensors 112 may generate data indicative of a sensed
characteristic or condition such as, for example, motion, heat,
light, water, smoke, etc.
[0111] Generally speaking, sensors 112 detect or sense various
dynamic characteristics and/or conditions of the building 130
and/or of its internal and/or external environment, and in some
cases, a degree or amount of the dynamic characteristic (e.g.,
temperature, flow, density, etc.). Appliances 114 may generate data
that is indicative of the operation of the appliances 114, such as
usage reports, appliance sensor data, and the like. Additionally or
alternatively, appliances 114 may generate data indicative of a
received command, such as a manual or automatic command to turn a
particular appliance on or off, to adjust a control on the
appliance, etc.
[0112] Displays 116 and/or user interfaces 118 may generate data
indicative of user input and/or responses that are received.
Generally speaking, dynamic characteristics of the building 130
that are monitored by the intelligent building products 110, 112,
114, 116/116R, 118/118R may be indicative of the usage of the
building 130, and/or of the usage and/or operations of components
and various systems (e.g., appliances, security system, smart
utility systems, HVAC systems, communication network systems, etc.)
that are included in and that service the building 130.
[0113] At any rate, the local monitor 224 may receive data
generated by the intelligent building products 110-118 (e.g., data
descriptive of various dynamic characteristics of and/or associated
with the building 130) and may store the received data into the
database 210 and/or the database 146. In some scenarios, the local
monitor 224 may process the data generated by the intelligent
building products 110-118 to determine one or more current
conditions associated with the building 130, and optionally one or
more resulting actions in response to the determined conditions.
For example, alerts or alarms may be sent to the remote system
monitor 142 based upon data generated by motion detectors, smoke
detectors, etc.
[0114] Additionally or alternatively, a user may be notified of a
detected condition, e.g., via a display 116 or user interface 118
at the building 130, and/or via a remote device 116R/118R. Other
actions may be possible. The local monitor 224 may store determined
and/or detected conditions and/or any resulting actions into the
database 210 and/or the database 146, e.g., as time-series
data.
[0115] The damage detector 226 may also implement functionality for
receiving and data processing third-party input or data, and
utilizing such data to detect or determine damage and/or other
conditions associated with the building 130. Third-party input or
data may include digitized information, such as digital images,
notes, text, numbers, and/or data of any suitable digital
format.
[0116] Typically, the content of third-party input or data is
generated by a party that or who is not an end-user (e.g., owner,
property manager, resident, staff, etc.) of the real property
monitoring system 100 and, in some situations, may not be
associated with the building 130. For example, a third-party may be
an agent, adjuster, call-center representative image-capturing
drone, or other representative of an insurance provider of an
insurance policy providing coverage for the building 130, and the
notes and/or images generated by the representative of the
insurance provider (e.g., during the processing of an insurance
claim and/or during a phone or email conversation) may be converted
into a digital format and provided to the real property monitoring
system 100 as third-party input.
[0117] In some scenarios, third-party input provided by
representative of an insurance provider may be included in a file
of an insurance claim, or otherwise attached thereto. A third-party
may be a reporting agency, such as a news reporting agency, a
weather service, local authorities, etc. Accordingly, third-party
input provided by such sources may include, for example, maps,
police reports, incident reports, and the like.
[0118] Additionally, the damage detector 226 may generate and/or
obtain dynamic characteristic data indicative of various dynamic
characteristics that have occurred at the building 130 and
optionally their times of occurrence, frequencies, magnitudes, etc.
The dynamic characteristic data that is associated with the
building 130 may be generated, for example, based upon signals
provided by the sensors 112 of the real property monitoring system
100, and optionally by other intelligent building products 110,
114, 116/116R, 118/118R.
[0119] At least some of the dynamic characteristic data may be
provided to the damage detector 226 By the Local Monitor 224.
Additionally or alternatively, the damage detector 226 may itself
generate at least a portion of the dynamic characteristic data,
and/or the damage detector 226 may read or access at least a
portion of the dynamic characteristic data from the data storage
area 146, 210.
[0120] Moreover, damage detector 228 may implement functionality
for determining and/or detecting damage to the building 130 and/or
other conditions associated with the building 130 using the
third-party input and the dynamic characteristic data of the
building 130, and thereby discovering and or determining one or
more conditions associated with the building 130 that, for example,
otherwise would not be characterized and/or even detected using
only sensor-generated data 112 and/or the human eye. Specifically,
in one implementation, the damage detector 228 may use the
third-party input and the dynamic characteristic data of the
building 130 to train a model, e.g., a statistical or analytical
model, which may be stored in the data storage area 146, 210. The
model may be predictive of one or conditions that may be associated
with the building 130, for example.
[0121] The damage detector 228 may apply the trained model to the
dynamic characteristic data of the building 130 and/or to another
set of dynamic characteristic data of the building 130. Outputs of
the application of the trained model may indicate one or more
conditions associated with the building 130 that are more strongly
correlated with the building 130 than are other conditions. For
example, the application of the trained model may indicate or
discover one or more conditions that are associated with both the
building 130 and the impacting event which was described by the
content of the third-party input.
[0122] In one embodiment, particular damage to the building 130,
e.g. that is at least in part caused by the impacting event, may be
determined or discovered by using the trained model. Additionally
or alternatively, other conditions associated with the building 130
such as, for example, causes of loss, quantified risk levels,
adjustments to insurance policies, etc., may be determined or
discovered by using the trained model. The damage detector 228 may
provide an indication of one or more discovered conditions
corresponding to the building 130 to other computing devices, to
user interfaces, or to other systems, e.g., via the network
132.
Exemplary Computer-Implemented Method
[0123] FIG. 3 depicts a flow diagram of an exemplary
computer-implemented method 300 of a method for monitoring a
building and/or detecting damage and other conditions at the
building. At least a portion of the method 300 may be performed,
for example, by one or more components of the real property
monitoring system 100 of FIGS. 1 and 2, and/or by other suitable
devices, apparatuses, and/or systems. For example, at least a
portion of the method 300 may be performed by the local monitor 224
and/or by the damage detector 226 of the system 100. Additionally
or alternatively, at least a portion of the method 300 may be
performed by the front-end components 102 and/or the back-end
components 104 of the system 100. For ease of illustration herein,
the method 300 is discussed with simultaneous reference to FIGS. 1
and 2.
[0124] As shown in FIG. 3, the method 300 may include monitoring
(block 302) a plurality of dynamic, physical characteristics
associated with a building. For example, referring to FIGS. 1 and
2, a plurality of sensors 112 of a real property monitoring system
100 may be utilized to monitor one or more dynamic, physical
characteristics of or associated with the building 130. The
plurality of sensors 112 may generate signals indicative of sensed,
respective dynamic, physical characteristics associated with the
building 130, such as movement, motion, temperature, moisture,
humidity, presence of smoke and/or gas, on/off (e.g., of various
devices, appliances, etc.), open/closed (e.g., of various windows,
doors, etc.), and the like. The plurality of sensors 112 may be
fixedly disposed at respective locations at the building 130 and/or
in its environment (e.g., on the interior of the building, on the
exterior of the building, on a fixture disposed on a parcel of land
or other real estate on which the building is located, etc.), and
at least some of the plurality of sensors 112 may be fixedly
attached to the building 100.
[0125] In one embodiment, monitoring the plurality of dynamic,
physical characteristics of the building (block 302) may
additionally include utilizing one or more controls 110, appliances
114, displays 116, and/or user interfaces 118 (e.g., one or more
intelligent building products) of the system 100 to monitor at
least some of the dynamic, physical characteristics, where the
intelligent building product(s)110, 114, 116, 118, 118R generate
respective signals indicative of one or more dynamic, physical
characteristics associated with the building 130. The signals
generated by the sensors 112 (and optionally by the intelligent
building products 110, 114, 116, 118, 118R) may be transmitted to
the monitoring controller 106, the remote monitoring controller
106R, and/or the server 140.
[0126] Based upon the signals generated by the sensors 112 (and
optionally by the intelligent building products 110, 114, 116, 118,
118R), dynamic characteristic data that is indicative of the
plurality of dynamic, physical characteristics that are associated
with the building 130 and that are being monitored by the plurality
of sensors (and optionally by the intelligent building products
110, 114, 116, 118, 118R) may be generated and stored (block 305).
For example, the intelligent monitoring application 222 may process
the received signals (either individually, or in combination with
other signals) to generate the dynamic characteristic data, and the
dynamic characteristic data associated with the building 130 may be
stored in a data storage entity that is included in the real
property monitoring system 130 and that is communicatively
connected to monitoring controller 106, the remote monitoring
controller 106R, the server 140, the plurality of sensors 112,
and/or to one or more of the intelligent building product(s) 110,
114, 116, 118, 118R (such as the data storage entities 146 and/or
210 shown in FIGS. 1 and 2, respectively).
[0127] Generally, the dynamic characteristic data is indicative of
detected, various dynamically occurring physical conditions inside
of, outside of, on, at, or near the building 130, and/or respective
measurements, amounts, or other indication of magnitudes of the
dynamically occurring, physical conditions associated with the
building 130. The dynamic conditions may include, for example,
dynamic conditions of a part or component of the building 130, or
dynamic conditions to which the part or component of the building
130 is subjected. For example, the foundation of the building may
be subjected to rising ground waters (a detectable dynamic
condition associated with the building), and the foundation itself
may suffer structural damage due to the exposure to rising ground
waters (another detectable dynamic condition associated with the
building).
[0128] Additionally or alternatively, the dynamic conditions may
include dynamic conditions of an object that is disposed inside, on
top of, on the property of, or otherwise near the building 130,
and/or dynamic conditions to which such an object is subjected. For
example, an electric kitchen oven may be subject to a power surge,
and the oven may short out due to the power surge, both of which
are examples of dynamic conditions associated with the building. In
some scenarios, at least a portion of the dynamic characteristic
data may be time-series data, and as such may include timestamps or
other indications of respective times/dates at which the detected
and/or measured dynamic conditions were observed or detected.
[0129] At a block 308, the method 300 may include receiving input
that has been generated by a third-party, where the third-party
input includes digitized information that is descriptive of an
event that impacts the building 130, e.g., an "impacting event."
Generally speaking, but not necessarily, an event that impacts the
building 130 may not be able to be detected, described, and/or
characterized (e.g., sufficiently characterized or described) only
by the intelligent building products of the building 130 (e.g., the
sensors 112, control(s) 110, appliance(s) 114, display(s) 116/116R,
and/or user interface(s) 118, 118R). Indeed, in some situations,
the intelligent building products 112, 110, 114, 116/116R, 118/118R
of the building 130 may remain ignorant of the occurrence of the
event impacting the building 130.
[0130] Some types of impacting events may be caused or precipitated
by an actor and/or other factors that are external to and
independent of the building 130 (e.g., aside from the impacting
event, the actor/other factors causing the impacting event do not
have a relationship or association with the building 130). Examples
of such types of impacting events include environmental,
situational, and/or weather-related events that occur in the area
in which the building 130 is located, such as hurricanes, floods,
wildfires, riots, earthquakes, manufacturing plant explosions,
train derailments, etc. Other examples of such impacting events
include events that are particular to the building 130, such as an
out-of-control vehicle running into the building, a malfunctioning
drone that falls onto the building or is propelled through a window
of the building, a failure of a gas or water pipe that delivers
utilities to the building, etc. Some types of events that impact
the building 130 may be caused or precipitated by objects or people
inside or around the building 130, for example, a clothes dryer
that catches on fire, a tree that falls on the roof of the
building, a person who slips and falls down a staircase or the
front steps of the building 130, etc.
[0131] At any rate, the digitized information included in the
third-party input may be of any suitable digital or digitized
format or formats, such as digital notes and/or text (e.g.,
free-form notes and/or text), images, numbers, files, and/or other
digital data formats. Similar to the dynamic characteristic data,
the third-party input data may be descriptive and/or indicative of
the impacting event and/or of various characteristics of the
impacting event, and optionally respective measurements, amounts,
or indications of magnitudes of various portions of the event.
Respective timestamps may capture the dates/times at which the
various third-party input data points were collected or
observed.
[0132] Typically, the third-party that generates or provides the
third-party input that is descriptive of the impacting event is not
a building owner, building property manager, resident, tenant, or
other end-user of the monitoring system 100. As such, the
third-party input data may identify and/or characterize various
aspects of the event from a perspective that is different than that
which is able to be sensed by the intelligent building products of
the building 130 and/or that is different than the immediate
experiences and observations of end-users of the building 130. For
example, sensors 112 at the building 130 may detect a high wind
speed, while the third-party input may describe a tornado, and thus
the resulting high wind speeds detected by the sensors 112. In
another example, sensors 112 at the building 130 may detect rising
waters in the basement, while the third-party input may describe a
break in a levee.
[0133] The content of the third-party input may be generated or
provided by one or more different third-parties. For example, a
third-party may be an agent, adjustor, call-center representative,
image-capturing drone, or other representative of an insurance
provider of an insurance policy for the building 130. Input
provided by such types of third-parties may be provided in
real-time, on demand, and/or in conjunction with an insurance claim
associated with the building 130, e.g., when maintained in or
attached to a file of the insurance claim.
[0134] A third-party may be another party who is not an end-user of
the monitoring system 100 of the building 130 and who is not a
representative of the building's insurance provider. For example, a
report on a travel path and strength of a hurricane provided by the
National Weather Service, a police report indicating the path of a
runaway vehicle, and a map of where and when city-wide power
outages occurred may be considered to be third-party input. The
third-party input may be received (block 308) via one or more
network interfaces 121, 134 of the real property monitoring system
100 and, in some scenarios, via the network 132.
[0135] At a block 310, the method 300 may include training a model
based upon the dynamic characteristic data associated with the
building 130 and the third-party input. The model may be, for
example, a statistical or analytical model, which may be a
publicly-available or proprietary model. The model may be
predictive of one or more conditions that may be associated with
the building 130. For example, the one or more conditions
associated with the building 130 may include particular damage at
the building 130 that was caused by the occurrence of the impacting
event, and that otherwise would not be discoverable via human
observation or investigation and, in some scenarios, would not be
discoverable via the intelligent building products 110, 112, 114,
116/116R, 118/118R of the building 130. For instance, the specific
damage to circuits, pipes, and other building support systems that
are positioned between walls of the building and that are not being
monitored by any intelligent building products 110, 112, 114,
116/116R, 118/118R may be discovered and quantified using the
model, without requiring any human, physical investigation such as
opening up the walls.
[0136] In one embodiment, the model may be trained (block 310)
using the dynamic characteristic data associated with the building
130, the third-party input, and additional types of data. For
example, the model may be trained by utilizing the dynamic
characteristic data of the building 130, the third-party input, and
static characteristic data associated with the building 130.
