U.S. patent application number 17/353621 was filed with the patent office on 2021-10-07 for automobile monitoring systems and methods for loss reserving and financial reporting.
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 | 20210312567 17/353621 |
Document ID | / |
Family ID | 1000005669650 |
Filed Date | 2021-10-07 |
United States Patent
Application |
20210312567 |
Kind Code |
A1 |
Hayward; Gregory L. ; et
al. |
October 7, 2021 |
Automobile Monitoring Systems and Methods for Loss Reserving and
Financial Reporting
Abstract
A method of determining loss reserves and/or providing automatic
financial reporting related thereto via one or more processors
includes (1) receiving a plurality of historical electronic claim
documents, each respectively labeled with a claim loss amount; (2)
normalizing each respective claim loss amount and training an
artificial intelligence or machine learning algorithm, module, or
model, such as an artificial neural network, by applying the
plurality of electronic claim documents to the artificial
intelligence or machine learning algorithm, module, or model. The
method may include receiving a user claim and predicting a loss
reserve amount by applying the user claim to the trained artificial
intelligence or machine learning algorithm, module, or model, and
may include unreported claims.
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: |
1000005669650 |
Appl. No.: |
17/353621 |
Filed: |
June 21, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16136401 |
Sep 20, 2018 |
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17353621 |
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62652121 |
Apr 3, 2018 |
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62646729 |
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62617851 |
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62610599 |
Dec 27, 2017 |
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62580655 |
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62580713 |
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62564055 |
Sep 27, 2017 |
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62646735 |
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62646740 |
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62632884 |
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62625140 |
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62622542 |
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62621797 |
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62621218 |
Jan 24, 2018 |
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62618192 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 40/08 20130101;
G06N 3/08 20130101 |
International
Class: |
G06Q 40/08 20060101
G06Q040/08; G06N 3/08 20060101 G06N003/08 |
Claims
1-20. (canceled)
21. A method implemented by one or more processors, comprising:
receiving a plurality of historical claim documents; rendering a
graphical user interface to a user, the graphical user interface
comprising a selection section and a query section; receiving a
user input corresponding to the selection section; receiving a user
query corresponding to the query section; selecting a subset of
historical claim documents from the plurality of historical claims
based at least in part upon the user input and the user query;
training a machine learning model using the selected subset of
historical claim documents; receiving a claim document; and
predicting a loss reserve amount by applying the trained machine
learning model to the claim document;
22. The method of claim 21, further comprising: selecting a first
subset of historical claim documents based upon the user input;
selecting a second subset of historical claim documents based upon
the user query; and generating the subset of historical claim
documents based upon the first subset of historical claim documents
and the second subset of historical claim documents.
23. The method of claim 21, further comprising: compiling a
structured query based upon the user query; and selecting the
subset of historical claim documents from the plurality of
historical claims based at least in part upon the user input and
the structured query.
24. The method of claim 21, wherein the machine learning model
comprises a first machine learning model and a second machine
learning model, the method further comprising: selecting a first
subset of historical claim documents based upon the user input;
selecting a second subset of historical claim documents based upon
the user query; training the first machine learning model using the
first subset of historical claim documents; training the second
machine learning model using the second subset of historical claim
document; predicting a first loss reserve amount by applying the
first trained machine learning model to the claim document;
predicting a second loss reserve amount by applying the second
trained machine learning model to the claim document; and
determining the loss reserve amount based at least in part upon the
first loss reserve amount and the second loss reserve amount.
25. The method of claim 24, further comprising: determining the
loss reserve amount to be the sum of the first loss reserve amount
and the second loss reserve amount.
26. The method of claim 21, wherein the claim document includes
free-form text and an image, the method further comprising:
determining a first cause of loss by applying the trained machine
learning model to the free-form text of the claim document;
determining a second cause of loss by applying the trained machine
learning model to the image of the claim document; and predicting
the loss reserve amount using the trained machine learning model
based at least in part upon the first cause of loss and the second
cause of loss.
27. The method of claim 26, wherein the trained machine learning
model comprising a natural language processing model, the method
further comprising: identifying a keyword in the free-form text of
the claim document using the natural language processing model; and
determining the first cause of loss based at least upon the
identified keyword.
28. The method of claim 21, wherein each historical claim document
of the plurality of history claim documents is associated with a
plurality of labels.
29. A system, comprising: one or more memories having instructions
stored thereon; and one or more processors configured to execute
the instructions and configured to perform operations comprising:
receiving a plurality of historical claim documents; rendering a
graphical user interface to a user, the graphical user interface
comprising a selection section and a query section; receiving a
user input corresponding to the selection section; receiving a user
query corresponding to the query section; selecting a subset of
historical claim documents from the plurality of historical claims
based at least in part upon the user input and the user query;
training a machine learning model using the selected subset of
historical claim documents; receiving a claim document; and
predicting a loss reserve amount by applying the trained machine
learning model to the claim document;
30. The system of claim 29, wherein the operations further
comprise: selecting a first subset of historical claim documents
based upon the user input; selecting a second subset of historical
claim documents based upon the user query; and generating the
subset of historical claim documents based upon the first subset of
historical claim documents and the second subset of historical
claim documents.
31. The system of claim 29, wherein the operations further
comprise: compiling a structured query based upon the user query;
and selecting the subset of historical claim documents from the
plurality of historical claims based at least in part upon the user
input and the structured query.
32. The system of claim 29, wherein the machine learning model
comprises a first machine learning model and a second machine
learning model, wherein the operations further comprise: selecting
a first subset of historical claim documents based upon the user
input; selecting a second subset of historical claim documents
based upon the user query; training the first machine learning
model using the first subset of historical claim documents;
training the second machine learning model using the second subset
of historical claim document; predicting a first loss reserve
amount by applying the first trained machine learning model to the
claim document; predicting a second loss reserve amount by applying
the second trained machine learning model to the claim document;
and determining the loss reserve amount based at least in part upon
the first loss reserve amount and the second loss reserve
amount.
33. The system of claim 32, wherein the operations further
comprise: determining the loss reserve amount to be the sum of the
first loss reserve amount and the second loss reserve amount.
34. The system of claim 29, wherein the claim document includes
free-form text and an image, wherein the operations further
comprise: determining a first cause of loss by applying the trained
machine learning model to the free-form text of the claim document;
determining a second cause of loss by applying the trained machine
learning model to the image of the claim document; and predicting
the loss reserve amount using the trained machine learning model
based at least in part upon the first cause of loss and the second
cause of loss.
35. The system of claim 34, wherein the trained machine learning
model comprising a natural language processing model, wherein the
operations further comprise: identifying a keyword in the free-form
text of the claim document using the natural language processing
model; and determining the first cause of loss based at least upon
the identified keyword.
36. The system of claim 29, wherein each historical claim document
of the plurality of history claim documents is associated with a
plurality of labels.
37. A non-transitory computer-readable storage media comprising
instructions that cause a programmable processor to: receive a
plurality of historical claim documents; render a graphical user
interface to a user, the graphical user interface comprising a
selection section and a query section; receive a user input
corresponding to the selection section; receive a user query
corresponding to the query section; select a subset of historical
claim documents from the plurality of historical claims based at
least in part upon the user input and the user query; train a
machine learning model using the selected subset of historical
claim documents; receive a claim document; and predict a loss
reserve amount by applying the trained machine learning model to
the claim document.
38. The non-transitory computer-readable storage media of claim 37,
wherein the instructions further cause the programmable processor
to: select a first subset of historical claim documents based upon
the user input; select a second subset of historical claim
documents based upon the user query; and generate the subset of
historical claim documents based upon the first subset of
historical claim documents and the second subset of historical
claim documents.
39. The non-transitory computer-readable storage media of claim 37,
wherein the instructions further cause the programmable processor
to: compile a structured query based upon the user query; and
select the subset of historical claim documents from the plurality
of historical claims based at least in part upon the user input and
the structured query.
40. The non-transitory computer-readable storage media of claim 37,
wherein the instructions further cause the programmable processor
to: determine a first cause of loss by applying the trained machine
learning model to the free-form text of the claim document;
determine a second cause of loss by applying the trained machine
learning model to the image of the claim document; and predict the
loss reserve amount using the trained machine learning model based
at least in part upon the first cause of loss and the second cause
of loss.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to and the benefit of:
[0002] U.S. Application 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 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 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 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 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 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 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 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 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 No.
