U.S. patent application number 16/136365 was filed with the patent office on 2021-08-19 for implementing machine learning for life and health insurance loss mitigation and claims handling.
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 | 20210256615 16/136365 |
Document ID | / |
Family ID | 1000003606772 |
Filed Date | 2021-08-19 |
United States Patent
Application |
20210256615 |
Kind Code |
A1 |
Hayward; Gregory L. ; et
al. |
August 19, 2021 |
Implementing Machine Learning For Life And Health Insurance Loss
Mitigation And Claims Handling
Abstract
Techniques for implementing machine learning for insurance loss
mitigation or prevention, and claims handling are disclosed. In
some scenarios, the insurance loss mitigation and claims handling
may be associated with a disability, worker's compensation, life or
health insurance policy, and the machine-learning analytics model
may be trained in accordance with data that is relevant to
identifying appropriate predictions in accordance with these
particular types of insurance products. For instance, the
machine-learning analytics model may utilize information within a
dynamic data set as training data, which may include electronically
accessible information. The machine-learning analytics model may
additionally be implemented to identify various predictions that
are indicative of a risk of insuring an individual as well as one
or more actions that, when performed, may reduce the initial
calculation of risk.
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 |
|
|
Family ID: |
1000003606772 |
Appl. No.: |
16/136365 |
Filed: |
September 20, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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62652121 |
Apr 3, 2018 |
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62646729 |
Mar 22, 2018 |
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62646735 |
Mar 22, 2018 |
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62646740 |
Mar 22, 2018 |
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62632884 |
Feb 20, 2018 |
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62625140 |
Feb 1, 2018 |
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62622542 |
Jan 26, 2018 |
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62621797 |
Jan 25, 2018 |
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62621218 |
Jan 24, 2018 |
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62618192 |
Jan 17, 2018 |
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62617851 |
Jan 16, 2018 |
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62610599 |
Dec 27, 2017 |
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62580655 |
Nov 2, 2017 |
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62580713 |
Nov 2, 2017 |
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62564055 |
Sep 27, 2017 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 10/10 20130101;
G06Q 40/08 20130101; G06N 20/00 20190101; G06N 3/08 20130101 |
International
Class: |
G06Q 40/08 20060101
G06Q040/08; G06Q 10/10 20060101 G06Q010/10; G06F 15/18 20060101
G06F015/18; G06N 3/08 20060101 G06N003/08 |
Claims
1. A computer-implemented method, comprising: accessing, via one or
more processors, a dynamic data set associated with one or more
users including electronic medical records, demographic
information, insurance records, and lifestyle information;
training, via one or more processors, a machine-learning analytics
model using the dynamic data set as training data to generate a
trained machine-learning analytics model; receiving, via the one or
more processors, user data associated with a user; applying, via
the one or more processors, the trained machine-learning analytics
model to the user data to predict a set of one or more
medical-related conditions associated with the user; determining,
via the one or more processors in accordance with the trained
machine-learning analytics model, a first level of risk associated
with insuring the user based upon the one or more predicted
medical-related conditions; identifying, via the one or more
processors in accordance with the trained machine-learning
analytics model, one or more intervening actions that, when
executed by the user within a future time period, reduce the first
level of risk associated with insuring the user to a second level
of risk; transmitting, via the one or more processors, the one or
more intervening actions to a computing device to be presented to
the user; monitoring user activity associated with the one or more
identified intervening actions to collect user activity monitoring
data; re-training, via the one or more processors, the trained
machine-learning analytics model using the user activity monitoring
data; and applying, via the one or more processors, the trained
machine-learning analytics model to the user activity monitoring
data to determine a likelihood of whether the user will continue to
execute the one or more intervening actions during the future time
period.
2. The computer-implemented method of claim 1, wherein the first
and second levels of risk associated with insuring the user
represent insuring the user for a health insurance, worker's
compensation, disability or life insurance policy.
3. The computer-implemented method of claim 1, further comprising:
calculating, via the one or more processors, a first insurance
premium associated with insuring the user in accordance with the
first level of risk; calculating, via the one or more processors, a
second insurance premium associated with insuring the user in
accordance with the second level of risk; and transmitting, via the
one of more processors, the first and the second insurance premium
to the computing device for presentation to the user.
4. The computer-implemented method of claim 1, further comprising:
calculating, via the one or more processors, a health or life
insurance premium associated with insuring the user in accordance
with the second level of risk; and upon insuring the user for the
health or the life insurance policy in accordance with the
calculated health or life insurance premium, accessing, via one or
more processors, the dynamic data set to collect user activity
monitoring data.
5. The computer-implemented method of claim 1, wherein the act of
training the machine-learning analytics model includes training a
neural net.
6. The computer-implemented method of claim 1, wherein the one or
more intervening actions include suggestions regarding (i) a type
and frequency of exercise, (ii) daily nutrition, and (iii)
lifestyle habits.
7. The computer-implemented method of claim 1, wherein the future
time period corresponds to a period of insurance coverage for a
health, worker's compensation, disability or life insurance
policy.
8. A computing device, comprising: a communication unit configured
to access a dynamic data set associated with one or more users
including electronic medical records, demographic information,
insurance records, and lifestyle information, and to receive user
data associated with a user; and a processing unit configured to:
train a machine-learning analytics model using the dynamic data set
as training data to generate a trained machine-learning analytics
model; apply the trained machine-learning analytics model to the
user data to predict a set of one or more medical-related
conditions associated with the user; determine a first level of
risk associated with insuring the user based upon the one or more
predicted medical-related conditions in accordance with the trained
machine-learning analytics model; identify one or more intervening
actions in accordance with the trained machine-learning analytics
model that, when executed by the user within a future time period,
reduce the first level of risk associated with insuring the user to
a second level of risk; transmit, via the communication unit, the
one or more intervening actions to a computing device to be
presented to the user; monitor user activity associated with the
one or more identified intervening actions to collect user activity
monitoring data; re-train the trained machine-learning analytics
model using the user activity monitoring data; and apply the
trained machine-learning analytics model to the user activity
monitoring data to determine a likelihood of whether the user will
continue to execute the one or more intervening actions during the
future time period.
9. The computing device of claim 8, wherein the first and second
levels of risk associated with insuring the user represent insuring
the user for a health, worker's compensation, disability or life
insurance policy.
10. The computing device of claim 8, wherein the processing unit is
further configured to: calculate a first insurance premium
associated with insuring the user in accordance with the first
level of risk; calculate a second insurance premium associated with
insuring the user in accordance with the second level of risk; and
transmit the first and the second insurance premium to the
computing device for presentation to the user.
11. The computing device of claim 8, wherein the processing unit is
further configured to: calculate a health or life insurance premium
associated with insuring the user in accordance with the second
level of risk; and upon insuring the user for the health or the
life insurance policy in accordance with the calculated health or
life insurance premium, access the dynamic data set to collect user
activity monitoring data.
12. The computing device of claim 8, wherein the processing unit is
further configured to train the machine-learning analytics model by
training a neural net.
13. The computing device of claim 8, wherein the one or more
intervening actions include suggestions regarding (i) a type and
frequency of exercise, (ii) daily nutrition, and (iii) lifestyle
habits.
14. The computing device of claim 8, wherein the future time period
corresponds to a period of insurance coverage for a health,
worker's compensation, disability or life insurance policy.
15. A non-transitory computer readable media having instructions
stored thereon that, when executed by one or more processors, cause
the one or more processors to: access a dynamic data set associated
with one or more users including electronic medical records,
demographic information, insurance records, and lifestyle
information; train a machine-learning analytics model using the
dynamic data set as training data to generate a trained
machine-learning analytics model; receive user data associated with
a user; apply the trained machine-learning analytics model to the
user data to predict a set of one or more medical-related
conditions associated with the user; determine, in accordance with
the trained machine-learning analytics model, a first level of risk
associated with insuring the user based upon the one or more
predicted medical-related conditions; identify, in accordance with
the trained machine-learning analytics model, one or more
intervening actions that, when executed by the user within a future
time period, reduce the first level of risk associated with
insuring the user to a second level of risk; transmit the one or
more intervening actions to a computing device to be presented to
the user; monitor user activity associated with the one or more
identified intervening actions to collect user activity monitoring
data; re-train the trained machine-learning analytics model using
the user activity monitoring data; and apply the trained
machine-learning analytics model to the user activity monitoring
data to determine a likelihood of whether the user will continue to
execute the one or more intervening actions during the future time
period.
16. The non-transitory computer readable media of claim 15, wherein
the first and second levels of risk associated with insuring the
user represent insuring the user for a health insurance or a life
insurance policy, and wherein the future time period corresponds to
a period of insurance coverage for the health or the life insurance
policy.
17. The non-transitory computer readable media of claim 15, further
including instructions that, when executed by one or more
processors, cause the one or more processors to (i) calculate a
first insurance premium associated with insuring the user in
accordance with the first level of risk, (ii) calculate a health or
life insurance premium associated with insuring the user in
accordance with associated with insuring the user in accordance
with the second level of risk, and (iii) transmit the first and the
second insurance premium to the computing device for presentation
to the user.
18. The non-transitory computer readable media of claim 15, further
including instructions that, when executed by one or more
processors, cause the one or more processors to (i) calculate a
health or life insurance premium associated with insuring the user
in accordance with the second level of risk, and (ii) upon insuring
the user for the health or the life insurance policy in accordance
with the calculated premium, access the dynamic data set to collect
user activity monitoring data.
19. The non-transitory computer readable media of claim 15, wherein
the instructions to train the machine-learning analytics model
further include instructions that, when executed by one or more
processors, cause the one or more processors to train the
machine-learning analytics model by training a neural net.
20. The non-transitory computer readable media of claim 15, wherein
the one or more intervening actions include suggestions regarding
(i) a type and frequency of exercise, (ii) daily nutrition, and
(iii) lifestyle habits.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority 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 ANI) METHODS FOR
DETECTING DAMAGE AND OTHER CONDITIONS;"
[0009] U.S. Application No. 62/625,140, filed Feb. 1, 2018 and
entitled "SYSTEMS ANI) 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 ANI) METHODS FOR RISK
DETERMINATION;"
[0012] U.S. Application No. 62/646,740; filed Mar. 22, 2018 and
entitled "SYSTEMS ANI) 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 ANI) 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 implementing machine
learning as part of insurance risk assessment and, more
particularly, to implementing machine learning to improve upon
aspects of life, worker's compensation, disability and health
insurance loss mitigation and prevention, and claims handling.
BACKGROUND
[0019] Insurance policies may typically require that the risk of
insuring a particular person or property be evaluated as part of an
initial underwriting process. The underwriting process may
typically require an insurer to assess several variables to
identify the overall risk of insuring a particular person or asset.
Based upon this assessed risk, the insurer may then decide, for
example, how much insurance to provide and/or the cost of premiums
for a specific type of insurance and amount of insurance coverage.
Moreover, once an insurance policy is issued, a claim may be made
by an insured, which is then reviewed by a claims handler who
partially approves, fully approves, or rejects the claim, allowing
the insured to be reimbursed accordingly.
[0020] However, conventional underwriting and claims handling may
typically rely on manual methods performed by an insurance
underwriter and claims handler, respectively, which may be
time-consuming, arduous, and prone to human error that may lead to
an inaccurate assessment of risk. Furthermore, current insurance
pricing and underwriting techniques may identify risks by looking
at previous and current medical history, but may be unable to
consistently and accurately predict and assess future risks, which
may likewise prevent an insurer from effectively managing loss
mitigation and prevention.