Generally speaking, static characteristic data associated with the
building 130 may include data that is descriptive or indicative of
one or more static characteristics of the building 130 such as, for
example, a type of the building (e.g., ranch, Cape Cod, apartment
building, storage warehouse, etc.), a material or product used to
construct the building (e.g., roofing, insulation, concrete, vapor
barriers, etc.), a make, model, and/or year of an appliance inside
the building, the grading of the parcel of land on which the
building is located, and other static characteristics.
[0137] Additionally or alternatively, the model may be trained
(block 310) by utilizing historical insurance claim data, which may
pertain to the building 130 and/or may pertain to other buildings.
Historical insurance claim data may include indications, for
example, of whether or not an insurance claim was paid; costs of
material and/or labor for replacement or repair; types of injuries,
where treated, how treated, etc.; disbursements related to the
claim such as hotel costs, rental car costs, and/or other types of
payouts; causes of loss; and the like. Historical insurance claim
data may include indications, for example, of static characteristic
data and dynamic characteristic data of the building 130 and/or of
other buildings, third-party input related to the historical
insurance claims, and/or any other types of data that is associated
with historical insurance claims of buildings and/or real
properties. Generally, historical insurance claim data may be
obtained from files or other records of insurance claims that been
filed for the building 130 and/or for other buildings.
[0138] As such, at a block 312, the method 300 may include applying
the trained analytics model to the dynamic characteristic data
corresponding to the building and/or to additional dynamic
characteristic data corresponding to the building, thereby
discovering particular damage to the building that corresponds to
the impacting event, e.g., particular damage that is caused, at
least in part, by the occurrence of the impacting event. For
example, the nature, the location, and/or the degree of particular
damage of the building 130 may be discovered at the block 312.
[0139] In one embodiment, one or more additional conditions may
also be discovered at the block 312. For example, a cause of loss
that is associated with both the building 130 and the impacting
event may be discovered at the block 312. The discovered cause of
loss may be a known cause of loss, e.g., the discovered cause of
loss is included in a set of causes of loss known to and utilized
by an insurance provider to assess insurance claims (e.g., wind,
fire, hail, mold, smoke, weight of snow or ice, freezing pipes,
etc.).
[0140] In some scenarios, a discovered cause of loss may be a new
cause of loss that is excluded from the set of known causes of
loss. In these scenarios, the method 300 may include updating the
set of known causes of loss to include the newly discovered cause
of loss. In another example, additional conditions corresponding to
the building 130 that may be discovered at the block 312 may
include adjustments to one or more terms of an insurance policy
that provides coverage for the building 130. For instance, an
adjustment to the pricing and/or other financial terms of the
insurance policy (e.g., a premium amount, a deductible amount, a
coverage amount, a replacement amount, etc.) may be discovered by
applying the trained analytics model to dynamic characteristic data
of the building 130 and to the third-party input.
[0141] The pricing and/or other financial terms of the insurance
policy may be adjusted to more accurately reflect the risk, or the
lack thereof, associated with the building 130, and in particular,
in light of the impacting event as described by the third-party
input. As such, an owner of the building 130 is able to obtain
insurance coverage for the building 130 with a policy and terms
that more accurately reflect the usage of the building 130 as well
as the impact of various events on the building 130.
[0142] At a block 315, the method 300 may include transmitting an
indication of the discovered condition(s), e.g., the discovered
particular damage of the building 130, to the remote computing
device and/or to a user interface. For example, an indication of
the particular damage to the building 130 and/or of other
conditions may be transmitted, via the network 132, to the remote
monitor 142, to a computing system of an insurance provider, to a
computing system of a first responder, to an end-user of the
monitoring system 100, etc. The recipient computing system (and, in
some embodiments, the real property monitoring system 100 itself)
may then initiate suitable actions and/or activities to mitigate
the discovered condition(s).
[0143] In one embodiment (not shown in FIG. 3), the method 300 may
include re-training or updating the model. For example, the model
may be re-trained or updated by using the third-party input, the
dynamic characteristic data of the building 130, and subsequently
received data. Subsequently received data may include, for example,
subsequently received third-party input, subsequently received
dynamic characteristic data of the building 130 and/or of other
buildings, subsequently received insurance claim data of the
building 130 and/or of other buildings, other types of data
corresponding to the building 130 and/or to other buildings that is
subsequently received, and/or other types of data corresponding to
the impacting event and/or similar events that is subsequently
received.
[0144] The re-trained or updated model may be then utilized to
discover additional information which, for example, may include
additional detail, aspects, accuracy, and/or precision to the
information descriptive of the previously-discovered condition(s),
and/or may include one or more new conditions that the previous
model was unable to discover. For example, as more insurance claim
data related to hurricane damage is used to train the model, future
applications of the updated model are more quickly and accurately
be able to differentiate hurricane damage from other types of wind
and/or water damage.
[0145] In one embodiment, the model may be updated periodically,
repeatedly, or upon demand. The updated model may then be applied
to discover one or more adjustments to an insurance policy and/or
to a group of insurance policies, such as an adjustment to pricing
and/or other insurance terms. As such, pricing models of insurance
policies are able to more accurately reflect current risk (or lack
thereof) to the buildings and other real property for which the
insurance policies provide coverage.
[0146] The benefits of just-in-time, accurate risk assessment using
real property monitoring systems may be continually adjusted and
passed along to end-users throughout the terms of their insurance
policies. Moreover, insurance providers are able to better
re-allocate pricing and other insurance terms amongst various
portions of their customer base to more efficiently mitigate
overall risk. Of course, any data corresponding to the building 130
that is collected and utilized by the real property monitoring
system 100 would be utilized with any of the systems and methods
disclosed herein with permission or affirmative consent of the
owner, tenant, property manager, and/or other end-user associated
with the building 130.
[0147] Thus, in view of the above, the systems, methods, and/or
techniques (or portions thereof) disclosed herein for using a real
property monitoring system to automatically detect damage and/or
other conditions at a building (and in particular, to detect damage
at the building caused, at least in part, by an impacting event)
enable such damage and/or other conditions to be more quickly and
accurately ascertained, discovered, and/or characterized as
compared to currently known techniques. Indeed, in some scenarios,
damage that was previously undetectable by non-invasive techniques
(e.g., damage that required human investigation and actions, such
as cutting into walls, testing electrical circuits, etc. to detect
and characterize the damage) and/or other conditions are able to be
automatically (as well as quickly and more accurately) detected and
identified using at least portions of the systems, methods, and/or
techniques disclosed herein.
[0148] As such, risk (or lack thereof) of loss associated with the
building is also able to be automatically, quickly, and accurately
identified. Accordingly, more appropriate and suitable risk
mitigation techniques may be able to be applied at or to the
building 130, e.g., in a more timely manner, to thereby prevent
additional damage and/or loss from occurring.
Overview of AI Platform for Real Property Insurance
[0149] The embodiments described herein may relate to, inter alia,
determining an accurate, granular real property insurance risk
level corresponding to a plurality of inputs. More particularly, in
some embodiments, one or more neural network models (or other
machine learning programs, algorithms, models, or modules, or other
artificial intelligence programs, algorithms, models, or modules)
may be trained using historical insurance claims data as training
input. Historical insurance claims data may include, for example,
indications of static characteristic data and dynamic
characteristic data of buildings and/or real properties;
third-party input related to the historical insurance claims;
whether or not an insurance claim was paid; costs of material
and/or labor for replacement or repair; types of injuries; where
treated, how treated, etc.; disbursements related to the claim such
as hotel costs, rental car costs, and/or other types of payouts;
causes of loss; and/or other data associated with historical
insurance claims corresponding to buildings and/or real properties.
Generally speaking, historical insurance claims data may be
obtained from files or other records of insurance claims that been
filed for buildings and/or other real properties.
[0150] Risk levels related to building and/or real property
insurance may be determined using the techniques described herein
for any number of assessments that are performed with respect to
building and/or real property insurance. In an example scenario, at
least some of the techniques disclosed herein may be utilized to
determine risk levels corresponding to an application for a new
insurance policy to provide coverage for a building or real
property, such as during the underwriting process and/or at other
stages of processing a building a real property insurance
application. In another example scenario, at least some of the
techniques disclosed herein may be utilized to determine risk
levels corresponding to a renewal or continuing eligibility of an
existing insurance policy for a building or real property, such as
during the re-underwriting process and/or other stages of
processing the renewal or continuing eligibility of the existing
insurance policy.
[0151] As such, an application for a new insurance policy for
building and/or real property insurance, a renewal or
re-underwriting of an existing insurance policy for building and/or
real property insurance, or information associated with a claim
against an existing insurance policy for building and/or real
property insurance may be provided to a client computing device
(e.g., a smartphone, tablet, laptop, desktop computing device,
wearable, or other computing device) of a user. A user of the
application, who may be an employee of a company or other entity
employing the methods described herein or a customer of that
company, may enter input into the application via a user interface
or other means. The input may be transmitted from the client
computing device to a remote computing device (e.g., one or more
servers) via a computer network, and then processed further,
including by applying input entered into the client to the one or
more trained neural network models (or other machine learning
programs, algorithms, models, or modules, or other artificial
intelligence programs, algorithms, models, or modules) to produce
labels and weights indicating net or individual risk factors.
Additionally or alternatively, input may be transmitted from a real
property monitoring system, such as the system 100, to the remote
computing device for additional processing by the one or more
trained neural network models.
[0152] For example, the remote computing device may receive the
input and determine, using a trained neural network (or other
machine learning program, algorithm, model, or module), one or more
risk indicators applicable to the input, and/or a risk level.
Herein risk indicators may be expressed numerically, as strings
(e.g., as labels), or in any other suitable format. Risk levels may
be expressed as Boolean values (e.g., risk/no risk), scaled
quantities (e.g., from 0.0-1.0), or in any other suitable format.
The determined risk indicators and/or risk level may be displayed
to the user, and/or may be provided as input to another application
(e.g., to an application which uses the risk indicators and
calculated risk in an insurance quotation calculation or for other
purposes). An insurance quotation may include a price, parameters
describing the real property, and/or one or more identified risk
indicators, among other information.
[0153] In some scenarios, additional or alternative information may
be generated (e.g., by one or more other applications) based upon
the determined risk indicators and/or risk level, and such
information may be provided to the client computing device and/or
to other computing devices of the insurance company. Examples of
additional or alternative risk-related information which may be
generated include risk mitigation imperatives or actions (and
optionally respective urgencies thereof) corresponding to a
building and/or real property insurance policy application, to an
associated insurance claim, to a renewal of an existing building
and/or real property insurance policy, or to a re-underwriting of
an existing building and/or real property insurance policy may be
determined based upon the determined risk indicators, and may be
provided to the client computing device and/or to other computing
devices of the insurance company. For example, the techniques
described herein may generate a risk mitigation imperative that is
transmitted to a customer's mobile device, e.g., "clean out dryer
vent." In another example, the techniques described herein may
generate a risk mitigation imperative to an insurance provider to
increase a deductible on a particular homeowner's policy, e.g.,
when a customer has a high frequency of small claims.
[0154] Other risk-related information such as a mitigation plan
(which may include multiple mitigation imperatives or actions, and
optionally respective urgencies thereof), notifications, etc. may
be additionally or alternatively determined based upon the
determined risk indicators, and may be provided to the client
computing device and/or to other computing devices of the insurance
company. By transmitting input to the remote computing device for
processing and analysis, an accurate risk level and/or other
risk-related information may be determined based upon a wealth of
historical knowledge and provided to the user in what may appear to
the user to be a very rapid, even instantaneous, manner.
Exemplary Environment for Identifying Risk Factors and Calculating
Risk in Data
[0155] Turning to FIG. 4, an exemplary computing environment 400,
representative of artificial intelligence platform for real
property insurance, is depicted. The computing environment 400 may
be at least partially included with the system 100, in some
implementations. Environment 400 may include input data 402 and
historical data 408, both of which may comprise a list of
parameters, a plurality (e.g., thousands or millions) of electronic
documents, or other information. As used herein, the term "data"
generally refers to information which exists in the environment 400
and is related to a real property (e.g., a house, a home, a
building, a parcel of land, or other type of real property). For
example, data may include an electronic document representing a
real property insurance claim, telematics information indicative of
environmental conditions at and/or human usage of the real
property, information related to the type of real property and/or
its characteristics and materials of which it is comprised, and/or
other information.
[0156] Data may be historical or current. Although data may be
related to an ongoing claim filed by an owner of real property, in
some embodiments, data may consist of raw data parameters entered
by a human user of the environment 400 or which is
retrieved/received from another computing system, such as the real
property monitoring system 100.
[0157] Data may or may not relate to the claims filing process, and
while some of the examples described herein refer to real property
insurance claims, it should be appreciated that the techniques
described herein may be applicable to other types of electronic
documents, in other domains. For example, the techniques herein may
be applicable to identifying risk factors in other insurance
domains, such as agricultural insurance, vehicle insurance, health
or life insurance, renters insurance, etc. In that case, the scope
and content of the data may differ.
[0158] As another example, data may be collected from an existing
customer filing a claim, a potential or prospective customer
applying for a new insurance policy or renewing an existing
insurance policy, an insurance provider (e.g., the proprietor of
the environment 400) renewing or re-underwriting an existing
insurance policy, etc., or data may be supplied by a third party
such as a company other than the proprietor of the environment 400.
In some cases, data may reside in paper files that are scanned or
entered into a digital format by a human or by an automated process
(e.g., via a scanner). Generally, data may comprise any digital
information, from any source, created at any time.
[0159] Input data 402 may be loaded into an artificial intelligence
system 404 to organize, analyze, and process input data 402 in a
manner that facilitates efficient determination of risk levels by
risk level analysis platform 406. The loading of input data 402 may
be performed by executing a computer program on a computing device
that has access to the environment 400, and the loading process may
include the computer program coordinating data transfer between
input data 402 and AI platform 404 (e.g., by the computer program
providing an instruction to AI platform 404 as to an address or
location at which input data 402 is stored). As previously
discussed, input data 402 may include data that has been entered
and stored by a user (e.g., via a mobile computing device or other
client device), and/or may include telematics data generated by one
or more buildings or other types of real property that is
automatically received by the system 400, e.g., from one or more
real property monitoring systems 100, and stored.
[0160] AI platform 404 may reference the address at which input
data 402 is stored to retrieve records from input data 402 to
perform risk level determination techniques. AI platform 404 may be
thought of as a collection of algorithms configured to receive and
process parameters, and to produce labels and, in some embodiments,
risk and/or pricing information.
[0161] As discussed below with respect to FIGS. 5, 6, and 7, AI
platform 404 may be used to train multiple neural network models
(or other machine learning programs, algorithms, models, or
modules), relating to different granular segments of real
properties. For example, AI platform 404 may be used to train a
neural network model (or other machine learning model) for real
properties that are over 100 years old. In another embodiment, AI
platform 404 may be used to train a neural network model (or other
machine learning model) for use in predicting risk of real
properties located in a particular state or locality. For example,
machine learning models may be used in underwriting and/or in
re-underwriting insurance, wherein the former may include
determining eligibility of a new applicant for an insurance
program, and the latter may include determining continued
eligibility of an existing insurance customer on an ongoing basis.