62/646,735, filed Mar. 22, 2018 and entitled "REAL PROPERTY
MONITORING SYSTEMS AND METHODS FOR RISK DETERMINATION;" [0012] U.S.
Application No. 62/646,740, filed Mar. 22, 2018 and entitled
"SYSTEMS AND METHODS FOR ESTABLISHING LOSS RESERVES FOR
BUILDING/REAL PROPERTY INSURANCE;" [0013] U.S. Application No.
62/617,851, filed Jan. 16, 2018 and entitled "IMPLEMENTING MACHINE
LEARNING FOR LIFE AND HEALTH INSURANCE PRICING AND UNDERWRITING;"
[0014] U.S. Application No. 62/622,542, filed Jan. 26, 2018 and
entitled "IMPLEMENTING MACHINE LEARNING FOR LIFE AND HEALTH
INSURANCE LOSS MITIGATION AND CLAIMS HANDLING;" [0015] U.S.
Application No. 62/632,884, filed Feb. 20, 2018 and entitled
"IMPLEMENTING MACHINE LEARNING FOR LIFE AND HEALTH INSURANCE LOSS
RESERVING AND FINANCIAL REPORTING;" [0016] U.S. Application No.
62/652,121, filed Apr. 3, 2018 and entitled "IMPLEMENTING MACHINE
LEARNING FOR LIFE AND HEALTH INSURANCE CLAIMS HANDLING;"
[0017] the entire disclosures of which are hereby incorporated by
reference herein in their entireties.
FIELD OF INVENTION
[0018] This disclosure generally relates to detecting damage, loss,
injury and/or other conditions associated with an automobile using
a computer system using an automobile monitoring system; and for
processing, estimating, and optimizing loss reserves and financial
reporting.
BACKGROUND
[0019] As computer and computer networking technology has become
less expensive and more widespread, more and more devices have
started to incorporate digital "smart" functionalities. For
example, controls and sensors capable of interfacing with a network
may now be incorporated into devices such as vehicles. Furthermore,
it is possible for one or more vehicle and/or central controllers
to interface with the smart devices or sensors.
[0020] However, conventional systems may not be able to
automatically detect and characterize various conditions (e.g.,
damage, injury, etc.) associated with a vehicle and/or the
vehicle's occupants, occupants of other vehicles, and/or
pedestrians. 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 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 reserving estimates.
[0021] In general, "loss reserves" may be funds that may be
pre-allocated by an insurer or other company (e.g., a mutual
insurance company or capital stock insurance company) to offset
known or anticipated losses. Some level of disclosure of loss
reserves may be required (e.g., by public statute or contractual
bylaws). Disclosure of loss reserves may be issued periodically
(e.g., yearly) and may be included in a financial report such as an
annual report, shareholder report (e.g., S.E.C. form 10-K), or
other statement.
[0022] Accurate loss reserves prediction historically may be a
manual process in which actuaries or other financial scientists
manually review claims and make guesses as to the final loss
amounts associated with those claims. This manual process may be
influenced by significant error margin due to human inexperience,
limitations of recollection, bias, and other frailties. Accurate
loss reserving practice may be very difficult to get right, and may
have serious consequences for companies that neglect to do it
properly. Underestimation of loss reserves may cause a company to
believe that it has adequate capitalization, when in reality, it
does not. Once the final loss amounts become known, the liquidity
of the company may be negatively affected. On the other hand,
overestimation of loss reserves may cause a company to set aside
funds in excess of the necessary capital reserves. Doing so may
prevent the company from using the capital for other purposes.
Conventional techniques may have other drawbacks as well.
BRIEF SUMMARY
[0023] The present disclosure generally relates to systems and
methods for detecting damage, loss, injury and/or other conditions
associated with a vehicle using a computer system; and methods and
systems for processing, estimating, and optimizing loss reserving
and financial reporting obligations. 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.
[0024] In one aspect, a computer-implemented method of determining
loss reserves is provided. The method may include receiving a
plurality of historical electronic claim documents, each
respectively labeled with a claim loss amount, normalizing each
respective claim loss amount, and training an artificial neural
network (or other artificial intelligence or machine learning
algorithm, program, module or model) by applying the plurality of
electronic claim documents to the artificial neural network. The
method may further include receiving a user claim and predicting a
loss reserve amount by applying the user claim to the trained
artificial neural network (or other artificial intelligence or
trained machine learning algorithm, program, module, or model).
[0025] In another aspect computing system having one or more
processor and one or more memories storing instructions that, when
executed by the one or more processors, cause the computing system
to receive a plurality of historical electronic claim documents,
each respectively labeled with a claim loss amount, normalize each
respective claim loss amount, and train an artificial neural
network (or other artificial intelligence or machine learning
algorithm, program, module, or model) by applying the plurality of
electronic claim documents to the artificial neural network (or
other artificial intelligence or machine learning algorithm,
program, module, or model). The instructions may further cause the
computing system to receive a user claim and predict a loss reserve
amount by applying the user claim to the trained artificial neural
network (or other artificial intelligence or machine learning
algorithm, program, module, or model).
[0026] 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
[0027] 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.
[0028] 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:
[0029] FIG. 1 depicts an exemplary computing environment in which
techniques for training a neural network (or other artificial
intelligence or machine learning algorithm, program, module, or
model) to determine a loss reserve associated with a vehicle and/or
vehicle operator may be implemented, according to one
embodiment;
[0030] FIG. 2 depicts an exemplary computing environment in which
techniques for collecting and processing user input, and training a
neural network (or other artificial intelligence or machine
learning algorithm, program, module, or model) to determine loss
reserving and financial reporting information may be implemented,
according to one embodiment;
[0031] 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;
[0032] FIG. 4 depicts an exemplary neuron, which may be included in
the artificial neural network of FIG. 3, according to one
embodiment and scenario;
[0033] FIG. 5 depicts text-based content of an exemplary electronic
claim record that may be processed by an artificial neural network,
in one embodiment;
[0034] 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;
[0035] FIG. 7 depicts a flow diagram of an exemplary
computer-implemented method of identifying risk indicators from
vehicle operator information, according to one embodiment;
[0036] 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;
[0037] FIG. 9A is an example flow diagram depicting an exemplary
computer-implemented method of determining damage to personal
property, according to one embodiment;
[0038] 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 (or
other artificial intelligence or machine learning algorithm,
program, module, or model) to facilitate handling an insurance
claim associated with the damaged insured vehicle, according to one
embodiment;
[0039] FIG. 10A is an example flow diagram depicting an exemplary
computer-implemented method for determining damage to personal
property, according to one embodiment;
[0040] 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 (or other artificial intelligence or machine learning
algorithm, program, module, or model) to facilitate generating an
insurance quote for the undamaged insurable vehicle, according to
one embodiment;
[0041] FIG. 11 depicts an example loss reserving user interface, in
which a user may train and/or operate a neural network (or other
artificial intelligence or machine learning algorithm, program,
module, or model) using a customized data set, according to one
embodiment and scenario;
[0042] FIG. 12 depicts a flow diagram of an exemplary
computer-implemented method of determining loss reserves, according
to one embodiment; and
[0043] FIG. 13 depicts a flow diagram of an exemplary
computer-implemented method of executing a trained artificial
neural network (or other artificial intelligence or machine
learning algorithm, program, module, or model) and data set, and
displaying the result of such execution, according to one
embodiment.
[0044] 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
[0045] The present embodiments are directed to, inter alia, machine
learning and/or training of models using historical automobile
claim data to determine optimal loss reserving amounts and
financial reporting information. 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 vehicle
controller, and/or into a chat bot or robo-advisor.
[0046] Other inputs to a machine learning/training model may be
harvested from historical claims, and may include make, model,
year, miles, technological features, and/or other characteristics
of a vehicle including any software updates that have been applied
to the vehicle (including versions thereof), claim paid or not
paid, liability (e.g., types of injuries, where treated, how
treated, etc.), disbursements related to claim such as rental costs
and other payouts, etc. Additional inputs to the machine
learning/training model may include vehicle telematics data, such
as how long and when are the doors unlocked, how often is the
security system armed, how long the vehicle is operated and/or
during which times of the day, etc.
