U.S. patent application number 17/752702 was filed with the patent office on 2022-09-08 for automobile monitoring systems and methods for detecting damage and other conditions.
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 | 20220284517 17/752702 |
Document ID | / |
Family ID | 1000006359518 |
Filed Date | 2022-09-08 |
United States Patent
Application |
20220284517 |
Kind Code |
A1 |
Hayward; Gregory L. ; et
al. |
September 8, 2022 |
Automobile Monitoring Systems and Methods for Detecting Damage and
Other Conditions
Abstract
A method of determining damage to property includes inputting
historical data into a machine learning model to identify an
insured type, features, and/or characteristics. The method may
include identifying a peril, repair and/or replacement cost of the
vehicle by analyzing a digital image from a device of an insured,
the digital image depicting damage to the vehicle, The method may
include inputting the digital image into the trained machine
learning model to identify a type, feature, and/or characteristic
of the vehicle, and may include identifying a peril, repair, and/or
replacement cost associated with the vehicle. A method may include
receiving and/or retrieving free-form text associated with an
insurance claim and/or a vehicle, identifying at least one key word
composing the free-form text, and determining based on the at least
one key word a cause of loss and/or peril that caused damage to the
vehicle.
Inventors: |
Hayward; Gregory L.;
(Bloomington, IL) ; Goldfarb; Meghan Sims;
(Bloomington, IL) ; Christopulos; Nicholas U.;
(Bloomington, IL) ; Donahue; Erik; (Normal,
IL) |
|
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: |
1000006359518 |
Appl. No.: |
17/752702 |
Filed: |
May 24, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16136357 |
Sep 20, 2018 |
11373249 |
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17752702 |
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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: |
G06V 30/194 20220101;
G06Q 40/08 20130101; G06V 20/00 20220101; G06N 3/088 20130101 |
International
Class: |
G06Q 40/08 20060101
G06Q040/08; G06N 3/08 20060101 G06N003/08; G06V 30/194 20060101
G06V030/194; G06V 20/00 20060101 G06V020/00 |
Claims
1. A computer-implemented method of determining damage to personal
property, the method comprising: inputting, via one or more
processors, historical claim data into a machine learning algorithm
to train the algorithm to identify an insured vehicle, a respective
type of the insured vehicle, respective insured vehicle features or
characteristics, a peril associated with the insured vehicle,
and/or a repair or replacement cost associated with the insured
vehicle; receiving, via the one or more processors and/or the one
or more transceivers, a digital image depicting damage to the
insured vehicle, the digital image submitted by an insured entity
via a webpage, website, and/or mobile device; and inputting, via
the one or more processors, the digital image of the damaged
insured vehicle into a processor having the trained machine
learning algorithm installed in a memory unit, the trained machine
learning algorithm identifying a type of the damaged insured
vehicle, a respective feature or characteristic of the damaged
insured vehicle, a peril associated with the damaged insured
vehicle, and/or a repair or replacement cost associated with the
damaged insured vehicle to facilitate handling an insurance claim
associated with the damaged insured vehicle or enhancing an online
customer experience.
2. The computer-implemented method of claim 1, wherein the
respective features or characteristics of the damaged insured
vehicle include one or more autonomous or semi-autonomous
technologies or systems.
3. The computer-implemented method of claim 2, wherein the peril
associated with the damaged insured vehicle comprises collision,
comprehensive, the or water.
4. The computer-implemented method of claim 1, the method further
comprising: retrieving, via the one or more processors and/or the
one or more transceivers, an insurance policy associated with the
damaged insured vehicle; and determining, via the one or more
processors, whether or not the peril associated with the damaged
insured vehicle is a covered peril under the insurance policy.
5. The computer-implemented method of claim 1, wherein the peril
associated with the damaged insured vehicle comprises fire, smoke,
water, hail, wind, or storm surge.
6.-17. (canceled)
18. A non-transitory computer readable medium containing program
instructions that when executed, cause a computer to: input
historical claim data into a machine learning algorithm to operate
the algorithm to identify a damaged insured vehicle, respective
damaged insured vehicle type, respective damaged insured vehicle
features or characteristics, a peril associated with the damaged
insured vehicle, and/or a repair or replacement cost associated
with the damaged insured vehicle; receive a digital image depicting
damage to the insured vehicle, the digital image being submitted by
an insured entity via a webpage, webpage, or mobile device: and
input the image of the damaged insured vehicle into a processor
having the trained machine learning algorithm installed in a memory
unit, the trained machine learning algorithm identifying a type of
the damaged insured vehicle, a feature or characteristic of the
damaged insured vehicle, a peril associated with the damaged
insured vehicle, and/or a repair or replacement cost associated
with the damaged insured vehicle to facilitate handling an
insurance claim associated with the damaged insured vehicle.
19. The non-transitory computer readable medium of claim 18,
wherein the features or characteristics of the damaged insured
vehicle include geographical area, make, model, transmission, tire,
engine, autonomous or semi-autonomous features, air conditioning,
power brakes, power windows, and/or color of the vehicle.
20. The non-transitory computer readable medium of claim 18,
containing further program instructions that when executed, cause
the computer to: retrieve an insurance policy associated with the
damaged insured vehicle; and determine whether or not the peril
associated with the damaged insured vehicle is a covered peril
under the insurance policy.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to and the benefit of:
[0002] U.S. application Ser. No. 62/564,055, filed Sep. 27, 2017
and entitled "REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR
DETECTING DAMAGE AND OTHER CONDITIONS;" [0003] U.S. application
Ser. No. 62/580,655, filed Nov. 2, 2017 and entitled REAL PROPERTY
MONITORING SYSTEMS AND METHODS FOR DETECTING DAMAGE AND OTHER
CONDITIONS;" [0004] U.S. application Ser. No. 62/610,599, filed
Dec. 27, 2017 and entitled "AUTOMOBILE MONITORING SYSTEMS AND
METHODS FOR DETECTING DAMAGE AND OTHER CONDITIONS;"
[0005] U.S. application Ser. No. 62/621,218, filed Jan. 24, 2018
and entitled "AUTOMOBILE MONITORING SYSTEMS AND METHODS FOR LOSS
MITIGATION AND CLAIMS HANDLING;"
[0006] U.S. application Ser. No. 62/621,797, filed Jan. 25, 2018
and entitled "AUTOMOBILE MONITORING SYSTEMS AND METHODS FOR LOSS
RESERVING AND FINANCIAL REPORTING;"
[0007] U.S. application Ser. No. 62/580,713, filed Nov. 2, 2017 and
entitled "REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR
DETECTING DAMAGE AND OTHER CONDITIONS;" [0008] U.S. application
Ser. No. 62/618,192, filed Jan. 17, 2018 and entitled "REAL
PROPERTY MONITORING SYSTEMS AND METHODS FOR DETECTING DAMAGE AND
OTHER CONDITIONS;" [0009] U.S. application Ser. No. 62/625,140,
filed Feb. 1, 2018 and entitled "SYSTEMS AND METHODS FOR
ESTABLISHING LOSS RESERVES FOR BUILDING/REAL PROPERTY
INSURANCE;"
[0010] U.S. application Ser. No. 62/646,729, filed Mar. 22, 2018
and entitled "REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR LOSS
MITIGATION AND CLAIMS HANDLING;" [0011] U.S. application Ser. No.
62/646,735, filed Mar. 22, 2018 and entitled "REAL PROPERTY
MONITORING SYSTEMS AND METHODS FOR RISK DETERMINATION,"-- U.S.
application Ser. No. 62/646,740, filed Mar. 22, 2018 and entitled
"SYSTEMS AND METHODS FOR ESTABLISHING LOSS RESERVES FOR
BUILDING/REAL PROPERTY INSURANCE;" [0012] U.S. application Ser. No.
62/617,851, filed Jan. 16, 2018 and entitled. "IMPLEMENTING MACHINE
LEARNING FOR LIFE AND HEALTH INSURANCE PRICING AND
UNDERWRITING;"
[0013] U.S. application Ser. No. 62/622,542, filed Jan. 26, 2018
and entitled "IMPLEMENTING MACHINE LEARNING FOR LIFE AND HEALTH
INSURANCE LOSS MITIGATION AND CLAIMS HANDLING;"
[0014] U.S. application Ser. No. 62/632,884, filed Feb. 20, 2018
and entitled "IMPLEMENTING MACHINE LEARNING FOR LIFE AND HEALTH
INSURANCE LOSS RESERVING AND FINANCIAL REPORTING;"
[0015] U.S. application Ser. No. 62/652,121, filed Apr. 3, 2018 and
entitled "IMPLEMENTING MACHINE LEARNING FOR LIFE AND HEALTH
INSURANCE CLAIMS HANDLING;"
[0016] the entire disclosures of which are hereby incorporated by
reference herein in their entireties.
FIELD OF INVENTION
[0017] This disclosure generally relates to detecting damage, loss,
and/or other conditions associated with an automobile and human
passengers, operators, and/or pedestrians to determine risk levels
for insurance to better and/or more efficiently match price to
risk.
BACKGROUND
[0018] 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 vehicles and/or
traffic control systems. Furthermore, it is possible for one or
more vehicle and/or central controllers to interface with the smart
devices or sensors.
[0019] However, conventional systems may not be able to
automatically detect and characterize various conditions or damage
associated with a vehicle or building. Additionally, conventional
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,
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 loss mitigation factors.
BRIEF SUMMARY
[0020] The present disclosure generally relates to systems and
methods for detecting damage, loss, and/or other conditions
associated with a vehicle using a computer system and/or a
building, land, structure, or other real property using a property
monitoring system. 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.