SUMMARY
[0021] The present disclosure generally relates to techniques
implementing machine learning for insurance loss mitigation and
prevention, and claims handling. In particular, electronically
accessible data may be analyzed that is relevant to specific types
of insurance policies. For instance, for life and health insurance
policies, disability and worker's compensation policies, electronic
medical records, demographic information, insurance records,
lifestyle information, psychographic information, etc., may be
collected to form part (or all) of a dynamic data set, which may
change over time as additional information is collected and/or as
additional users contribute to an overall data pool. In some cases,
the information collected and/or analyzed may pertain only to
humans. In some embodiments the information collected and/or
analyzed may pertain to domesticated animals (e.g., dogs, cats,
thoroughbreds, etc.) and/or livestock.
[0022] Information contained within this dynamic data set may then
be used to train one or more machine-learning analytics models,
algorithm, or module (and/or other artificial intelligence models,
algorithms, or modules) such that, when a user requests information
regarding a new or existing insurance product, his application
information or "user data," may be analyzed in accordance with a
trained machine-learning analytics model, algorithm, or module to
predict certain risk variables that may be indicative of risk.
These risk variables may include, for instance, predicted
medical-related conditions that are likely to occur in accordance
with the data analyzed via the trained machine-learning analytics
model, algorithm, or module. From these risk variables, an initial
risk assessment may be made, which may include a scaled risk score
or other suitable indicator to quantify the risk of insuring the
user given the likelihood, for example (in the case of a life or
health insurance policy) of the various medical-related conditions
occurring within some future time horizon that coincides with the
insurance coverage.
[0023] Moreover, the machine-learning analytics models, algorithms,
or modules (and/or other artificial intelligence models,
algorithms, or modules) may be further implemented to identify one
or more loss-mitigation, and/or loss-prevention, variables that
represent a reduction in the initial risk assessment. For instance,
the loss-mitigation, and/or loss-prevention, variables may include
or more intervening actions (e.g., a type and frequency of
exercise, daily nutritional guidance, lifestyle habits, etc.) that,
when executed by the user, reduce the initial risk assessment value
to a new, reduced risk level. These loss-mitigation, and/or
loss-prevention, variables may be shared with a user applying for a
new insurance policy and/or renewing an existing policy along with
calculated insurance premiums, which may reflect each respective
level of risk. In this way, an insurer may mitigate the loss
(and/or prevent losses from occurring) of insuring a particular
person while providing options to the user to reduce a premium rate
as long as the user complies with the actions, such as health
improving actions, indicated by the various identified
loss-mitigation, and/or loss-prevention, variables.
[0024] Furthermore, upon insuring a user (via traditional insurance
application techniques or those described herein designed to
mitigate insurer loss certain aspects described herein implement
trained machine-learning analytics models, algorithms, or modules
(and/or other artificial intelligence models, algorithms, or
modules) to improve upon the claims handling process. In
particular, some aspects include using data contained within the
dynamic data set (e.g., insurer data such as previous claims
history and insurance records) to streamline the claims handling
process. These aspects may additionally or alternatively include
predicting a claim amount or settled amount, and/or partially
executing portions of electronic claims and/or performing other
claims-based actions using these predictions.
[0025] In one aspect, a computer-implemented method for
implementing a machine-learning analytics model, algorithm, or
module (and/or other artificial intelligence model, algorithm, or
module) to calculate a level of risk of insuring a user and/or how
to reduce this risk may be provided. The method may include one or
more processors and/or associated transceivers (I) accessing a
dynamic data set associated with one or more users including
electronic medical records, demographic information, insurance
records, and/or lifestyle information; (2) training a
machine-learning analytics model, algorithm, or module (and/or
other artificial intelligence model, algorithm, or module) using
the dynamic data set as training data to generate a trained
machine-learning analytics model, algorithm, or module (and/or
other trained artificial intelligence model, algorithm, or module);
(3) receiving user data associated with a user; (4) applying the
trained machine-learning analytics model, algorithm, or module
(and/or other trained artificial intelligence model, algorithm, or
module) to the user data to predict one or more medical-related
conditions associated with the user based upon the user data; (5)
determining, in accordance with the trained machine-learning
analytics model, algorithm, or module (and/or other trained
artificial intelligence model, algorithm, or module) a first level
of risk associated with insuring the user based upon the one or
more predicted medical-related conditions; (6) identifying, in
accordance with the trained machine-learning analytics model,
algorithm, or module (and/or other trained artificial intelligence
model, algorithm, or module) one or more intervening actions that,
when executed by the user within a future time period, reduce the
first level of risk associated with insuring the user to a second
level of risk; and/or (7) transmitting the one or more intervening
actions to a computing device to be presented to the user. The
method may include additional, less, or alternate actions,
including those discussed elsewhere herein.
[0026] In another aspect, a computing device for implementing a
machine-learning analytics model (and/or other artificial
intelligence model, algorithm, or module) to calculate a level of
risk of insuring a user and/or how to reduce this risk may be
provided. The computing device may include a communication unit
configured to access a dynamic data set associated with one or more
users including electronic medical records, demographic
information, insurance records, and/or lifestyle information, and
to receive user data associated with a user. Additionally, the
computing device may include a processing unit that is configured
to (1) train a machine-learning analytics model using the dynamic
data set as training data to generate a trained machine-learning
analytics model; (2) apply the trained machine-learning analytics
model to the user data to predict a set of one or more
medical-related conditions associated with the user; (3) determine
a first level of risk associated with insuring the user based upon
the one or more predicted medical-related conditions in accordance
with the trained machine-learning analytics model; and/or (4)
identify one or more intervening actions in accordance with the
trained machine-learning analytics model that, when executed by the
user within a future time period, reduce the first level of risk
associated with insuring the user to a second level of risk.
Moreover, the communication unit may be further configured to
transmit the one or more intervening actions to a computing device
to be presented to the user. The computing device may include
additional, less, or alternate components, including those
discussed elsewhere herein.
[0027] In yet another aspect, a non-transitory computer readable
media may be provided to calculate a level of risk of insuring a
user and/or how to reduce this risk. The instructions stored on the
non-transitory computer readable may, when executed by one or more
processors, cause the one or more processors to: (1) access a
dynamic data set associated with one or more users including
electronic medical records, demographic information, insurance
records, and/or lifestyle information; (2) train a machine-learning
analytics model (and/or other artificial intelligence model,
algorithm, or module) using the dynamic data set as training data
to generate a trained machine-learning analytics model; (3) receive
user data associated with a user; (4) apply the trained
machine-learning analytics model to the user data to predict one or
more medical-related conditions associated with the user based upon
the user data; (5) determine, in accordance with the trained
machine-learning analytics model, a first level of risk associated
with insuring the user based upon the one or more predicted
medical-related conditions; (6) identify, in accordance with the
trained machine-learning analytics model, one or more intervening
actions that, when executed by the user within a future time
period, reduce the first level of risk associated with insuring the
user to a second level of risk; and/or (7) transmit the one or more
intervening actions to a computing device to be presented to the
user. The non-transitory computer readable media device may include
additional, less, or alternate instructions stored thereon,
including those discussed elsewhere herein.
[0028] 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
[0029] 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.
[0030] 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:
[0031] FIG. 1 illustrates a block diagram of an exemplary computer
system 100 implementing machine-learning for insurance loss
mitigation and claims handling, in accordance with certain aspects
of the present disclosure;
[0032] FIG. 2 illustrates a block diagram of an exemplary
machine-learning analytics engine 200; in accordance with certain
aspects of the present disclosure;
[0033] FIG. 3 illustrates an exemplary data set 300 including a
dynamic data set and user data, in accordance with certain aspects
of the present disclosure;
[0034] FIG. 4 depicts an exemplary artificial neural network 400,
which may be trained by the machine-learning analytics engine 200
of FIG. 2, in accordance with certain aspects of the present
disclosure;
[0035] FIG. 5 depicts an exemplary neuron 500 that may correspond
to the neuron labeled as "1,1" in hidden layer 404-1 of FIG. 4, in
accordance with certain aspects of the present disclosure;
[0036] FIG. 6 depicts text-based content of an exemplary electronic
claim record 600 that may be processed by a machine-learning
analytics engine, in accordance with certain aspects of the present
disclosure; and
[0037] FIG. 7 illustrates an exemplary computer-implemented method
flow 700, in accordance with certain aspects of the present
disclosure.
[0038] 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
[0039] Artificial Intelligence System for Life & Health
Insurance
[0040] The present embodiments are directed to, inter cilia,
machine learning via training a model using electronically
accessible data that is associated with various users and/or is
relevant to particular types of insurance, such as health and life
insurance, for example. The electronically accessible data may
include information that changes over time as additional data is
collected, aggregated, accessed, and/or retrieved, and thus may be
considered a dynamic data set in that it may periodically or
continuously change over time.
[0041] Techniques are disclosed to train a machine-learning
analytics model using various portions of the dynamic data set as
inputs, and may include re-training the machine-learning analytics
model as information within the dynamic data set changes, thus
adapting and improving over time as additional information is
received and processed. This machine-learning analytics model, once
trained, may then be applied to user data received from one or more
users seeking to purchase insurance coverage (e.g., health or life
insurance). The machine-learning analytics model may be executed,
for example, on an external computing device that receives user
data from various computing devices utilized by one or more users
requesting insurance coverage and/or from internal insurer systems
(e.g., via an agent). Thus, the machine-learning analytics model
may automate and improve upon the efficiency and accuracy of
existing insurance loss mitigation and prevention, and claims
handling processes, in accordance with aspects of the present
disclosure. The techniques disclosed herein may allow for real-time
loss mitigation, and may be less costly because machines are
capable of working around the clock, do not take vacation or
observe holidays, and are able to more comprehensively use large
volumes (e.g., terabytes or more) of empirical data than are
humans.
[0042] The dynamic data set, portions of which (or the entirely of)
may function as inputs to the machine-learning analytics model, may
be obtained from various sources. For instance, the dynamic data
set may include information harvested from historical claims,
electronic medical records, demographic information, insurance
records, surveys, collected lifestyle information, application
data, etc. Other inputs to the machine-learning analytics model
(which may be additionally or alternatively included as part of the
dynamic data set) may include health-related data received from
fitness trackers, health-related software applications such as
weight loss applications, activity loggers, physical sensors (e.g.,
heart rate monitors, blood pressure monitors, thermometers, weights
scales, glucose monitors, baby monitors, pregnancy monitors, sleep
monitors, etc.), social media, etc.
[0043] Thus, the present aspects may facilitate predicting new
loss-mitigation variables (and/or loss-prevention variables) that
allow an insurer to better mitigate (or prevent) loss by
identifying present and future risks that would otherwise not be
foreseeable using traditional underwriting. The present aspects may
dynamically characterize the risk of providing health and/or life
insurance to new applicants, and/or dynamically update pricing
models as new information is collected and the machine-learning
analytics model is re-trained. As a result, the present aspects
allow an insurer to improve upon the accuracy and efficiency in how
the insurance underwriting process assesses certain risks,
identifies variables to mitigate these risks, prices insurance
policies to accurately reflect those risks, and/or streamlines the
overall insurance claims process or customer experience, in
particular with regards to life and health insurance.
[0044] Computer System Overview
[0045] FIG. 1 illustrates a block diagram of an exemplary computer
system 100 implementing machine-learning for insurance loss
mitigation and claims handling, in accordance with certain aspects
of the present disclosure. The high-level architecture may include
both hardware and software applications, as well as various data
communication channels for communicating data between the various
hardware and software components. Generally, the system 100 may
automatically monitor data (which may be dynamically occurring)
associated with various electronic records, data sources, and/or
users, and use this data set to implement the various
machine-learning implementations discussed herein to facilitate
improvements to the insurance loss mitigation and prevention, and
claims handling process.