[0047] Vehicle inspection and/or maintenance records may be of
particular interest in some embodiments, as being highly correlated
to vehicle malfunction. A driver's history may also be used as
inputs to artificial intelligence or machine learning algorithms or
models in some embodiments, including without limitation, the
driver's age, number and type of moving violations and any fines
associated therewith, etc.
[0048] As noted above, "loss reserves" are amounts of capital set
aside in advance of claim settlement, and in some cases, prior to
the filing of claims. For example, an insurer may learn via
statistical analysis that a given amount of broken-windshield
claims may occur each year, and may be able to derive a claim
payout average. With this information, the insurer may be able to
extrapolate the amount of loss reserves that should be set aside
for the next year's worth of broken-windshield claims. However, a
model that is specific to broken-windshield claims will not provide
the insurer with information related to other claim types, and may
not be very accurate. The methods and systems herein may be used to
build general models for determining loss reserves taking into
account decades worth of historical claims information, which may
appear counter-intuitive to human observers.
Artificial Intelligence System for Vehicle Insurance
[0049] The present embodiments may also be directed to machine
learning and/or training a model using historical auto claim data
to discover loss reserving data. 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", "surgery",
"hospital", etc.), 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. It should be appreciated that the inputs to the
trained model may be very complex and may include many (e.g.,
millions) of inputs. For example, a single network may base an
amount of loss reserve on a zip code, medical diagnosis, treatment
plan, age of injured person, gender of injured person, point of
impact, G-forces at impact, air bag deployment(s), striking vehicle
weight and/or size, etc. Many more, or fewer, inputs may be
included in some embodiments. The presence or absence of autonomous
vehicle features may be determined with respect to historical auto
claims, and may be used in the training process.
Exemplary Environment for Determining Loss Reserves and Financial
Reporting in Data
[0050] The embodiments described herein may relate to, inter alia,
determining one or more loss reserves. The embodiments described
herein may also relate to financial reporting. Different loss
reserve amounts may be generated by separate models examining a set
of inputs, in some embodiments. In some embodiments, one or more
neural network models (or other artificial intelligence or machine
learning algorithms, programs, modules, or models) may be trained
using a subset of historical claims data as training input. A
separate subset of the historical claims data may be used for
validation and cross-validation purposes. 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 (or other artificial
intelligence or machine learning algorithms, programs, modules, or
models) to produce labels and weights indicating net or individual
risk factors, based upon existing claim data.
[0051] For example, the remote computing device may receive the
input and determine, using a trained neural network (or other
artificial intelligence or machine learning algorithm, program,
module, or model), one or more loss reserve amounts applicable to
the input. Herein loss reserve amounts may be expressed
numerically, as strings (e.g., as labels), or in any other suitable
format. Loss reserves may be expressed as a dollar amount, or as a
multiplier with respect to a past amount (e.g., 1.2 or 120%). The
determined loss reserve amounts may be displayed to an end user, or
an employee or owner of a business utilizing the methods and
systems described herein. Similarly, the loss reserve amounts may
be provided as input to another application (e.g., to an
application which provides the loss reserve amounts to an end
user). The loss reserve amounts may be joined with other
information (e.g., claim category) and may be formatted (e.g., in a
table or other suitable format) and automatically inserted in a
financial report, such as a PDF file.
[0052] A loss reserve aggregate may include one or more loss
reserve amounts respective of a claim category or subtype, and may
include a gross or net reserve amount. For example, an "automobile"
loss reserve aggregate may be created which predicts a $1 m total
loss reserve. This aggregate may include a plurality of categorical
loss reserve, which may themselves be aggregate loss reserves or
individual loss reserves. For example, the automobile loss reserve
aggregate may include a "motorcycle" loss reserve and a "passenger
car" loss reserve, wherein the motorcycle loss reserve may be
associated with an amount of ($2 m) and the passenger car loss
reserve may be associated with an amount of $3.2 m. Herein,
parentheses may be used to denote negative loss reserves (e.g.,
shortfalls) and lack of parentheses may be used to denote positive
loss reserves (e.g., surpluses). The gross amount of the automobile
(i.e., combined motorcycle and passenger car) loss reserves may be
$1.2 m. An amount of money may be deducted from the gross amount
for miscellaneous expenses associated with the automobile loss
reserve and/or constituent loss reserves (e.g., audit fees, storage
fees, etc.) to arrive at a net loss reserve amount. Gross and/or
net loss reserves may be included in financial reports.
[0053] It should be appreciated that the fully automated and
dynamic learning methods and systems described herein may estimate
loss reserves not only for insurance claims that have been reported
but also for claims that have occurred but have not yet been
reported or recorded, as may be required by actuarial standards of
practice and/or applicable law.
[0054] Turning to FIG. 1, an exemplary computing environment 100,
representative of automobile monitoring systems and methods for
loss reserve determination and financial reporting, 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.
[0055] 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. 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 determining loss reserves
and generating financial reports 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.
[0056] 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.
[0057] 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 determining optimal loss reserves via a
loss reserve aggregation 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). AI platform
may reference this address to retrieve records from input data 102
to perform loss reserves determinations. 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,
loss reserves and financial reports.
[0058] 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 in determining loss reserves related to motorcycle
operators. In another embodiment, AI platform 104 may be used to
train a neural network model (or other artificial intelligence or
machine learning algorithm, program, module, or model) for use in
determining optimal loss reserves, a priori, relating to windshield
damage claims. The precise manner in which neural networks are
created and trained is described below. In some embodiments,
large-scale/distributed computing tools (e.g., Apache Hadoop) may
be used to implement some of artificial intelligence platform
104.
[0059] 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 (or other
artificial intelligence or machine learning algorithms or models).
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.
[0060] 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., "hospital" or "surgery") which may be
indicative of particular types of injury. Such keywords may be
associated with a respective occurrence (e.g., the number 5 may
indicate that a person sustained five surgeries). 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). NIT unit 130 may identify human speech
patterns in data, including semantic information relating to
entities, such as people, vehicles, homes, and other objects. For
example, the location and time of an accident may be identified, as
well as a quantity related to an accident (e.g., the number of
passengers)
[0061] 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. For example, text analysis unit 126 may receive output
from a trained neural network.
[0062] In the embodiment of FIG. 1, AI platform 104 may include a
loss classifier 140 to classify, or group, losses. Such
classification may use standard clustering techniques used in
machine learning, such as k-means algorithms. In some embodiments,
loss classifier 140 may group losses into groups by pre-defined
categories (e.g., large/small, personal injury/property, etc.). In
other embodiments, classification may determine categories by
agglomeration or other known methods. Loss classifier may associate
claims with loss category information, and such information may be
stored in a loss data 142. Loss classifier 140 may be used to build
a predictive model that pertains to a category (e.g., motorcycle
operators) as described above.
[0063] Loss classifier 140 may analyze a subset of claims in
historical data 110. The subset of claims may contain a mixture of
severe claims (e.g., those claims in which complications from
surgery post-accident resulted in the greatest level of damage,
whether quantified by pecuniary loss or the loss of human life,
motor function, and/or cognitive function) and non-severe claims
(e.g., those claims in which only minor first aid was rendered
post-accident). Loss classifier 140 may be trained to categorize
claims based upon membership in one or more "severity" categories.
Once loss classifier 140 has classified a given claim, its severity
may be saved to and/or retrieved from an electronic database, such
as loss data 142, or associated with a set of input data 102. Loss
classifier 140 severity information may also be passed to other
components, such as neural network unit 150. Random forest trees
may be used to classify claims, and may be capable of determining
which of several criteria or features associated with each claim
was paramount in the classifier's decision.
[0064] 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, feed-forward neural network, and/or deep
learning 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. In some embodiments, neural network unit 150 may use
other types of artificial intelligence or machine learning
algorithms or models, including those discussed elsewhere
herein.
[0065] In some embodiments, neural network models may be chained
together, so that output from one model is piped or transferred
into another model as input. For example, loss classifier 140 may,
in one embodiment, apply input data 102 to a first neural network
model that is trained to categorize claims (e.g., by vehicle type,
severity and/or other criteria). The output of this first neural
network model may be fed as input to a second neural network model
which has been trained to generate loss reserves for claims
corresponding to the categories.