[0021] In one aspect, the present embodiments may relate to
determining an automobile-based risk level via one or more
processors, training a neural network to identify risk factors that
are predictive electronic claim features, receiving information
corresponding to (i) an automobile, and/or (ii) an automobile
operator, analyzing the information using the trained neural
network to generate one or more risk indicators, determining, by
analyzing the risk indicators, a risk level corresponding to the
automobile, and/or displaying, to a user, an insurance quotation
based upon analyzing the risk indicators. The automobile may be a
smart, autonomous, or semi-autonomous vehicle, and have sensors,
software, and electronic components that direct autonomous or
semi-autonomous vehicle features or technologies -- each of which
may have a various levels of risk, or lack thereof, that may be
analyzed and determined by the present embodiments. Systems and
methods may automatically generate risk models for various types of
vehicle insurance types and loss types, such as by the application
of artificial intelligence and machine learning methods as
disclosed herein, to provide more granular risk models, leading to
more accurate commercial offerings, and more appropriate matching
premium price to actual risk.
[0022] In another aspect.sub.; a computer-implemented method of
determining an automobile-based risk level via one or more
processors may include training, via one or more processors, a
neural network to identify risk factors that are predictive of
electronic vehicle claim records. The neural network may include a
plurality of layers, and an input layer from among the plurality of
layers may include a plurality of input parameters--with each
corresponding to a different claim attribute. The method may
include, via one or more processors, receiving information
corresponding to (i) an automobile, and/or (ii) an automobile
operator; and analyzing the information using the trained neural
network. Analyzing the information may include generating, within
the plurality of layers, one or more risk indicators corresponding
to the information. The method may also include determining a risk
level corresponding to the vehicle. The method may include
additional, less, or alternate actions, including those discussed
elsewhere herein.
[0023] In another aspect, a computing system may include one or
more processors, and one or more memories storing instructions.
When the instructions are executed by the one or more processors,
they may cause the computing system to provide a first application
to a user of a client computing device. The first application, when
executing on the client computing device, may cause the client
computing device to obtain a set of information from an input
device of the client computing device, and transmit, via a
communication network interface of the client computing device, the
set of information to a remote computing system. The instructions
may cause the computing system to receive, at the remote computing
system, the set of information and process, at the remote computing
system, the set of information. The instructions may cause the
computing system to identify, by the remote computing system, one
or more risk indications, at least in part, by applying the set of
information to a trained neural network and generate, by the remote
computing system analyzing the one or more risk indications, a
quotation, such as quote for auto insurance. The instructions may
cause the computing system to (i) display the quotation to the
user, and (ii) provide the quotation as input to a second
application. The system may include additional, less, or alternate
functionality, including that discussed elsewhere herein.
[0024] 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
[0025] 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.
[0026] 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:
[0027] FIG. 1 depicts an exemplary computing environment in which
techniques for training a neural network to identify a risk level
of a vehicle may be implemented, according to one embodiment;
[0028] FIG. 2 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 vehicle may be
implemented, according to one embodiment;
[0029] FIG. 3 depicts an exemplary artificial neural network which
may be trained by the neural network unit of FIG. 1 or the neural
network training application of FIG. 2, according to one embodiment
and scenario;
[0030] FIG. 4 depicts an exemplary neuron, which may be included in
the artificial neural network of FIG. 3, according to one
embodiment and scenario;
[0031] FIG. 5 depicts text-based content of an exemplary electronic
claim record that may be processed by an artificial neural network,
in one embodiment;
[0032] FIG. 6 depicts a flow diagram of an exemplary
computer-implemented method of determining a risk level posed by an
operator of a vehicle, according to one embodiment;
[0033] FIG. 7 depicts a flow diagram of an exemplary
computer-implemented method of identifying risk indicators from
vehicle operator information, according to one embodiment;
[0034] FIG. 8 is a flow diagram depicting an exemplary
computer-implemented method of detecting and/or estimating damage
to personal property, according to one embodiment;
[0035] FIG. 9A is an example flow diagram depicting an exemplary
computer-implemented method of determining damage to personal
property, according to one embodiment;
[0036] FIG. 9B is an example data flow diagram depicting an
exemplary computer-implemented method of determining damage to an
insured vehicle using a trained machine learning algorithm to
facilitate handling an insurance claim associated with the damaged
insured vehicle, according to one embodiment;
[0037] FIG. 10A is an example flow diagram depicting an exemplary
computer-implemented method for determining damage to personal
property, according to one embodiment; and
[0038] FIG. 10B is an example data flow diagram depicting an
exemplary computer-implemented method of determining damage to an
undamaged insurable vehicle using a trained machine learning
algorithm to facilitate generating an insurance quote for the
undamaged insurable vehicle, according to one embodiment.
[0039] 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 Systems for Insurance
[0040] The present embodiments are directed to, inter cilia,
machine learning and/or training a model using historical
automobile and/or home insurance claim data to discover risk levels
and price automobile 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 or smart vehicle controller, and/or into a
chat bot or robo-advisor.
[0041] Other inputs to a machine learning/training model may be
harvested from historical claims may, and may include make, model,
year, miles, technological features, and/or other characteristics
of a vehicle, vehicle operation monitoring systems, whether a claim
is 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 vehicle telematics data
for automobiles, and for real property, home telematics data
received from a smart vehicle controller, such as how long and when
are the doors unlocked, how often is the security system armed, how
long is the vehicle in operation during time periods, etc.
[0042] The present embodiments may facilitate discovering new
causes of loss that may be utilized to set pricing of insurance.
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 also dynamically update pricing models to
facilitate better matching insurance premium price to actual
risk.
Artificial Intelligence System for Vehicle Insurance
[0043] Noted above, the present embodiments may also be directed to
machine learning and/or training a model using historical auto
claim data to discover risk levels, and then price vehicle
insurance accordingly. The present embodiments may include natural
language processing of free-form notes recorded by call center
and/or claim adjustor (e.g., "hit a deer"), photos, and/or other
evidence to use as input to machine learning/training model. Other
inputs to a machine learning/training model may be harvested from
historical claims, and may include make, model, year, claim paid or
not paid, liability (e.g., types of injuries, where treated, how
treated, etc.), disbursements related to the claim such as rental
car and other payouts, etc.
Exemplary Environment for Identifying Risk Factors and Calculating
Risk in Data
[0044] The embodiments described herein may relate to, inter alia,
determining an accurate, granular vehicle insurance risk level
corresponding to a plurality of inputs. More particularly, in some
embodiments, one or more neural network models may be trained using
historical claims data as training input. An application 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 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 to produce labels and
weights indicating net or individual risk factors. The risk factors
may be identified in electronic claim records, and/or may be
predictive of certain real-world risks. Although historical claims
may be used in training one or more neural network models,
electronic claims information may be streaming in realtime or with
near-realtime latencies (e.g., on the order of 10 ms or less) along
with all input information to tune the artificial intelligence
system, in a dynamic process.
[0045] For example, the remote computing device may receive the
input and determine, using a trained neural network, 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 a quotation calculation or for other
purposes).
[0046] A quotation may include a price, parameters describing the
vehicle, and/or one or more identified risk indicators, among other
information. By transmitting input to the remote computing device
for processing and analysis, an accurate risk level based upon a
wealth of historical knowledge may be determined, and provided to
the user in what may appear to the user to be a very rapid, even
instantaneous, manner.
[0047] Turning to FIG. 1, an exemplary computing environment 100,
representative of artificial intelligence platform for vehicle
insurance, is depicted. Environment 100 may include input data 102
and historical data 108, 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 related to a vehicle operator,
which exists in the environment 100. For example, data may include
an electronic document representing a vehicle (e.g., automobile,
truck, boat, motorcycle, etc.) insurance claim, demographic
information about the vehicle operator and/or information related
to the type of vehicle or vehicles being operated by the vehicle
operator, and/or other information.
[0048] Data may be historical or current. Although data may be
related to an ongoing claim filed by a vehicle operator, in some
embodiments, data may consist of raw data parameters entered by a
human user of the environment 100 or which is retrieved/received
from another computing system.
[0049] Data may or may not relate to the claims filing process, and
while some of the examples described herein refer to auto 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, homeowners insurance, health or life
insurance, renters insurance, etc. In that case, the scope and
content of the data may differ, in addition to the domain-specific
training and operational requirements applicable to the neural
network(s).
[0050] As another example, data may be collected from an existing
customer filing a claim, a potential or prospective customer
applying for an insurance policy, or may be supplied by a third
party such as a company other than the proprietor of the
environment 100. 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.
[0051] Input data 102 may be loaded into an artificial intelligence
system 104 to organize, analyze, and process input data 102 in a
manner that facilitates efficient determination of risk levels by
risk level analysis platform 106. The loading of input data 102 may
be performed by executing a computer program on a computing device
that has access to the environment 100, and the loading process may
include the computer program coordinating data transfer between
input data 102 and AI platform 104 (e.g., by the computer program
providing an instruction to AI platform 104 as to an address or
location at which input data 102 is stored).
[0052] AI platform may reference this address to retrieve records
from input data 102 to perform risk level determination techniques.
AI platform 104 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.
[0053] As discussed below with respect to FIGS. 3, 4, and 5; AI
platform 104 may be used to train multiple neural network models
relating to different granular segments of vehicle operators. For
example, AI platform 104 may be used to train a neural network
model for use by operators of autonomous vehicles who are over the
age of 30. In another embodiment, AI platform 104 may be used to
train a neural network model for use in predicting risk of
motorcycle operators in a particular state or locality. The precise
manner in which neural networks are created and trained is
described below.
[0054] In the embodiment of FIG. 1, AI platform 104 may include
claim analysis unit 120. Claim analysis unit 120 may include
speech-to-text unit 122 and image analysis unit 124 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 104. In some embodiments, customer behavior
represented in data--including the accuracy and truthfulness of a
customer--may be encoded by claim analysis unit 120 and used by AI
platform 104 to train and operate neural network models.