A re-underwriting action based upon a machine learning risk
determination may include cancellation or required premium
adjustment due to a changed risk of loss. For example, a customer
filing a high frequency of small claims may be required to increase
a deductible amount in order to keep an insurance policy in force.
In other cases, a customer filing a low frequency of claims may be
provided with an automatic discount or reduced deductible. At any
rate, whether for underwriting or for re-underwriting, one
embodiment of a manner in which neural networks are created and
trained is described below.
[0162] In the embodiment of FIG. 4, AI platform 404 may include
claim analysis unit 420 (which is also interchangeably referred to
herein as "input analysis unit 420"). Claim analysis unit 420 may
include speech-to-text unit 422 and image analysis or image
processing unit 424 which may comprise, respectively, algorithms
for converting human speech into text and analyzing images (e.g.,
extracting information from hotel and rental receipts). In this
way, data may comprise audio recordings (e.g., recordings made when
a customer telephones a customer service center) that may be
converted to text and further used by AI platform 404. Additionally
or alternatively, data may include images of handwritten, typed, or
printed notes (e.g., that are attached to an insurance claim, that
are transcribed by an employee or other staff member, that are
received in an email, etc.) that may be converted to text and
further used by the AI platform 404. In some embodiments, customer
behavior represented in data--including the accuracy and
truthfulness of a customer--may be encoded by claim analysis unit
420 and used by AI platform 404 to train and operate neural network
models.
[0163] Claim analysis unit 420 may also include text analysis unit
426, which may include pattern matching unit 428 and natural
language processing (NLP) unit 430. In some embodiments, text
analysis unit 426 may determine facts regarding claim inputs (e.g.,
the amount of money paid under a claim). Amounts may be determined
in a currency- and inflation-neutral manner, so that claim loss
amounts may be directly compared. In some embodiments, text
analysis unit 426 may analyze text produced by speech-to-text unit
422 or image analysis unit 424.
[0164] In some embodiments, pattern matching unit 428 may search
textual claim data loaded into AI platform 404 for specific strings
or keywords in text (e.g., "dryer vent blocked") which may be
indicative of particular types of risk. NLP unit 430 may be used to
identify, for example, entities or objects indicative of risk
(e.g., that an injury occurred to a person, and that the person's
leg was injured). NLP unit 430 may identify human speech patterns
in data, including semantic information relating to entities, such
as people, vehicles, homes, and other objects.
[0165] Relevant verbs and objects, as opposed to verbs and objects
of lesser relevance, may be determined by the use of a machine
learning algorithm analyzing historical claims. For example, both a
dryer vent, occurrences of dryer-related fires, and dates/times of
general usage of the dryer may be relevant objects. Verbs
indicating the setting of an alarm system and/or the turning on and
off of outside lighting may be relevant verbs. In some embodiments,
text analysis unit 426 may comprise text processing algorithms
(e.g., lexers and parsers, regular expressions, etc.) and may emit
structured text in a format which may be consumed by other
components.
[0166] In the embodiment of FIG. 4, AI platform 404 may include a
risk level unit 440 to determine risk based upon analysis of data.
Risk may be calculated with respect to individual attributes or
elements of data, such as by assigning a risk score between 0 and 1
to a given attribute (e.g., dryer vent). In other embodiments, risk
level unit 440 may determine an indication of risk by generating
labels which pertain to data in whole or in part. This labeling may
be accomplished in various different ways, depending on the
embodiment.
[0167] For example, risk level unit 440 may label input data 402,
or portions thereof, according to positive or negative pattern
matching according to pattern matching unit 428. For example, if
input data 402 matches the pattern "dryer vent blocked," then input
data 402 may receive labels such as (BLOCKED, VENT, DRYER) or
(FIRE, APPLIANCE). Alternately, in some embodiments, risk level
unit 440 may label input data 402, which may be raw data or a claim
filed by a customer, according to results obtained from natural
language processing unit 430 (e.g., JEWELRY, THEFT). Risk level
unit 440 may label input data 402 according to Boolean values
(e.g., PAID/NOT-PAID) or pre-determined ranges (e.g., claims having
a payout of $0-$50,000; $50,000-$500,000; $500,000-$1,000,000; or
>=$1,000,000).
[0168] Labels may be saved to and/or retrieved from an electronic
database, such as risk indication data 442, and claim labels may be
generated from already-existing labels, and/or dynamically created
labels (i.e., labels created at runtime) by risk level unit 440. A
set of labels may be associated with a set of input data 402, and
the creation of new labels may be partially or entirely based upon
existing labels and/or input data 402.
[0169] Dynamic creation of labels may, in some embodiments, be
based upon user attributes and/or metadata. For example, a resident
of the Eastern United States may be assigned a label related to
weather or another attribute unique to the region; for example, a
hurricane- or flood-related label.
[0170] As noted, in some embodiments, risk level unit 440 may
analyze input data 402 (e.g., label claims) through the use of a
neural network unit 450. Neural network unit 450 may use an
artificial neural network, or simply "neural network." The neural
network may be any suitable type of neural network, including,
without limitation, a recurrent neural network or feed-forward
neural network. The neural network may include any number (e.g.,
thousands) of nodes or "neurons" arranged in multiple layers, with
each neuron processing one or more inputs to generate a decision or
other output.
[0171] In some embodiments, neural network models may be chained
together, so that output from one model is fed into another model
as input. For example, risk level unit 440 may, in one embodiment,
apply input data 402 to a first neural network model that is
trained to generate labels. The output (e.g., labels) of this first
neural network model may be fed as input to a second neural network
model which has been trained to predict, for example, claim
settlement amounts based upon the presence of labels. The second
neural network may be trained using an inflation-adjusted set of
claim payout amounts, and respective set of risk labels, to very
accurately predict the amount of money likely to be paid on a new
claim, given only a new set of risk labels from the first model. In
another arrangement, the output of the first neural network model
may be fed as an input to a third neural network model which has
been trained to predict, for example, a likelihood of damage to a
dwelling and respective repair and/or replacement costs. The third
neural network may be trained based upon insurance claim data and
respective sets of risk labels, for example.
[0172] Other neural network models may be trained (and optionally
chained) to predict other parameters corresponding to and/or
attributes of buildings, real properties, and/or associated risk
and risk mitigation based upon labeled input data. For example,
sets of neural networks may collectively operate on input data 402
and/or historical claim data 408 to predict parameters and/or
attributes such as claim risk factors (and optionally respective
measures, levels, or quantifications of risk for each risk factor);
risk mitigation imperatives or actions; claim settlement amounts;
confidence levels, other labels; etc.
[0173] Neural network unit 450 may include training unit 452, and
risk indication unit 454. To train the neural network to identify
risk, neural network unit 450 may access electronic claims within
historical data 408. Historical data 408 may comprise a corpus of
documents including many (e.g., millions) of insurance claims which
may contain data linking customers or claimants to one or more real
properties, and which may also contain, or be linked to,
information pertaining to the customers. In particular, historical
data 408 may be analyzed by AI platform 404 to generate claim
records 410-1 through 410-n, where n is any positive integer. Each
claim 410-1 through 410-n may be processed by training unit 452 to
train one or more neural networks or other machine learning model,
module, algorithm or program) to identify claim risk factors,
including by pre-processing of historical data 408 using input
analysis unit 420 as described above, e.g., to generate
corresponding labels. For example, the training unit 452 may train
an artificial neural network (or other artificial intelligence or
machine learning algorithm, model, or module) by using a subset of
the historical claim data 408 that has respective labels applied
thereto. The training unit 452 may test and/or validate the trained
network (or the trained, other artificial intelligence or machine
learning algorithm, model, or module) by using another
non-overlapping subset of the historical claim data 408 (which may
or may not have corresponding labels) to determine the accuracy of
the fit of the trained network/algorithm/model/module, and in some
cases, to avoid or mitigate over- or under-fitting.
[0174] Generally speaking, training an artificial neural network,
machine learning algorithm, model, or module may include
establishing a network architecture, or topology, by adding layers
such as activation functions (e.g., a rectified linear unit,
softmax, etc.), loss function, and optimizer, to name a few. The
data used to train, test, and/or validate the neural network (e.g.,
the historical claim data 408) may include respective data
corresponding to a large group of inputs, which may be labeled, and
which may be divided into training, validation, and testing data
(e.g., mutually exclusive subsets of the historical claim data
408). Data that is input to the neural network (e.g., for training,
testing, or validation purposes) may be encoded in an N-dimensional
tensor, array, matrix, or other suitable data structure.
[0175] In one embodiment, a different or specific neural network
type may be selected or chosen to be trained (e.g., a recurrent
neural network, a convolutional neural network, a deep learning
neural network, etc.). Training may be performed by successive
evaluation (e.g., looping) of the network by using labeled training
samples, e.g., subsets of the labeled historical claim data 408.
The process of training the artificial neural network may cause
weights or parameters of the artificial neural network to be
created. The created weights may correspond to, for example, one or
more labels, either alone or in combination; static characteristics
of buildings and/or real properties, dynamic characteristics of
buildings and/or real properties, and/or combinations thereof;
and/or other information, attributes, characteristics, or
parameters included in and/or derived from the historical claim
data. In some implementations, the weights may be initialized to
random values. The weights may be adjusted as the network is
successively trained, e.g., by using one of several gradient
descent algorithms, to reduce loss and to cause the values output
by the network to converge to expected, or "learned," values.
[0176] In one embodiment, a regression neural network, which has no
activation function, may be selected or chosen. Therein, input data
may be normalized by mean centering, and a mean squared error loss
function may be used, in addition to mean absolute error, to
determine the appropriate loss as well as to quantify the accuracy
of the outputs.
[0177] Trained networks, algorithms, models, and/or modules may be
subject to validation and cross-validation using standard
techniques (e.g., by hold-out, K-fold, etc.). In some embodiments,
multiple neural networks may be separately trained and
operated.
[0178] At any rate, neural network 450 may, from a trained model,
identify labels that correspond to specific data, metadata, and/or
attributes within input data 402, depending on the embodiment. For
example, neural network 450 may be provided with instructions from
input analysis unit 420 indicating that one or more particular type
of insurance is associated with one or more portions of input data
402.
[0179] Neural network 450 may identify one or more insurance types
associated with the one or more portions of input data 402 (e.g.,
dwelling coverage, personal property or contents coverage, personal
liability, earthquake insurance, flood insurance, water back up of
sewer, other structures insurance, medical payments, etc.) and by
input analysis unit 420. In one embodiment, the one or more
insurance types may be identified by training the neural network
450 based upon types of peril. For example, the neural network
model may be trained to determine that fire, theft, or vandalism
may indicate comprehensive property owner's insurance coverage.
[0180] In addition, input data 402 may indicate a particular or
"target" real property. In that case, risk level unit 440 may look
up additional real property information from customer data 460
corresponding to the owner of the particular real property, and
real property data 462 corresponding to the particular real
property, respectively. For example, the age and/or type of the
particular real property (e.g., single family home, apartment
building, business storefront, etc.) may be obtained. In another
example, if a customer is a business or corporation that owns
multiple buildings, customer data 460 may include historical data
of claims filed by the owner for any of the multiple buildings. The
additional customer and/or real property information may be
provided to neural network unit 450 and may be used to analyze and
label input data 402 and, ultimately, may be used to determine
risk. For example, neural network unit 450 may be used to predict
risk based upon inputs obtained from a party applying for an
insurance policy for the real property, or based upon a claim
submitted by a party who is a holder of an existing insurance
policy. That is, in some embodiments where neural network unit 450
is trained on claim data, neural network unit 450 may predict risk
based upon raw information unrelated to the claims filing process,
or based upon other data obtained during the filing of a claim
(e.g., a claim record retrieved from historical data 408).
[0181] In one embodiment, the training process may be performed in
parallel, and training unit 452 may analyze all or a subset of
claims 410-1 through 410-n. Specifically, training unit 452 may
train a neural network to identify claim risk factors in claim
records 410-1 through 410-n. As noted, AI platform 404 may analyze
input data 402 to arrange the historical claims into claim records
410-1 through 410-n, where n is any positive integer.
[0182] Claim records 410-1 through 410-n may be organized in a flat
list structure, in a hierarchical tree structure, or by means of
any other suitable data structure. For example, the claim records
may be arranged in a tree wherein each branch of the tree is
representative of one or more customer. There, each of claim
records 410-1 through 410-n may represent a single non-branching
claim, or may represent multiple claim records arranged in a group
or tree.
[0183] Further, claim records 410-1 through 410-n may comprise
links to customers and real properties whose corresponding data is
located elsewhere. In this way, one or more claims may be
associated with one or more customers and one or more real
properties via one-to-many and/or many-to-one relationships. Risk
factors may be data indicative of a particular risk or risks
associated with a given claim, customer, and/or real property. The
status of claim records may be completely settled or in various
stages of settlement.
[0184] As used herein, the term "claim" or "real property claim"
generally refers to an electronic document, record, or file, that
represents an insurance claim (e.g., an insurance claim on a house,
home, building, or other type of real property) submitted by a
policy holder of an insurance company. Herein, "claim data" or
"historical data" generally refers to data directly entered by the
customer or insurance company including, without limitation,
free-form text notes, photographs, audio recordings, written
records, receipts (e.g., hotel and rental car, purchase of
replacement materials, repair labor, etc.), and other information
including data from legacy, including pre-Internet (e.g., paper
file), systems. Notes from claim adjusters and attorneys may also
be included.
[0185] In one embodiment, claim data may include claim metadata or
external data, which generally refers to data pertaining to the
claim that may be derived from claim data or which otherwise
describes, or is related to, the claim but may not be part of the
electronic claim record. Claim metadata may have been generated
directly by a developer of the environment 400, for example, or may
have been automatically generated as a direct product or byproduct
of a process carried out in environment 400. For example, claim
metadata may include a field indicating whether a claim was settled
or not settled, and amount of any payouts, and the identity of
corresponding payees.
[0186] Another example of claim metadata is the geographic location
in which a property is located. Yet another example of claim
metadata includes a category of the claim type (e.g., damage to the
building structure, theft of articles, liability, etc.). For
example, a single claim in historical data 408 may be associated
with a company that owns and/or leases several buildings, and may
include the name, address, and other information relating to the
company and well as information pertaining to the building
portfolio owned/leased by the company.
[0187] The claim may include a plurality of claim data and claim
metadata, including metadata indicating a relationship or linkage
to other claims in historical claim data 408. In this way, neural
network unit 450 may produce a neural network that has been trained
to associate the presence of certain input parameters with higher
or lower risk levels. A specific example of a claim is discussed
with respect to FIG. 5, below.