[0046] In the present aspect, the computer system 100 may include
one or more client devices 102, one or more back-end computing
devices 120, one or more health institutions 150, and/or a
communication network 180. The system 100 may further include any
suitable number X of back-end computing devices 120, which may
include back-end computing devices 120.1-120.X, for example. The
system 100 may include additional, less, or alternate components,
including those discussed elsewhere herein.
[0047] For the sake of brevity, the system 100 is illustrated as
including a single client device 102, three back-end computing
devices 120, two health institutions 150, and a single
communication network 180. However, the aspects described herein
may include any suitable number of such components. For example,
back-end computing devices 120 may include several hundred
components, including a server configured to communicate with
several client devices 102, each of which may be operated by a
separate user. Moreover, several back-end computing devices 120 may
receive data from each separate client device 102, from several
health institutions 150, and/or transmit data to each separate
client device 102 and several health institutions 150, as further
discussed herein.
[0048] To provide another example, one or more of back-end
computing devices 120 may receive user data from one or more client
devices 102 such that insurance policy pricing may be calculated
and transmitted to each client device 102, where it is then
displayed to each respective user. To provide additional examples,
client device 102 may represent one client device from several
different client devices for the same user or for different users.
For example, client device 102 may represent a user's smartphone as
well as a user's desktop computer, each of which may collect and
communicate with one or more health institutions 150 and/or one or
more back-end computing devices 120, as further discussed
below.
[0049] Communication network 180 may be configured to facilitate
communications between one or more client devices 102, one or more
health institutions 150, and/or one or more back-end computing
devices 120 using any suitable number of wired and/or wireless
links, which may be represented as links 117.1-117.3, for example.
For example, communication network 180 may include any suitable
number of nodes, radio frequency links, wireless or digital
communication channels, additional wired and/or wireless networks
that may facilitate one or more landline connections, internet
service provider (ISP) backbone connections, satellite links,
public switched telephone network (PSTN), etc.
[0050] To provide additional examples, the present aspects include
communication network 180 being implemented, for example, as a
local area network (LAN), a metropolitan area network (MAN), a wide
area network (WAN), or any suitable combination of local and/or
external network connections. To provide further examples,
communications network 180 may include wired telephone and cable
hardware, satellite, cellular phone communication networks, base
stations, macrocells, femtocells, etc. In the present aspects,
communication network 180 may provide one or more client devices
102 with connectivity to network services, such as Internet
services, for example, and/or support application programming
interface (API) calls between one or more client devices 102, one
or more health institutions 150, and/or one or more back-end
computing devices 120.
[0051] One or more health institutions 150 may include any suitable
number and/or type of health institutions that are associated with
various medical records. For example, one or more health
institutions 150 may include hospitals, physician offices,
dentists, mental health providers, pediatric care facilities,
emergency care facilities, psychiatric care providers, imaging
(e.g., X-ray, ultrasound, MRI, CT) facilities, chiropractors,
therapists, nurses, pharmacists, dieticians, laboratories, etc. In
various aspects, one or more users (e.g., a user associated with
client device 102) may have electronic medical records that are
created by one or more health institutions 150 (e.g., via medical
staff entering or maintaining electronic medical records), stored
locally at the one or more health institutions 150, or otherwise
stored on one or more suitable storage devices and accessible via
the one or more health institutions 150. Thus, each user's
electronic medical records may be held at a single institution (or
accessible storage device) that is part of or accessed by the one
or more health institutions 150, or spread out across several
different health institutions 150 or storage devices. In this way,
the health institutions 150 may function, in some instances, as
gateways of medical records such that the provisions of the Health
Insurance Portability and Accountability Act of 1996 (HIPAA) may be
met by obtaining user authorization to access such information.
[0052] In one aspect, electronic medical records held at one or
more health institutions 150 may be accessible via a secure
connection to communication network 180, for example, by client
device 102 and/or one or more back-end computing devices 120. For
example, one or more health institutions 150 may provide online
services that allow a user to access her accounts using client
device 102 and/or another suitable computing device. To provide
another example, a user may authorize a third party (e.g., an
insurer) upon applying or requesting information (and providing
proof of user consent) regarding health and/or life insurance
policies to access electronic medical records associated with one
or more health institutions 150.
[0053] In any event, upon receipt of a valid and authenticated
request for medical record data, one or more health institutions
150 may transmit medical-related information to client device 102
and/or one or more back-end computing devices 120. The
medical-related information may include any suitable data relevant
to assessing the current and future risk of insuring a user for
various insurance products (e.g., life and health insurance),
and/or how to reduce this risk to mitigate insurer loss. For
example; the medical-related data transmitted by one or more health
institutions 150 may include individual and/or family medical
history, details regarding specific medical procedures (e.g., when,
how much, where treated), information regarding a user's current
health from previous checkups, whether the user and/or user's
family member have any congenital defects, whether the user or
user's family members have been diagnosed with a particular disease
or condition, in addition to genomic information (e.g., raw data
from consumer-grade and/or personalized medicine genetic testing),
etc.
[0054] In various aspects, back-end computing devices 120 may
include any suitable number and/or type of components configured to
receive, send, store, and/or analyze data to facilitate the
functionality performed via the various embodiments as described
herein. For example, as shown in FIG. 1, back-end computing devices
120 may include one or more machine-learning analytics engines
120.1, one or more databases 120.2, and/or one or more database
servers 120.X.
[0055] In the present aspects, machine-learning analytics engine
120.1 may be configured to access data from and/or store data to
one or more additional data sources that may be included as one or
more of back-end computing devices 120. Additionally or
alternatively, the machine-learning analytics engine 120.1 may
access data from one or more health institutions 150 and/or data
provided by one or more users associated with one or more client
devices 120. In various aspects, any combination and/or subset of
this aforementioned data may form a dynamic data set that changes
over time as additional data is collected, which may be stored
and/or updated in one or more back-end components 120 and/or
accessed by the machine-learning analytics engine 120.1. For
example, machine-learning analytics engine 120.1 may use any
suitable portion of the dynamic data set as training data to train
a machine-learning analytics model.
[0056] Once the machine-learning analytics model is trained in this
way, the machine-learning analytics model may be applied to
received user data to predict various medical-related conditions
associated with a new or updated life or health insurance policy.
Moreover, once such predictions are made, aspects include the
machine-learning analytics engine 120.1 determining an initial
level of risk associated with insuring the user based upon the one
or more predicted medical-related conditions for a particular life
or health insurance policy as part of an artificial intelligence
(AI) driven underwriting process. The machine-learning analytics
engine 120.1 may then identify one or more intervening actions
that, when executed by the user within a future time period, reduce
the initial level of risk associated with insuring the user to a
second level of risk. Moreover, the machine-learning analytics
engine 120.1 may calculate pricing (e.g., premiums) associated with
the initial and the reduced level of risk, and transmit this
information and/or the one or more intervening actions to a
computing device (e.g., client device 102) for presentation to the
user.
[0057] To provide additional examples, as shown in FIG. 1, the
dynamic data set used to train the machine-learning analytics model
may be accessed via the one or more back-end computing devices 120
and/or any other suitable data sources. For instance, the dynamic
data set may include data associated with third-party data
providers that may not be readily obtained from the user (e.g., via
communications with client device 102), and/or from the health
institutions 150. For example, the additional data sources may
include information such as data mined from social media (which may
detail lifestyle and activities, and sporting, fitness, and eating
habits), data provided by the insurer (e.g., insurance claim or
other history known by the insurer or the user's previous
insurers), psychographic information, demographic information,
lifestyle information, etc. In other words, the dynamic data set
may include, for example, any suitable type of information that is
relevant to the calculation of an initial and reduced level of risk
and/or the identification of intervening actions taken to reduce
the initial level of assessed risk of insuring the user and, in
turn, relevant to calculating pricing for a specific type of
insurance product.
[0058] For example, a user's demographic information may include
any suitable type of information that is relevant to the
calculation of risks and/or actions to reduce this assessed risk,
which may be used to calculate insurance pricing for a particular
user and for a particular insurance product. For instance, the
demographic information may include a user's age or age bracket,
gender, marital status, household size, name and address, a
particular region where the user currently lives (e.g., a city,
state, zip code, county, etc.), whether the user has any children,
total household income, languages spoken, whether the user owns a
home, whether the user rents, level of education, employment
status, number and type of vehicles (or other assets) owned, where
the user works, etc.
[0059] In one embodiment, a severe influenza outbreak or epidemic
may occur, and a machine learning model may be trained using
historical medical claims data to determine those current patients
or users who are most at risk. A second model may be used, in
conjunction with multiple mitigation approaches (e.g., therapies
and/or medications) to determine the relative effectiveness of the
approaches. Effectiveness may be measured by comparing a patient's
first condition to the same patient's second condition with respect
to one or more ailments and/or medical/lifestyle conditions. Each
condition may be assigned a weight, which may improve or worsen
over time in response to the patient taking action. Effectiveness
may be measured with respect to each weight, or aggregated to form
an overall effectiveness score. Future mitigation advice may be
modified based upon A/B testing of loss mitigation approaches. For
example, whether diet, exercise, or some combination of the two was
more successful for patients of a particular type.
[0060] Additionally, a user's psychographic information may also
include any suitable type of information that is relevant to the
calculation of risks and/or actions to reduce this assesse risk,
which may be used to calculate insurance pricing for a particular
user and for a particular insurance product. For instance, the
psychographic information may include a level of risk tolerance in
general and/or for various specific types of activities, aspects of
the user's personality, values, opinions, attitudes, interests,
lifestyles, etc.
[0061] To provide yet another example, a user's lifestyle
information may also include any suitable type of information that
is relevant to the calculation of risks and/or actions to reduce
this assesse risk, which may be used to calculate insurance pricing
for a particular user and for a particular insurance product. For
instance, the lifestyle information may include data received from
fitness trackers, data received via connected (e.g., smart) medical
devices, data indicative of a user's frequency and intensity of
exercise, information indicative of a user's diet such as caloric
intake and/or nutritional information, etc.
[0062] In one aspect, the machine-learning analytics engine 120.1
may be implemented as any suitable number and/or type of computing
device (e.g., one or more computer servers) configured to
communicate with other components such as other back-end components
120.2-120.X, one or more client devices 102, and/or one or more
health institutions 150 (or suitable databases and/or storage
devices associated therewith), etc. In various aspects,
machine-learning analytics engine 120.1 may be configured to
process application programming interface (API) service calls, to
support one or more applications installed on one or more client
devices 102, etc., the details of which are further discussed
below.
[0063] Furthermore, certain aspects described herein allocate the
calculations and functionality for executing the machine-learning
based loss mitigation and prevention, and claims handling primarily
with machine-learning analytics engine 120.1, for ease of
explanation. However, aspects include machine-learning analytics
engine 120.1 working in conjunction with any suitable number of
other components of system 100 (or others not shown in FIG. 1) to
facilitate the functionality associated with the aspects of the
disclosure as described herein. For example, machine-learning
analytics engine 120.1 may work in conjunction with other servers,
databases, cloud-based servers, etc., included as part of the one
or more back-end computing devices 120. To provide another example,
machine-learning analytics engine 120.1 may work in conjunction
with client device 102. Additionally or alternatively, one or more
functions described herein with respect to machine-learning
analytics engine 120.1 may also be performed via one or more client
devices 102, which similarly may work in conjunction with work one
or more of back-end computing devices 120.