[0055] Claim analysis unit 120 may also include text analysis unit
126, which may include pattern matching unit 128 and natural
language processing (NLP) unit 130. In some embodiments, text
analysis unit 126 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 126 may analyze text produced by speech-to-text unit
122 or image analysis unit 124.
[0056] In some embodiments, pattern matching unit 128 may search
textual claim data loaded into AI platform 104 for specific strings
or keywords in text (e.g., "hit a deer") which may be indicative of
particular types of risk. NLP unit 130 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 130 may identify human speech patterns in data,
including semantic information relating to entities, such as
people, vehicles, homes, and other objects.
[0057] 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
driver and a deer may be relevant objects. Verbs indicating
collision or injury may be relevant verbs. in some embodiments,
text analysis unit 126 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.
[0058] In the embodiment of FIG. 1, AI platform 104 may include a
risk level unit 140 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., deer). In other embodiments, risk level
unit 140 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.
[0059] For example, risk level unit 140 may label input data 102,
or portions thereof, according to positive or negative pattern
matching according to pattern matching unit 128. For example, if
input data 102 matches the pattern "hit [a] deer," wherein the
article "a" is optional, then input data 102 may receive labels
such as (ACCIDENT, DEER) or (COLLISION, ANIMAL). Alternately, in
some embodiments, risk level unit 140 may label input data 102,
which may be raw data or a claim filed by a customer, according to
results obtained from natural language processing unit 130 (e.g.,
LIMB-INJURY). Risk level unit 140 may label input data 102
according to Boolean values (e.g., PAID/NOT-PAID) or pre-determined
ranges (e.g., claims having a payout of 50-$50,000;
550,000-$500,000; $500,000-$1,000,000; or >=$1,000,000).
[0060] Labels may be saved to and/or retrieved from an electronic
database, such as risk indication data 142, 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 140. A
set of labels may be associated with a set of input data 102, and
the creation of new labels may be partially or entirely based upon
existing labels and/or input data 102.
[0061] 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.
[0062] As noted, in some embodiments, risk level unit 140 may
analyze input data 102 (e.g., label claims) through the use of a
neural network unit 150. Neural network unit 150 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.
[0063] 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 140 may, in one embodiment,
apply input data 102 to a first neural network model that is
trained to generate labels. The output (e.g., labels) of this first
neural network model may he fed as input to a second neural network
model which has been trained to predict 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.
[0064] Neural network unit 150 may include training unit 152, and
risk indication unit 154. To train the neural network to identify
risk, neural network unit 150 may access electronic claims within
historical data 108. Historical data 108 may comprise a corpus of
documents comprising many (e.g., millions) of insurance claims
which may contain data linking a particular customer or claimant to
one or more vehicles, and which may also contain, or be linked to,
information pertaining to the customer. In particular, historical
data 108 may be analyzed by AI platform 104 to generate claim
records 110-1 through 110-n, where n is any positive integer. Each
claim 110-1 through 110-n may be processed by training unit 152 to
train one or more neural networks to identify claim risk factors,
including by pre-processing of historical data 108 using input
analysis unit 120 as described above.
[0065] Neural network 150 may, from a trained model, identify
labels that correspond to specific data, metadata, and/or
attributes within input data 102, depending on the embodiment. For
example, neural network 150 may be provided with instructions from
input analysis unit 120 indicating that one or more particular type
of insurance is associated with one or more portions of input data
102.
[0066] Neural network 150 may identify one or more insurance types
associated with the one or more portions of input data 102 (e.g.,
bodily injury, property damage, collision coverage, comprehensive
coverage, liability insurance, med pay, or personal injury
protection (PIP) insurance) and by input analysis unit 120. in one
embodiment, the one or more insurance types may be identified by
training the neural network 150 based upon types of peril. For
example, the neural network model may be trained to determine that
fire, theft, or vandalism may indicate comprehensive insurance
coverage.
[0067] In addition, input data 102 may indicate a particular
customer and/or vehicle. In that case, risk level unit 140 may look
up additional customer and/or vehicle information from customer
data 160 and vehicle data 162, respectively. For example, the age
of the vehicle operator and/or vehicle type may be obtained. The
additional customer and/or vehicle information may be provided to
neural network unit 150 and may be used to analyze and label input
data 102 and, ultimately, may be used to determine risk. For
example, neural network unit 150 may be used to predict risk based
upon inputs obtained from a person applying for an auto insurance
policy, or based upon a claim submitted by a person who is a holder
of an existing insurance policy. That is, in some embodiments where
neural network unit 150 is trained on claim data, neural network
unit 150 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 108).
[0068] In one embodiment, the training process may be performed in
parallel, and training unit 152 may analyze all or a subset of
claims 110-1 through 110-n. Specifically, training unit 152 may
train a neural network to identify claim risk factors in claim
records 110-1 through 110-n. As noted, AI platform 104 may analyze
input data 102 to arrange the historical claims into claim records
110-1 through 110-n, where n is any positive integer.
[0069] Claim records 110-1 through 110-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 110-1 through 110-n may represent a single non-branching
claim, or may represent multiple claim records arranged in a group
or tree.
[0070] Further, claim records 110-1 through 110-n may comprise
links to customers and vehicles 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 vehicles 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 vehicle. The status of claim records may be
completely settled or in various stages of settlement.
[0071] As used herein, the term "claim" or "vehicle claim"
generally refers to an electronic document, record, or file, that
represents an insurance claim (e.g., an automobile insurance claim)
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), 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. Claim data may include data entered
by third parties, such as information from a repair shop, hospital,
doctor, police report, etc.
[0072] 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 100, for example, or may
have been automatically generated as a direct product or byproduct
of a process carried out in environment 100. 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.
[0073] Another example of claim metadata is the geographic location
in which a claim is submitted, which may be obtained via a global
positioning system (GPS) sensor in a device used by the person or
entity submitting the claim. Yet another example of claim metadata
includes a category of the claim type (e.g., collision, liability,
uninsured or underinsured motorist, etc.). For example, a single
claim in historical data 108 may be associated with a married
couple, and may include the name, address, and other demographic
information relating to the couple. Additionally, the claim may be
associated with multiple vehicles owned or leased by the couple,
and may contain information pertaining to those vehicles including
without limitation, the vehicles' make, model, year, condition,
mileage, etc.
[0074] 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 108. In this way, neural
network unit 150 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.
[0075] Once the neural network has been trained, risk indication
unit 154 may apply the trained neural network to input data 102 as
processed by input analysis unit 120. In one embodiment, input
analysis unit 120 may merely "pass through" input data 102 without
modification. The output of the neural network, indicating risk
indications, such as labels pertaining to the entirety of, or
portions of input data 102, may then be provided to risk level unit
140. Risk level unit 140 may insert the output of the neural
network (e.g., labels) into an electronic database, such as risk
indication data 142. Alternatively, or additionally, risk
indication unit 154 may use label information output by the neural
network to determine attributes of input data 102, and may provide
those attributes to risk level unit 140.
[0076] 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 vehicle data 274, 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 140 may then forward the labels and/or scores to risk
level analysis platform 106. In some embodiments, determining a
single label may require neural network unit 150 to analyze several
attributes within input data 102. For example, a new customer
applying for an auto insurance policy may be required to provide
their name, make and model of their car, and a scanned copy of
their driver's abstract to determine a risk that is reflective of
all three pieces of information. Some models may include validation
that will produce an error state if a required piece of information
is not provided.
[0077] AI platform 104 may further include customer data 160 and
vehicle data 162, which risk level unit 140 may leverage to provide
useful input parameters to neural network unit 150. Customer data
160 may be an integral part of AI platform 104, or may be located
separately from AI platform 104. In some embodiments, customer data
160 or vehicle data 162 may be provided to AI platform 104 via
separate means (e.g., via an API call), and may be accessed by
other units or components of environment 100. Either may be
provided by a third-party service.
[0078] Vehicle data 162 may be a database comprising information
describing vehicle makes and models, including information about
model years and model types (e.g., model edition information,
engine type, any upgrade packages, etc.). Vehicle data 162 may
indicate whether certain make and model year vehicles are equipped
with safety features (e.g., lane departure warnings). The vehicle
data 162 may also relate to autonomous or semi-autonomous vehicle
features or technologies of the vehicle, and/or sensors, software,
and electronic components that direct the autonomous or
semi-autonomous vehicle features or technologies.
[0079] Both of customer data 160 and vehicle data 162 may be used
to train a neural network model. For example, to continue the above
new customer application example, risk level unit 140 may look up
the applicant's age and other demographic information in customer
data 160, and may obtain from vehicle data 162 the knowledge that
the car is a convertible. Further, the driver abstract may be
analyzed by image processing unit 124 and pattern matching unit
128, which--together--may determine that the applicant's driver's
license was suspended within the prior year.
[0080] All of the information pertaining to the applicant may then
be provided to neural network unit 150, which may--based upon its
prior training on claims from historical data 108--determine that a
plurality of labels apply to the applicant. For example, the labels
may include SUSPENDED, CONVERTIBLE, YOUTH. 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 142, in an
embodiment. It should be appreciated that the use of additional
vehicle labels (e.g., DIESEL, V8, MANUAL-TRANSMISSION, REVOKED) is
envisioned in label generation.
[0081] In some embodiments, pattern matching unit 128 and natural
language processing unit 130 may act in conjunction to determine
labels. For example, pattern matching unit 128 may include
instructions to identify words indicating contact (e.g., "hit",
"crash", or "collide"). Matched data may be provided to natural
language processing unit 130, which may further process the matched
data to determine parts of speech such as verbs and objects, as
well as relationships between the objects.
[0082] The output of natural language processing unit 130 may be
provided to neural network unit 150 and used by training unit 152
to train a neural network model to label insurance types. For
example, if natural language processing unit 152 indicates a
collision with an inanimate object, such as a fence, pole, or
otherwise, then the neural network may generate a label of
COLLISION, indicating that the input data 102 may indicate a
collision insurance policy. On the other hand, if natural language
processing unit 152 indicates a collision with an animal, such as a
deer, then the neural network may generate a label of
COMPREHENSIVE.