[0066] As noted, a neural network may include a series of nodes
connected by weighted links, and the neural network may be
continuously trained from a randomized initial state, using a
subset of historical claims as input, until the outputs
corresponding to the sum of the weights at each layer in the neural
network converge to the particular values assigned to a "truth"
data set. A truth data set may contain claims along with correct,
or optimal, loss reserving amounts, and may be based upon
historical loss reserves of an insurer. For example, the insurer
may include a plurality of claims with corresponding loss reserves
that resulted in the insurer not overestimating or underestimating
the payouts of the plurality of claims. The error of the network
may be measured as the difference between the particular values and
the weights. Once trained, the neural network (or other artificial
intelligence or machine learning algorithm, program, module, or
model) may be validated with another subset of data, and its
parameters and/or structure adjusted accordingly.
[0067] Neural network unit 150 may include training unit 152, and
loss reserving 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 predict loss reserves,
including by pre-processing of historical data 108 using input
analysis unit 120 as described above. Claim records 110-1 through
110-n may be assigned to a time series by, for example, date
parsing or other methods.
[0068] 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.
[0069] Neural network 150 (or other artificial intelligence or
machine learning algorithm, program, module, or model) 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,
need 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 (or other artificial intelligence or machine learning algorithm
or model) based upon types of peril. For example, the neural
network model (or other artificial intelligence or machine learning
algorithm or model) may be trained to determine that fire, theft,
or vandalism may indicate comprehensive insurance coverage.
Insurance types and/or types of peril may be used to categorize
claim records for the purpose of training a model. For example, a
"vandalism" loss reserve model may be trained using a categorized
subset of such data.
[0070] In addition, input data 102 may indicate a particular
customer and/or vehicle. In that case, loss classifier 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 (or other artificial intelligence or
machine learning algorithm or model) and may be used to analyze and
label input data 102 and, ultimately, may be used to train the
artificial neural network model (or other artificial intelligence
or machine learning algorithm or model). For example, neural
network unit 150 (or other artificial intelligence or machine
learning algorithm or model) 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 (or other artificial intelligence or
machine learning algorithm or model) is trained on claim data,
neural network unit 150 (or other artificial intelligence or
machine learning algorithm or model) may determine loss reserves
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).
[0071] 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 (or other artificial intelligence or machine
learning algorithm or model) to predict loss reserves 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. 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.
[0072] 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. 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.
[0073] 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.
[0074] 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. 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.
[0075] 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. 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.
[0076] Once the neural network (or other artificial intelligence or
machine learning algorithm or model) has been trained, loss
reserving unit 154 may analyze, combine, and/or validate prediction
information from training unit 152. For example, loss reserving
unit 154 may check whether predicted loss reserving amounts or
percentages are within a given range (e.g., positive or negative).
Loss reserving unit 154 may use pre-determined parameters retrieved
from loss data 142 or another electronic database in conjunction
with training unit 152 output. A trained neural network (or other
artificial intelligence or machine learning algorithm or model)
may, based upon analyzing claim data, output a loss reserving
amount that is analyzed by loss reserving unit 154. Multiple loss
reserving outputs, or estimates, may be aggregated by loss reserve
aggregation platform 106.
[0077] AI platform 104 may further include customer data 160 and
vehicle data 162, which loss classifier 140 may use to provide
useful input parameters to neural network unit 150 (or other
artificial intelligence or machine learning algorithm or model).
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. For example, in
some embodiments, a trained neural network (or other artificial
intelligence or machine learning algorithm or model) may require a
vehicle type as a parameter. Based solely on a claim input from
claim 110-1 through 110-n, loss classifier 140 may look up the
vehicle type from vehicle data 162 as the claim is being passed to
neural network unit 150. It should be appreciated that many sources
of additional data may be used as inputs to train and operate
artificial neural network models. The neural network modules may
include other types of artificial intelligence or machine learning
algorithms, models, and/or modules.
[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. For example, the
information describing vehicle makes and models may specify, at the
model type and/or model year level, the degree to which a vehicle
is equipped with autonomous and/or semiautonomous capabilities,
and/or the degree to which a vehicle may be adequately retrofitted
to accept such capabilities.
[0079] In some embodiments, the failure of an autonomous or
semi-autonomous vehicle system may be discovered via the neural
network (or other artificial intelligence or machine learning
algorithm or model) analysis described above. An autonomous vehicle
system failure such as "lane departure malfunction" may be used by
loss reserving unit 154. In one embodiment, a user's completing a
repair within a pre-set window of time, or one computed based upon
loss probability, may cause the user to receive advantageous
pricing as regards an existing or new insurance policy.
[0080] Vehicle capabilities may be listed individually. For
example, a database table may be constructed within the electronic
database which specifies whether a vehicle has a steering wheel,
gas pedal, and/or brake pedal. In addition, or alternately, the
database table may classify a vehicle as belonging to a particular
category/taxonomic classification of autonomous or semi-autonomous
vehicles as measured by a vehicle automation ratings system (e.g.,
by Society of Automotive Engineers (S.A.E.) automation ratings
system), by which the set of features may be automatically
determined, by reference to the standards established by the
vehicle automation ratings system. In some embodiments, autonomous
and/or semi-autonomous capabilities known to be installed in a
vehicle, or which may be determined based upon a known vehicle
classification/adherence to a standard, may be provided as input to
an artificial neural network or other algorithm used to mitigate
loss and/or handle claims. Vehicle owners may be advised (e.g., via
a message displayed in a display such as display 224) that moving
from one level of vehicle autonomy to another, may improve
aggregate risk and decrease premiums.
[0081] In some embodiments, users who have been involved in an
accident recently (e.g., within one month) may be incentivized to
mitigate further injury by utilizing autonomous driving features.
Such incentives may be communicated to users after a trained neural
network analyzes a claim as described above.
[0082] The types of autonomous or semi-autonomous vehicle-related
functionality or technology that may be used with the present
embodiments to replace human driver actions may include and/or be
related to the following types of functionality: (a) fully
autonomous (driverless); (b) limited driver control; (c)
vehicle-to-vehicle (V2V) wireless communication; (d)
vehicle-to-infrastructure (and/or vice versa), and/or
vehicle-to-device (such as mobile device or smart vehicle
controller) wireless communication; (e) automatic or semi-automatic
steering; (f) automatic or semi-automatic acceleration; (g)
automatic or semi-automatic braking; (h) automatic or
semi-automatic blind spot monitoring; (i) automatic or
semi-automatic collision warning; (j) adaptive cruise control; (k)
automatic or semi-automatic parking/parking assistance; (l)
automatic or semi-automatic collision preparation (windows roll up,
seat adjusts upright, brakes pre-charge, etc.); (m) driver
acuity/alertness monitoring; (n) pedestrian detection; (o)
autonomous or semi-autonomous backup systems; (p) road mapping
systems; (q) software security and anti-hacking measures; (r) theft
prevention/automatic return; (s) automatic or semi-automatic
driving without occupants; and/or other functionality.
[0083] All of the information pertaining to a claim, including
customer and vehicle information, may be provided to neural network
unit 150 for training a model to determine loss reserving amounts.
In some embodiments, loss reserve overrides that are stored
separately from AI platform 104, may be used to force human
oversight of. For example, the methods and systems herein may
contain instructions which, when executed, cause any set of claim
being analyzed by a neural network (or other artificial
intelligence or machine learning algorithm or model) that cause a
loss reserving amount of over $1 m to be predicted to require human
review and confirmation. Over time, as the model is trained, such
overrides may be removed. In other embodiments, the models may be
completely automated and unattended.
[0084] 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.
In one embodiment, a claim may be related to the operation of a
vehicle. In other words, the claim may relate to physical injury
sustained by a driver and/or passenger, damage to the vehicle being
driven by the vehicle operator, another vehicle, or other
persons/property. The models trained using the methods and systems
herein may be trained incrementally, so that when new claims are
settled, they are used to improve an existing model without
completely retraining the model on all data.
[0085] The methods and systems described herein may help
risk-averse customers to lower their insurance premiums by taking
affirmative steps to mitigate risk of loss before, during, and
after the filing of a claim. The methods and systems may also allow
customers to interact with claims handling in a transparent,
streamlined, and scalable fashion. All of the benefits provided by
the methods and systems described herein may be realized much more
quickly than traditional modeling approaches.