[0188] Once the neural network (or other machine learning model or
program) has been trained, risk indication unit 454 may apply the
trained neural network to input data 402 as processed by input
analysis unit 420. In one embodiment, input analysis unit 420 may
merely "pass through" input data 402 without modification. The
output of the neural network, indicating risk indications, such as
labels pertaining to the entirety of, or portions of input data
402, may then be provided to risk level unit 440. Risk level unit
440 may insert the output of the neural network (e.g., labels) into
an electronic database, such as risk indication data 442.
Alternatively, or additionally, risk indication unit 454 may use
label information output by the neural network to determine
attributes of input data 402, and may provide those attributes to
risk level unit 440.
[0189] In some embodiments, each label or attribute may be
associated with a confidence score and/or weight. Confidence scores
may be assigned based upon the source of the information (e.g., if
the information is from real property data 574, such as telematics
data, then a score of 1.0 may be assigned; whereas, if the
information is inferred and/or provided by a user, a lower
confidence score may be assigned). Risk level unit 440 may then
forward the labels and/or scores to risk level analysis platform
406. In some embodiments, determining a single label may require
neural network unit 450 to analyze several attributes within input
data 402. For example, an application for a new homeowners
insurance policy may be required to provide the home's age, type
(e.g., ranch, two-story, split-level, etc.), and geographical
location. Some models may include validation that will produce an
error state if a required piece of information is not provided.
[0190] AI platform 404 may further include customer data 460 and
real property data 462, which risk level unit 440 may leverage to
provide useful input parameters to neural network unit 450.
Customer data 460 may be an integral part of AI platform 404, or
may be located separately from AI platform 404. In some
embodiments, customer data 460 or real property data 462 may be
provided to AI platform 404 via separate means (e.g., via an API or
Application Programming Interface call), and may be accessed by
other units or components of environment 400. Either may be
provided by a third-party service.
[0191] Real property data 462 may include a database comprising
information describing various types of real property, including
information about legal names or identification of properties, the
year a structure was built, square footage, location, materials
used, amount of personal property insured, whether or not
additional types of insurance such as flood or earthquake insurance
was purchased for the property, etc. Real property data 462 may
indicate whether or not a property is equipped with various
features which may affect risk (e.g., security sensors and/or
systems, automatic sprinkler systems, motion detectors, etc.).
[0192] Both of customer data 460 and real property data 462 may be
used to train a neural network model. For example, in an example of
a new property insurance application to cover a target property,
risk level unit 440 may look up the applicant in the customer data
460 to determine the presence and contents of the applicant's
property insurance claim history (e.g., for other properties that
have been owned by the applicant), and may obtain from real
property data 462 the knowledge of various characteristics of the
target property and/or any property insurance claims that were
filed by previous owners of the target property.
[0193] All of the information pertaining to the applicant may then
be provided to neural network unit 450, which may--based upon its
prior training on claims from historical data 408--determine that a
plurality of labels apply to the applicant and/or to the target
property. For example, the labels may include (e.g., FLOODPLAIN,
BASEMENT). As noted, the labels may have a respective confidence
factor, and may be sorted in terms of criticality, and/or given
pre-assigned weights. The labels and/or weights may be stored in
risk indication data 442, in an embodiment. It should be
appreciated that the use of additional real property labels (e.g.,
FINISHED-BASEMENT, SUMP PUMP, GENERATOR) is envisioned in label
generation.
[0194] In some embodiments, pattern matching unit 428 and natural
language processing unit 430 may act in conjunction to determine
labels. For example, pattern matching unit 428 may include
instructions to identify words indicating flooding or the undesired
presence of water (e.g., "leak," "damp," "puddle," "mold"). Matched
data may be provided to natural language processing unit 430, which
may further process the matched data to determine parts of speech
such as verbs and objects, as well as relationships between the
objects.
[0195] The output of natural language processing unit 430 may be
provided to neural network unit 450 and used by training unit 452
to train a neural network model to label insurance types. For
example, if natural language processing unit 452 indicates a theft
of electronics or other personal property, then the neural network
may generate a label of THEFT, indicating that the input data 402
may indicate a personal property or personal articles insurance
policy. On the other hand, if natural language processing unit 452
indicates damage to multiple electronics within a home (e.g., due
to a power surge), then the neural network may generate a label of
COMPREHENSIVE.
[0196] It should be appreciated that in this example, the two
labels (THEFT and COMPREHENSIVE) are not mutually exclusive. That
is, the neural network model may generate multiple labels
corresponding to an indication by pattern matching unit 428 and/or
natural language processing unit 430 that both types of insurance
coverage are indicated. For example, due to a power surge,
electronic locks may be disabled, thus enabling the theft of the
personal articles. Further, additional processing, including by the
use of an additional neural network model, maybe used to assign
weight to a label. For example, an injury of a person who slipped
and fell on an ice dam located on the front steps may receive a
higher weight than an injury of a person who tripped over his or
her own feet and fell in the middle of a room.
[0197] The labels in risk indication data 442 may be provided to
risk level analysis platform which may perform a calculation using
the labels and/or weights. For example, in one embodiment, risk
level analysis platform 406 may sum the weights and scale the price
of a policy offered to the applicant. In other embodiments, the
risk level analysis platform 406 may apply a cut-off level, beyond
which no policy may be offered. In yet another embodiment, a
maximum and/or minimum weight may be computed, and used to scale a
base price. A maximum or minimum weight may correspond to a local
maximum (e.g., the deepest or highest level of flood waters
measured in a neighborhood), a global maximum (e.g., the homeowner
of a set of homeowners with the most claims filed in a five-year
period), or a maximum among a set of property owners.
[0198] It should be appreciated that there are many possibilities
for using the risk-related information generated by the neural
network. For example, when claim data related to a real property is
received as input data 402 and analyzed using the trained neural
network, resulting information that is generated by the neural
network and associated with identified risk may include one or more
labels (which may be the same or different from input labels), one
or more mitigation imperatives or actions that may be taken to
reduce risk at the real property, a claim mitigation plan (which
may include, for example, multiple mitigation imperatives
addressing different risk factors), and the like. The resulting
information may be generated directly by the trained neural
network, or may be generated by one or more other units (e.g.,
within the risk level analysis platform 406) operating on output of
a trained neural network (or of a chained set of trained neural
networks) that is indicative or risk types and/or degrees of
risk.
[0199] In some embodiments, labels may be associated with pre-set
weights that are stored separately from AI platform 404, and which
may be updated independently. It should also be appreciated that
the methods and techniques described herein may not be applied to
seek profit in an insurance marketplace. Rather, the methods and
techniques may be used to more fairly and equitably allocate risk
among customers in a way that is revenue-neutral, yet which strives
for fairness to all market participants, and may only be used on an
opt-in basis. For example, a homeowner may opt-in to having
telematics data generated by his or her home (and/or various
appliances, systems, and components therein) automatically utilized
to help set an insurance premium that is more reflective of risk to
the home.
[0200] Historically, claim losses may be categorized using loss
cause codes. These may be a handful of mutually-exclusive labels or
categories into which claims are categorized that only permit
coarse analysis of risk. However, the methods and systems described
herein may help risk-averse customers to lower their insurance
premiums by more granularly quantifying risk. The methods and
systems may also allow new customers to receive more accurate
pricing when they are shopping for real property insurance
products. All of the benefits provided by the methods and systems
described herein may be realized much more quickly than traditional
modeling approaches.
Exemplary Training Model System
[0201] With reference to FIG. 5, a high-level block diagram of real
property insurance risk training model system 500 is illustrated
that may implement communications between a client device 502 and a
server device 504 via network 506 to provide real property
insurance loss classification and/or risk level analysis. For
example, the training model system 500 may be utilized to analyze
the risk associated with a particular building or real property for
use in underwriting and/or pricing an insurance policy for a
particular building and/or real property. Additionally or
alternatively, the training model system 500 may be utilized to
handle a filed insurance claim and/or mitigate loss pertaining to a
particular building/real property that is covered by insurance.
[0202] FIG. 5 may correspond to one embodiment of the system 100 of
FIG. 1 and/or the environment 400 of FIG. 4, and also may include
various user/client-side components. For simplicity, client device
502 is referred to herein as client 502, and server device 504 is
referred to herein as server 504, but either device may be any
suitable computing device (e.g., a laptop, smart phone, tablet,
server, wearable device, etc.). Indeed, in one embodiment, the
client 502 may comprise one or more intelligent monitoring system
controllers 106, 106R and/or intelligent monitoring system servers
140 such as shown in FIG. 1. In some implementations, monitoring
system server 140 and training model system server 504 may be an
integral server, or may be separate and distinct servers that are
communicatively connected, e.g., via one or more networks 132.
Generally speaking, server 504 may host services relating to neural
network training and operation, and may be communicatively coupled
to client 502 via network 506.
[0203] Although only one client device is depicted in FIG. 5, it
should be understood that any number of client devices 502 may be
supported. Client device 502 may include a memory 508 and a
processor 510 for storing and executing, respectively, a module
512. While referred to in the singular, processor 510 may include
any suitable number of processors of one or more types (e.g., one
or more CPUs, graphics processing units (GPUs), cores, etc.).
Similarly, memory 508 may include one or more persistent memories
(e.g., a hard drive and/or solid state memory).
[0204] Module 512, stored in memory 508 as a set of
computer-readable instructions, may be related to an input data
collection application 516 which, when executed by the processor
510, causes input data to be stored in memory 508. The data stored
in memory 508 may correspond to, for example, raw data retrieved
from input data 402. Input data collection application 516 may be
implemented as web page (e.g., HTML, JavaScript, CSS, etc.) and/or
as a mobile application for use on a standard mobile computing
platform.
[0205] Input data collection application 516 may store information
in memory 508, including the instructions required for its
execution. While the user is using input data collection
application 516, scripts and other instructions comprising input
data collection application 516 may be represented in memory 508 as
a web or mobile application. Additionally or alternatively, while
the client device 502 is automatically collecting telematics data
generated by one or more real properties, input data collection
application 516 may execute, e.g., in the background, of the client
device 502. In one exemplary usage scenario, the collected or
acquired input data may pertain to an insurance applicant and a
target building or real property that the applicant desires to
insure. In another exemplary usage scenario, the collected or
acquired input data may pertain to an insurance claim that has been
filed for an insured target building or real property.
[0206] The input data collected by input data collection
application 516 may be stored in memory 508 and/or transmitted to
server device 504 by network interface 514 via network 506, where
the input data may be processed as described above to determine a
series of risk indications and/or a risk level. In one embodiment,
input data collection application 516 may be data used to train a
model (e.g., scanned claim data).
[0207] Client device 502 may also include GPS sensor 518, an image
sensor 520, user input device 522 (e.g., a keyboard, mouse,
touchpad, and/or other input peripheral device), and display
interface 524 (e.g., an LED screen). User input device 522 may
include components that are integral to client device 502, and/or
exterior components that are communicatively coupled to client
device 502, to enable client device 502 to accept inputs from the
user. Display 524 may be either integral or external to client
device 502, and may employ any suitable display technology.
[0208] In some embodiments, input device 522 and display 524 are
integrated, such as in a touchscreen display. Execution of the
module 512 may further cause the processor 510 to associate device
data collected from client 502 such as a time, date, and/or sensor
data (e.g., a camera for photographic or video data) with real
property and/or customer data, such as data retrieved from customer
data 460 and real property data 462, respectively.
[0209] In some embodiments, client 502 may receive data from risk
indication data 442 and risk level analysis platform 406. Such
data, indicating risk labels and/or a risk level computation, may
be presented to a user of client 502 by a display interface
524.
[0210] Execution of the module 512 may further cause the processor
510 of the client 502 to communicate with the processor 550 of the
server 504 via network interface 514 and network 506. As an
example, an application related to module 512, such as input data
collection application 516, may, when executed by processor 510,
cause a user interface to be displayed to a user of client device
502 via display interface 524. The application may include
graphical user input (GUI) components for acquiring data (e.g.,
photographs) from image sensor 520, GPS coordinate data from GPS
sensor 518, and textual user input from user input device(s) 522.
Additionally or alternatively, and as previously discussed, the
application related to the module 512, such as the data collection
application 516, may, when executed by processor 510, automatically
collect telematics data generated by one or more buildings/real
properties. For example, the input data collection application 516
may execute in the background of the client device 502.
[0211] At any rate, the processor 510 may transmit the
aforementioned acquired data to server 504, and processor 550 may
pass the acquired data to a neural network (or other machine
learning model or program), which may accept the acquired data and
perform a computation (e.g., training of the model, or application
of the acquired data to a trained neural network model (or other
trained machine learning model) to obtain a result). With specific
reference to FIG. 5, the data acquired by client 502 may be
transmitted via network 506 to a server implementing AI platform
404, and may be processed by input analysis unit 420 before being
applied to a trained neural network by risk level unit 440.
[0212] As described with respect to FIG. 5, the processing of the
input data acquired from client 502 may include associating
customer data 460 and real property data 462 with the acquired
data. The output of the neural network (or other machine learning
model) may be transmitted, by a risk level unit corresponding to
risk level unit 440 in server 504, back to client 502 for display
(e.g., in display 524) and/or for further processing.
[0213] Network interface 514 may be configured to facilitate
communications between client 502 and server 504 via any hardwired
or wireless communication network, including network 506 which may
be a single communication network, or may include multiple
communication networks of one or more types (e.g., one or more
wired and/or wireless local area networks (LANs), and/or one or
more wired and/or wireless wide area networks (WANs) such as the
Internet). Client 502 may cause insurance risk related data to be
stored in server 504 memory 552 and/or a remote insurance related
database such as customer data 460.
[0214] Server 504 may include a processor 550 and a memory 552 for
executing and storing, respectively, a module 554. Module 554,
stored in memory 552 as a set of computer-readable instructions,
may facilitate applications related to processing and/or collecting
insurance risk related data, including claim data and claim
metadata, and/or insurance policy application data. For example,
module 554 may include input analysis application 560, risk level
application 562, and neural network training application 564, in
one embodiment.
[0215] Input analysis application 560 may correspond to input
analysis unit 420 of environment 400 of FIG. 4. Risk level
application 562 may correspond to risk level unit 440 of
environment of FIG. 4, and neural network training application 564
may correspond to neural network unit 450 of environment 400 of
FIG. 4. Module 554 and the applications contained therein may
include instructions which, when executed by processor 550, cause
server 504 to receive and/or retrieve input data from (e.g., raw
data and/or an electronic claim) from client device 502. In one
embodiment, input analysis application 560 may process the data
from client 502, such as by matching patterns, converting raw text
to structured text via natural language processing, by extracting
content from images, by converting speech to text, and so on.
[0216] Throughout the aforementioned processing, processor 550 may
read data from, and write data to, a location of memory 552 and/or
to one or more databases associated with server 504. For example,
instructions included in module 554 may cause processor 550 to read
data from a historical data 570, which may include historical
property insurance claim data, among other data, stored at a data
storage area or system, which may be communicatively coupled to
server device 504, either directly or via communication network
506. Historical data 570 may correspond to historical data 408, and
processor 550 may contain instructions specifying analysis of a
series of electronic claim documents from historical data 570, as
described above with respect to claims 410-1 through 410-n of
historical data 408 in FIG. 4.