[0083] It should be appreciated that in this example, the two
labels (COLLISION and COMPREHENSIVE) are not mutually exclusive.
That is, the neural network model may generate multiple labels
corresponding to an indication by pattern matching unit 128 and/or
natural language processing unit 130 that both types of insurance
coverage are indicated. Further, additional processing, including
by the use of an additional neural network model, maybe used to
assign weight to a label. For example, a collision involving a deer
may receive a higher weight than one involving a rabbit.
[0084] The labels in risk indication data 142 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 106 may sum the weights and scale the price
of a policy offered to the applicant. In other embodiments, the
risk level analysis platform 106 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.
[0085] A maximum or minimum weight may correspond to a local
maximum (e.g., the longest trip taken by a given driver), a global
maximum (e.g., the vehicle operator in a vehicle operator cohort
with the most claims filed in a five-year period), or a maximum
among a set of vehicle operators. It should be appreciated that
there are many possibilities for using the information generated by
the neural network.
[0086] In some embodiments, labels may be associated with pre-set
weights that are stored separately from AI platform 104, 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.
[0087] 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.
[0088] 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 vehicle insurance products. All of the benefits
provided by the methods and systems described herein may be
realized much more quickly than traditional modeling approaches.
The methods and systems herein may reduce, in some cases
dramatically, insurance company expenses and/or insurance customer
premiums, due to increased efficiencies and improved predictive
accuracies.
Exemplary Training Model System
[0089] With reference to FIG. 2, a high-level block diagram of
vehicle insurance risk training model system 200 is illustrated
that may implement communications between a client device 202 and a
server device 204 via network 206 to provide vehicle insurance loss
classification and/or risk level analysis. FIG. 2 may correspond to
one embodiment of environment 100 of FIG. 1, and also includes
various user/client-side components. For simplicity, client device
202 is referred to herein as client 202, and server device 204 is
referred to herein as server 204, but either device may be any
suitable computing device (e.g., a laptop, smart phone, tablet,
server, wearable device, etc). Server 204 may host services
relating to neural network training and operation, and may be
communicatively coupled to client 202 via network 206.
[0090] Although only one client device is depicted in FIG. 2, it
should be understood that any number of client devices 202 may be
supported. Client device 202 may include a memory 208 and a
processor 210 for storing and executing, respectively, a module
212. While referred to in the singular, processor 210 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 208 may include one or more persistent memories
(e.g., a hard drive and/or solid state memory).
[0091] Module 212, stored in memory 208 as a set of
computer-readable instructions, may be related to an input data
collection application 216 which, when executed by the processor
210, causes input data to be stored in memory 208. The data stored
in memory 208 may correspond to, for example, raw data retrieved
from input data 102. Input data collection application 216 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.
[0092] Input data collection application 216 may store information
in memory 208, including the instructions required for its
execution. While the user is using input data collection
application 216, scripts and other instructions comprising input
data collection application 216 may be represented in memory 208 as
a web or mobile application. The input data collected by input data
collection application 216 may be stored in memory 208 and/or
transmitted to server device 204 by network interface 214 via
network 206, 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 216 may
be data used to train a model (e.g., scanned claim data).
[0093] Client device 202 may also include GPS sensor 218, an image
sensor 220, user input device 222 (e.g., a keyboard, mouse,
touchpad, and/or other input peripheral device), and display
interface 224 (e.g., an LED screen). User input device 222 may
include components that are integral to client device 202, and/or
exterior components that are communicatively coupled to client
device 202, to enable client device 202 to accept inputs from the
user. Display 224 may be either integral or external to client
device 202, and may employ any suitable display technology. In some
embodiments, input device 222 and display 224 are integrated, such
as in a touchscreen display. Execution of the module 212 may
further cause the processor 210 to associate device data collected
from client 202 such as a time, date, and/or sensor data (e.g., a
camera for photographic or video data) with vehicle and/or customer
data, such as data retrieved from customer data 160 and vehicle
data 162, respectively.
[0094] In some embodiments, client 202 may receive data from risk
indication data 142 and risk level analysis platform 106. Such
data, indicating risk labels and/or a risk level computation, may
be presented to a user of client 202 by a display interface
224.
[0095] Execution of the module 212 may further cause the processor
210 of the client 202 to communicate with the processor 250 of the
server 204 via network interface 214 and network 206. As an
example, an application related to module 212, such as input data
collection application 216, may, when executed by processor 210,
cause a user interface to be displayed to a user of client device
202 via display interface 224. The application may include
graphical user input (GUI) components for acquiring data (e.g.,
photographs) from image sensor 220, GPS coordinate data from GPS
sensor 218, and textual user input from user input device(s)
222,
[0096] The processor 210 may transmit the aforementioned acquired
data to server 204, and processor 250 may pass the acquired data to
a neural network, 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 to obtain a
result). With specific reference to FIG. 1, the data acquired by
client 202 may be transmitted via network 206 to a server
implementing AI platform 104, and may be processed by input
analysis unit 120 before being applied to a trained neural network
by risk level unit 140.
[0097] As described with respect to FIG. 1, the processing of input
from client 202 may include associating customer data 160 and
vehicle data 162 with the acquired data. The output of the neural
network may be transmitted, by a risk level unit corresponding to
risk level unit 140 in server 204, back to client 202 for display
(e.g., in display 224) and/or for further processing.
[0098] Network interface 214 may be configured to facilitate
communications between client 202 and server 204 via any hardwired
or wireless communication network, including network 206 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 202 may cause insurance risk related data to be
stored in server 204 memory 252 and/or a remote insurance related
database such as customer data 160.
[0099] Server 204 may include a processor 250 and a memory 252 for
executing and storing, respectively, a module 254. Module 254,
stored in memory 252 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 insurance policy application data. For example,
module 254 may include input analysis application 260, risk level
application 262, and neural network training application 264, in
one embodiment.
[0100] Input analysis application 260 may correspond to input
analysis unit 120 of environment 100 of FIG. 1. Risk level
application 262 may correspond to risk level unit 140 of
environment of FIG. 1, and neural network training application 264
may correspond to neural network unit 150 of environment 100 of
FIG. 1. Module 254 and the applications contained therein may
include instructions which, when executed by processor 250, cause
server 204 to receive and/or retrieve input data from (e.g., raw
data and/or an electronic claim) from client device 202. In one
embodiment, input analysis application 260 may process the data
from client 202, 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.
[0101] Throughout the aforementioned processing, processor 250 may
read data from, and write data to, a location of memory 252 and/or
to one or more databases associated with server 204. For example,
instructions included in module 254 may cause processor 250 to read
data from an historical data 270, which may be communicatively
coupled to server device 204, either directly or via communication
network 206. Historical data 270 may correspond to historical data
108, and processor 250 may contain instructions specifying analysis
of a series of electronic claim documents from historical data 270,
as described above with respect to claims 110-1 through 110-n of
historical data 108 in FIG. 1.
[0102] Processor 250 may query customer data 272 and vehicle 274
for data related to respective electronic claim documents and raw
data, as described with respect to FIG. 1. In one embodiment
customer data 272 and vehicle data 274 correspond, respectively,
customer data 160 and 162. In another embodiment, customer data 272
and/or vehicle data 274 may not be integral to server 204. Module
254 may also facilitate communication between client 202 and server
204 via network interface 256 and network 206, in addition to other
instructions and functions.
[0103] Although only a single server 204 is depicted in FIG. 2, it
should be appreciated that it may be advantageous in some
embodiments to provision multiple servers for the deployment and
functioning of AI system 102. For example, the pattern matching
unit 128 and natural language processing unit 130 of input analysis
unit 120 may require CPU-intensive processing. Therefore, deploying
additional hardware may provide additional execution speed. Each of
historical data 270, customer data 272, vehicle data 274, and risk
indication data 276 may be geographically distributed.
[0104] While the databases depicted in FIG. 2 are shown as being
communicatively coupled to server 204, it should be understood that
historical claim data 270, for example, may be located within
separate remote servers or any other suitable computing devices
communicatively coupled to server 204. 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 262). 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.
[0105] In a manner similar to that discussed above in connection
with FIG. 1, historical claims from historical claim data 270 may
be ingested by server 204 and used by neural network training
application 264 to train an artificial neural network. Then, when
module 254 processes input from client 202, the data output by the
neural network(s) (e.g., data indicating labels, risks, weights,
etc.) may be passed to risk level application 262. 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 202 and/or another
device. The calculated risk level may be used for further
processing by client device 202, server device 204, or another
device.
[0106] It should be appreciated that the client/server
configuration depicted and described with respect to FIG. 2 is but
one possible embodiment. In some cases, a client device such as
client 202 may not be used. In that case, input data may be
entered--programmatically, or manually--directly into device 204. 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).
[0107] 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 202 (i.e., the trained
neural network may be operated on the client 202 without the use of
a server 204).
[0108] In operation, the user of client device 202, by operating
input device 222 and viewing display 224, may open input data
collection application 216, 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 104 or a customer or
end user of the company. For example, input data collection
application 216 may walk the user through the steps of submitting a
claim.
[0109] Before the user can fully access input data collection
application 216, the user may be required to authenticate (e.g.,
enter a valid username and password). The user may then utilize
input data collection application 216. Module 212 may contain
instructions that identify the user and cause input data collection
application 216 to present a particular set of questions or prompts
for input to the user, based upon any information input data
collection application 216 collects, including without limitation
information about the user or any vehicle.
[0110] Further, module 212 may identify a subset of historical data
270 to be used in training a neural network, and/or may indicate to
server device 204 that the use of a particular neural network model
or models is appropriate. For example, if the user is applying for
liability vehicle insurance on a particular make and model year
car, then module 212 may transmit the user's name and personal
information, the location of the user as provided by GPS 218, a
photograph of the vehicle to be insured captured by image sensor
220; and the make, model, and year of the vehicle to server device
204.