Exemplary Training Model System
[0086] With reference to FIG. 2, a high-level block diagram of
vehicle insurance loss reserving model training 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 mitigation and/or claims handling. 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. In
general, training the neural network model may include establishing
a network architecture, or topology, and adding layers that may be
associated with one or more activation functions (e.g., a rectified
linear unit, softmax, etc.), loss functions and/or optimization
functions. Multiple different types of artificial neural networks
may be employed, including without limitation, recurrent neural
networks, convolutional neural networks, and deep learning neural
networks. Data sets used to train the artificial neural network(s)
may be divided into training, validation, and testing subsets;
these subsets may be encoded in an N-dimensional tensor, array,
matrix, or other suitable data structures. Training may be
performed by iteratively training the network using labeled
training samples. Training of the artificial neural network may
produce byproduct weights, or parameters which may be initialized
to random values. The weights may be modified as the network is
iteratively trained, by using one of several gradient descent
algorithms, to reduce loss and to cause the values output by the
network to converge to expected, or "learned", values, in an
embodiment, a regression neural network may be selected which lacks
an activation function, wherein input data may be normalized by
mean centering, to determine loss and quantify the accuracy of
outputs. Such normalization may use a mean squared error loss
function and mean absolute error. The artificial neural network
model may be validated and cross-validated using standard
techniques such as hold-out, K-fold, etc. In some embodiments,
multiple artificial neural networks may be separately trained and
operated, and/or separately trained and operated in
conjunction.
[0087] 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).
[0088] Module 212, stored in memory 208 as a set of
computer-readable instructions, may be related to an loss reserve
client 216 which, when executed by the processor 210, may cause
input data to be stored in memory 208 or data to be transferred
to/from server 204 via network 206. The data stored in memory 208
may correspond to, for example, raw data retrieved from input data
102. Loss reserve client 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.
[0089] Loss reserve client 216 may store information in memory 208,
including the instructions required for its execution. While the
user is using loss reserve client 216, scripts and other
instructions comprising loss reserve client 216 may be represented
in memory 208 as a web or mobile application. The input data
collected by loss reserve client 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, loss reserve client 216 may be data used
to train a model (e.g., scanned claim data).
[0090] 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.
[0091] In some embodiments, client 202 may receive data from loss
data 142 and loss reserve aggregation platform 106. Such data, loss
labels and plan data, may be presented to a user of client 202 by a
display interface 224. Aggregation data may include gross and net
amounts related to categories of loss reserves, in some
embodiments. Aggregation data may include an acceptability
indicator, demonstrative of whether aggregate amounts are more or
less than an acceptable dollar amount or multiplier. An action may
be taken if an acceptability indicator demonstrates an amount
beyond an acceptable range (e.g., a warning message emitted or an
email sent). In this way, the loss reserve aggregation platform 106
may provide an insurer with a view of loss reserves at a global
level (e.g., across a division, such as automotive, or with respect
to an organizational unit or subsidiary of a company) or at a level
wherein the granularity is configurable by the insurer all the way
down to the individual customer level.
[0092] 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 loss reserve
client 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.
[0093] The processor 210 may transmit the aforementioned acquired
data to server 204, and processor 250 may pass the acquired data to
an artificial neural network (or other artificial intelligence or
machine learning algorithm, program, module, or model), 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
artificial neural network model (or other artificial intelligence
or machine learning algorithm or 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 (or other artificial
intelligence or machine learning algorithm or model) by loss
classifier 140.
[0094] 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 (or other artificial intelligence or machine learning
algorithm or model) may be transmitted, by a loss classifier
corresponding to loss classifier 140 in server 204, back to client
202 for display (e.g., in display 224) and/or for further
processing.
[0095] 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/loss/claim related
data and/or metadata to be stored in server 204 memory 252 and/or a
remote insurance related database such as customer data 160.
[0096] 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 loss reserving and financial
reporting including data storage and retrieval (e.g., data and
claim metadata, and insurance policy application data). For
example, module 254 may include input analysis application 260,
loss reserving application 262, and neural network training
application 264, in one embodiment. Module 254 may be responsible
for interpreting output from trained neural network models (or
other types of artificial intelligence or machine learning
algorithms or models), and for generating loss reserving
information, in some embodiments.
[0097] Input analysis application 260 may correspond to input
analysis unit 120 of environment 100 of FIG. 1. Loss reserving
application 262 may correspond to loss reserving unit 154 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.
[0098] In another embodiment, client device 202 may he used by an
employee of the insurer to view results produced by loss reserving
application 262. For example, loss reserving unit 262 may
display/interpret results of a trained neural network (or other
artificial intelligence or machine learning algorithm or model). In
some cases, loss reserving unit 262 may continuously interpret
results produced by a trained neural network (e.g., hourly, weekly,
or monthly). As time passes and the neural network receives
additional claim data, the predicted loss reserves may be updated.
In one embodiment, an increase in loss reserves predicted by a
neural network model may cause a withdrawal or transfer of funds
into a bank account or trust specifically created for the purpose
of holding loss reserving funds.
[0099] 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.
[0100] 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.
[0101] 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.
[0102] 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., loss reserving 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.
[0103] 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 (or other
artificial intelligence or machine learning algorithm or model). In
one embodiment, a claim may be classified according to a
multilabel, multiclass scheme. For example, an algorithm may be
trained using a portion of historical claims as input that are
pre-labeled with a set of labels. The set of labels may comprise
any information found in a claim before processing (e.g., whether
settled, and a payout amount, if any) and after processing by input
analysis unit 120. A set of several thousand, or even millions, of
claims may be associated with such informational labels, and a
percentage (e.g., 80%) may be used to train a neural network (or
other artificial intelligence or machine learning algorithm or
model). For example, a recurrent neural network may be created that
uses a number of hidden layers and has as its last layer a densely
connected network in which all neurons are interconnected.
Additional layers or "chains" may be formed, in which models of
differing network architectures are coupled to the recurrent neural
network. The output of the chained network may be a set of labels
to which the claim is predicted to belong (e.g., MOTORCYCLE,
PASSENGER-CAR, etc.).
[0104] Then, when module 254 processes input from client 202, the
data output by the neural network(s) (e.g., data indicating labels,
loss reserving amounts, weights, etc.) may be passed to loss
reserving application 262 for analysis/display. As discussed, loss
reserving application 262 may take additional actions based upon
the output of the trained model(s).
[0105] 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).
[0106] In one embodiment, a client device 202 may be an integral
device to a vehicle of a user (not depicted), or may be
communicatively coupled to a network communication device of a
vehicle. The vehicle may be an autonomous or semi-autonomous
vehicle, and loss reserve client 216 may include instructions
which, when executed, may collect information pertaining to the
autonomous capabilities of the vehicle. For example, the loss
reserve client 216 may periodically receive/retrieve the status of
individual autonomous vehicle components (e.g., the
engagement/disengagement of a collision avoidance mechanism) and/or
whether a particular dynamic driving system is active or disabled
(e.g., by intentional interference or accidental damage).
[0107] The status of autonomous (and in some embodiments,
semi-autonomous) systems may be determined by polling input
devices, such as input device 222, or by other methods (e.g., by
receiving streamed status information, or by retrieving cached
values). Such status information may be used as training data for
an artificial neural network (or other artificial intelligence or
machine learning algorithm or model) (e.g., by neural network
training application 264), and/or may be used as input to a trained
artificial neural network (or other artificial intelligence or
machine learning algorithm or model) to determine the risk
represented by a vehicle and/or a driver. Vehicle risk and driver
risk may be independently calculated. For example, an SAE Level 3
autonomous vehicle may be associated with a baseline risk level,
and a user's risk may be factored in to the baseline level risk.
Multiple variables e.g., vehicle category, driver age, autonomous
features, etc.) may be used to make a single prediction of loss
reserves.
[0108] As noted, the risk factors or labels determined by trained
neural networks (or other artificial intelligence or machine
learning algorithms or models) analyzing historical claim data may
appear counter-intuitive or unrelated to the optimal loss reserve
level. For example, in a vehicle wherein a dynamic driving system
includes functionality to take control away from the automated
system, a neural network (or other artificial intelligence or
machine learning algorithm or model) may predict high risk wherein
the instances of revocation of control from the automated system is
low. This may indicate an over-reliance on the automated system by
a vehicle operator. It may also be the case that revocation of
control may indicate high risk with respect to some vehicle
operators, and lower risk with respect to others.