[0217] Processor 550 may query customer data 572 and real property
data 574 for data related to respective electronic claim documents
and raw data, e.g., as described with respect to FIG. 4. In one
embodiment customer data 572 and real property data 574 correspond,
respectively, customer data 460 and real property 462. In another
embodiment, customer data 572 and/or real property data 574 may not
be integral to server 504. Module 554 may also facilitate
communication between client 502 and server 504 via network
interface 556 and network 506, in addition to other instructions
and functions.
[0218] Although only a single server 504 is depicted in FIG. 5, it
should be appreciated that it may be advantageous in some
embodiments to provision multiple servers for the deployment and
functioning of AI system 402. For example, the pattern matching
unit 428 and natural language processing unit 430 of input analysis
unit 420 may require CPU-intensive processing. Therefore, deploying
additional hardware may provide additional execution speed. Each of
historical data 570, customer data 572, real property data 574, and
risk indication data 576 may be geographically distributed. For
example, at least a portion of the server 504 may be implemented
using a cloud computing system or other suitable distributed
processing system.
[0219] While the databases depicted in FIG. 5 are shown as being
communicatively coupled to server 504, it should be understood that
historical claim data 570, for example, may be located within
separate remote servers or any other suitable computing devices
communicatively coupled to server 504. For example, at least a
portion of the historical claim data 570 may be stored using a
cloud data storage system or other suitable distributed data
storage system. As such, distributed database techniques (e.g.,
sharding and/or partitioning) may be used to distribute data. In
one embodiment, a free or open source software framework such as
Apache Hadoop.RTM. may be used to distribute data and run
applications (e.g., risk level application 562). It should also be
appreciated that different security needs, including those mandated
by laws and government regulations, may in some cases affect the
embodiment chosen, and configuration of services and
components.
[0220] In a manner similar to that discussed above in connection
with FIG. 4, historical claims from historical claim data 570 may
be ingested by server 504 and used by neural network training
application 564 to train an artificial neural network. In one
exemplary implementation, the neural network training application
564 may include a plurality of input layers for customer data, a
plurality of input layers for static characteristic data of
buildings/real properties, and a plurality of layers for
building/real property telematics data or dynamically generated
characteristic data of buildings/real properties. As discussed
above, the customer data may include, for example, an insurance
applicant or policyholder, such as an individual person, a property
management company, etc.; the static characteristic data of
building/real properties may include, for example, geospatial
location, building materials, year built, square footage, roof
type, number of bathrooms, presence or absence of back-up
generator, storm shutters, fireplace, basement, etc.; and the
building/real property telematics data may include, for example,
dynamic data generated by sensors disposed at the building/real
property, such as alarm sensors, energy sensors, appliance sensors,
equipment sensors, environmental condition detection sensors,
video/image sensors, audio/sound sensors, and the like.
[0221] The artificial neural network (or other machine learning
model or program) may be trained, e.g., to determine risk factors,
to detect damage, etc., by using historical building/property
claims data in addition to the customer data, the static
characteristic data of building/real properties, and/or the dynamic
characteristic data of building/real properties. In some
embodiments, the set of historical claims data utilized to train
the artificial neural network (or other machine learning model or
program) may be a subset of the claims data that is stored in
historical data 570. For example, the subset of claims data may be
limited to that related to properties that are located in a
particular zip code or other designated geographical area, to
insurance applicants whose outstanding mortgage balance is less
than X percent of the building/real property value, to multi-family
buildings, and/or otherwise as desired. Such a subset of claims may
be identified by querying the electronic databases described above,
or by any other suitable method.
[0222] The artificial neural network (or other machine learning
model or program) may be trained to process input data pertaining
to a particular building/real property, and output one or more
corresponding indications related to building/real property risk
(e.g., one or more risk indicators corresponding to the particular
building/real property). In one exemplary usage scenario, the
trained artificial neural network (or other machine learning model
or program) may output one or more numeric value(s) that represent
the risk of various aspects of the particular building/real
property, and/or may provide indications of identified risk factors
or labels associated with, and/or descriptive of, the particular
building/real property.
[0223] In this exemplary scenario, when module 554 processes input
from client 502, the data output by the neural network(s) (or other
machine learning model or program) (e.g., data indicating labels,
risks, weights, etc.) may be passed to risk level application 562
for computation of an overall risk level, which as discussed, may
be expressed in Boolean, decimal, or any other suitable format. The
calculated risk level may then be transmitted to client device 502
and/or another device. The calculated risk level may be used for
further processing by client device 502, server device 504, or
another device, e.g., to determine insurance policy underwriting
and/or pricing.
[0224] In another exemplary usage scenario, the trained artificial
neural network (or other machine learning model or program) may
output one or more indications of detected damage to the particular
building/real property and optionally, related data such as costs
to repair the damage. In this exemplary scenario, when module 554
processes input from client 502, the data output by the neural
network(s) (or other machine learning model or program) (e.g., data
indicating damaged portions of the building, degree of damage,
costs and/or parts and labor required to repair and/or replace,
other costs, etc.) may be passed to risk level application 562.
[0225] In one embodiment, the risk level application 562 determines
or computes a claim cost corresponding to the detected damage. The
calculated claim cost and/or other data output by the neural
network(s) (or other machine learning model or program) may be
transmitted to client device 502 and/or another device for further
processing by client device 502, server device 504, or another
device, e.g., to handle the processing of an insurance claim and/or
to mitigate loss associated with the claim. Additional details
pertaining to artificial neural networks (or other machine learning
model or program) and their training are provided in later sections
of this disclosure.
[0226] It should be appreciated that the client/server
configuration depicted and described with respect to FIG. 5 is but
one possible embodiment. In some cases, a client device such as
client 502 may not be used. In that case, input data may be
entered--programmatically, or manually--directly into device 504. A
computer program or human may perform such data entry. In that
case, device may contain additional or fewer components, including
input device(s) and/or display device(s).
[0227] The most useful embodiment may vary according to the purpose
for which the AI platform is being utilized--for example, a
different hardware configuration may be preferable if the AI
platform is being used to provide a risk analysis to an end user or
customer, whereas another embodiment may be preferable if the AI
platform is being used to provide risk as part of a backend
service. Furthermore, it may be possible to package the trained
neural network for distribution to a client 502 (i.e., the trained
neural network (or other machine learning model or program) may be
operated on the client 502 without the use of a server 504).
[0228] In operation, the user of client device 502, by operating
input device 522 and viewing display 524, may open input data
collection application 516, which depending on the embodiment, may
allow the user to enter personal information. The user may be an
employee of a company controlling AI platform 404 or a customer or
end user of the company. For example, input data collection
application 516 may walk the user through the steps of applying for
homeowners, building, or real property insurance, or may walk the
user through the steps of submitting a claim.
[0229] Before the user can fully access input data collection
application 516, the user may be required to authenticate (e.g.,
enter a valid username and password). The user may then utilize
input data collection application 516. Module 512 may contain
instructions that identify the user and cause input data collection
application 516 to present a particular set of questions or prompts
for input to the user, based upon any information input data
collection application 516 collects, including without limitation
information about the user or any real property. Generally
speaking, though, the module 512 does not collect any information
about (e.g., that is indicative of and/or descriptive of) the user
and/or about (e.g., that is indicative of and/or descriptive of) a
target building/real property without first obtaining an indication
that the user has granted permission to do so. The acquired or
collected information corresponding to the user and to the
particular building may be transmitted to the artificial
intelligence platform 404 and/or server device 504 for further
analysis.
[0230] Further, module 512 may identify a subset of historical data
570 to be used in training a neural network, and/or may indicate to
server device 504 that the use of a particular neural network model
or models is appropriate. For example, if the user is applying for
earthquake insurance for a particular building, or submitting an
insurance claim pertaining to damage to the particular building
that occurred due to an earthquake, then module 512 may transmit
the user's name and personal information, the location of the
building, a photograph of the building to be insured (which may be
captured by image sensor 520); information indicative of building
materials and techniques used to construct the building; and/or
other information to server device 504 in conjunction with an
indication that an earthquake-related neural network would be
appropriate to use to process the information.
[0231] At the server device 504, the input analysis application 560
may receive the transmitted information corresponding to the user
and to the particular building (and optionally, respective
indications of one or more suitable neural network model(s) (or
other machine learning model or program)), and may format and/or
store the received data in a database, such as in the real property
data 574, so that the received data is available for use for
analysis in conjunction with other types of stored data 570, 572,
576.
[0232] In exemplary usage scenarios in which the real property
insurance risk training model system 500 is used to assess risk in
association with an application for building or real property
insurance, an insurance applicant may access client 502 to
electronically apply for insurance for a target building or
property, and/or receive an electronic quote for such insurance.
The client 502 may request and collect (e.g., with the applicant's
permission) various information that is indicative of and/or
descriptive of the applicant and of the target building/property,
which may include static characteristic data of the target
building/property, and data that is indicative or descriptive of
the applicant.
[0233] In some scenarios, the client 502 may request and collect
(with the applicant's permission) various dynamic characteristic
data that has been generated at the target property. For example,
the applicant may download or otherwise transfer dynamic
characteristic data that has been or is being collected by an
intelligent real property monitoring system 100 associated with the
target property to the client 502.
[0234] Upon reception of the applicant-provided information
collected by the client 502, risk level application 562 at the
server device 504 may utilize a trained neural network (or other
machine learning model or program) to immediately (or at a later
time) process the applicant-provided information to determine a set
of risk factors corresponding to the target building/property,
e.g., by data stored respectively in the real property data 574 and
in the customer data 572. The risk factors may be associated with
the target building/property and/or with the applicant or user. In
some embodiments, the determined set of risk factors may be stored
in electronic database such as risk indication data 576. In some
embodiments, the determined set of risk factors may be provided to
an additional application, such as to the risk level analysis
platform 406, or to an application executing in the module 512. As
noted, when a set of risk factors identified, they may be used to
determine or compute an aggregate risk level for the particular
building and, in some cases, also for the particular insurance
applicant. An aggregate risk level may be used for many purposes,
such as pricing, quoting, or underwriting of insurance
policies.
[0235] In some embodiments, location and/or other data from client
device 502 may be used by a neural network to label risk, and
labels may be linked, in that a first label implies a second label.
As noted above, location may be provided to one or more neural
networks in the AI platform to generate labels and determine risk.
For example, the zip code of a piece of property, whether provided
via GPS or entered manually by a user, may cause the neural network
to generate a label applicable to the property such as RURAL,
SUBURBAN, or URBAN. Such qualifications may be used in the
calculation of risk, and may be weighted accordingly. For example,
the neural network may assign a higher risk weight to the URBAN
label, due to the increased likelihood of theft of personal
property. Due to the increased risk of theft of personal property,
the generation of an URBAN label may be accompanied by additional
labels such as THEFT. Alternatively, or in addition, the personal
property theft label weight may be increased along with the
addition of the URBAN label.
[0236] Another label, such as LIGHTNING, may be associated with
buildings which the neural network labels as (RURAL, PLAINS). In
some embodiments, label generation may be based upon seasonal
information, in whole or in part. Additionally or alternatively,
the neural network may generate labels, and/or adjust label weights
based upon location provided in input data. For example, the
trained neural network model may learn to associate buildings
located on the eastern seaboard of the United States with higher
risk during hurricane season.
[0237] All other inputs being equal, real property risk may differ
based upon the time of year when an applicant is applying for real
property insurance. Indeed, using the techniques described herein,
risk of a particular real property may vary throughout a calendar
year (e.g., based upon seasons and/or weather), and the varying
levels of risk may be reflected in varying premium amounts, which
may be adjusted throughout the calendar year. It should be
appreciated that the quick and automatic generation of such
associations is a benefit of the methods and systems disclosed
herein, and that some of the associations may appear
counter-intuitive when analyzing large data sets.
[0238] In some implementations, by the time the user of client 502
submits an application for real property insurance or files a
claim, server 504 may have already processed the electronic claim
records in historical data 570 and trained a neural network model
(or other machine learning model or program) to analyze the
information provided by the user to output risk indications,
labels, and/or weights.
[0239] In another exemplary usage scenario of the real property
insurance risk training model system 500, the real property
insurance risk training model system 500 may be utilized to handle
the processing of a building/real property insurance claim,
generate proposed insurance claims for customer approval, and/or to
mitigate loss associated with the claim. For example, a homeowner
may access client 502 to submit a claim under the homeowner's
insurance policy related to damage to the home's kitchen due to a
cooking fire.
[0240] Client 502 may collect information from the homeowner
related to the circumstances of the cooking fire in addition to
demographic information of the home (e.g., smoke detectors,
auto-shut-off of appliances, sprinkler system, etc.), such as
photographs from image sensor 520, dynamic telematics data provided
by the home's monitoring system in the period of time during which
the cooking fire occurred, historical telematics data over time,
etc. In some embodiments, the homeowner may be prompted to make a
telephone call to discuss the filing of the claim, which may be
recorded and later provided to server 504. Additionally or
alternatively, a report generated by the fire department that put
out the fire and/or corresponding 911 call records may be obtained
and provided to the server 504.
[0241] All of the information collected may be associated with a
claim identification number so that it may be referenced as a
whole. Server 504 may process the information as it arrives, and
thus may process information collected by input data collection
application 516 at a different time than server 504 processes the
audio recording, the current and historical home telematics data,
the fire department report, and the 911 call records in the above
example. Once information sufficient to process the claim has been
collected, server 504 may pass all of the processed information
(e.g., from input analysis application) to risk level application
562, which may apply the information to the trained neural network
model (or other trained machine learning model or program).
[0242] While the claim or application processing is pending, client
device 502 may display an indication that the processing of the
claim is ongoing and/or incomplete. When the claim is ultimately
processed by server 504, an indication of completeness may be
transmitted to client 502 and displayed to user, for example via
display 524. Missing information may cause the model to abort with
an error.
[0243] In some embodiments, the labels and/or characterization of
input data (claims and otherwise) performed by the systems and
methods described herein may be capable of dynamic, incremental,
and or online training. Specifically, a model that has been trained
on a set of electronic claim records from historical data 570 may
be updated dynamically, such that the model may be updated on a
much shorter time scale. For example, the model may be adjusted
weekly or monthly to take into account newly-settled claims.
[0244] In one embodiment, the settlement of a claim may trigger an
immediate update of one or more neural network models included in
the AI platform. For example, the settlement of a claim involving
roof and gutter repair due to the weight of ice and snow may
trigger updates to a set of neural network models (or other machine
learning models or programs) pertaining to coverage due to ice and
snow weight for a particular geographical regions. In addition, or
alternatively, as new claims are filed and processed, new labels
may be dynamically generated, based upon risks identified and
generated during the training process. In some embodiments, a human
reviewer or team of reviewers may be responsible for approving the
generated labels and any associated weightings before they are
used.