[0064] Database 102.2 may include one or more storage devices
configured to collect, store, delete, update, and/or modify data in
accordance with one or more instructions received from one or more
other back-end components 120, one or more client devices 102,
and/or one or more health institutions 150. For example, database
120.2 may include any suitable combination of one or more storage
mediums such as hard disk drives, solid state memory, cloud-based
storage devices, etc. In various aspects, database 120.2 may store
data in addition to or instead of data stored locally by
machine-learning analytics engine 120.1. In doing so,
machine-learning analytics engine 120.1, database 120.2, and/or
other back-end components 12.0 may store any suitable type of data
used to facilitate the various functionalities of certain aspects
as described herein.
[0065] Examples of the data stored among the various components of
one or more back-end components 120 include information contained
in the dynamic data sets as discussed herein and/or subsets of
information included in the dynamic data sets, insurance plan
information, one or more intervening actions to reduce the initial
assessed level of risk, logs or monitored data used to determine
whether a user has been or will execute the various intervening
actions, executable code, algorithms, instructions, etc., used to
train, re-train, and otherwise execute the machine-learning
analytics model, other calculations as discussed herein, etc.
Moreover, data stored in database 120.2 (and/or one or more other
back-end components 120) may be accessed via client device 102
and/or the one or more health institutions 150 as needed.
[0066] In some aspects, data stored in database 120.2 (and/or one
or more other back-end components 120) may include private or
confidential information such as electronic medical records,
insurance-related information (e.g., a history of insurance claims
and/or retrieved insurance information maintained for one or more
users etc. Thus, some aspects include utilizing secure data storage
and access procedures when data is written to or retrieved from
database 120.2 (and/or one or more other back-end components 120)
via machine-learning analytics engine 120.1. These procedures may
include, for example, secure login and authentication procedures
and/or the encryption of data stored in database 120.2 (and/or one
or more other back-end components 120).
[0067] Database server 120.X may be configured as any suitable
number and/or type of storage devices to perform substantially
similar functions as machine-learning analytics engine 120.1. In
some embodiments, machine-learning analytics engine 120.1 and
database server 120.X may be implemented as a single device, and
thus both machine-learning analytics engine 120.1 and database
server 120.X may not be present in some aspects. But in other
aspects, database server 120.X may perform dedicated database
operations, while machine-learning analytics engine 120.1 may
perform communication and analytical-based functions.
[0068] For example, machine-learning analytics engine 120.1 may
handle communications with client computing devices 102, one or
more health institutions 150, and/or one or more other back-end
components 120, and perform calculations related to training,
re-training, and executing a machine-learning analytics model.
Continuing this example, in such a case, database server 120.X may
facilitate the receipt of data included in the dynamic data set
that is used to train and re-train the machine-learning analytics
model. For example, as new information is received over time,
database server 120.X may append, substitute, update, or otherwise
modify the information contained within the dynamic data set so
that it remains up-to-date, allowing the machine-learning analytics
model to "learn" over time.
[0069] To provide another example, database 120.2 (and/or one or
more other back-end components 120) may store user information,
logon credentials, and contact information for one or more users.
Machine-learning analytics engine 120.1 may access the information
contained within the dynamic data set stored in database 120.2
(and/or one or more other back-end components 120) to correlate
data received from various data sources to a particular user to
facilitate the application of the machine-learning analytics model
for that user.
[0070] In the present aspects, client device 102 may include a
processing unit 104, a communication unit 106, a user interface
108, a display 110, and a memory unit 114. Client device 102 may
include additional, less, or alternate components, including those
discussed elsewhere herein. In various aspects, client device 102
may be implemented as any suitable computing device configured to
receive user input, display information, and/or communicate with
other components of the system 100. For example, client device 102
may be implemented as a smartphone or other suitable mobile
computing device. To provide additional examples, client device 102
may be implemented as a personal digital assistant (PDA), a desktop
computer, a tablet computer, a laptop computer, a phablet, a
GNSS-enabled device, a smart watch, smart glasses, a smart
bracelet, wearable electronics, a pager, a computing device
configured for wireless communication, etc.
[0071] Client device 102 may be configured to communicate using any
suitable number and/or type of communication protocols, such as
Wi-Fi, cellular, BLUETOOTH, NFC, RFID, Internet Protocols, etc. For
example, client device 102 may be configured to communicate with
communication network 180 using a cellular communication protocol
to send data to and/or receive data from the one or more health
institutions 150 and/or the one or more back-end computing devices
120 via communication network 180 using one or more communication
links, such as links 117.1-117.3, for example.
[0072] To this end, communication unit 106 may be configured to
facilitate data communications between client device 102 and one or
more of communication network 180, one or more health institutions
150, and/or one or more back-end computing devices 120 in
accordance with any suitable number and/or type of communication
protocols. In the present aspects, communication unit 106 may be
configured to facilitate data communications based upon the
particular component and/or network with which client device 102 is
communicating.
[0073] Such communications may facilitate the transmission of user
data collected from client device 102, which is then utilized by
machine-learning analytics engine 120.1 in accordance with the
execution of the trained machine-learning analytics model to
predict a set of one or more medical-related conditions associated
with the user, to determine an initial level of risk associated
with insuring the user in accordance with the predicted
medical-related conditions, to identify one or more intervening
actions to reduce the first level of risk, to calculate insurance
premiums, etc., as further discussed herein.
[0074] In the present aspects, communication unit 106 may be
implemented with any suitable combination of hardware and/or
software to facilitate this functionality. For example,
communication unit 106 may be implemented with any suitable number
and type of wired and/or wireless transceivers, network interfaces,
physical layers (PHY), ports, antennas, etc.
[0075] User interface 108 may be configured to facilitate user
interaction with client device 102. For example, user interface 108
may include a user-input device such as an interactive portion of
display 110 (e.g., a "soft" keyboard displayed on display 110), an
external hardware keyboard configured to communicate with client
device 102 via a wired or a wireless connection (e.g., a BLUETOOTH
keyboard), an external mouse, or any other suitable user-input
device.
[0076] Display 110 may be implemented as any suitable type of
display that may facilitate user interaction, such as a capacitive
touch screen display, a resistive touch screen display, etc. In
various aspects, display 110 may be configured to work in
conjunction with user-interface 108 and/or processing unit 104 to
detect user inputs upon a user selecting a displayed interactive
icon or other graphic, to identify user selections of objects
displayed via display 110, to display notifications and/or pricing
information for specific insurance products, etc.
[0077] Processing unit 104 may be implemented as any suitable type
and/or number of processors, such as a host processor for the
relevant device in which client device 102 is implemented, for
example. Processing unit 104 may be configured to communicate with
one or more of communication unit 106, user interface 108, display
110, and/or memory unit 114 to send data to and/or to receive data
from one or more of these components.
[0078] For example, processing unit 104 may be configured to
communicate with memory unit 114 to store data to and/or to read
data from memory unit 114. In accordance with various embodiments,
memory unit 114 may be a computer-readable non-transitory storage
device, and may include any suitable combination of volatile (e.g.,
a random access memory (RAM)), or non-volatile memory (e.g.,
battery-backed RAM, FLASH, etc.). In the present aspects, memory
unit 114 may be configured to store instructions executable by
processing unit 104. These instructions may include machine
readable instructions that, when executed by processing unit 104,
cause processing unit 104 to perform various acts.
[0079] In the present aspects, insurer application 115 is a portion
of memory unit 114 configured to store instructions, that when
executed by processing unit 104, cause processing unit 104 to
perform various acts in accordance with applicable aspects as
described herein. In certain aspects, a user may utilize insurer
application 115 (or other suitable component(s) of client device
102), to begin the process of requesting information regarding
various insurance products, such as purchasing new life or health
insurance policies or updating existing ones. This may be
implemented, for example, upon launching life planning application
115 to facilitate communications with machine-learning analytics
engine 120.1 and/or other suitable components.
[0080] For example, instructions stored in insurer application 115
may facilitate processing unit 104 performing functions such as
displaying various prompts in accordance with an insurer-based
application. This may include, for instance, prompts regarding
various types of insurance products for which a user desires to
obtain pricing information and/or the details associated with such
insurance products. Insure application 115 may thus facilitate the
collection of portions of, or the entirety of, user data in
conjunction with user interface 108, which may then be transmitted
to the one or more backend components 120 via communication unit
106. Insurer application 115 may also facilitate a user consenting
to the insurer accessing electronic medical records or other
sensitive information, as well as presenting information associated
with the calculated insurance premium pricing via display 110.
[0081] In some aspects, insurer application 115 may reside in
memory unit 114 as a default application bundle that may be
included as part of the operating system (OS) utilized by client
device 102. But in other aspects, insurer application 115 may be
installed on client device 102 as one or more downloads, such as an
executable package installation file downloaded from a suitable
application source via a connection to the Internet or other
suitable device, network, external memory storage device, etc.
[0082] For example, insurer application 115 may be stored in any
suitable portions of memory unit 114 upon installation of a package
file downloaded in such a manner. Examples of package download
files may include downloads via the iTunes store, the Google Play
Store, the Windows Phone Store, a package installation file
downloaded from another computing device, etc. Once downloaded,
insurer application 115 may be installed on client device 102 as
part of an installation package such that, upon installation of
insurer application 115, memory unit 114 may store executable
instructions such that, when executed by processing unit 104, cause
client device 102 to implement the various functions of the aspects
as described herein.
[0083] Exemplary Machine-Learning Analytics Engine
[0084] FIG. 2 illustrates a block diagram of an exemplary
machine-learning analytics engine 200, in accordance with aspects
of the present disclosure. In one aspect, machine-learning
analytics engine 200 may be an implementation of machine-learning
analytics engine 120.1, as shown and discussed with respect to FIG.
1. In the present aspects, machine-learning analytics engine 200
may include a processor unit 222, a communication unit 224, and a
memory unit 226. Machine-learning analytics engine 200 may include
additional, less, or alternate components, including those
discussed elsewhere herein.
[0085] It should be noted that, although only a single
machine-learning analytics engine 200 is shown in FIG. 2, this is
only one of many aspects. In some aspects, multiple computing
devices, servers, etc., may be configured to have a logical
presence of a single entity, such as a server bank or an
arrangement known as "cloud computing," for example. These
configurations may provide various advantages, such as enabling
near real-time uploads and downloads of information as well as
periodic uploads and downloads of information. However, for ease of
discussion and not limitation, the machine-learning analytics
engine 200 is referred to herein using the singular tense.
[0086] Machine-learning analytics engine 200 may be configured to
communicate using any suitable number and/or type of communication
protocols, such as Wi-Fi, cellular, BLUETOOTH, NFC, RFID, Internet
Protocols, etc. For example, the machine-learning analytics engine
200 may be configured to communicate via wireless communication or
data transmission over one or more radio frequency links or
communication channels, and/or communicate with one or more
communication networks (e.g., communication network 180) using a
cellular communication protocol to send data to and/or receive data
from one or more health institutions (e.g., one or more health
institutions 150), one or more back-end computing devices (e.g.,
one or more back-end computing devices 120), and/or one or more
client devices 102 (e.g., client device 102) via such
communications.
[0087] To this end, communication unit 22.4 may be configured to
facilitate data communications between various components in
accordance with any suitable number and/or type of communication
protocols. In the present aspects, communication unit 224 may be
configured to facilitate data communications based upon the
particular component and/or network with which machine-learning
analytics engine 200 is communicating. In the present aspects,
communication unit 224 may be implemented with any suitable
combination of hardware and/or software to facilitate this
functionality. For example, communication unit 224 may be
implemented with any suitable number of wired and/or wireless
transceivers, network interfaces, physical layers (PHY), ports,
antennas, etc.