[0111] In some embodiments, location data from client device 202
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 vehicle operator, whether provided via GPS or
entered manually by a user, may cause the neural network to
generate a label applicable to the vehicle operator such as RURAL,
SUBURBAN, or URBAN.
[0112] 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 RURAL label, due to the
increased likelihood of collision with animals. Due to the
increased risk of collision with animals, the generation of a RURAL
label may be accompanied by additional labels such as COLLISION.
Alternatively, or in addition, the collision label weight may be
increased along with the addition of the RURAL label.
[0113] Another label, such as LONG-TRIP, to reflect that the
vehicle operator drives longer trips than other drivers, on
average, may be associated with vehicle operators who the neural
network labels as RURAL. In some embodiments, label generation may
be based upon seasonal information, in whole or in part. For
example, the neural network may generate labels, and/or adjust
label weights based upon location provided in input data. The
trained neural network model may learn to associate drivers who
drive in the city in summer with higher risk.
[0114] All other inputs being equal, vehicle operator risk may
differ based upon the time of year when the vehicle operator is
applying for insurance. 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.
[0115] By the time the user of client 202 submits an application
for vehicle insurance or files a claim, server 204 may have already
processed the electronic claim records in historical data 270 and
trained a neural network model to analyze the information provided
by the user to output risk indications, labels, and/or weights.
[0116] For example, the operator of a 2012 Jeep Cherokee may access
client 202 to submit a claim under the driver's collision insurance
policy related to damage to the vehicle sustained while the driver
was on vacation in a state other than the driver's home state.
Client 202 may collect information from the vehicle operator
related to the circumstances of the collision, in addition to
demographic information of the vehicle operator, including location
and photographs from GPS 218 and image sensor 220, respectively. In
some embodiments, the vehicle operator may be prompted to make a
telephone call to discuss the filing of the claim, which may be
recorded and later provided to server 204.
[0117] All of the information collected may be associated with a
claim identification number so that it may be referenced as a
whole. Server 204 may process the information as it arrives, and
thus may process information collected by input data collection
application 216 at a different time than server 204 processes the
audio recording in the above example. Once information sufficient
to process the claim has been collected, server 204 may pass all of
the processed information (e.g., from input analysis application)
to risk level application 262, which may apply the information to
the trained neural network model.
[0118] While the claim or application processing is pending, client
device 202 may display an indication that the processing of the
claim is ongoing and/or incomplete. When the claim is ultimately
processed by server 204, an indication of completeness may be
transmitted to client 202 and displayed to user, for example via
display 224. Missing information may cause the model to abort with
an error.
[0119] 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 270 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.
[0120] 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
personal injury that occurs on a boat may trigger updates to a set
of personal injury neural network models pertaining to boat
insurance. 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.
[0121] In some embodiments, AI platform 104 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.
[0122] While FIG. 2 depicts a particular embodiment, the various
components of environment 100 may interoperate in a manner that is
different from that described above, and/or the environment 100 may
include additional components not shown in FIG. 2. For example, an
additional server/platform may act as an interface between client
device 202 and server device 204, and may perform various
operations associated with providing the labeling and/or risk
analysis operations of server 204 to client device 202 and/or other
servers.
Exemplary Artificial Neural Network
[0123] FIG. 3 depicts an exemplary artificial neural network 300
which may be trained by neural network unit 150 of FIG. 2 or neural
network training application 264 of FIG. 2, according to one
embodiment and scenario. The example neural network 300 may include
layers of neurons, including input layer 302, one or more hidden
layers 304-1 through 304-n, and output layer 306. Each layer
comprising neural network 300 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. 3.
[0124] Input layer 302 may receive different input data. For
example, input layer 302 may include a first input a.sub.1 which
represents an insurance type (e.g., collision), a second input
a.sub.2 representing patterns identified in input data, a third
input a.sub.3 representing a vehicle make, a fourth input a.sub.4
representing a vehicle model, 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 302 may comprise thousands or more
inputs. In some embodiments, the number of elements used by neural
network 300 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.
[0125] Each neuron in hidden layer(s) 304-1 through 304-n may
process one or more inputs from input layer 302, and/or one or more
outputs from a previous one of the hidden layers, to generate a
decision or other output. Output layer 306 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
(ACCIDENT, DEER) or absence (DROUGHT) of a condition. in some
embodiments, however, outputs of neural network 300 may be obtained
from a hidden layer 304-1 through 304-n in addition to, or in place
of, output(s) from output layer(s) 306.
[0126] 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 dimensions may
correspond to aspects of a vehicle operator considered strongly
determinative, then those that are considered of intermediate
importance, and finally those that are of less relevance.
[0127] 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 304-1 through 304-n may
share decisions relating to labeling, with no single layer making
an independent decision as to labeling.
[0128] In some embodiments, neural network 300 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 100 of FIG. 1. For example, in one
embodiment, a recurrent neural network may be trained and utilized
as part of image processing unit 124 to automatically label
images.
[0129] FIG. 4 depicts an example neuron 400 that may correspond to
the neuron labeled as "1,1" in hidden layer 304-1 of FIG. 3,
according to one embodiment. Each of the inputs to neuron 400
(e.g., the inputs comprising input layer 302) 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 300.
[0130] 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 420, labeled as f.sub.1,1(z.sub.1). The
function 420 may be any suitable linear or non-linear, or sigmoid,
function. As depicted in FIG. 4, the function 420 may produce
multiple outputs, which may be provided to neuron(s) of a
subsequent layer, or used directly as an output of neural network
300. 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.
[0131] It should be appreciated that the structure and function of
the neural network 300 and neuron 400 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
[0132] 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 108 of FIG. 1 and
historical data 270 of FIG. 2) and the input data provided by
customers or users of the AI platform (e.g., input data 102 of FIG.
1 and the data collected by input data collection application 216
of FIG. 2), as well as the data that is joined to the historical
data and input data, such as customer data 160 of FIG. 1 and
customer data 272 of FIG. 2, and customer data 160 of FIG. 1 and
vehicle data 274 of FIG. 2.
[0133] 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. 5.
[0134] FIG. 5 depicts text-based content of an example electronic
claim record 500 which may be processed using an artificial neural
network, such as neural network 300 of FIG. 3 or a different neural
network generated by neural network unit 150 of FIG. 1 or neural
network training application 264 of FIG. 2. 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.
[0135] Although text-based-content is depicted in the embodiment of
FIG. 5, 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 ted directly into the
neural network(s) without being converted to text first.
[0136] With respect to FIG. 5, electronic claim record 500 includes
three sections 510a-510c, which respectively represent policy
information, loss information, and external information. Policy
information 510a may include information about the insurance policy
under which the claim has been made, including the person to whom
the policy is issued, the name of the insured and any additional
insureds, the location of the insured, etc. Policy information 510a
may be read, for example by input analysis unit 120 analyzing
historical data such as historical data 108 and individual claims,
such as claims 110-1 through 110-n. Similarly, vehicle information
may be included in policy information 510a, such as a vehicle
identification number (VIN).
[0137] Additional information about the insured and the vehicle
(e.g., make, model, and year of manufacture) may be obtained from
data sources and joined to input data. For example, additional
customer data may be obtained from customer data 160 or customer
data 272, and additional vehicle data may be obtained from vehicle
data 162 and vehicle data 274. in some embodiments, make and model
information may be included in electronic claim record 500, and the
additional lookup may be of vehicle attributes (e.g., the number of
passengers the vehicle seats, the available options, etc.).
[0138] In addition to policy information 510a, electronic claim
record 500 may include loss information 510b. Loss information
generally corresponds to information regarding a loss event in
which a vehicle covered by the policy listed in policy information
510a sustained loss, and may be due to an accident or other peril.
Loss information Slob may indicate the date and time of the loss,
the type of loss (e.g., whether collision, comprehensive, 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.
[0139] In some embodiments, more the than one loss may be
represented in loss information 510b. For example, a single
accident may give rise to multiple losses under a given policy, for
example to two vehicles involved in a crash operated by vehicle
operators not covered under the policy. In addition to loss
information, electronic claim record 500 may include external
information 510c, including but not limited to correspondence with
the vehicle operator, statements made by the vehicle operator, etc.
External information 510c 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
tiles or data sources, such as linked data 520a-g. It should be
appreciated that although only links 520a-g are shown, more or
fewer links may be included, in some embodiments.
[0140] Electronic claim record 500 may include links to other
records, including other electronic claim records. For example,
electronic claim record 500 may link to notice of loss 520a, one or
more photographs 520b, one or more audio recordings 520c, one or
more investigator's reports 520d, one or more forensic reports
520e, one or more diagrams 520f, and one or more payments 520g.
Data in links 520a-520g may be ingested by an AI platform such as
AI platform 120. For example, as described above, each claim may be
ingested and analyzed by input analysis unit 120.
[0141] AI platform 104 may include instructions which cause input
analysis unit 120 to retrieve, for each link 520a-520g, 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. 1, input analysis unit 120 may process, first, all
images from one or more photograph 520b using image processing unit
124 Input analysis unit 120 may process audio recording 520c using
speech-to-text unit 122.
[0142] 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.
[0143] Once the various input data comprising electronic claim
record 500 has been processed, the results of the processing may,
in one embodiment, be passed to a text analysis unit, and then to
neural network. If the AI platform is being trained, then the
output of input analysis unit 120 may be passed directly to neural
network unit 150. The neurons comprising a first input layer of the
neural network being trained by neural network unit 150 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 510a, loss information 510b,
and external information 510c.
[0144] Similarly, one or more input neurons may be configured to
receive particular input(s) from links 520a-520g. 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. 2.