[0245] In some embodiments, AI platform 404 may be trained and/or
updated to provide one or more dynamic insurance rating models
which may be provided to, for example, a governmental agency. As
discussed above, models are historically difficult to update and
updates may be performed on a yearly basis. Using the techniques
described herein, models may be dynamically updated in real-time,
or on a shorter schedule (e.g., weekly) based upon new claim
data.
[0246] While FIG. 5 depicts a particular embodiment, the various
components of environment 500 may interoperate in a manner that is
different from that described above, and/or the environment 500 may
include additional components not shown in FIG. 5. For example, an
additional server/platform may act as an interface between client
device 502 and server device 504, and may perform various
operations associated with providing the labeling and/or risk
analysis operations of server 504 to client device 502 and/or other
servers.
Exemplary Artificial Neural Network
[0247] FIG. 6 depicts an exemplary artificial neural network 600
which may be trained by neural network unit 450 of FIG. 4 or neural
network training application 564 of FIG. 5, according to one
embodiment and scenario. The example neural network 600 may include
layers of neurons, including input layer 602, one or more hidden
layers 604-1 through 604-n, and output layer 606. Each layer
comprising neural network 600 may include any number of
neurons--i.e., q and r may be any positive integers. It should be
understood that neural networks may be used to achieve the methods
and systems described herein that are of a different structure and
configuration than those depicted in FIG. 6.
[0248] Input layer 602 may receive different input data. For
example, input layer 602 may include a first input a.sub.1 which
represents an insurance type for property (e.g., dwelling), a
second input a.sub.2 representing patterns identified in input
data, a third input a.sub.3 representing a type of dwelling or
building, a fourth input a.sub.4 representing one or materials from
which the dwelling or building is constructed, a fifth input
a.sub.5 representing whether a claim was paid or not paid, a sixth
input a.sub.6 representing an inflation-adjusted dollar amount
disbursed under a claim, and so on. Input layer 602 may comprise
thousands or more inputs. In some embodiments, the number of
elements used by neural network 600 may change during the training
process, and some neurons may be bypassed or ignored if, for
example, during execution of the neural network, they are
determined to be of less relevance.
[0249] Each neuron in hidden layer(s) 604-1 through 604-n may
process one or more inputs from input layer 602, and/or one or more
outputs from a previous one of the hidden layers, to generate a
decision or other output. Output layer 606 may include one or more
outputs each indicating a label, confidence factor, and/or weight
describing one or more inputs. A label may indicate the presence
(ROOF, HAIL) or absence (DROUGHT) of a condition. In some
embodiments, however, outputs of neural network 600 may be obtained
from a hidden layer 604-1 through 604-n in addition to, or in place
of, output(s) from output layer(s) 606.
[0250] In some embodiments, each layer may have a discrete,
recognizable, function with respect to input data. For example, if
n=3, a first layer may analyze one dimension of inputs, a second
layer a second dimension, and the final layer a third dimension of
the inputs, where all dimensions are analyzing a distinct and
unrelated aspect of the input data. For example, the first
dimension may correspond to aspects of a real property that are
considered as strongly determinative, then the second dimension may
correspond to those that are considered of intermediate importance,
and finally the third dimension may correspond to those that are of
less relevance.
[0251] In other embodiments, the layers may not be clearly
delineated in terms of the functionality they respectively perform.
For example, two or more of hidden layers 604-1 through 604-n may
share decisions relating to labeling, with no single layer making
an independent decision as to labeling.
[0252] In some embodiments, neural network 600 may be constituted
by a recurrent neural network, wherein the calculation performed at
each neuron is dependent upon a previous calculation. It should be
appreciated that recurrent neural networks may be more useful in
performing certain tasks, such as automatic labeling of images.
Therefore, in one embodiment, a recurrent neural network may be
trained with respect to a specific piece of functionality with
respect to environment 400 of FIG. 4. For example, in one
embodiment, a recurrent neural network may be trained and utilized
as part of image processing unit 424 to automatically label
images.
[0253] FIG. 7 depicts an exemplary neuron 700 that may correspond
to the neuron labeled as "1,1" in hidden layer 604-1 of FIG. 6,
according to one embodiment. Each of the inputs to neuron 700
(e.g., the inputs comprising input layer 602) may be weighted, such
that input a.sub.1 through a.sub.p corresponds to weights w.sub.1
through w.sub.p, as determined during the training process of
neural network 600.
[0254] In some embodiments, some inputs may lack an explicit
weight, or may be associated with a weight below a relevant
threshold. The weights may be applied to a function .alpha., which
may be a summation and may produce a value z.sub.1 which may be
input to a function 720, labeled as f.sub.1,1(z.sub.1). The
function 720 may be any suitable linear or non-linear, or sigmoid,
function. As depicted in FIG. 7, the function 720 may produce
multiple outputs, which may be provided to neuron(s) of a
subsequent layer, or used directly as an output of neural network
600. For example, the outputs may correspond to index values in a
dictionary of labels, or may be calculated values used as inputs to
subsequent functions.
[0255] It should be appreciated that the structure and function of
the neural network 600 and neuron 700 depicted are for illustration
purposes only, and that other suitable configurations may exist.
For example, the output of any given neuron may depend not only on
values determined by past neurons, but also future neurons.
Exemplary Processing of a Claim
[0256] The specific manner in which the one or more neural networks
employ machine learning to label and/or quantify risk may differ
depending on the content and arrangement of training documents
within the historical data (e.g., historical data 408 of FIG. 4 and
historical data 570 of FIG. 5) and the input data provided by
customers or users of the AI platform (e.g., input data 402 of FIG.
4 and the data collected by input data collection application 516
of FIG. 5), as well as the data that is joined to the historical
data and input data, such as customer data 460 of FIG. 4 and
customer data 572 of FIG. 5, and real property data 462 of FIG. 4
and real property data 574 of FIG. 5.
[0257] The initial structure of the neural networks (e.g., the
number of neural networks, their respective types, number of
layers, and neurons per layer, etc.) may also affect the manner in
which the trained neural network processes the input and claims.
Also, as noted above, the output produced by neural networks may be
counter-intuitive and very complex. For illustrative purposes,
intuitive and simplified examples will now be discussed in
connection with FIG. 8.
[0258] FIG. 8 depicts text-based content of an exemplary electronic
claim record 800 which may be processed using an artificial neural
network, such as neural network 600 of FIG. 6 or a different neural
network generated by neural network unit 450 of FIG. 4 or neural
network training application 564 of FIG. 5. The term "text-based
content" as used herein includes printing (e.g., characters A-Z and
numerals 0-9), in addition to non-printing characters (e.g.,
whitespace, line breaks, formatting, and control characters).
Text-based content may be in any suitable character encoding, such
as ASCII or UTF-8 and text-based content may include HTML.
[0259] Although text-based-content is depicted in the embodiment of
FIG. 8, as discussed above, claim input data may include images,
including hand-written notes, and the AI platform may include a
neural network trained to recognize hand-writing and to convert
hand-writing to text. Further, "text-based content" may be
formatted in any acceptable data format, including structured query
language (SQL) tables, flat files, hierarchical data formats (e.g.,
XML, JSON, etc.) or as other suitable electronic objects. In some
embodiments, image and audio data may be fed directly into the
neural network(s) without being converted to text first.
[0260] With respect to FIG. 8, electronic claim record 800 includes
three sections 810a-810c, which respectively represent policy
information, loss information, and external information. Policy
information 810a may include information about the insurance policy
under which the claim has been made, including the person to whom
the policy is issued, the address of the insured property, the
different types of property coverages (e.g., dwelling, contents,
liability, etc.), liabilities, conditions, limits, deductibles,
etc. Policy information 810a may be read, for example by input
analysis unit 420 analyzing historical data such as historical data
408 and individual claims, such as claims 410-1 through 410-n.
[0261] Additional information about the insured property (e.g.,
location, type of property, year of construction, square footage,
building materials, historical claim data, historical telematics
data, etc.) may be obtained from data sources and joined to input
data. For example, additional customer data may be obtained from
customer data 460 and/or customer data 572, and additional real
property data may be obtained from real property data 452 and/or
real property data 574. In some embodiments, in addition to policy
information 810a, electronic claim record 800 may include loss
information 810b. Loss information generally corresponds to
information regarding a loss event in which a real property covered
by the policy listed in policy information 810a sustained loss, and
may be due to an accident, weather conditions, failure of building
component (such as a pipe or electrical circuit), theft, fire, or
other peril. Loss information 810b may indicate the date and time
of the loss, the type of loss (e.g., damage, total loss, theft,
etc.), whether personal injury occurred, whether the insured made a
statement in connection with the loss, whether the loss was
settled, and if so for how much money. Some real property
information may be included in electronic claim record 800, and the
additional lookup may be of real property attributes (e.g.,
building materials, square footage, etc.).
[0262] In some embodiments, more the than one loss may be
represented in loss information 810b. For example, a single event
may give rise to multiple losses under a given policy, for example,
when a tree on the property falls and damages a part of the
building as well as a visitor's automobile parked on the property.
In addition to loss information, electronic claim record 800 may
include external information 810c, including but not limited to
correspondence with the homeowner, statements made by the visitor,
before and after photographs or images, etc. External information
810c may be textual, audio, or video information. The information
may include file name references, or may be file handles or
addresses that represent links to other files or data sources, such
as linked data 820a-g. It should be appreciated that although only
links 820a-g are shown, more or fewer links may be included, in
some embodiments.
[0263] Electronic claim record 800 may include links to other
records, including other electronic claim records. For example,
electronic claim record 800 may link to notice of loss 820a, one or
more photographs 820b, one or more audio recordings 820c, one or
more investigator's reports 820d, one or more forensic reports
820e, one or more diagrams 820f, and one or more payments 820g.
Data in links 820a-820g may be ingested by an AI platform such as
AI platform 420. For example, as described above, each claim may be
ingested and analyzed by input analysis unit 420.
[0264] AI platform 404 may include instructions which cause input
analysis unit 420 to retrieve, for each link 820a-820g, all
available data or a subset thereof. Each link may be processed
according to the type of data contained therein; for example, with
respect to FIG. 4, input analysis unit 420 may process, first, all
images from one or more photograph 820b using image processing unit
424. Input analysis unit 420 may process audio recording 820c using
speech-to-text unit 422.
[0265] In some embodiments, a relevance order may be established,
and processing may be completed according to that order. For
example, portions of a claim that are identified as most
dispositive of risk may be identified and processed first. If, in
that example, they are dispositive of pricing, then processing of
further claim elements may be abated to save processing resources.
In one embodiment, once a given number of labels is generated
(e.g., 50) processing may automatically abate.
[0266] Once the various input data comprising electronic claim
record 800 has been processed, the results of the processing may,
in one embodiment, be passed to a text analysis unit, and then to a
neural network (or other machine learning model or program). If the
AI platform is being trained, then the output of input analysis
unit 420 may be passed directly to neural network unit 450. The
neurons comprising a first input layer of the neural network being
trained by neural network unit 450 may be configured so that each
neuron receives particular input(s) which may correspond, in one
embodiment, to one or more pieces of information from policy
information 810a, loss information 810b, and external information
810c.
[0267] Similarly, one or more input neurons may be configured to
receive particular input(s) from links 820a-820g. If the AI
platform is being used to accept input to predict a claim value
during the claims filing process, or to estimate the risk posed by
a new customer during the application process, then the processing
may begin with the use of an input collection application, as
discussed with respect to one embodiment in FIG. 8.
[0268] In some embodiments, analysis of input entered by a user may
be performed on a client device, such as client device 502. In that
case, output from input analysis may be transmitted to a server,
such as server 504, and may be passed directly as input to neurons
of an already-trained neural network, such as a neural network
trained by neural network training application 564.
[0269] In one embodiment, the value of a new claim may be predicted
directly by a neural network model (or other machine learning model
or program) trained on historical data 408, without the use of any
labeling. For example, a neural network (or other machine learning
model or program) may be trained such that input parameters
correspond to, for example, policy information 810a, loss
information 812b, external information 812c, and linked information
820a-820g.
[0270] The trained model may be configured so that inputting sample
parameters, such as those in the example electronic claim record
800, may accurately predict, for example, the estimate of damage
($95,000) and settled amount ($94,500). In this case, random
weights may be chosen for all input parameters.
[0271] The model may then be provided with training data from
claims 410-1 through 410-n, which are each pre-processed by the
techniques described herein with respect to FIGS. 4 and 5 to
extract individual input parameters. The electronic claim record
800 may then be tested against the model, and the model trained
with new training data claims, until the predicted dollar values
and the correct dollar values converge.
[0272] In one embodiment, the AI platform may modify the
information available within an electronic claim record. For
example, the AI platform may predict a series of labels as
described above that pertain to a given claim. The labels may be
saved in a risk indication data store, such as risk indication data
442 with respect to FIG. 4. Next, the labels and corresponding
weights, in one embodiment, may be received by risk level analysis
platform 406, where they may be used in conjunction with base rate
information to predict a claim loss value.
[0273] In some embodiments, information pertaining to the claim,
such as the coverage amount and real property type from policy
information 810a, may be passed along with the labels and weights
to risk analysis platform 406 and may be used in the computation of
a claim loss value. After the claim loss value is computed, it may
be associated with the claim, for example by writing the amount to
the loss information section of the electronic claim record (e.g.,
to the loss information section 810b of FIG. 8).
[0274] As noted above, the methods and systems described herein may
be capable of analyzing decades of electronic claim records to
build neural network models (or other machine learning models), and
the formatting of electronic claim records may change significantly
from decade to decade, even year to year. Therefore, it is
important to recognize that the flexibility built into the methods
and systems described herein allows electronic claim records in
disparate formats to be consumed and analyzed.
Exemplary Computer-Implemented Methods
[0275] Turning to FIG. 9, an exemplary computer-implemented method
900 for determining a risk level posed by a particular real
property is depicted. The method 900 may include training a neural
network (or other machine learning model or program) to identify
risk factors within electronic insurance claim records
corresponding to the particular real property and/or to the owners
of the particular real property (e.g., by an AI platform such as AI
platform 404 training a neural network (or other machine learning
model or program) by an input analysis unit 420 processing data
before passing the results of the analysis to a training unit 452
that uses the results to train a neural network model (or other
machine learning model or program)) (block 910). The method 900 may
include receiving information corresponding to the particular real
property by an AI platform (e.g., the AI platform 404 may accept
input data such as input data 402 and may process that input by the
use of an input analysis unit such as input analysis unit 420)
(block 920). The method 900 may include analyzing the information
using the trained neural network (e.g., a risk indication unit 454
applies the output of the input analysis unit 420 to trained neural
network model (or other machine learning model or program)) to
generate one or more risk indicators corresponding to the
information (e.g., the neural network (or other machine learning
model or program) produces a plurality of labels and/or
corresponding weights) (block 930) which are used to determine a
risk level corresponding to the particular real property based upon
the one or more risk indicators (e.g., risk indications are stored
in risk indication data 442, and/or passed to risk level analysis
platform 406 for computation of a risk level, which may be based
upon weights also generated by the trained neural network (or other
machine learning model or program)) (block 940). The method may
include additional, less, or alternate actions, including those
discussed elsewhere herein.