[0088] Again, such communications may facilitate the receipt of
user data and/or data collected from one or more health
institutions, one or more back-end computing devices, and/or one or
more client devices 102. This collected data may form part of, or
entirely of, the dynamic data set, which may be used by the
machine-learning analytics engine 200 to train one or more
machine-learning analytics models. Once trained, aspects include
the machine-learning analytics engine 200 applying the
machine-learning analytics model to the user data to predict
various risk-based variables, to assess risk in accordance with the
risk-based variables, to identify loss-mitigation variables, to
calculate insurance premiums in accordance with varying levels of
assessed risk, and/or to assist in the claim handling process.
[0089] Processing unit 222 may be implemented as any suitable type
and/or number of processors, such as a host processor for the
relevant device in which machine-learning analytics engine 200 is
implemented, for example. Processing unit 222 may be configured to
communicate with one or more of communication unit 224 and/or
memory unit 226 to send data to and/or to receive data from one or
more of these components.
[0090] For example, processing unit 222 may be configured to
communicate with memory unit 226 to store data to and/or to read
data from memory unit 226. In accordance with various embodiments,
memory unit 226 may be a computer-readable non-transitory storage
device, and may include any combination of volatile (e.g., a random
access memory (RAM)), or a non-volatile memory (e.g.,
battery-backed RAM, FLASH, etc.). In the present aspects, memory
unit 226 may be configured to store instructions executable by
processor unit 222. These instructions may include machine readable
instructions that, when executed by processor unit 222, cause
processor unit 222 to perform various acts.
[0091] In the present aspects, machine-learning application 227 is
a portion of memory unit 226 configured to store instructions, that
when executed by processing unit 222, cause processing unit 222 to
perform various acts in accordance with applicable aspects as
described herein. For example, instructions stored in
machine-learning application 227 may facilitate processing unit 222
executing the various functions described below with respect to
each of the modules stored in memory unit 226. Some of these
functions may include, for example, collecting and aggregating
various types of data, training one or more machine-learning
analytic models, predicting one or more medical-related conditions,
assessing the risk of insuring one or more users, identifying
intervening actions that mitigate the insurer's loss, calculating
premiums, transmitting premiums and/or other notifications to
relevant computing devices (e.g., client device 102), etc. These
functions are further discussed below with respect to the each of
the additional modules stored in memory unit 226.
[0092] Some aspects include machine-learning application 227, data
aggregation module 229, machine learning training module 231,
prediction module 233, risk assessment module 235, loss-mitigation
variable identification module 237, premium calculation module 239,
and/or claim handling module 241 being implemented as one or more
software applications, sets of computer-executable instructions,
algorithms, etc., which are stored on the memory unit 226 and
executable by the processing unit 222. For example, memory unit 226
may represent a tangible, non-transitory computer-readable medium,
with each of machine-learning application 227, data aggregation
module 229, machine learning training module 231, prediction module
233, risk assessment module 235, loss-mitigation variable
identification module 237, premium calculation module 239, and/or
claim handling module 241 including instructions executable by one
or more processors (e.g., processing unit 222) that, when executed
by the one or more processors, cause the one or more processors to
perform various acts as described herein. To provide another
example, the machine-learning application 227, data aggregation
module 229, machine learning training module 231, prediction module
233, risk assessment module 235, loss-mitigation variable
identification module 237, premium calculation module 239, and/or
claim handling module 241 may be implemented at least partially in
firmware and/or in hardware of the machine-learning analytics
engine 200.
[0093] The various applications and modules shown in FIG. 2 and
discussed herein may be executed on the same processing unit 222 or
on different computer processors (which may also be part of
separate components not pictured in FIG. 2) in some aspects, as
desired. Further, while the machine-learning application 227, data
aggregation module 229, machine learning training module 231,
prediction module 233, risk assessment module 235, loss-mitigation
variable identification module 237, premium calculation module 239,
and/or claim handling module 241 are depicted as separate
components of memory unit 226, two or more of these components may
be integrated into different integrated applications and/or
integrated modules. Moreover, one or more of the machine-learning
application 227, data aggregation module 229, machine learning
training module 231, prediction module 233, risk assessment module
235, loss-mitigation variable identification module 237, premium
calculation module 239, and/or claim handling module 241 may be
implemented in conjunction with other applications (not shown) that
are stored and executed via the machine-learning analytics engine
200 and/or other components in communication with machine-learning
analytics engine 200.
[0094] In the present aspects, data aggregation module 229 is a
portion of memory unit 226 configured to store instructions, that
when executed by processing unit 222, cause processing unit 222 to
perform various acts in accordance with applicable aspects as
described herein. For example, instructions stored in data
aggregation module 229 may facilitate processing unit 222
performing functions associated with receiving, storing, and
updating user data and/or other data that is used as part of a
dynamic data set. For instance, data aggregation module 229 may
include instructions to facilitate machine-learning analytics
engine 200 monitoring various data sources and/or receiving data
from one or more data sources over time to build dynamic data sets
that may change over time as new data is acquired. To provide
another example, data aggregation module 229 may include
instructions to facilitate machine-learning analytics engine 200
receiving user data prompted from users making inquiries about
health and life insurance products.
[0095] Again, the data sources may include, for example, data
received from one or more financial institutions (e.g., one or more
financial institutions 150), one or more back-end computing devices
(e.g., one or more back-end computing devices 120), and/or one or
more client devices 102 (e.g., client device 102). In various
aspects, this data may represent user input (e.g., via client
device 102) and/or other types of data that may be acquired with or
without user input. For instance, a user may be solicited via a
suitable computing device (e.g, via client device 102) in the form
of survey questions, prompts, etc., for information that is then
aggregated with other data included in the user's data profile.
Some examples of data that may be acquired from a user in this way
may include, for example, any suitable portion of medical
information, demographic information, insurance information,
psychographic information, lifestyle information, etc., as further
discussed herein.
[0096] Exemplary Data Set
[0097] To provide an illustrative example with reference to FIG. 3,
which illustrates an exemplary data set 300 including a dynamic
data set and user data, in accordance with certain aspects of the
present disclosure, data aggregation module 229 may facilitate
processing unit 222 (e.g., via communication unit 224) aggregating
various portions of data to form the dynamic data set.
Additionally, data aggregation module 229 may facilitate processing
unit 222 receiving and storing user data, which is used to
calculate an insurance premium for a specific type of insurance
product. The dynamic data set and user data may thus represent data
that is received, accessed, and/or stored in any suitable portion
of machine-learning analytics engine 200 (e.g., memory unit 226)
and/or another suitable storage device that is accessible by
machine-learning analytics engine 200 (e.g., one or more back-end
components 120).
[0098] It will be understood that the examples shown in FIG. 3
associated with the dynamic data set and user data, such as the
electronic medical records, demographic information, insurance
records, lifestyle information, etc., are but some examples of the
types of information that may be relevant to train and execute a
machine-learning analytics model for one or more particular users.
The dynamic data set and user data may thus include, for example,
any suitable number and/or type of information that is useful or
otherwise relevant to calculate health and/or life insurance
policies for one or more users, including social media information
gathered with the user's permission or affirmative consent. For
example, although not illustrated in FIG. 3, the dynamic data set
may include psychographic information that is relevant to or
indicative of the risk of insuring a user for a particular type of
insurance policy.
[0099] As shown in FIG. 3, electronic medical records for various
users may include data such as a history of various symptoms and
diagnoses, pre-existing conditions, congenital defects, a history
of blood work data (e.g., triglycerides, cholesterol levels,
glucose levels, etc.), and other recorded health metrics (e.g.,
height, weight, BMI, pulse, blood pressure, body temperature,
etc.). Thus, the electronic medical records may include any
suitable type of information that is relevant to assessing an
initial risk of providing health and/or life insurance for a user
and/or intervening actions that may be taken by the user to reduce
this initially assessed risk, which may represent part of the
dynamic data set that is used as training data for a
machine-learning analytics model, as further discussed herein.
[0100] To provide another example, as shown in FIG. 3, demographic
information may include age (or age bracket), gender, location data
such as the user's current address or residential region, blood
type, etc., for various users. In various aspects, this demographic
information may provide various insights when used as training
data, such that correlations may be made amongst similar users and
compared to future users as part of a machine-learning analytics
model, as further discussed herein. For example, the demographic
information may allow a correlation to be made among other users
with similar demographic data, for which similar risk assessments
may thus be identified.
[0101] As yet another example, insurance records may include, for
various users, a history of medical claims, a history of other
types of insurance claims, current insurance policy information for
various users (e.g., policy numbers, dates, coverage, premiums,
etc.), insurance pricing information, risk tables and/or data
mapping various conditions, behaviors, etc., to specific levels of
risk, etc. As further discussed herein, this insurance information
may be used to train a machine-learning analytics model by
establishing an initial correlation between specific types of
insurance policy information, user data for specific policies, and
the assessed risk and pricing amongst similar insurance plans.
[0102] To provide an additional example, lifestyle information may
indicate, for several users, each user's general preference
regarding various lifestyle choices, which may represent
preferences regarding how often each user prefers to travel (and
where), how often a user receives a health-related checkup, how
often each user exercises (and the type of exercise), self-logged
health data (e.g., information from weight loss applications such
as caloric intake, data accessed via fitness trackers such as heart
rate, etc.), how each user prefers to commute to work, each user's
occupation, etc. Again, like the aforementioned demographic
information and other data that may form part of the dynamic data
set, the lifestyle information may be utilized to identify risk
correlations amongst users, which may then be used, for example, to
predict future risks for similar users via the machine-learning
analytics model.
[0103] In the present aspects, machine learning training module 231
is a portion of memory unit 226 configured to store instructions,
that when executed by processing unit 222, cause processing unit
222 to perform various acts in accordance with applicable aspects
as described herein. For example, instructions stored in machine
learning training module 231 may facilitate processing unit 222
performing functions associated with identifying, accessing, and
using various portions of the dynamic data set as training data for
a machine-learning analytics model.
[0104] In various aspects, any suitable number and type of
machine-learning analytics model may be implemented, and therefore
the data selected from the dynamic data set, as well as the
particular type of training process, may be adapted to the
particular type of a machine-learning analytics model and/or
artificial intelligence system that is implemented. For example, a
machine-learning analytics model may be implemented in accordance
with decision tree learning, association rule learning, an
artificial neural network, deep learning, inductive logic
programming, support vector machines, clustering, Bayesian
networks, reinforcement learning, reinforced learning, combined
learning, representation learning, similarity and metric learning,
sparse dictionary learning, genetic algorithms, rule-based
learning, a MapReduce programming model used in accordance with the
HADOOP framework, etc.
[0105] Generally, the overall process of training the
machine-learning analytics model may include defining the sample
inputs, the importance (e.g., weighting) of these inputs, and
defining one or more outputs that are determined using the weighted
inputs. Based upon this initial training framework, the
machine-learning process allows correlations to be made among
different subsets of data within the dynamic data set, correlations
to be made among data contained in the data set and received user
data, and/or specific predictions to be formulated. Over time, as
additional data becomes available, or as the dynamic data set is
updated, the trained machine-learning model may identify new
correlations, vary the weighing of certain inputs, change the
inputs, etc., such that different correlations may be made, the
accuracy of predictions may be improved, and/or new predictions may
be made.
[0106] For instance, the data contained as part of the dynamic data
set may represent time-series data, with each data point including
a particular value and a corresponding indication of time at which
the value was collected, observed, or generated by a particular
data source. An example of a machine-learning training process is
provided below with reference to FIGS. 4 and 5, which uses a neural
network as an example. However, as discussed above, aspects include
any suitable type of machine-learning model being trained and
executed to facilitate the aspects as described herein.