[0145] In some embodiments, analysis of input entered by a user may
be performed on a client device, such as client device 202. In that
case, output from input analysis may be transmitted to a server,
such as server 204, 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 264.
[0146] In one embodiment, the value of a new claim may be predicted
directly by a neural network model trained on historical data 108,
without the use of any labeling. For example, a neural network may
be trained such that input parameters correspond to, for example,
policy information 510a, loss information 512b, external
information 512c, and linked information 520a-520g.
[0147] The trained model may be configured so that inputting sample
parameters, such as those in the example electronic claim record
500, may accurately predict, for example, the estimate of damage
($25,000) and settled amount ($24,500). In this case, random
weights may be chosen for all input parameters.
[0148] The model may then be provided with training data from
claims 110-1 through 110-n, which are each pre-processed by the
techniques described herein with respect to FIGS. 1 and 2 to
extract individual input parameters. The electronic claim record
500 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.
[0149] 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
142 with respect to FIG. 1. Next, the labels and corresponding
weights, in one embodiment, may be received by risk level analysis
platform 106, where they may be used in conjunction with base rate
information to predict a claim loss value.
[0150] In some embodiments, information pertaining to the claim,
such as the coverage amount and vehicle type from policy
information 510a, may be passed along with the labels and weights
to risk analysis platform 106 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 510b of FIG. 5).
[0151] As noted above, the methods and systems described herein may
be capable of analyzing decades of electronic claim records to
build neural network 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
[0152] Turning to FIG. 6, an exemplary computer-implemented method
600 for determining a risk level posed by an operator of a vehicle
is depicted. The method 600 may be implemented via one or more
processors, sensors, servers, transceivers, and/or other computing
or electronic devices. The method 600 may include training a neural
network to identify risk factors that are predictive of electronic
vehicle claim records (e.g., by an AI platform such as AI platform
104 training a neural network by an input analysis unit 120
processing data before passing the results of the analysis to a
training unit 152 that uses the results to train a neural network
model) (block 610), The method 600 may include receiving
information corresponding to the vehicle by an AI platform (e.g.,
the AI platform 104 may accept input data such as input data 102
and may process that input by the use of an input analysis unit
such as input analysis unit 120) (block 620). The method 600 may
include analyzing the information using the trained neural network
(e.g., a risk indication unit 154 applies the output of the input
analysis unit 120 to trained neural network model) to generate one
or more risk indicators corresponding to the information (e.g., the
neural network produces a plurality of labels and/or corresponding
weights) (block 630) which are used to determine a risk level
corresponding to the vehicle based upon the one or more risk
indicators (e.g., risk indications are stored in risk indication
data 142, and/or passed to risk level analysis platform 106 for
computation of a risk level, which may be based upon weights also
generated by the trained neural network) (block 640). The method
may include additional, less, or alternate actions, including those
discussed elsewhere herein.
[0153] Turning to FIG. 7, a flow diagram for an exemplary
computer-implemented method 700 of determining risk indicators from
vehicle operator information. The method 700 may be implemented by
a processor (e.g., processor 250) executing, for example, a portion
of AI platform 104, including input analysis unit 120, pattern
matching unit 128, natural language processing unit 130, and neural
network unit 150. In particular, the processor 220 may execute an
input data collection application 216 and an input device 222 to
cause the processor 225 to acquire application input 710 from a
user of a client 202.
[0154] The processor 220 may further execute the input data
collection application 216 to cause the processor 220 to transmit
application input 710 from the user via network interface 214 and a
network 206 to a server (e.g., server 204). Processor 250 of server
204 may cause module 254 of server 204 to process application input
710. input analysis application 260 may analyze application input
710 according to the methods describe above. For example, vehicle
information maybe queried from a vehicle data such as vehicle data
274. A VIN number in application input 710 may be provided as a
parameter to vehicle data 274.
[0155] Vehicle data 274 may return a result indicating that a
corresponding vehicle was found in vehicle data 274, and that it is
a gray minivan that is one year old. Similarly, the purpose
provided in application input 710 may be provided to a natural
language processing unit (e.g., NLP unit 130), which may return a
structured result indicating that the vehicle is being driven by a
person who is an employed student athlete. The result of processing
the application input 710 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.
[0156] In one embodiment, the trained neural network model may
produce a set of labels and confidence factors 720. The set of
labels and confidence factors 720 may contain labels that are
inherent in the application input 710 (e.g., LOW-MILEAGE) or that
are queried based upon information provided in the application
input 710 (e.g., MINIVAN, based upon VIN). However, the set of
labels and confidence factors 720 may include additional labels
(e.g., COLLISION and DEER) that are not evident from the
application input 710 or any related/queried information. After
being generated by the neural network, the set of labels and
confidence factors 720 may then be saved to an electronic database
such as risk indication data 276, 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 202.
[0157] It should be appreciated that many more types of information
may be extracted from the application input 710 (e.g., from example
links 520a-520g as shown in FIG. 5). In one embodiment, the pricing
quote may be a weighted average of the products of label weights
and confidences. The method 700 may be implemented, for example, in
response to a vehicle operator accessing client 202 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.
[0158] With respect to FIG. 8, a flow diagram for an exemplary
computer-implemented method 800 of detecting and/or estimating
damage to personal property is depicted, according to an
embodiment. The method 800 may be implemented, for instance, by a
processor (e.g., processor 250) executing, for example, a portion
of AI platform 104, including input analysis unit 120, pattern
matching unit 128, natural language processing unit 130, and neural
network unit 150. In particular, the processor 250 may execute an
input analysis application 260 to cause processor 250 to receive
free-form text or voice/speech associated with a submitted
insurance claim for a damaged insured vehicle (block 802). The
method may include identifying one or more key words within the
free-form text or voice/speech (block 804). The identification of
key words within free-form text may be performed by a module of AI
platform 104 (e.g., by text analysis unit 126, pattern matching
unit 128, and/or natural language processing unit 130). The
identification of key words within voice/speech may be performed
by, for example, speech-to-text unit 122. The method may further
include determining a cause of loss and/or peril that caused damage
to the damaged insured vehicle (block 806). A cause of loss and/or
peril may be chosen from a set of causes of loss known to the
insurer (e.g., a set stored in risk indication data 142) or may be
identified or generated by risk indication unit 154.
[0159] In some embodiments, the free-form text may be associated
with a webpage or user interface of a client device accessed by a
customer or employee of the proprietor of AI system 104 (e.g., an
insurance agent) or by a user interface of an intranet page
accessed by an employee of a call center. For example, the
free-form text may be entered by a person utilizing input device
222 and display 224 of client device 202, and the input may be
caused to be collected by processor 210 executing instructions in
input data collection application 216. Voice/speech of a user may
be collected by processor 210 causing instructions in input data
collection 216 to be executed which read audio signals from an
input device such as a microphone. In one embodiment, free-form
text or voice/speech may be input to server device 204 via other
means (e.g., directly loaded onto server device 204). In some
embodiments, a neural network may be trained (e.g., by neural
network training unit 264) to identify, or determine, a key word
(or words) associated with a cause of loss and/or peril using
free-form text or voice/speech and a type corresponding to the
insured vehicle as training data. For example, multiple neural
networks may be trained that individually correspond to multiple
different respective vehicle types and sets of free-form text or
voice/speech.
[0160] In one embodiment, the machine learning algorithms may be
dynamically or continuously trained (i.e., trained online) to
dynamically update a set of key words associated with respective
cause of loss and/or peril information. The cause of loss and/or
peril information may be similarly dynamically updated. Such a
dynamic set may be stored and updated in an electronic database,
such as risk indication data 276.
[0161] In one embodiment, a first cause of loss and/or first a
peril may be identified, and an image may be received. For example,
a user may capture an image, e.g., a digital image, of a vehicle
(e.g., a vehicle that is damaged and/or insured) via image sensor
220, or other type of camera. The image may be collected by module
212 and transmitted via network interface 214 and network 206 to
network interface 256, whereupon the image may be analyzed by input
analysis application 260. The image may be input to neural network
unit 150 and passed to a trained neural network model or algorithm,
which may analyze the image determine a second cause of loss and/or
second peril. Then, the first cause of loss and/or peril (e.g.,
that were identified in a free-form submission, such as a claim)
may be compared to the second cause of loss and/or peril
corresponding to the image, to verify the accuracy of the submitted
claim and/or to identify potential fraud or inflation of otherwise
legitimate claims. In some embodiments the image received via image
sensor 220 may be analyzed to estimate damages, in terms of cost
and/or severity. Repair and replacement cost may be determined, in
one embodiment, by training a neural network model to accept an
image of a damaged vehicle, and to output an estimate of the
severity or cost of damages, repair, and/or replacement cost. Such
models may be trained using the methods described herein including,
without limitation, using a subset of historical data 108 as
training data.
[0162] In some embodiments, an insurance policy associated with the
damaged insured vehicle may be received or retrieved. The cause of
loss and/or peril may be analyzed to determine whether the cause of
loss and/or peril are covered under the insurance policy. For
example, a user of client device 202 may be required to login to an
application in module 212 using a username or password. The user
may be prompted to upload an image of a damaged vehicle during the
claims submission process by the application in module 212, and the
user may do so by capturing an image of a damaged vehicle the user
owns via image sensor 220. The image, and an indication of the
user's identity, may be transmitted via network 206 to server
device 204.
[0163] Server device 204 may determine the cause of loss as
described above by analyzing the image, and may retrieve an
insurance policy corresponding to the user by querying, for
example, customer data 272. Server 204 may contain instructions
that cause the cause of loss or peril associated with the uploaded
image to be analyzed in light of the insurance policy. The
insurance policy may be machine readable, such at the cause of loss
and peril information is directly comparable to the insurance
policy.