[0109] Artificial neural network models (or other artificial
intelligence or machine learning algorithms or models) may be
trained to output compound labels (e.g., AUTONOMOUS-RURAL). Once
autonomous vehicle information is determined, it may be transmitted
to a remote computing system, such as AI platform 104, or server
device 204 for further analysis. Input analysis application 260 may
format and/or store autonomous vehicle information in a database,
such as vehicle data 274, and/or a trained neural network (or other
artificial intelligence or machine learning algorithm or model) may
immediately (or at a later time) process the autonomous vehicle
information to determine loss reserving amounts, whether individual
or aggregated.
[0110] The loss reserving and financial reporting information may
be associated with one or both of a vehicle and a vehicle operator,
by, respectively storage in vehicle data 274 and customer data 272.
In some embodiments, the set of loss reserving information may be
stored in an electronic database such as loss data 142. In some
embodiments, the set of loss reserving and financial reporting
information may be provided to an additional application, such as
loss reserve aggregation platform 106, or an application executing
in module 212. As noted, once a set of loss reserving information
is identified, the set may be used to compute an aggregate, which
may be used for many purposes, such as underwriting insurance
policies, adjusting capitalization, forecasting profit/loss,
etc.
[0111] In one embodiment, an automated control system may perform
dynamic vehicle control, which may include instructions to operate
the vehicle, including without limitation, real-time functions,
trip generation, steeling control, acceleration and deceleration,
environmental monitoring, and instructions for operating various
vehicle components (e.g., headlights, turn signals, traction
control, etc.). The automated control system may perform dynamic
vehicle control for a period of time (e.g., hours or days) with
respect to the vehicle, during which time an application executing
in module 212 may collect telematics data. Telematics data may
include such data as GPS information, vehicle location, braking,
speed, acceleration, cornering, movement, status, orientation,
position, behavior, mobile device, and/or other types of data; and
may be determined using a combination of sensors and
computing/storage devices. For example, loss reserve client 216 may
determine the vehicle's position by reading data from GPS device
218. Other sensors may provide information regarding the vehicle's
speed, acceleration, instrumentation, and path.
[0112] Telematics data may be periodically sampled, or retrieved on
a continuous basis. Telematics data may be transmitted in real-time
from a wireless networking transceiver e.g., network interface 214)
via a network (e.g., network 206) to a communicatively coupled
server (e.g., server device 204). In some embodiments, telematics
data may be cached in a memory 208 and transmitted to server 204 at
a later time, or processed in situ. Telematics data may be provided
as input to a trained artificial neural network (or other
artificial intelligence or machine learning algorithm or model).
Each individual data type within telematics data may be referred to
as a "telematics attribute." For example, "speed" may be a
telematics attribute.
[0113] In one embodiment, the real-time use of autonomous vehicle
features and telematics data may be used to train a neural network
(or other artificial intelligence or machine learning algorithm or
model) to predict loss reserving amounts. For example, the
percentage of vehicles equipped with autonomous vehicle features in
which the drivers do not take sharp corners may be directly
correlated with lower loss reserving requirements. As noted above,
such models may be continuously trained using data input from
client device 202. Client device 202 may be located inside/integral
to a vehicle, according to some embodiments.
[0114] A set of periodic telematics data, (e.g., a month's worth of
telematics data) may be stored in association with a user's account
in an electronic database coupled to client device 202. and/or
server device 204. The electronic database may include physical
and/or software anti-tampering measures intended to prevent
unauthorized alteration of modification of the telematics data. For
example, telematics data stored in a device onboard the vehicle may
be encrypted in a server computing device using a secret key that
is not stored in the vehicle. As noted above, customer data 272 may
be associated with data corresponding to one or more vehicle in
vehicle data 274.
[0115] In some embodiments, a neural network may be trained to
automatically provide financial reporting. The provision of
automatic financial reporting may be based on output of a first
neural network establishing loss reserve amounts. Financial
reporting may be triggered in response to a set of inputs or a
learned value. For example, in an embodiment, financial reporting
may be performed if a trained algorithm encounters a series of
inputs that increase loss reserving beyond a preconfigured amount
or cause another threshold value to be exceeded. In other
embodiments, financial reports may include aggregations of loss
reserve amounts. For example, the output of a loss reserving neural
network may be collected over a period of time (e.g., hourly,
quarterly, etc.). A financial report may be generated which
includes a summary and/or aggregation of the loss reserve outputs,
in textual and/or graphical format.
[0116] An artificial neural network (or other artificial
intelligence or machine learning algorithm or model) may be trained
in neural network training application 264 which includes a
plurality of input layers for customer data, a plurality of input
layers for vehicle data, and a plurality of layers for telematics
data, wherein the vehicle data, customer data, and telematics data
relate to the operation of a vehicle by a vehicle operator. For
example, the vehicle data may include the make and model of the
vehicle, as well as a manifest of the autonomous or dynamic driving
capabilities standardly supported by the vehicle, including a
status indication of each respective capability. The customer data
may include demographic or other customer data as described herein,
and the telematics data may include the information as described
above.
[0117] In one embodiment, telematics data may include indications
of user driving such as braking, cornering, speed, and
acceleration. The neural network (or other artificial intelligence
or machine learning algorithm or model) may associate such
behaviors with higher loss reserves. The neural network (or other
artificial intelligence or machine learning algorithm or model) may
learn to weight such activities higher due to association with
factors in other data sets (e.g., higher claim payouts, and
vehicles having higher top speeds and/or lacking automated driving
capabilities).
[0118] The artificial neural network (or other artificial
intelligence or machine learning algorithm or model) may be trained
to output loss reserving information with respect to customers by
analyzing historical claims data in addition to the telematics
data, vehicle data, and/or claims data. For example, the artificial
neural network (or other artificial intelligence or machine
learning algorithm or model) may be trained using claims data filed
customers in a geographic area who are between the ages of 16 and
25, wherein the vehicle subject to the claim is a pickup truck, and
wherein the pickup truck includes partial driving automation (e.g.,
minimally, lane departure warnings). Such a subset of claims may be
identified by querying the electronic databases described above, or
by any other suitable method.
[0119] It should be appreciated that the ability to create models
that are able to calculate loss reserves for an arbitrary set of
customers may be a very valuable tool, and may have applications
beyond merely setting loss reserves. It should be appreciated that
the foregoing example is simplified for expository purposes, and
that more complex training scenarios are envisioned. Although some
scenarios may include a trained neural network executing in module
254, it may be possible to package the trained neural network for
distribution to a client 202 (i.e., the trained neural network (or
other artificial intelligence or machine learning algorithm or
model) may be operated on the client 202 without the use of a
server 204).
[0120] In operation, the user of client device 202, by operating
input device 222 and viewing display 224, may open loss reserve
client 216, which depending on the embodiment, may allow the user
to enter 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, loss reserve client 216 may walk the user
through the steps of training a loss reserving neural network (or
other artificial intelligence or machine learning algorithm or
model) using a specific subset of training data, and also operating
the trained model, as described with respect to FIG. 11.
[0121] Before the user can fully access loss reserve client 216,
the user may be required to authenticate (e.g., enter a valid
username and password). The user may then utilize loss reserve
client 216. Module 212 may contain instructions that identify the
user and cause loss reserve client 216 to present a particular set
of questions or prompts for input to the user, based upon any
information loss reserve client 216 collects, including without
limitation information about the user or any vehicle. Further,
module 212 may identify a subset of historical data 270 to be used
in training a neural network (or other artificial intelligence or
machine learning algorithm or model), and/or may indicate to server
device 204 that the use of a particular neural network (or other
artificial intelligence or machine learning) model or models is
appropriate.
[0122] In some embodiments, location data from client device 202
may be used by a neural network (or other artificial intelligence
or machine learning algorithm or model) 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 (or other artificial intelligence or machine learning
algorithms or models) 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 (or other artificial intelligence or machine
learning algorithm or model) to generate a label applicable to the
vehicle operator such as RURAL, SUBURBAN, or URBAN. Such
qualifications may be used in the calculation of optimal loss
reserve estimations, and may be weighted accordingly. For example,
the neural network (or other artificial intelligence or machine
learning algorithm or model) may assign a higher severity score to
the RURAL label, due to the fact that the vehicle operator recently
underwent surgery and should not be driving longer distances. 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.
[0123] 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 (or other artificial intelligence or machine learning
algorithm or model) labels as RURAL. In some embodiments, label
generation may be based upon seasonal information, in whole or in
part. For example, the neural network (or other artificial
intelligence or machine learning algorithm or model) may generate
labels, and/or adjust label weights based upon location provided in
input data. It should be appreciated that the quick and automatic
generation of labels 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.