[0107] Exemplary Artificial Neural Network
[0108] In various aspects, a processor or a processing element
(e.g., processing unit 222) 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, deep learning or
reinforced learning model or module, or a combined learning module
or program that learns in two or more fields or areas of interest.
Other types of artificial intelligence or machine learning may be
used. Machine learning may involve identifying and recognizing
patterns in existing data in order to facilitate making predictions
for subsequent data. Models may be created based upon example
inputs in order to make valid and reliable predictions for novel
inputs.
[0109] Additionally or alternatively, the machine learning programs
may be trained by inputting sample data sets or certain data into
the programs, such as via images, electronic records, mobile
devices, smart or autonomous vehicles, fitness devices, etc. 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 any suitable number and type of programs in accordance
with the particular artificial intelligence system that is
implemented, such as Bayesian program learning (BPL), voice
recognition and synthesis, image or object recognition, optical
character recognition, natural language processing, etc., either
individually or in combination. The machine learning programs may
also include, for example, natural language processing, semantic
analysis, automatic reasoning, etc.
[0110] Supervised or unsupervised machine learning techniques may
also be used. In supervised machine learning, a processing element
(e.g., processing unit 222) may be provided with example inputs and
their associated outputs, and may seek to discover a general rule
that maps inputs to outputs. As a result, 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.
[0111] For instance, FIG. 4 depicts an exemplary artificial neural
network 400, which may be trained by the machine-learning analytics
engine 200 of FIG. 2, in accordance with aspects of the present
disclosure. For example, processing unit 222 may execute
instructions stored in machine learning training module 231 to
facilitate this process. The example neural network 400 may include
layers of neurons, including input layer 402, one or more hidden
layers 404-1 through 404-n, and output layer 406. Each layer
comprising neural network 400 may include any number of neurons
i.e., q and r may be any positive integers. Again, although a
neural network is illustrated, aspects include the implementation
of any suitable type of machine-learning system to achieve the
methods and systems described herein, which may be of a different
structure and configuration than those depicted in FIGS. 4 and
5.
[0112] Input layer 402 may receive different input data from within
the dynamic data set. For example, input layer 402 may include a
first input at that represents an insurance type (e.g., health or
life insurance), a second input a.sub.2 representing patterns
identified in input data, a third input a.sub.3 representing
various age groups, a fourth input a.sub.4 representing a gender, a
fifth input a.sub.5 representing a particular level, frequency,
and/or type of exercise, a sixth input a.sub.6 representing body
mass index, and so on. Six inputs are shown in FIG. 4 for purposes
of brevity, although aspects include input layer 402 utilizing any
suitable number of (e.g., hundreds or thousands or more) inputs. It
should be appreciated that life and health insurance policies may
relate to humans or animals (e.g., pets, livestock, etc.). Input
may relate to farm/ranch insurance.
[0113] In some embodiments, the number of elements used by neural
network 400 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. Furthermore, this training process may be repeated
periodically or continuously as additional data is collected and
added to the dynamic data set or as data within the dynamic data
set is updated. In this way, the neural network 400 may be
re-trained over time to continuously provide the most accurate
predictions in accordance with the most recent dynamic data
set.
[0114] Each neuron in hidden layer(s) 404-1 through 404-n may
process one or more inputs from input layer 402, and/or one or more
outputs from a previous one of the hidden layers, to generate a
decision or other output. Output layer 406 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
(e.g., frequent exercise, intensive and/or long duration of
exercise, high cholesterol, a high stress job) or absence (e.g.,
lack of exercise, low cholesterol, low intensity or duration of
exercise) of a condition. In some embodiments, however, outputs of
neural network 400 may be obtained from a hidden layer 404-1
through 404-n in addition to, or in place of, output(s) from output
layer(s) 406.
[0115] 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 user considered strongly determinative
of risk of insuring a user for health or life insurance, then those
that are considered of intermediate importance, and finally those
that are of less relevance. 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
404-1 through 404-n may share decisions, with no single layer
making an independent decision.
[0116] In some embodiments, neural network 400 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. Therefore, in one embodiment, a recurrent
neural network may be trained with respect to a specific piece of
functionality with respect to system 100 of FIG. 1. For example, in
one embodiment, a recurrent neural network may be trained and
utilized as part of machine-learning analytics engine 200 to
automatically identify specific information that may render a
person ineligible for certain types of insurance coverage (e.g., a
user may be ineligible for life insurance if he has a specific type
of pre-existing condition such as HIV).
[0117] FIG. 5 depicts an example neuron 500 that may correspond to
the neuron labeled as 1,1'' in hidden layer 404-1 of FIG. 4,
according to one embodiment. Again, each of the inputs to neuron
500 (e.g., the inputs comprising input layer 402) 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 400.
[0118] 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 cc, which may be a summation
and may produce a value z.sub.1, which may be input to a function
520, labeled as f.sub.1,1(z.sub.1). The function 520 may be any
suitable function such as a linear function, a non-linear function,
a sigmoid function, etc. In any event, as depicted in FIG. 5, the
function 520 may produce multiple outputs, which may be provided to
neuron(s) of a subsequent layer, or used directly as an output of
neural network 500. For example, the outputs may correspond to
various medical-related predictions for a particular user, or may
be calculated values used as inputs to subsequent functions.
[0119] It should be appreciated that the structure and function of
the neural network 400 and neuron 500 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.
[0120] Exemplary Predictive and Risk Assessment Functions
[0121] Referring back to FIG. 2, in the present aspects, prediction
module 233 is a portion of memory unit 226 configured to store
instructions, that when executed by processing unit 222, cause
processing unit 222 to identify various risk-based variables in
accordance with applicable aspects as described herein. For
example, instructions stored in prediction module 233 may
facilitate processing unit 222 applying the trained
machine-learning analytics model to identify, from model outputs,
various predictions regarding one or more users for whom health or
life insurance coverage is sought. These predictions may include,
for example, any suitable type of forecasted information that may
be derived from the machine-learning analytics model in accordance
with the particular type of model, the manner in which the model is
trained, and/or the specific portions of the dynamic data set and
how they are mapped and weighted in accordance with the trained
machine-learning analytics model.
[0122] For instance, certain aspects include the predictions being
related to one or more medical-related conditions associated with a
user based upon his user data. For example, the user data may be
received as a result of a user inquiring about a particular type of
health and/or life insurance product, coverage associated with a
new policy, coverage regarding a renewed policy, etc., and include
information about that user and/or information received from that
user. As shown in FIG. 3, the user data may include, for example,
user identifying information (e.g., a name and contact
information), the parameters associated with a particular type of
insurance product, such as deductibles, a type of insurance (e.g.,
HMO health, PPO health, life insurance, etc.), a life insurance
term, the specific benefits that are sought (e.g., life insurance
benefits), answers to prompts presented via client device 102, etc.
Moreover, and as further discussed in the examples below, the user
data may include data retrieved from other sources (e.g., the
dynamic data set) that is correlated to a particular user.
[0123] To provide an illustrative example, assume that a User A
inquires about a PPO health insurance plan, and provides user data
indicating the user's name, contact information, age, gender, and a
desired deductible. User A may also consent to the insurer
collecting additional information from various sources that may
then be used to supplement this data, such as information obtained
via electronic medical records (e.g., prescription history obtained
from one's pharmacy records). The type of information to include as
part of the user data, as well as the various sources to provide
the user data, may be defined as part of the trained
machine-learning analytics model and/or a result of the processing
unit 222 executing instructions stored in prediction module 233, in
various embodiments.
[0124] Continuing this example, once an adequate amount of user
data is collected for User A, the trained machine-learning
analytics model may be applied to the user data to predict specific
medical-related conditions that, within some future time period,
are likely to be associated with or experienced by User A.
Continuing the previous example, assume that the user data
indicates that User A is a 25-year old male having a slightly above
average BMI of 26. Further assume that the user data includes
electronic medical record information, and indicates from recent
blood work that User A has normal levels of cholesterol and
triglycerides. Moreover, assume that the information obtained via
one or more data sources indicates that User A has an occupation
that is known as being relatively sedentary, and that although User
A lives within walking or bicycling distance from his place of
work, User A decides to commute by driving each day.
[0125] This information, taken at one snapshot in time, may not
reveal anything that indicates an excessively high risk of
providing User A with health insurance. In other words, a
traditional underwriting process may only consider present
health-related information, and may not consider or identify other
sources of information as contributors to future risk. On the other
hand, because the present aspects use a trained machine-learning
analytics model, this model may be leveraged to identify patterns
or correlations using data from these additional sources.
Continuing the previous example for User A, the machine-learning
analytics model may predict that certain behaviors, lifestyles, and
medical conditions in the present may correlate to a high
likelihood of certain medical conditions occurring within a future
time horizon, thus predicting their occurrence within some future
time period. These medical-related conditions may include, for
instance, conditions that may develop, injuries that are likely to
occur, etc.
[0126] For instance, User A has only a slightly than normal BMI and
normal blood work metrics. However, the lifestyle information
associated with User A (i.e., that User A could partake in exercise
as part of a daily commute but chooses not to) is indicative of a
desire to not regularly exercise. Because this trend is likely to
be maintained in the future given that User A is now 25 years old,
it is also likely User A's BMI will increase over time as a result
of a sedentary lifestyle. In various aspects, these predictions may
be made in accordance with the trained machine-learning analytics
model. For example, the machine-learning analytics model may
identify users from within the dynamic data set that have similar
(or identical) metrics as User A, such as other male users that had
same age or were within a range matching User A, had (at that age)
the same BMI or a BMI within a range matching User A, had sedentary
occupations, and who did, in fact, not regularly exercise from the
age of 25-35.
[0127] The trained machine-learning analytics model may then
determine an overall trend by analyzing the users within this
subset of users that are similar to User A, and determine that most
of these users had a BMI that increased during the next 3 years, 5
years, etc. in other words, aspects include the machine-learning
analytics model, upon being executed, identifying similar users
having corresponding information matching that of a particular
user, and then predicting one or more medical-related conditions
associated with that particular user based upon one or more
medical-related conditions and/or trends that were observed within
the set of similar users.
[0128] Additionally, certain aspects include re-training the
machine-learning analytics model over time as additional
information is added to the dynamic data set and/or as information
in the dynamic data set is updated. This may include, for instance,
defining new inputs to the machine-learning analytics model and/or
defining new outputs (e.g., alternative or new predictions). In
this way, the machine-learning analytics model may be periodically
or continuously re-trained such that the accuracy of predictions
determined by the model is increased over time.
[0129] For example, it may later be determined that the correlation
between driving to work instead of walking or bicycling for users
similar to User A is not strongly correlated to a trend of
subsequently-increased BMI Instead, additional information included
in the dynamic data set may identify that, in spite of User A's
sedentary occupation and lack of exercise during a commute, fitness
tracker data shows that User A still maintains 10,000 steps per
day, and therefore this additional information may be considered
for future users to alter subsequent predictions.
[0130] To provide another illustrative example, assume that a User
B inquires about a term life insurance plan, and provides user data
indicating the user's name, contact information, age, gender, and a
desired amount of coverage (i.e., benefit). User B may also consent
to the insurer collecting additional information from various
sources, which may then be used to supplement this data, such as
information obtained via electronic medical records, for example.
Again, the type of information to include as part of the user data,
as well as the various sources to provide the user data, may be
defined as part of the trained machine-learning analytics model
and/or a result of the processing unit 222 executing instructions
stored in prediction module 233, in various embodiments.