[0164] In one embodiment, another means of comparison may be
employed (e.g., a deep learning or Bayesian approach). Server 204,
or more precisely an application executing in server 204, may then
determine whether or not, or to what extent, the cause of loss
associated with the image captured by the user is covered under the
user's insurance policy. in one embodiment, an indication of the
coverage may be transmitted to the user (e.g., via network 206).
The causes of loss, perils, and key words/concepts that may be
identified and/or determined by the above-described methods
include, without limitation: collision, comprehensive, bodily
injury, property damage, liability, medical, rental, towing, and
ambulance.
[0165] FIG. 9A is an example flow diagram depicting an exemplary
computer-implemented method 900 of determining damage to personal
property, according to one embodiment. The method 900 may include
inputting historical claim data into a machine learning algorithm,
or model, to train the algorithm to identify an insured vehicle, a
type of insured vehicle, vehicle features or characteristics, a
peril associated with the vehicle, and/or a cost associated with
the vehicle (block 902). The method 900 may be implemented by a
processor (e.g., processor 250) executing, for example, a portion
of AI platform 104, including input analysis unit 120, and/or
otherwise implemented via, for instance, one or more processors,
sensors, servers, and/or transceivers. Processor 250 may execute an
input analysis application 260 to cause processor 250 to receive an
image of the damaged insured vehicle (block 904).
[0166] The method may further include inputting an image of the
damaged insured vehicle into the trained machine learning algorithm
to identify a type of insured vehicle, vehicle features or
characteristics, peril associated with the vehicle, and/or a cost
associated with the vehicle. A type of vehicle may include any
attribute of the vehicle, including without limitation, whether the
body type (e.g., coupe, sedan), make, model, model year, options
(e.g., sport package), whether the vehicle is autonomous or not,
etc. In some embodiments, the features and characteristics may
include an indication of whether the vehicle includes autonomous or
semi-autonomous technologies or systems. In some embodiments, the
peril associated with the damaged insured vehicle may comprise
collision, comprehensive, fire, water, smoke, hail, wind, or storm
surge.
[0167] In one embodiment, an insurance policy associated with the
damaged insured vehicle may be retrieved by AI platform 104, for
example, from customer data 160, and the type of peril compared to
the insurance policy to determine whether or not the peril is a
covered peril under the insurance policy. As noted above, the
applicable policy may be identified by a user identification passed
from a client device, but in some embodiments, the applicable
policy may be identified by other means. For example, a VIN number
or license plate may be digitized by optical character recognition
(e.g., by image processing unit 124) from the image provided to the
AI platform 104, and the digitization used to search customer data
160 for a matching insurance policy.
[0168] FIG. 9B is an example data flow diagram depicting an
exemplary computer-implemented method 910 of determining damage to
an insured vehicle using a trained machine learning algorithm to
facilitate handling an insurance claim associated with the damaged
insured. vehicle, according to one embodiment. The method 910 may
be implemented, for instance, via one or more processors, sensors,
servers, transceivers, and/or other computing or electronic
devices.
[0169] The method 910 may include receiving a photograph of a
damaged insured vehicle 912. The image may be received by, for
example, image processing unit 124 of AI platform 104. The image
may originate in a sensor of a client device, such as image sensor
220 of client device 202, and may be captured in response to an
action taken by a user, such as the user pressing a user interface
button (e.g., a button or screen element of input device 222). The
photograph may be analyzed by image processing unit 124 (e.g.,
sharpened, contrasted, or converted to a dot matrix) before being
passed to neural network unit 150, where it may be input to a
trained machine learning algorithm, or neural network model (block
914). The trained neural network model in block 914 may correspond
to the machine learning algorithm trained in block 904 of FIG. 9A,
The method may include identifying information 916 which may
include a type of the damaged insured vehicle, a respective feature
or characteristic of the damaged insured vehicle, a peril
associated with the damaged insured vehicle, and/or a repair or
replacement cost associated with the damaged insured vehicle. The
information 916 may be used to facilitate handling an insurance
claim associated with the damaged insured vehicle.
[0170] FIG. 10A is an example flow diagram depicting an exemplary
computer-implemented method 1000 for determining damage to personal
property, according to one embodiment. The method 1000 may be
implemented, for instance, via one or more processors, sensors,
servers, transceivers, and/or other computing or electronic
devices.
[0171] The method 1000 may include inputting historical claim
information into a machine learning algorithm, or model, to train
the algorithm to develop a risk profile for an undamaged insurable
vehicle based upon a type, feature, and/or characteristic of the
vehicle (block 1002), The type, feature, and/or characteristic of
the vehicle may include an indication of the geographic area of the
vehicle, the vehicle make or model, information about the vehicle's
transmission, information about the type and condition of the
vehicle's tires, information about the vehicle's engine,
information pertaining to whether the vehicle includes autonomous
or semi-autonomous features, information about the vehicle's air
conditioning or lack thereof, information specifying whether the
vehicle has power brakes and windows, and the color of the vehicle.
The method may further include receiving an image of an undamaged
insurable vehicle (block 1004). The method may further include
inputting the image of the undamaged insurable vehicle into a
machine learning algorithm to identify a risk profile for the
undamaged insurable vehicle (block 1006).
[0172] A risk profile may include a predicted loss amount,
likelihood of loss, or a risk relative to other vehicles. For
example, for a minivan may be lower than a risk profile for a
sports car. Similarly, the risk of being rear-ended in a sports car
may be lower than the risk of being rear-ended in a minivan. A risk
profile may also include multiple risks with respect to one or more
peril (e.g., respective risks for collision, liability, and
comprehensive) in addition to an overall, or aggregate, risk
profile.
[0173] FIG. 10B is an example data flow diagram depicting an
exemplary computer-implemented method 1010 of using a trained
machine learning algorithm to facilitate generating an insurance
quote for an undamaged insurable vehicle, according to one
embodiment. The method 1010 may be implemented, for instance, via
one or more processors, sensors, servers, transceivers, and/or
other computing or electronic devices.
[0174] The method may include receiving an image, or photograph, of
an undamaged vehicle 1012. The photograph may originate in a client
device, such as client 202, and may be captured and transmitted to
a server via the methods described above. The method 1010 may
include inputting the image of an undamaged vehicle into a trained
machine learning algorithm 1014. The trained neural network may
correspond to the neural network trained block 1002 of FIG. 10A,
and the machine learning algorithm may be trained using historical
claim information corresponding to historical data 108 of FIG. 1.
The neural network may be configured to accept historical claim
data and to predict damage amounts, or other risks.
[0175] The method may include inputting the image of the undamaged
insurable vehicle into the trained machine learning algorithm to
identify a risk profile for the undamaged insurable vehicle,
wherein the risk profile may correspond to the risk profile
described above with respect to block 1006. It should be
appreciated that the use of neural networks may cause variables to
emerge from large data sets that are not expected, but which are
highly correlated to risk. In sonic cases, the risk profile
associated with a given vehicle may contain information that seems
unforeseeable and/or counter-intuitive.
[0176] In one embodiment, the risk profile described above may be
used to generate an insurance policy and/or determine a rate
quotation corresponding to the undamaged insurable vehicle wherein
the policy and/or rate are based upon the risk profile. In one
embodiment, the rate may include a usage-based insurance (UBI)
rate. In some embodiments, the generated insurance policy and/or
rate quotation may be transmitted to the vehicle owner for a review
and/or approval process. For example, a user of client device 202
may submit an image of their vehicle via processor 210 and module
212, and the above-described analysis involving the trained neural
network model may then take place on server 204. Then, when a rate
quote or policy is generated on the server, the quote or policy may
be transmitted by network interface 256 to network 206 and
ultimately to network interface 214, back on the client.
[0177] The client may include an application in module 212 which
causes the policy or rate to be displayed to the user of client 202
(e.g., via display 224), and the user may review the policy/quote,
and may be prompted to enter (e.g., via input device 222) their
approval with the terms of the policy/quote. The user's approval
may be transmitted back to the server 204 via network 206, and a
contract for insurance formed. In this way, a user may successfully
register for an insurance policy coveting an insurable vehicle, by
capturing an image of the vehicle, uploading the image of that
vehicle, and reviewing a policy corresponding to that vehicle that
has been generated by a neural network model analyzing the image,
wherein the neural network model has been trained on historical
claim data and/or images of similar vehicles, according to at least
one preferred embodiment.
[0178] 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
[0179] 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.
[0180] 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.
[0181] 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
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.
[0182] 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, vehicle telematics, smart or autonomous
vehicle, and/or intelligent home telematics data. The machine
learning programs may utilize deep learning algorithms 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.
[0183] 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 EMBODIMENTS
[0184] In one aspect, a computer-implemented method of detecting
and/or estimating damage may be provided. The method may include
(1) receiving, via one or more processors and/or associated
transceivers (such as via wireless communication or data
transmission over one or more radio links or communication
channels), free form text or free form speech associated with a
submitted insurance claim or a damage insured asset (such as home
or vehicle), for instance the free form text or free form speech
may be associated with, or input via, a webpage accessed by a
customer or insurance agent, or an intranet page accessed by a call
center representative; (2) identifying, via one or more processors,
one or more key words within the free form text or free form
speech; and/or (3) based upon the one or more keywords,
determining, via one or more processors, a cause of loss and/or
peril that caused damage to the damaged insured asset to facilitate
handling insurance claims and enhancing the online customer
experience. The method may include additional, less, or alternate
actions, including those discussed elsewhere herein.
[0185] For instance, the damaged insured asset may be a home, and
the cause of loss and/or peril may be wind, water, storm surge,
smoke, fire, hail, hurricane, or tornado. The damaged insured asset
may an autonomous or semi-autonomous vehicle, and the cause of loss
may be related to a collision or comprehensive (non-vehicle
collision) cause of loss. For vehicles, the cause of loss may
include animals, such as deer, and the damage may relate to one or
more damaged or worn out sensors or other electronic
components.