[0124] All of the information collected by loss reserve client 216
may be associated with a session 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 loss reserve client 216. Once information sufficient
to process the user's request has been collected, server 204 may
pass all of the processed information (e.g., from input analysis
application) to loss reserving application 262, which may apply the
information to the trained neural network model (or other
artificial intelligence or machine learning algorithm or model).
While the loss reserve calculation is ongoing, client device 202
may display an indication to that effect.
[0125] When the loss reserving estimate is available, 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. In one embodiment, the settlement
of a claim may trigger an immediate update of one or more neural
network models (or other artificial intelligence or machine
learning algorithms or 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 (or other artificial intelligence or machine
learning algorithms or models) pertaining to boat insurance, or to
a monolithic model.
[0126] In addition, or alternatively, as new claims are filed and
processed, new labels may be dynamically generated, based upon
claim mitigation or loss information 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. For
example, claims may be labeled with settlement amounts, as well as
the amount of time that the claim remained unsettled, wherein such
time is normalized across all claims (e.g., represented as
seconds). Both the dollar amount and timing information may be used
to train a neural network (or other artificial intelligence or
machine learning algorithm or model), such that the loss reserving
prediction may include both a dollar amount as well as an amount of
time that the claim may remain unsettled.
[0127] 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.
[0128] 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 loss reserving and
financial reporting operations of server 204 to client device 202
and/or other servers.
Exemplary Artificial Neural Network
[0129] 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.
[0130] Input layer 302 may receive different input data. For
example, input layer 302 may include a first input al 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.sub.2 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.
[0131] 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.
[0132] 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.
[0133] 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.
[0134] 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.
[0135] 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.
[0136] 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.
[0137] 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.
[0138] In some embodiments, a percentage of the data set used to
train the neural network (or other artificial intelligence or
machine learning algorithm or model) may be held back as testing
data until after the neural network (or other artificial
intelligence or machine learning algorithm or model) is trained
using the balance of the data set. In embodiments wherein the
neural network involves a time series or other temporally-ordered
data, all elements composing the testing data set may be posterior
of those composing training data set in time.
Exemplary Processing of a Claim
[0139] 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 loss reserve client 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.
[0140] The nature and characteristics of the data to be predicted
may also necessitate changes to the structure of the neural network
(e.g., number of layers, number of input parameters, number of
output parameters, number of neurons per layer), as well as the
determination of whether or not to chain or stack multiple neural
networks together to form predictions based upon multiple input
types (e.g., text, images, etc.). 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.
[0141] 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.
[0142] 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 (or other artificial intelligence or machine
learning algorithm or model) trained to recognize hand-writing and
to convert hand-writing to text. Further, "text-based content" may
be formatted in any acceptable data format, including structured
query language (SQL) tables, flat files, hierarchical data formats
(e.g., XML, JSON, etc.) or as other suitable electronic objects. In
some embodiments, image and audio data may be fed directly into the
neural network(s) without being converted to text first.
[0143] 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).
[0144] 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.).
[0145] 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 510b 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, the number of vehicle
operators and/or passengers involved, whether traffic citations
were issued, whether the loss was settled, and if so for how much
money.
[0146] 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
files 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.
[0147] 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.
[0148] 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.
[0149] 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.
[0150] 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 (or other artificial intelligence or machine
learning algorithm or model). 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. Similarly, one or
more input neurons may be configured to receive particular input(s)
from links 520a-520g.
[0151] 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.
[0152] In one embodiment, the value of a new claim may be predicted
directly by a neural network model (or other artificial
intelligence or machine learning algorithm or model) trained on
historical data 108, without the use of any labeling. For example,
a neural network (or other artificial intelligence or machine
learning algorithm or model) 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.
[0153] 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.
[0154] The model may then be provided with a subset of 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 or "truth" dollar values converge.
[0155] 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 loss data 142 with
respect to FIG. 1. Next, the labels and corresponding weights, in
one embodiment, may be received by loss reserve aggregation
platform 106, where they may be used in conjunction with base rate
information to predict a claim loss value. Claims labeled with
historical loss amounts may be used as training data.
[0156] 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 loss reserve aggregation platform 106 and may be used in the
computation of a gross or net claim loss value. After the
aggregated loss reserve 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).
[0157] 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. Additionally, unlike human actuaries, who may
naturally weight the most recently-analyzed information most
heavily, and may not recall all information analyzed, the
computerized methods described may treat all claims equally,
regardless of temporal ordering, and may have practically unlimited
memory capacity.
Exemplary Computer-Implemented Methods
[0158] 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 (or other artificial intelligence or machine learning
algorithm or model) to identify risk factors within electronic
vehicle claim records (e.g., by an AI platform such as AI platform
104 training a neural network (or other artificial intelligence or
machine learning algorithm or model) 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 (or other artificial intelligence or machine learning
algorithm or model)) (block 610).
[0159] 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 (or
other artificial intelligence or machine learning algorithm or
model) (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 (or other
artificial intelligence or machine learning algorithm or model))
(block 640). The method may include additional, less, or alternate
actions, including those discussed elsewhere herein.
[0160] 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.
[0161] 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 may be 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.
[0162] 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 (or other artificial intelligence
or machine learning algorithm or model).
[0163] In one embodiment, the trained neural network model (or
other artificial intelligence or machine learning algorithm or
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 (or other artificial
intelligence or machine learning algorithm or model), 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.
[0164] 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.
[0165] 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.
[0166] 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 (or other artificial intelligence or
machine learning algorithm or model) 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.
[0167] 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.
[0168] 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
(or other artificial intelligence or machine learning algorithm or
model), 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.
[0169] Repair and replacement cost may be determined, in one
embodiment, by training a neural network model (or other artificial
intelligence or machine learning algorithm or 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.
[0170] 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.
[0171] 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 that the cause of
loss and peril information is directly comparable to the insurance
policy.
[0172] 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.
[0173] 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).
[0174] 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, tire, water, smoke, hail, wind, or storm
surge.
[0175] 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.
[0176] 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.
[0177] 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.
[0178] 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.
[0179] 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).
[0180] 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.
[0181] The risk profile may include an indication of behaviors
and/or vehicle features that may be adopted to lower aggregate
risk. For example, the risk profile may indicate that upgrading a
vehicle to include a rear-facing camera may lower risk by a certain
percentage, or that trading a vehicle of a first model year to a
vehicle of a second model year may result in an insurance premium
discount with respect to the risk level or underwriting price of
the first model year.
[0182] Such determinations may be based upon a vehicle owner making
smaller, more granular changes. For example, a neural network (or
other artificial intelligence or machine learning algorithm or
model) may determine that such discounts may be available to a
hybrid or electric vehicle owner by the vehicle owner charging the
vehicle battery to a greater than or equal level (e.g., >=60%),
or up/downgrading the firmware of an onboard computer from a first
version to a second version.
[0183] In some embodiments, the methods and systems herein may
prompt a vehicle operator to improve their risk profile, and/or
reduce an insurance premium linked to such a profile, by adopting
certain behaviors. For example, in vehicles wherein driving
automation or dynamic driving is user-selectable, or optional, a
driver may be encouraged to activate (or deactivate) automated
driving capabilities (e.g., steering control). It will be
appreciated by those skilled in the art that the foregoing are
intended to be simple examples for purposes of illustration, and
that more complex embodiments are envisioned.
[0184] 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.
[0185] 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 (or
other artificial intelligence or machine learning algorithm or
model) 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.
[0186] 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 some cases, the risk profile
associated with a given vehicle may contain information that seems
unforeseeable and/or counter-intuitive.
[0187] 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 (or other artificial intelligence or machine learning
algorithm or 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.
[0188] 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 covering 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 (or other artificial
intelligence or machine learning algorithm or model) analyzing the
image, wherein the neural network model (or other artificial
intelligence or machine learning algorithm or model) has been
trained on historical claim data and/or images of similar vehicles,
according to at least one preferred embodiment.