[0131] Continuing this example, assume that the user data indicates
that User B is a 33-year old female having a normal BMI of 21, and
that User B's electronic medical record indicates healthy blood
metrics. Thus, this information may not reveal anything that
indicates an excessively high risk of providing User B with life
insurance. However, the present aspects include the use of a
trained machine-learning analytics model to identify other
indicators and/or correlations that may be relevant to an increased
risk of providing life insurance for User B. For example, further
assume that User B's psychographic profile indicates that User B is
not particularly risk averse, and User B's lifestyle information
indicates that User B often travels to adventuresome destinations
and partakes (or is likely to partake) in life-threatening
activities, such as base jumping and skydiving. Moreover, assume
that the insurance records associated with User B indicate a
driving history of several accidents in which User B was at fault,
and that police reports associated with User B's insurance records
indicate several instances of excessive speeding.
[0132] Continuing this example for User B, the machine-learning
analytics model may thus predict that, even though User B may be
quite healthy, her personality traits and lifestyle represent a
high risk with regards to life insurance coverage. As a result,
aspects may include the trained machine-learning analytics model
predicting a high likelihood of User B dying at an age that is much
less than average among users with similar health information. And,
if the likelihood of User B dying within a time period matches that
of the requested life insurance term, this prediction may be taken
into consideration when pricing the life insurance to adequately
reflect this risk, as further discussed below.
[0133] Again, the machine-learning analytics model may be
periodically or continuously re-trained such that the accuracy of
predictions determined by the model is increased over time. In this
example, it may be later determined that a set of users similar to
User B, upon having a child, often cease their high-risk lifestyle.
Thus, upon re-training the machine-learning analytics model, if may
be determined that a new User C (similar otherwise to User B) has a
6-month-old child from information available via the dynamic data
set. Thus, this prediction may later be changed for future users to
indicate a less likelihood of User C (and other future users
similar to User C) suffering a premature death.
[0134] In the present aspects, risk assessment module 235 is a
portion of memory unit 226 configured to store instructions, that
when executed by processing unit 222, cause processing unit 222 to
perform various acts in accordance with applicable aspects as
described herein. For example, instructions stored in risk
assessment module 235 may facilitate processing unit 222 performing
functions associated with identifying an initial level of risk to
insure a user in accordance with a particular insurance policy,
based upon the outputs of the trained machine-learning analytics
model. This may include, for example, generating a level of risk
associated with insuring a user for health or life insurance based
upon the one or more predicted medical-related conditions (or other
suitable risk-based variables) output by the trained
machine-learning analytics model, as discussed above.
[0135] This initial level of risk may represent, for example, any
suitable type of system that assesses the overall risk of insuring
a user, which may include a numeric scoring and/or weighted system,
for example. In various aspects, the level of risk may include a
weighting to each predicted medical-related condition output by the
trained machine-learning analytics model. Thus, the level of risk
may identify a particular risk associated with individually
identified conditions, and/or a level of risk associated with the
combined probability of all the conditions occurring in accordance
with the specific type of insurance coverage that is to be
provided.
[0136] To provide an illustrative example, an overall level of risk
may indicate, for each prediction that is made, the impact or
contribution towards the overall risk of insuring the user. For
instance, BMI may be considered a relatively accurate factor in
assessing the risk of insuring an individual for health insurance,
although a trend indicating increasing LDL cholesterol and
triglycerides may represent an even higher risk. Therefore,
assuming that the trained machine-learning analytics model projects
increases to BNB, LDL cholesterol levels, and triglycerides within
the next 3 years, a weight may be assigned to each of these
conditions to represent the severity or likelihood of each
prediction towards an overall risk assessment of insuring the
individual. In various aspects, these weightings may be applied to
any suitable number of predicted conditions, and may be combined in
any suitable manner to derive an overall risk score.
[0137] In the present aspects, loss-mitigation variable
identification module 237 is a portion of memory unit 226
configured to store instructions, that when executed by processing
unit 222, cause processing unit 222 to perform various acts in
accordance with applicable aspects as described herein. For
example, instructions stored in loss-mitigation variable
identification module 237 may facilitate processing unit 222
performing functions associated with identifying various
loss-mitigation variables that are directed to reducing the initial
risk assessment of the user. In various aspects, the
loss-mitigation variables may be any suitable type of behavior,
action, and/or decision that, when performed by the user within a
certain future time period, may reduce or eliminate the impact of
the one or more predicted risk-based variables. In other words, the
identified loss-mitigation variables allow an insurer to mitigate
loss by better insulating the insurer from certain types of claims
being made that are associated with high claim payouts.
[0138] In some aspects, this future time period may correspond to
the term of insurance coverage, and thus the predictions and
loss-mitigation variables may be particularly relevant to the
insurer. For instance, the identification of the loss-mitigation
variables may proactively, attempt to prevent certain claims from
occurring at all or, if these claims are made, their severity (and
thus cost) may be reduced. Therefore, in either case, loss to both
the insurer and the user is mitigated.
[0139] Continuing the previous example with a health and life
insurance policy, loss mitigation and/or prevention in this context
may include preventing certain injuries or medical conditions from
occurring or reducing their severity if their occurrence cannot be
entirely prevented. For example, the cost of cancer treatment may
be very high if caught in a later stage, but much less if diagnosed
early. Thus, the identified loss-mitigation variables may include,
in this example, a user agreeing to follow a medical wellness
examination schedule that is recommended for similar users. In
doing so, cancers more common for people over certain ages may be
diagnosed early on, mitigating the loss borne by the insurer
regarding the protracted and expensive treatments that would
otherwise be needed if diagnosed at a later stage.
[0140] To provide an illustrative example continuing the previous
one with User A, assume that User A, as noted above, has only a
slightly than normal BMI and normal blood work metrics, but that
the machine-learning analytics model has identified, as a predicted
medical-related condition, that User A's BMI will likely (e.g., a
75% likelihood) increase during the next 2 years by 20%. Continuing
this example, loss-mitigation variable identification module 237
may include instructions that, when executed by processing unit
222, identifies one or more intervening action. These actions may
include those that, when performed by the user within the next 2
years, reduce this likelihood to less than 50%.
[0141] In various aspects, these intervening actions may include
suggestions regarding any suitable type of activity, suggestions
regarding changes in behavior, suggestions regarding daily
nutritional intake, suggestions regarding a type and frequency of
regular exercise, suggestions regarding changes to or adopting new
lifestyle habits, etc. For example, lifestyle habits may include
medical options, such as taking one's medicine regularly, being
vaccinated against influenza or another pathogen, visiting a
cardiologist, lowering one's glucose levels, etc. Using the present
example, these intervening actions may include a suggestion for
User A to begin taking a commute to work that involves more walking
and the accompanying routes to do so, the use of a fitness monitor
to provide additional information in this regard, and a suggestion
for User A to install a calorie-tracking application on his mobile
computing device.
[0142] In various aspects, any suitable number and/or type of
loss-mitigation variables may be calculated and transmitted to the
user's computing device (e.g., client device 102) for presentation
to the user depending upon the specific type of insurance product,
the determined risk-based variables, their likelihood of
occurrence, etc. In any event, certain aspects may include the
machine-learning analytics model calculating these loss-mitigation
variables in a manner that reduces the likelihood of the risk-based
variables (e.g., one or more predicted medical-related conditions)
occurring. Likewise, these aspects may include calculating the
loss-mitigation variables such that the initial assessed level of
risk (which was calculated assuming that the likelihood of these
risk-based variables occurring will remain unchanged over a future
time horizon) is reduced. In other words, any suitable number of
loss-mitigation variables may be identified to sufficiently reduce
the probability of various medical-related conditions occurring
over a future time horizon.
[0143] These loss-mitigation variables may be calculated in
accordance with the machine-learning analytics model in various
ways. For instance, in some aspects, the machine-learning analytics
model may identify previous suggestions regarding intervening
actions, and identify whether these suggestions were followed by
the user and/or whether these suggestions were correlated to a
reduction in the identified conditions. In other aspects, the
machine-learning analytics model may look at data inputs that
identify the most successful types of suggestions from third-party
databases, psychological profiling data, demographic data
correlations, etc., in an attempt to present suggestions that will
be the most successful to modify behavior for users similar to, in
this example, User A.
[0144] To provide another illustrative example using User B as
identified above, assume that User B has a normal BMI of 21 and
healthy blood metrics, but that the machine-learning analytics
model identified, as a predicted medical-related condition, that
User B will likely suffer from a premature death (e.g., a 60%
chance of accidental death prior to age 45). Continuing this
example, loss-mitigation variable identification module 237 may
include instructions that, when executed by processing unit 222,
identify one or more intervening actions to reduce this likelihood
to less than 40%. Using the present example, these intervening
actions may include a suggestion for User B to take additional
safety or training courses regarding specific high risk activities.
To provide additional examples, these intervening actions may
include warning User B about the dangers involved in participating
in certain activities, or suggesting that User B have a wellness
examination to determine the health of her heart. Again, these
intervening actions may be transmitted to a computing device
associated with User B (e.g., client device 102) in the form of
notifications or suggestions, such that they may be presented to
User B.
[0145] Additionally or alternatively, certain aspects may include
loss-mitigation variable identification module 237 including
instructions that, when executed by processing unit 222, cause
machine-learning analytics engine 200 to monitor data and/or track
feedback regarding the actions of a user. Continuing the previous
examples, this may include, for instance, machine-learning
analytics engine 200 determining whether a user has actually
executed, or continued to execute, the previously identified
loss-mitigation variables. Continuing the previous example for User
A, assume that the loss-mitigation variables include suggestions
for User A to average 10,000 steps per day and to have a wellness
examination each year. In certain aspects, machine-learning
analytics engine 200 may monitor data provided from User A and/or
other sources to determine whether User A has actually followed
through with these actions.
[0146] For instance, machine-learning analytics engine 200 may
periodically ask User A for feedback, require that User A link
relevant fitness tracking device account information to his
insurance policy, or otherwise request access to this information
from User A. In this way, machine-learning analytics engine 200 may
monitor user activity to determine if the various intervening
actions are actually being executed by User A. Certain aspects may
include machine-learning analytics engine 200 confirming that User
A is performing the suggested activities in this way, and may send
updated notifications to a relevant computing device (e.g., client
device 102). However, if User A is not executing the actions
represented by the loss-mitigation variables, machine-learning
analytics engine 200 may send the computing device 102 warnings or
notifications that a failure to do so may impact current and future
insurance premiums, as further discussed below.
[0147] In any event, the machine-learning analytics engine 200 may
re-train the implemented machine-learning analytics model over time
as new data is acquired via the monitoring activities described
above. For instance, in the event that User A is not executing the
suggested actions described immediately above, the machine-learning
analytics model may use different inputs, different weights, and/or
different portions of the dynamic data set to determine new or
alternate actions that are more likely to be carried out by the
user.
[0148] Continuing the example above with User A, assume that User A
is going to annual wellness examinations but only averaging 7000
steps a day for the last 18 months instead of 10,000. In this case,
machine-learning analytics engine 200 may re-train the
machine-learning analytics model to identify other users similar in
age, activity level, and occupation to User A. From this set of
users, the machine-learning analytics model may then identify that
similar users increased their activity level when they joined a gym
that was close (e.g., within 0.25 miles) to their place of work.
Using this new information, the machine-learning analytics engine
200 may replace the previous loss-mitigation variable requiring the
user to accumulate 10,000 steps a day with a new one that requires
the user to attend a gym for 3 hours a week.
[0149] And, by continuing to monitor location data consensually
shared by User A, machine-learning analytics engine 200 may further
track User A's activity to determine whether User A is performing
this new intervening activity. In this way, aspects include
machine-learning analytics engine 200 continuously receiving
feedback regarding the calculated loss-mitigating variables.