[0186] Identifying, via one or more processors, one or more key
words within the free form text may include inputting the free form
text or free form text into a processor having a machine learning
algorithm trained accept the free form text or free form speech
and/or type of insured asset as input, and then identify key words
associated with cause of loss and/or insurance perils. The machine
learning algorithm may be dynamically or continuously updated or
trained to dynamically update the key words associated with cause
of loss and/or insurance perils.
[0187] Determining, via one or more processors, a cause of loss
and/or peril that caused damage based upon the one or more key
words may include inputting the free form text or free form speech
into a processor having a machine learning algorithm trained to
accept one or more key words and/or type of insured asset as input,
and then identify a cause of loss and/or peril based upon the one
or more key words and/or type of insured asset. The machine
learning algorithm may be dynamically or continuously updated or
trained to dynamically update the causes of loss and/or perils.
[0188] The method may include receiving, via one or more processors
and/or transceivers (such as via wireless communication or data
transmission over one or more radio links or communication
channel), images of the damaged insured asset (such as images
submitted by the insured via a webpage); analyzing, via one or more
processors, the images of the damaged insured asset to determine a
second cause of loss and/or second peril; and/or comparing, via one
or more processors, the second cause of loss and/or second peril
with the cause of loss and/or peril associated with the submitted
insurance claim, respectively, to verify the accuracy of the
submitted insurance claim, or identify potential fraud or
buildup.
[0189] The method may include receiving, via one or more processors
and/or transceivers (such as via wireless communication or data
transmission over one or more radio links or communication
channel), images of the damaged insured asset (such as images
submitted by the insured via a webpage); and/or analyzing, via one
or more processors, the images of the damaged insured asset to
estimate damages and/or a repair or replacement cost.
[0190] Analyzing, via one or more processors, the images of the
damaged insured asset to estimate damages and/or a repair or
replacement cost for the insured asset may include inputting the
images into a processor having a machine learning algorithm trained
to accept the images of a damage insured asset as input, and
estimate damages and/or repair/replacement cost for the insured
asset.
[0191] The method may include retrieving or receiving, via one or
more processors, an insurance policy associated with the insured
asset; and/or determining, via one or more processors, whether the
cause of loss and/or peril is covered under the insurance
policy.
[0192] The damaged insured asset may be a vehicle, such as a smart
or autonomous vehicle, and the cause of loss and/or peril may be,
or may be associated with, collision, comprehensive, bodily injury,
property damage, liability, or medical. Additionally or
alternatively, the damaged insured asset may be a vehicle, such as
a smart or autonomous vehicle, and the one or more key words may
be, or may be associated with collision, comprehensive, bodily
injury, property damage, liability, medical, rental, towing, or
ambulance.
[0193] The damaged insured asset may be a home or vehicle, and the
one or more key words may be, or may be associated with, fire,
smoke, wind, hail, water, storm surge, tornado, hurricane,
electrical, plumping, property damage, liability, medical,
ambulance, materials, cabinets, fireplace, bathroom, bedroom,
kitchen, upstairs, roof, downstairs, basement, structural security
system, appliance, refrigerator, washer, dryer, oven, stove, and/or
lightning.
[0194] In another aspect, a computer-implemented method of
determining damage to property may be provided. The method may
include (1) inputting, via one or more processors, historical claim
data into a machine learning algorithm to train the algorithm to
identify an insured asset (or type thereof), insured asset features
or characteristics, a peril, and/or a repair or replacement cost;
(2) receiving, via one or more processors and/or transceivers (such
as via wireless communication or data transmission over one or more
radio links or communication channel), images of the damaged
insured asset (such as images submitted by the insured via a
webpage); and/or (3) 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, the
trained machine learning algorithm identifying a type of the
damaged insured asset, features or characteristics of the damaged
insured asset, a peril, and/or a repair or replacement cost to
facilitate handling an insurance claim associated with the damaged
insured asset. The method may include additional, less, or
alternate actions, including those discussed elsewhere herein.
[0195] For instance, the damaged insured asset may be a vehicle,
and the features or characteristics of the damaged insured asset
include one or more autonomous or semi-autonomous technologies or
systems. Additionally or alternatively, the damaged insured asset
may be a vehicle, and the features or characteristics of the
damaged insured asset include one or more autonomous or
semi-autonomous technologies or systems, and/or the peril is
collision, comprehensive, fire, or water.
[0196] The method may include retrieving, via one or more
processors, an insurance policy associated with the damaged insured
asset; and/or determining, via one or more processors, whether the
peril is a covered peril under the insurance policy.
[0197] In another aspect, a computer system configured to detect
and/or estimate damage may be provided. The system may include one
or more processors, sensors, transceivers, and/or servers
configured to: (1) receive (such as via wireless communication or
data transmission over one or more radio links or communication
channels) free form text or free form speech associated with a
submitted insurance claim or a damage insured asset (such as home
or vehicle, which may be an autonomous vehicle), for instance the
free form text or free form speech may be associated with a webpage
or website accessed by a customer or insurance agent, or an
intranet page accessed by a call center representative; (2)
identify one or more key words within the free form text or free
form speech; and/or (3) 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 insurance claims and
enhancing the online customer experience. The computer system may
include additional, less, or alternative functionality, including
that discussed elsewhere herein.
[0198] For instance, the system is further configured to: receive
(such as via wireless communication or data transmission over one
or more radio links or communication channel), images of the
damaged insured asset (such as images submitted by the insured via
a webpage); analyze the images of the damaged insured asset to
determine a second cause of loss and/or second peril; and/or
compare the second cause of loss and/or second peril with the cause
of loss and/or peril associated with the submitted insurance claim,
respectively, to verify the accuracy of the submitted insurance
claim, or identify potential fraud or buildup.
[0199] The system may be further configured to: receive (such as
via wireless communication or data transmission over one or more
radio links or communication channel) images of the damaged insured
asset (such as images submitted by the insured via a webpage,
website, and/or mobile device); and/or analyze the images of the
damaged insured asset to estimate damages and/or a repair or
replacement cost.
[0200] In another aspect, a computer system configured to determine
damage to property may be provided. The system may include one or
more processors, servers, sensors, and/or transceivers configured
to: (1) input historical claim data into a machine learning
algorithm to train the algorithm to identify an insured asset (or
type thereof), insured asset features or characteristics, a peril,
and/or a repair or replacement cost; (2) receive (such as via
wireless communication or data transmission over one or more radio
links or communication channel), images of the damaged insured
asset (such as images submitted by the insured via a webpage);
and/or (3) input the images of the damaged insured asset into a
processor having the trained machine learning algorithm installed
in a memory unit, the trained machine learning algorithm
identifying a type of the damaged insured asset, features or
characteristics of the damaged insured asset, a peril, and/or a
repair or replacement cost to facilitate handling an insurance
claim associated with the damaged insured asset. The system may
include additional, less, or alternative functionality, including
that discussed elsewhere herein.
[0201] In another aspect, a computer system configured to determine
damage to property may be provided. The system may include one or
more processors, servers, sensors, and/or transceivers configured
to: (1) input historical claim data into a machine learning
algorithm to train the algorithm to develop a risk profile for an
insurable asset based upon type of insurable asset and insurable
asset features or characteristics; (2) receive (such as via
wireless communication or data transmission over one or more radio
links or communication channel), images of an undamaged insurable
asset (such as images submitted by the insured via a webpage);
and/or (3) input the 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
identifying or determining a risk profile for the insurable asset
to facilitate generating an insurance quote for the insurable
asset. The system may include additional, less, or alternative
functionality, including that discussed elsewhere herein.
[0202] The insurable asset may be a home, and the features or
characteristics may include location, square footage, cabinet type,
roof type, siding type, type of fireplace, type of windows, and/or
material type, and/or other home features or characteristics.
[0203] The insurable asset may be a vehicle, and the features or
characteristics include geographical area, make, model,
transmission, tire, engine, autonomous or semi-autonomous features,
types of sensors or electronic components, versions of software
(such as software directing or controlling autonomous or
semi-autonomous features or technologies), air conditioning, power
brakes, power windows, and/or color of the vehicle, and/or other
vehicle features or characteristics.
[0204] The system may be configured to generate an insurance policy
and/or determine an insurance rate, including a usage-based
insurance (UBI) rate, for the insurable asset based at least in
part upon the risk profile developed for the insurable asset;
and/or transmit the insurance policy and/or insurance rate to an
asset owner for review and/or approval.
[0205] In another aspect, a computer-implemented method for
determining damage to property may be provided. The method may
include, via one or more processors, servers, sensors, and/or
transceivers configured to: (1) inputting historical claim data
into a machine learning algorithm to train the algorithm to develop
a risk profile for an insurable asset based upon type of insurable
asset and insurable asset features or characteristics; (2)
receiving (such as via wireless communication or data transmission
over one or more radio links or communication channel) images of an
undamaged insurable asset (such as images submitted by the insured
via a webpage); and/or (3) inputting the 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 identifying or determining a risk profile for
the insurable asset to facilitate generating an insurance quote for
the insurable asset. The method may include additional, less, or
alternate actions, including those discussed elsewhere herein.
Additional Considerations
[0206] 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 or
autonomous vehicle controller, smart home controller, 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. In the above description, neural networks may also refer
to other methods of artificial intelligence and machine
learning.
[0207] In other embodiments, deployment and use of neural network
models at a user device (e.g., the client 202 of FIG. 2) 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 204 of FIG. 2).
[0208] 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.
[0209] The patent claims at the end of this patent application are
not intended to be construed under 35 U.S.C. .sctn. 112(i) 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.
[0210] 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.
[0211] 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.
[0212] 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).
[0213] 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.
[0214] 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.
[0215] 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.
[0216] 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.
[0217] 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.
[0218] 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).
[0219] 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.
[0220] 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.
[0221] 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.
[0222] 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,
[0223] 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 performing the methods and
systems disclosed herein, using 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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