[0276] Turning to FIG. 10, a flow diagram for an exemplary
computer-implemented method 1000 of determining risk indicators
from real property information. The method 1000 may be implemented
by a processor (e.g., processor 550) executing, for example, a
portion of AI platform 404, including input analysis unit 420,
pattern matching unit 428, natural language processing unit 130,
and neural network unit 150. In particular, the processor 520 may
execute an input data collection application 516 and an input
device 522 to cause the processor 525 to acquire application input
1010 from a user of a client 502 and/or automatically from the
client 502 (such as when the client 502 is included in an
intelligent building monitoring system 100).
[0277] The processor 510 may further execute the input data
collection application 516 to cause the processor 510 to transmit
application input 1010 from the user via network interface 514 and
a network 506 to a server (e.g., server 504). Processor 550 of
server 504 may cause module 554 of server 504 to process
application input 1010. Input analysis application 560 may analyze
application input 1010 according to the methods describe above. For
example, real property information may be queried from real
property data such as real property data 574. An address or other
geographical indication of the real property in application input
1010 may be provided as a parameter to real property data 574.
[0278] Real property data 574 may return a result indicating that a
corresponding real property was found in real property data 574,
and that it is a vacation rental home located in on the Eastern
seaboard of the United States. Similarly, the purpose provided in
application input 1010 may be provided to a natural language
processing unit (e.g., NLP unit 130), which may return a structured
result indicating that the real property is owned by a company that
owns and rents out multiple vacation rental homes in the area. The
result of processing the application input 1010 may be provided to
a risk level unit (e.g., risk level unit 140) which will apply the
input parameters to a trained neural network model.
[0279] In one embodiment, the trained neural network model (or
other machine learning model or program) may produce a set of
labels and confidence factors 1020. The set of labels and
confidence factors 1020 may contain labels that are inherent in the
application input 1010 (e.g., RENTAL-PROPERTY) or that are queried
based upon information provided in the application input 1010
(e.g., BEACHFRONT, based upon address). However, the set of labels
and confidence factors 1020 may include additional labels (e.g.,
HURRICANE SHUTTERS and RAISED STRUCTURE) that are not evident from
the application input 1010 or any related/queried information.
After being generated by the neural network, the set of labels and
confidence factors 1020 may then be saved to an electronic database
such as risk indication data 576, and/or passed to a risk level
analysis platform 106, whereupon a total risk may be computed and
used in a pricing quote provided to the user of client 502.
[0280] It should be appreciated that many more types of information
may be extracted from the application input 1010 (e.g., from
example links 520a-520g as shown in FIG. 8). In one embodiment, the
pricing quote may be a weighted average of the products of label
weights and confidences. The method 1000 may be implemented, for
example, in response to a party accessing client 502 for the
purpose of applying for an insurance policy, or adding (via an
application) an additional insured to an existing policy. The
method may include additional, less, or alternate actions,
including those discussed elsewhere herein.
[0281] FIG. 11 depicts a flow diagram of an exemplary
computer-implemented method 1030 of detecting and/or estimating
damage to real property. In one embodiment, one or more processors,
servers, sensors, and/or transceivers are configured to perform at
least a portion of the method 1030. For example, at least a portion
of the method 1030 may be performed by one or more components of
the system 100, the system 400, and/or the system 500. Additionally
or alternatively, in some implementations, the method 1030 may
operate in conjunction with one or more portions of one or more
other methods described elsewhere herein.
[0282] At any rate, at a block 1032, the method 1030 may include
receiving free form text, voice, and/or speech associated with a
submitted insurance claim for a damaged insured asset, where the
damaged insured asset comprises a building, home, or another type
of real property. For example, one or more processors and/or
associated transceivers (such as via wired communication or data
transmission, and/or via wireless communication or data
transmission over one or more radio links or communication
channels) may receive the free form text, voice, and/or speech. The
free-form text or voice/speech may be associated with or input via
webpage accessed by customer or by an insurance agent, for example,
or via an Internet page accessed by call center representative.
[0283] Additionally, at a block 1035, the method 1030 may include
identifying, e.g., via the one or more processors, one or more key
words within the free form text or voice/speech. The one or more
key words may be or may be associated with, for example, fire,
smoke, wind, hail, water, storm surge, tornado, hurricane,
electrical, plumbing, property damage, liability, medical,
ambulance, materials, cabinets, fireplace, bathroom, bedroom,
kitchen, upstairs, roof, downstairs, basement, structure or
structural components, security system, appliance, refrigerator,
washer, dryer, oven, stove, and/or lightning, to name a few. In one
embodiment, the free-form text or voice/speech may be input into a
processor that has and/or executes a first machine learning
algorithm that is trained to accept, example, at least one type of
free from text or voice/speech and/or an indication of at least one
type of insured asset, and to identify at least one keyword
associated with the previous word at least one respective cause of
loss and/or peril based upon the accepted input. The first machine
learning algorithm may be dynamically or continuously updated or
trained to dynamically update a set of keywords associated with at
least one respective cause of loss and/or peril, if desired.
[0284] At a block 1038, the method 1030 may include determining,
e.g., via the one or more processors, a cause of loss and/or peril
that caused damage to the damaged insured asset to facilitate
handling an insurance claim and enhancing online customer
experience. The determination may be made at the block 1038 based
upon the one or more keywords, for example, and the cause of loss
and/or peril may be wind, water, storm surge, smoke, fire, hail,
hurricane, tornado, etc. In one embodiment, the block 1038 may
include inputting the one or more keywords into a processor having
a second machine learning algorithm that is trained to accept, as
input, at least one keyword and/or an indication of at least one
type of insured asset, and to identify at least one respective
cause of loss and/or peril based upon the accepted input. In some
scenarios, the second machine learning algorithm may be dynamically
or continuously updated or trained to dynamically update a set of
causes of loss and/or perils.
[0285] Further, in some implementations (not shown in FIG. 11), the
method 1030 may additionally include retrieving or receiving, e.g.,
via the one or more processors and/or transceivers, an insurance
policy associated with the damaged insured asset, and/or
determining whether or not the determined cause of loss and/or
peril is covered under the insurance policy. Still further, in some
implementations (also not shown in FIG. 11), the method 1030 may
include receiving, e.g., via the one or more processors and/or
transceivers, one or more images of the damaged insured asset (such
digital or electronic images acquired via a mobile device or smart
home controller), analyzing the one or more images to determine a
second cause of loss and/or peril, and comparing the second cause
of loss or peril with the first determined cause of loss and/or to
verify an accuracy of the submitted insurance claim or to identify
potential fraud or build up. For example, at least some of the
received images may be input into a machine learning algorithm
trained to accept images of assets as input and determine a cause
of loss and/or peril and/or to generate damage estimates and/or
repair/replacement costs for the asset based upon the accepted
images.
[0286] FIG. 12 depicts a flow diagram of a computer-implemented
method 1040 of determining damage to property. In one embodiment,
one or more processors, servers, sensors, and/or transceivers are
configured to perform at least a portion of the method 1040. For
example, at least a portion of the method 1040 may be performed by
one or more components of the system 100, the system 400, and/or
the system 500. Additionally or alternatively, in some
implementations, the method 1040 may operate in conjunction with
one or more portions of one or more other methods described
elsewhere herein.
[0287] The method 1040 may include inputting (block 1042), e.g.,
via one or more processors, historical property insurance claim
data into a machine learning algorithm to train the algorithm to
identify one or more insured assets (and/or respective types
thereof), one or more respective insured asset features or
characteristics, one or more perils associated with the one or more
insured assets, and/or respective repair or replacement costs of at
least a portion of the one or more insured assets. The one or more
insured assets may include one or more buildings and/or types of
real property, for example, a house or a home, and the one or more
features or characteristics of the damaged insured asset may
include location, square footage, cabinet type, roof type, siding
type, type of fireplace, and/or material type, to name a few. At a
block 1045, the method 1040 may include receiving one or more
images, such as one or more digital images acquired via a mobile
device or smartphone or a smart home controller, of a damaged
insured asset that is or includes real property (such as images
submitted by the insured via a webpage).
[0288] The one or more images of the damaged insured asset may be
received (block 1045) via the one or more processors and/or one or
more transceivers (such as via wired communication or data
transmission, and/or via wireless communication or data
transmission over one or more radio links or communication
channels), for example. Additionally, the method 1040 may include
inputting (block 1048), e.g., via one or more processors, the
images of the damaged insured asset into a processor having or
having access to the trained machine learning algorithm installed
in a memory unit. The trained machine learning algorithm may, based
upon the input, determine a type of the damaged insured asset, one
or more features or characteristics of the damaged insured asset, a
peril associated with the damaged insured asset, and/or a repair or
replacement cost of at least a portion of the damaged insured asset
to facilitate handling an insurance claim associated with the
damaged insured asset. The peril associated with the damaged
insured asset may be at least one of fire, smoke, water, hail,
wind, storm surge, hurricane, or tornado.
[0289] Further, in some implementations (not shown in FIG. 12), the
method 1040 may additionally include retrieving or receiving, e.g.,
via the one or more processors and/or transceivers, an insurance
policy associated with the damaged insured asset, and/or
determining whether or not the determined cause of loss and/or
peril is covered under the insurance policy.
[0290] FIG. 13 depicts a flow diagram of a computer-implemented
method 1050 for determining damage to real property. In one
embodiment, one or more processors, servers, sensors, and/or
transceivers are configured to perform at least a portion of the
method 1050. For example, at least a portion of the method 1050 may
be performed by one or more components of the system 100, the
system 400, and/or the system 500. Additionally or alternatively,
in some implementations, the method 1050 may operate in conjunction
with one or more portions of one or more other methods described
elsewhere herein.
[0291] At a block 1052, the method 1050 may include inputting
historical claim data into a machine learning algorithm to train
the algorithm to develop a risk profile for an insurable asset
based upon a type of the insurable asset and at least one feature
or characteristic of the insurable asset, where the insurable asset
comprises real property, such as a house, home, building, or other
type of real property. The at least one feature characteristic of
the insurable asset may include, for example, one or more static
characteristics of the real property, such as location, square
footage, cabinet type, roof type, siding type, type of fireplace,
type of windows, or material type, to name a few. In some
embodiments, the at least one feature characteristic of the
insurable asset may include, for example, one or more dynamic
characteristics of the real property, for example, the alarm system
is typically set while occupants are away, the thermostat is
automatically adjusted throughout the day, a surveillance camera is
automatically turned on when a motion sensor is tripped, etc.
[0292] At a block 1055, the method 1050 may further include
receiving (such as via wired communication or data transmission,
and/or via wireless communication or data transmission over one or
more radio links or communication channels), one or more images,
such as a digital image acquired via a mobile device or smart home
controller, of an undamaged insurable asset (such as one or more
images submitted by an insured party via a webpage, web site,
mobile device, and/or smart home controller). Additionally, at a
block 1058, the method 1050 may include inputting the one or more
images of the undamaged insurable asset into a processor having the
trained machine learning algorithm installed in a memory unit
(block 1058). The trained machine learning algorithm may, based
upon the one or more images, identify or determine a risk profile
for the undamaged insurable asset to facilitate generating an
insurance quote for the undamaged insurable asset.
[0293] It is noted that the methods and systems herein may prompt
an insurance applicant and/or an insured party to improve a risk
profile of a target real property. For example, an intelligent home
monitoring system 100 may automatically prompt a user to modify
various automatic settings, to enable certain behaviors and usages
of the system 100 and various situations, etc. Those skilled in the
art will appreciate that the foregoing are intended to be simple
examples for purposes of illustration, and that more complex
embodiments and scenarios are envisioned.
[0294] In one embodiment (not shown in FIG. 13), the method 1050
may include generating an insurance policy and/or determining an
insurance rate for the undamaged insurable asset based at least in
part upon the risk profile developed for the undamaged insurable
asset. For example, the insurance rate may include a usage-based
insurance (UBI) rate. The insurance policy and/or the insurance
rate may be electronically transmitted to an owner of the undamaged
insurable asset for review and/or approval, which may be provided
by the owner electronically, if desired.
[0295] FIG. 14 depicts a flow diagram of an example
computer-implemented method 1060 for determining damage to real
property. In one embodiment, one or more processors, servers,
sensors, and/or transceivers are configured to perform at least a
portion of the method 1060. For example, at least a portion of the
method 1060 may be performed by one or more components of the
system 100, the system 400, and/or the system 500. Additionally or
alternatively, in some implementations, the method 1060 may operate
in conjunction with one or more portions of one or more other
methods described elsewhere herein.
[0296] At a block 1062, the method 1060 may include inputting,
e.g., via the one or more processors, historical claim data into a
machine learning algorithm to train the algorithm to develop
respective risk profiles for at least one insurable asset based
upon a type of the at least one insurable asset and at least one
feature or characteristic of the at least one insurable asset,
where the at least one insurable asset comprises real property,
such as a house or a home. The at least one feature characteristic
of the insurable asset may include, for example, at least one of
location, square footage, cabinet type, roof type, siding type,
type of fireplace, type of windows, or material type, etc. At a
block 1065, the method 1060 may include receiving, e.g., via the
one or more processors and/or transceivers (such as via wired
communication or data transmission, and/or via wireless
communication or data transmission over one or more radio links or
communication channels), one or more images, such as digital images
acquired via a mobile device or smart home controller, of an
undamaged insurable asset (such as one or more images submitted by
an insured party via a webpage, website, mobile device, and/or
smart home controller).
[0297] Further, at a block 1068, the method 1080 may include
inputting, e.g., via the one or more processors, the one or more
images of the undamaged insurable asset into a processor having the
trained machine learning algorithm installed in a memory unit. The
trained machine learning algorithm may, based upon the one or more
images, identify or determine a risk profile for the undamaged
insurable asset to facilitate generating an insurance quote for the
undamaged insurable asset.
[0298] In one embodiment (not shown in FIG. 14), the method 1060
may include generating an insurance policy and/or determining an
insurance rate for the undamaged insurable asset based at least in
part upon the risk profile developed for the undamaged insurable
asset. For example, the insurance rate may include a usage-based
insurance (UBI) rate. The insurance policy and/or the insurance
rate may be electronically transmitted to an owner of the undamaged
insurable asset for review and/or approval, which may be provided
by the owner electronically, if desired.
[0299] Although the present invention has been described in
considerable detail with reference to certain preferred versions
thereof, other versions are possible, which may include additional
or fewer features. For example, additional knowledge may be
obtained using identical methods. The labeling techniques described
herein may be used in the identification of fraudulent claim
activity. The techniques may be used in conjunction with
co-insurance to determine the relative risk of pools of customers.