[0189] Turning to FIG. 11, an exemplary user interface environment
1100 for training and operating artificial neural network models
(or other types of artificial intelligence or machine learning
algorithms or models) is depicted, according to one embodiment and
scenario. User interface environment 1100 may include a user
interface 1110, which may be implemented in a web browser, mobile
application, or other suitable user interface display program. User
interface 1110 may correspond to loss reserve client 216, and may
be executing in memory 208, and may be displayed in display 224. A
user may interact with user interface 1110 via input device
222.
[0190] User interface 1110 may include pages/sections 1112-A
through 1112-D. Section 1110-A may allow the user to select an
existing data set, and may include a button or other suitable
graphical user interface (GUI) element which, when pressed, causes
a request to be transmitted to a server (e.g., server 204)
including an indication of the user's selected data set(s). The
server may query an electronic database, retrieve the selected data
set(s), and train a model based upon the selection. The user of
section 1112-A may then be redirected to a result page, such as
section 1112-D, wherein the results of operating the trained model
using the selected data set(s) may be displayed. Section 1112-B may
allow the user to specify a custom query (e.g., in Structured Query
Language or another suitable query language) of an electronic
database for records (e.g., claim, user, and/or vehicle records),
along with a button or other suitable GUI element.
[0191] In this way, a user may train a model using arbitrarily
complex subsets and/or aggregations of claim, user, and vehicle
data. Once the user activates the "Train" button in section 1112-B,
the model may be trained using a data set corresponding to the
user's custom query. The model may then be added to the "Trained
Models" list of section 1112-C, and the user may be directed to
section 1112-D, wherein the user may view the results of the query
being fed to the trained loss reserving model. The training that
occurs when a user activates the "Train" button in sections 1112-A
and 1112-B may be fully automated, including the validation
steps.
[0192] Section 1112-C may be a list of all trained models. The user
may edit or operate the individual models by interacting with user
interface 1100. Section 1112-D may be a results page which lists
the output of executing a trained neural network model (or other
artificial intelligence or machine learning algorithm or model).
For example, as depicted, section 1112-D displays a first output,
representative of executing a model trained using a data set
containing all historical passenger car claim records, and a second
output, representative of executing a model trained using a complex
data set including motorcycle claims relating to mopeds, sport
bikes, and tricycles. The first output includes an indication of
the data set used and a loss reserve amount. The second output
includes an indication of a complex data set used including three
subsets, each having a respective loss reserve amount which is
aggregated into a loss reserve aggregate. It should be understood
that additional standard scaffolding may be included in some
embodiments, for example, to create, update, delete, and retrieve
trained models. In some embodiments, the structure of neural
networks (or other artificial intelligence or machine learning
algorithms or models), and the parameters used in their creation,
may be accessible via loss reserving client 1100.
[0193] With regard to FIG. 12, an exemplary method 1200 of
determining loss reserves is depicted, according to an embodiment.
Method 1200 may include receiving a plurality of labeled historical
claim documents (block 1210). The labels may be a claim payout
amount, a claim pendency time, a number indicating whether the loss
reserve was adequate or inadequate, and any shortfall or surplus
that was associated with the loss reserves allocated before the
claim was settled. Method 1200 may include normalizing the claim
loss/payout/settlement amount (block 1220). Normalization may
include converting the claim amount into a standard currency (e.g.,
USD) and/or adjusting the claim settlement amount for inflation or
other circumstances related to monetary policy.
[0194] Method 1200 may include training an artificial neural
network (or other artificial intelligence or machine learning
algorithm or model) using the historical claim documents (block
1230). Training the artificial neural network may include creating
a neural network having a plurality of input neurons in an input
layer, and a plurality of hidden layers, each having a respective
number of neurons. The neural network may be dense and
interconnected, and may have an output layer having one or more
output neurons. A subset of the labeled historical claims may be
used to train the neural network (or other artificial intelligence
or machine learning algorithm or model) to predict an optimal loss
reserve for a type of claim (e.g., a motorcycle claim) or across
all claim types. An optimal loss reserve may be neither too large
nor too small, with respect to historical claim settlement amounts.
A validation set of historical claims may be held back for testing
the trained neural network (or other trained artificial
intelligence or machine learning algorithm or model) for
accuracy.
[0195] Method 1200 may include receiving a user claim (block 1240).
The user claim may be submitted by the user via an application,
such as an application executing in module 212 of client device
202. In some embodiments, a user claim may be retrieved from
historical data 108. The user claim may correspond to electronic
claim record 500. A plurality of attributes of claims (e.g.,
payments, type of loss, policy deductible, etc.) may be used to
train the neural network (or other artificial intelligence or
machine learning algorithm or model) and, the same attributes
respective to the user claim may be provided as inputs to the
neural network (or other artificial intelligence or machine
learning algorithm or model) by applying the user claim to the
neural network (or other artificial intelligence or machine
learning algorithm or model) to predict a loss reserve amount
(block 1250). In some embodiments, additional or fewer steps may be
used, and in some embodiments, loss reserving models may be created
that apply to a specific type of claim, vehicle, and/or
customer.
[0196] Turing to FIG. 13, an exemplary method 1300 of training and
executing artificial neural networks using a customized data set is
depicted, according to one embodiment. Method 1300 may include
receiving an indication of a trained artificial neural network (or
other artificial intelligence or machine learning algorithm or
model) and a data set (block 1310). The indication may be a pair of
integers or other values respectively uniquely identifying a
trained neural network (or other artificial intelligence or machine
learning algorithm or model) and a data set. The trained neural
network (or other artificial intelligence or machine learning
algorithm or model) may have been trained in advance by a user,
using, for example, loss reserving application 216.
[0197] In one embodiment, the neural network (or other artificial
intelligence or machine learning algorithm or model) may have been
trained using module 254 (e.g., using command line tools by a user
accessing server device 204). The data set may be a pre-existing
labeled data set that is listed on a user interface and selectable
by the user, or may be built by the user by the user entering an
SQL expression into an input box (e.g., via input device 222 and
display 224). The user may press a button, in response to which,
method 1300 may transmit an execution request including the
indication to a remote computing device (e.g., server 204).
[0198] Server 204 may include instructions for receiving the
indication, selecting the appropriate neural network (or other
artificial intelligence or machine learning algorithm or model) and
data set, applying the data set to the neural network (or other
artificial intelligence or machine learning algorithm or model),
and returning execution output including at least identification of
the selected trained artificial neural network (or other artificial
intelligence or machine learning algorithm or model) and a loss
reserve amount produced via operation of the selected trained
artificial neural network (or other artificial intelligence or
machine learning algorithm or model).
[0199] An application such as loss reserve client 216 may receive
the execution result/output (block 1330) and may display the output
of the trained artificial neural network (or other artificial
intelligence or machine learning algorithm or model) (block 1340).
In some embodiments, the data set may be a compound data set, and
the output of the trained artificial neural network (or other
artificial intelligence or machine learning algorithm or model) may
be a result that includes individual loss reserving amounts with
respect to a plurality of data subsets, and/or an aggregate loss
reserving amount applicable to all of the plurality of data
subsets.
[0200] 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
[0201] 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.
[0202] 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.
[0203] 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, a
reinforcement or reinforced learning algorithm or model, or a
combined learning module or program that learns in two or more
fields or areas of interest, In some embodiments, deep learning
strategies may be applied, in addition to random forest trees for
classification. 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.
[0204] Additionally or alternatively, the machine learning programs
may be trained by inputting sample data sets or certain data into
the programs, such as drone, autonomous or semi-autonomous drone,
image, mobile device, smart or autonomous vehicle, and/or
intelligent vehicle 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 deep,
combined, or reinforced learning algorithms or models, 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 other
types of machine learning, such as deep learning, combined
learning, and/or reinforced learning.
[0205] Supervised or unsupervised machine learning may also be
employed. 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
a 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.
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 vehicle 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.
[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(f) unless
traditional means-plus-function language is expressly recited, such
as "means for" or "step for" language being explicitly recited in
the claim(s). The systems and methods described herein are directed
to an improvement to computer functionality, and improve the
functioning of conventional computers.
[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 vehicle 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 vehicle 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 the method and systems described herein through the
principles disclosed herein. Thus, while particular embodiments and
applications have been illustrated and described, it is to be
understood that the disclosed embodiments are not limited to the
precise construction and components disclosed herein. Various
modifications, changes and variations, which will be apparent to
those skilled in the art, may be made in the arrangement, operation
and details of the method and apparatus disclosed herein without
departing from the spirit and scope defined in the appended
claims.
* * * * *