External customer features, such as payment histories, may be taken
into account in pricing risk. Therefore, the spirit and scope of
the appended claims should not be limited to the description of the
preferred versions described herein.
Machine Learning & Other Matters
[0300] The computer-implemented methods discussed herein may
include additional, less, or alternate actions, including those
discussed elsewhere herein. The methods may be implemented via one
or more local or remote processors, transceivers, servers, and/or
sensors (such as processors, transceivers, servers, and/or sensors
mounted on drones, vehicles or mobile devices, or associated with
smart infrastructure or remote servers), and/or via
computer-executable instructions stored on non-transitory
computer-readable media or medium.
[0301] Additionally, the computer systems discussed herein may
include additional, less, or alternate functionality, including
that discussed elsewhere herein. The computer systems discussed
herein may include or be implemented via computer-executable
instructions stored on non-transitory computer-readable media or
medium.
[0302] A processor or a processing element may be trained using
supervised or unsupervised machine learning, and the machine
learning program may employ a neural network, which may be a
convolutional neural network, a deep learning neural network, or a
reinforced or combined learning module or program that learns in
two or more fields or areas of interest. Machine learning may
involve identifying and recognizing patterns in existing data in
order to facilitate making predictions for subsequent data. For
instance, machine learning may involve identifying and recognizing
patterns in existing text or voice/speech data in order to
facilitate making predictions for subsequent data. Voice
recognition and/or word recognition techniques may also be used.
Models may be created based upon example inputs in order to make
valid and reliable predictions for novel inputs.
[0303] Additionally or alternatively, the machine learning programs
may be trained by inputting sample data sets or certain data into
the programs, such as drone, autonomous or semi-autonomous drone,
image, mobile device, smart or autonomous vehicle, and/or
intelligent home, building, and/or real property telematics data.
The machine learning programs may utilize deep learning, combined
learning, and/or reinforced learning algorithms or modules that may
be primarily focused on pattern recognition, and may be trained
after processing multiple examples. The machine learning programs
may include Bayesian program learning (BPL), voice recognition and
synthesis, image or object recognition, optical character
recognition, and/or natural language processing--either
individually or in combination. The machine learning programs may
also include natural language processing, semantic analysis,
automatic reasoning, and/or machine learning.
[0304] Supervised and/or unsupervised machine learning techniques
may be used. In supervised machine learning, a processing element
may be provided with example inputs and their associated outputs,
and may seek to discover a general rule that maps inputs to
outputs, so that when subsequent novel inputs are provided the
processing element may, based upon the discovered rule, accurately
predict the correct output. In unsupervised machine learning, the
processing element may be required to find its own structure in
unlabeled example inputs.
Exemplary Machine Learning Embodiments
[0305] In one aspect, a computer-implemented method of determining
a risk level of a building or real property may be provided. The
computer-implemented method may include, via one or more
processors, servers, sensors, and/or transceivers: (1) training,
via the one or more processors and/or servers, a neural network, or
other machine learning module or algorithm (such as deep, combined,
or reinforced learning module or algorithm), to identify risk
factors within a set of historical insurance claims corresponding
to buildings and/or real properties, the neural network, or other
machine learning module or algorithm, including a plurality of
input layers (or otherwise being configured to accept a plurality
of input layers, or data in multiple data fields), and each input
layer of the plurality of input layers including a respective
plurality of input parameters, each of which corresponds to a
respective characteristic of buildings and/or real properties; (2)
receiving, via the one or more processors and/or transceivers,
information corresponding to a target building or real property,
the received information including respective indications of one or
more characteristics of the target building or real property; (3)
analyzing, via the one or more processors and/or servers, the
received information using the trained neural network, or other
machine learning module or algorithm, including generating, within
the plurality of layers, one or more risk indicators of the target
building or real property based upon the received information; (4)
determining, via the one or more processors and/or servers, a risk
level of the target building or real property based upon the one or
more risk indicators; and/or (5) providing, via the one or more
processors, servers, and/or transceivers, an indication of the risk
level of the target building or real property to at least one of a
user interface, an application executing on the one or more
processors and/or servers, or an application executing on another
one or more processors, devices, and/or servers. The method may
include additional, less, or alternate actions, including those
discussed elsewhere herein.
[0306] For instance, the received information corresponding to the
target building or real property may include respective indications
of one or more static characteristics of the target building or
real property. Additionally or alternatively, the received
information corresponding to the target building or real property
may include respective indications of one or more dynamic
characteristics of the target building or real property.
[0307] The respective plurality of input parameters of the
plurality of input layers may include one or more characteristics
of applicants and/or insured parties of the set of historical
insurance claims; at least a portion of the received information
corresponding to the target building or real property may be
obtained from an application for insurance for the target building
or real property; and/or the at least the portion of the received
information may include respective indications of one or more
characteristics of an applicant of the insurance application.
[0308] At least a portion of the received information corresponding
to the target building or real property may be obtained from an
application for insurance for the target building or real property,
and the computer-implemented method further may include at least
one of: underwriting an insurance policy for the target building or
real property based upon the risk level of the target building or
real property, or determining a pricing of the insurance policy for
the target building or real property based upon the risk level of
the target building or real property.
[0309] In another aspect, a computer system for determining a risk
level of a building or real property may be provided. The computer
system may include one or more processors, servers, sensors, and/or
transceivers configured to: (1) train a neural network, or other
machine learning module or algorithm (such as deep, combined, or
reinforced learning module), to identify risk factors within a set
of historical insurance claims corresponding to buildings and/or
real properties, the neural network, or other machine learning
module or algorithm, including a plurality of input layers (or
otherwise being configured to accept a plurality of input layers,
or data in multiple data fields), and each input layer of the
plurality of input layers includes a respective plurality of input
parameters, each of which corresponds to a respective
characteristic of buildings and/or real properties; (2) receive
wired communication and/or wireless communication or data
transmission over one or more radio links or communication
channels, the wired communication and/or wireless communication or
data transmission including information corresponding to a target
building or real property, and the received information including
respective indications of one or more characteristics of the target
building or real property; (3) analyze the received information
using the trained neural network, or other machine learning module
or algorithm, including generating, within the plurality of layers,
one or more risk indicators of the target building or real property
based upon the received information; and/or (4) determine a risk
level of the target building or real property based upon the one or
more risk indicators. The system may include additional, less, or
alternate functionality, including that discuss elsewhere
herein.
[0310] For instance, the received information corresponding to the
target building or real property may include respective indications
of one or more static characteristics of the target building or
real property. Additionally or alternatively, the received
information corresponding to the target building or real property
may include respective indications of one or more dynamic
characteristics of the target building or real property.
Additional Considerations
[0311] With the foregoing, any users (e.g., insurance customers)
whose data is being collected and/or utilized may first opt-in to a
rewards, insurance discount, or other type of program. After the
user provides their affirmative consent, data may be collected from
the user's device (e.g., mobile device, smart home controller,
smart or autonomous vehicle, or other smart devices). In return,
the user may be entitled insurance cost savings, including
insurance discounts for auto, homeowners, mobile, renters, personal
articles, and/or other types of insurance.
[0312] In other embodiments, deployment and use of neural network
models at a user device (e.g., the client 502 of FIG. 5) may have
the benefit of removing any concerns of privacy or anonymity, by
removing the need to send any personal or private data to a remote
server (e.g., the server 504 of FIG. 5).
[0313] The following additional considerations apply to the
foregoing discussion. Throughout this specification, plural
instances may implement operations or structures described as a
single instance. Although individual operations of one or more
methods are illustrated and described as separate operations, one
or more of the individual operations may be performed concurrently,
and nothing requires that the operations be performed in the order
illustrated. These and other variations, modifications, additions,
and improvements fall within the scope of the subject matter
herein.
[0314] The patent claims at the end of this patent application are
not intended to be construed under 35 U.S.C. .sctn. 112(f) unless
traditional means-plus-function language is expressly recited, such
as "means for" or "step for" language being explicitly recited in
the claim(s). The systems and methods described herein are directed
to an improvement to computer functionality, and improve the
functioning of conventional computers.
[0315] Unless specifically stated otherwise, discussions herein
using words such as "processing," "computing," "calculating,"
"determining," "presenting," "displaying," or the like may refer to
actions or processes of a machine (e.g., a computer) that
manipulates or transforms data represented as physical (e.g.,
electronic, magnetic, or optical) quantities within one or more
memories (e.g., volatile memory, non-volatile memory, or a
combination thereof), registers, or other machine components that
receive, store, transmit, or display information.
[0316] As used herein any reference to "one embodiment" or "an
embodiment" means that a particular element, feature, structure, or
characteristic described in connection with the embodiment is
included in at least one embodiment. The appearances of the phrase
"in one embodiment" in various places in the specification are not
necessarily all referring to the same embodiment.
[0317] As used herein, the terms "comprises," "comprising,"
"includes," "including," "has," "having" or any other variation
thereof, are intended to cover a non-exclusive inclusion. For
example, a process, method, article, or apparatus that comprises a
list of elements is not necessarily limited to only those elements
but may include other elements not expressly listed or inherent to
such process, method, article, or apparatus. Further, unless
expressly stated to the contrary, "or" refers to an inclusive or
and not to an exclusive or. For example, a condition A or B is
satisfied by any one of the following: A is true (or present) and B
is false (or not present), A is false (or not present) and B is
true (or present), and both A and B are true (or present).
[0318] In addition, use of the "a" or "an" are employed to describe
elements and components of the embodiments herein. This is done
merely for convenience and to give a general sense of the
description. This description, and the claims that follow, should
be read to include one or at least one and the singular also
includes the plural unless it is obvious that it is meant
otherwise.
[0319] Throughout this specification, plural instances may
implement components, operations, or structures described as a
single instance. Although individual operations of one or more
methods are illustrated and described as separate operations, one
or more of the individual operations may be performed concurrently,
and nothing requires that the operations be performed in the order
illustrated. Structures and functionality presented as separate
components in example configurations may be implemented as a
combined structure or component. Similarly, structures and
functionality presented as a single component may be implemented as
separate components. These and other variations, modifications,
additions, and improvements fall within the scope of the subject
matter herein.
[0320] Additionally, certain embodiments are described herein as
including logic or a number of routines, subroutines, applications,
or instructions. These may constitute either software (e.g., code
embodied on a machine-readable medium) or hardware. In hardware,
the routines, etc., are tangible units capable of performing
certain operations and may be configured or arranged in a certain
manner. In example embodiments, one or more computer systems (e.g.,
a standalone, client or server computer system) or one or more
hardware modules of a computer system (e.g., a processor or a group
of processors) may be configured by software (e.g., an application
or application portion) as a hardware module that operates to
perform certain operations as described herein.
[0321] In various embodiments, a hardware module may be implemented
mechanically or electronically. For example, a hardware module may
comprise dedicated circuitry or logic that is permanently
configured (e.g., as a special-purpose processor, such as a field
programmable gate array (FPGA) or an application-specific
integrated circuit (ASIC) to perform certain operations. A hardware
module may also comprise programmable logic or circuitry (e.g., as
encompassed within a general-purpose processor or other
programmable processor) that is temporarily configured by software
to perform certain operations. It will be appreciated that the
decision to implement a hardware module mechanically, in dedicated
and permanently configured circuitry, or in temporarily configured
circuitry (e.g., configured by software) may be driven by cost and
time considerations.
[0322] Accordingly, the term "hardware module" should be understood
to encompass a tangible entity, be that an entity that is
physically constructed, permanently configured (e.g., hardwired),
or temporarily configured (e.g., programmed) to operate in a
certain manner or to perform certain operations described herein.
Considering embodiments in which hardware modules are temporarily
configured (e.g., programmed), each of the hardware modules need
not be configured or instantiated at any one instance in time. For
example, where the hardware modules comprise a general-purpose
processor configured using software, the general-purpose processor
may be configured as respective different hardware modules at
different times. Software may accordingly configure a processor,
for example, to constitute a particular hardware module at one
instance of time and to constitute a different hardware module at a
different instance of time.
[0323] Hardware modules can provide information to, and receive
information from, other hardware modules. Accordingly, the
described hardware modules may be regarded as being communicatively
coupled. Where multiple of such hardware modules exist
contemporaneously, communications may be achieved through signal
transmission (e.g., over appropriate circuits and buses) that
connect the hardware modules. In embodiments in which multiple
hardware modules are configured or instantiated at different times,
communications between such hardware modules may be achieved, for
example, through the storage and retrieval of information in memory
structures to which the multiple hardware modules have access. For
example, one hardware module may perform an operation and store the
output of that operation in a memory product to which it is
communicatively coupled. A further hardware module may then, at a
later time, access the memory product to retrieve and process the
stored output. Hardware modules may also initiate communications
with input or output products, and can operate on a resource (e.g.,
a collection of information).
[0324] The various operations of example methods described herein
may be performed, at least partially, by one or more processors
that are temporarily configured (e.g., by software) or permanently
configured to perform the relevant operations. Whether temporarily
or permanently configured, such processors may constitute
processor-implemented modules that operate to perform one or more
operations or functions. The modules referred to herein may, in
some example embodiments, comprise processor-implemented
modules.
[0325] Similarly, the methods or routines described herein may be
at least partially processor-implemented. For example, at least
some of the operations of a method may be performed by one or more
processors or processor-implemented hardware modules. The
performance of certain of the operations may be distributed among
the one or more processors, not only residing within a single
machine, but deployed across a number of machines. In some example
embodiments, the processor or processors may be located in a single
location (e.g., within a building environment, an office
environment or as a server farm), while in other embodiments the
processors may be distributed across a number of locations.
[0326] The performance of certain of the operations may be
distributed among the one or more processors, not only residing
within a single machine, but deployed across a number of machines.
In some example embodiments, the one or more processors or
processor-implemented modules may be located in a single geographic
location (e.g., within a building environment, an office
environment, or a server farm). In other example embodiments, the
one or more processors or processor-implemented modules may be
distributed across a number of geographic locations.
[0327] Some embodiments may be described using the expression
"coupled" and "connected" along with their derivatives. For
example, some embodiments may be described using the term "coupled"
to indicate that two or more elements are in direct physical or
electrical contact. The term "coupled," however, may also mean that
two or more elements are not in direct contact with each other, but
yet still co-operate or interact with each other. The embodiments
are not limited in this context.
[0328] Upon reading this disclosure, those of skill in the art will
appreciate still additional alternative structural and functional
designs for a system and a process of automatically obtaining
and/or maintaining insurance coverage through the principles
disclosed herein. Thus, while particular embodiments and
applications have been illustrated and described, it is to be
understood that the disclosed embodiments are not limited to the
precise construction and components disclosed herein. Various
modifications, changes and variations, which will be apparent to
those skilled in the art, may be made in the arrangement, operation
and details of the method and apparatus disclosed herein without
departing from the spirit and scope defined in the appended
claims.
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