Moreover, the machine-learning analytics engine 200 may calculate
new or alternate loss-mitigating variables as needed to
continuously ensure that the likelihood of the predicted risk-based
variables occurring is being reduced.
[0150] In other words, in the context of health and life insurance,
machine-learning analytics engine 200 may facilitate loss
mitigation for an insurer by acting as a virtual "health coach
assistant," In accordance with such aspects, the machine-learning
analytics engine 200 may identify an ongoing health action plan for
an insured user. This health action plan may include, for instance,
transmitting one or more loss-mitigation variables to the user's
computing device periodically. Again, these loss-mitigation
variables may include suggestions to improve an insured's health
over time, to prevent unnecessary premature deaths, and to ensure
that serious medical conditions either do not occur or are
diagnosed in their early stages.
[0151] In the present aspects, premium calculation module 239 may
be a portion of memory unit 226 configured to store instructions,
that when executed by processing unit 222, cause processing unit
222 to perform various acts in accordance with applicable aspects
as described herein. For example, instructions stored in premium
calculation module 239 may facilitate processing unit 222
performing functions associated with calculating a health or life
insurance premium based upon different levels of risk of insuring
users for a particular insurance policy. Again, these insurance
policy types may be, for instance, life and insurance policies with
various details identified by the user (e.g., deductible, term,
etc.).
[0152] This may include, for example, calculating premium pricing
as an output of the trained machine-learning analytics model and/or
in accordance with a correlation between premiums and corresponding
assessed risk levels. In any event, insurance premiums may be
calculated for a particular type of insurance policy in accordance
with the details associated with that insurance policy. Of course,
in the event that the machine-learning analytics model is used for
pricing, the machine-learning analytics model may also be trained
using pricing metrics associated with various insurance policies as
input, such that the pricing may be modified and become more
accurate over time.
[0153] In various aspects, this may include calculating different
insurance premiums associated with different levels of assessed
risk. For instance, one insurance premium may be calculated that is
associated with the initially calculated level of risk, i.e.,
assuming that the determined risk-based variables will occur with a
particular likelihood over a future time horizon. A separate,
lower, insurance premium may then be calculated in accordance with
a reduced level of risk, which assumes that the user will execute
the one or more intervening actions within the future time horizon
(or sooner, if applicable). In some aspects, each calculated
premium may then be transmitted to the user's computing device
(e.g., client device 102), and the user may be given an option to
purchase an insurance product in accordance with the lower premium
conditioned upon the user performing the one or more
loss-mitigating variables.
[0154] Furthermore, in the event that the user decides to purchase
the insurance product at the lower calculated premium (i.e.,
agreeing to perform the suggested actions), some aspects include
machine-learning analytics engine 200 monitoring the user's
activity monitoring data once an insurance policy is actually
issued (at this premium) to determine whether the user is
performing the suggested actions. Again, the user activity
monitoring data may include any suitable type of information
relevant to make this determination, such as tracking the user's
location, requesting feedback from the user, requesting proof of
medical examinations, periodically accessing medical records,
monitoring fitness tracker data, etc. Thus, any portion of the user
activity monitoring data may be stored as part of the dynamic data
set and/or the user data, as described herein, and accessed via the
machine-learning analytics engine 200 as needed.
[0155] As discussed above, the machine-learning analytics model may
also be re-trained based upon the collection of the user activity
monitoring data. In some aspects, as described above, this may
include the determination of new or alternative loss-mitigation
variables. Additionally or alternatively, aspects may include
re-training the machine-learning analytics model to determine the
likelihood that the user will continue to perform the one or more
intervening actions during the future time horizon.
[0156] To provide an illustrative example using User A, assume that
User A is provided with one premium for health insurance associated
with an initial level of assessed risk, i.e., assuming that there
is a 75% likelihood that User A's BMI will increase during the next
2 years by 20%. Further assume that a second health insurance
premium is calculated based upon the user performing various
actions that will decrease the likelihood of the user's BMI
increasing to less than 50%. It is then assumed that User A agrees
to perform the suggested activities and elects to purchase the
health insurance at the second, lower premium (as it reflects a
lower risk to the insurer). Then, the machine-learning analytics
engine 200 may continue to monitor the aforementioned user data to
determine not only that User A is performing the suggested acts,
but the likelihood that User A will continue to do so.
[0157] Continuing this example, assume that the user activity
monitoring data associated with User A for the first 2 weeks after
the health insurance policy issued indicates that User A is
consuming an average of only 60% the recommended daily caloric
intake. Furthermore, assume that the user activity monitoring data
indicates that User A has averaged 18,000 steps per day --well in
excess of the recommended 10,000. In one aspect, the
machine-learning analytics engine 200 may re-train the
machine-learning analytics model (or use a different
machine-learning analytics model) to calculate the likelihood of
the user continuing to perform the suggested actions during the
next two years (less the initial two weeks). In this example, the
machine-learning analytics model may identify one or more patterns
amongst users similar to User A (e.g., a similar age, gender,
occupation, interests, etc.) who have not performed similar
suggested activities. This may include, for instance, a pattern
indicating that most of these similar users shared a common trait
of excessively partaking in diet and exercise initially, but then
fail to maintain this activity over the course of the calculated
future time horizon.
[0158] Assuming that User A fits into this pattern given this
example of user activity monitoring data, then certain aspects may
include the machine-learning analytics model 200 identifying new
loss-mitigation variables, alternative loss-mitigation variables,
and/or updating the existing loss-mitigation variables, which may
then be transmitted to a suitable computing device associated with
User A (client device 102). Additionally or alternatively, certain
aspects may include the machine-learning analytics model 200
transmitting notifications to User A regarding updates or
adjustments to the loss-mitigation variables.
[0159] For instance, machine-learning analytics model 200 may
generate a message informing User A that users with similar habits
usually fail to maintain this activity in the long run, that it is
suggested for User A to reduce his daily activity and to increase
his caloric intake. In other words, certain aspects may include
attempting to further change User A's behavior to achieve the
overall goal of preventing weight gain.
[0160] It should be appreciated that in some embodiments, a machine
learning model may be constructed as discussed above to mitigate
and/or prevent loss with respect to worker's compensation,
disability, life, health, or other types of insurance. Similarly,
User A above may be, in some embodiments, a corporation or other
legal entity.
[0161] In various aspects, the machine-learning analytics engine
200 may continuously or periodically use the user activity
monitoring data to update the likelihood of the user continuing to
perform the suggested actions, and provide updated and/or new
suggestions as needed. In this way, machine-learning analytics
engine 200 may dynamically update the loss-mitigation variables
and/or generate new loss-mitigation variables to adapt to changes
in the user's behavior. In doing so, the machine-learning analytics
engine 200 helps to ensure that the user continues to perform the
suggested actions, thereby minimizing the insurer's loss for health
and life-related insurance claims.
[0162] In the present aspects, claim handling module 214 is a
portion of memory unit 226 configured to store instructions, that
when executed by processing unit 222, cause processing unit 222 to
perform various acts in accordance with applicable aspects as
described herein. For example, instructions stored in claim
handling module 241 may facilitate processing unit 222 performing
functions associated with implementing streamlined claims handling
processes and/or improving upon traditional claims handling via by
leveraging various aspects of machine-learning.
[0163] Exemplary Electronic Claim Record
[0164] FIG. 6 depicts text-based content of an exemplary electronic
claim record 600 that may be processed by a machine-learning
analytics engine in accordance with various aspects of the present
disclosure, such as machine-learning analytics engine 200 of FIG.
2, for example. 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.
[0165] Although text-based-content is depicted in the embodiment of
FIG. 6, as discussed above, data input and/or used as part of the
electronic claim file may include images, including hand-written
notes, and the AI platform (e.g., machine-learning analytics engine
200) may include a neural network trained to recognize hand-writing
and to convert hand-writing to text. Further, "text-based content"
may be formatted in any acceptable data format, including
structured query language (SQL) tables, flat files, hierarchical
data formats 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.
[0166] With respect to FIG. 6, electronic claim record 600 includes
two sections 610a-610b, which respectively represent policy
information and loss information (i.e., the cost of health-related
services rendered). Policy information 610a may include information
about the insurance policy under which the claim has been made,
including the person to whom the policy is issued, contact
information, the type of plan, deductibles, maximum payouts per
year, etc. Policy information 610a may be read, for example, by
machine-learning analytics engine 200 analyzing claim data and/or
individual claims.
[0167] Additional information about the insured (e.g., location, if
the issue was related to a pre-existing condition, historical claim
data, historical telematics data, family medical history, etc.) may
be obtained from various data sources to supplement the input data
included with the electronic claim record 600. For example,
additional data may be obtained from the dynamic data set and/or
the user data (e.g., insurance records/data, as shown in FIG. 3).
In some embodiments, in addition to policy information 610a, the
electronic claim record 600 may include loss information 610b. In
the context of health insurance, the loss information generally
corresponds to costs associated with a particular medical condition
or accident that necessitated some type of medical treatment for
which a claim amount is initially submitted. In the context of a
life insurance claim, the loss information 610b may correspond to
the total payout in accordance with the life insurance policy
benefit.
[0168] In any event, the loss information 610b may include the
total fees the date and time the services were rendered, whether
personal injury occurred, whether medical professionals made any
statements in connection with the loss, etc. For instance, the loss
information 610b may include (for health insurance, as shown in
FIG. 6) a medical diagnosis, services rendered, details associated
with the procedures required, the length of a hospital stay, etc.
For life insurance policy claims (not shown), the loss information
610b may include, for instance, additional details such as a time
and cause of death, whether an autopsy was performed, etc.
[0169] In addition to the loss information 610b, the electronic
claim record 600 may include additional information such as linked
data 620a-g. It should be appreciated that although only links
620a-g are shown in FIG. 6, more or fewer links may be included, in
some embodiments. For example, electronic claim record 600 may link
to notice of loss 620a, one or more photographs 620b, one or more
audio recordings 620c, one or more investigator's reports 620d, one
or more forensic reports 620e, one or more diagrams 620f, and/or
one or more payments 620g. Data in links 620a-620g may be ingested
by an AI platform, such as machine-learning analytics engine 200,
for example. Moreover, as described above, each insurance claim (or
various details associated with each claim) may be used as inputs
to a neural net as part of training a machine-learning analytics
model.
[0170] Instructions stored in claim handling module 241 may cause
processing unit 222 to retrieve, for each link 620a-620g, all
available data or a subset thereof. The data represented by each of
links 620a-620g may be included as part of the dynamic data set, as
part of the user data, and/or as part of any other suitable
additional data sources. Each of links 620a-620g may also be
processed, weighted, and/or analyzed according to the type of data
contained therein. For instance, machine-learning analytics engine
200 may analyze images included and/or associated with photograph
link 620b using any suitable type of image processing to recognize,
classify, and/or categorize images (e.g., endoscopic images,
ultrasound images, etc.) for use in a health or life insurance
claim. To provide another example, machine-learning analytics
engine 200 may analyze audio recordings (e.g., doctor's notes,
annotations, telephone calls, etc.) included and/or associated with
audio recording link 620c using a speech-to-text algorithm to
translate audio to text for use in a health or life insurance
claim.
[0171] In various aspects, a relevance order may be established for
each of the links 620a-620g, and processing of the data associated
with each respective link 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.
[0172] Once the various input data comprising electronic claim
record 600 have been processed, instructions stored in claim
handling module 241 may cause processing unit 222, in one aspect,
to execute a text-based analysis of that information, which is then
further utilized by the machine-learning analytics engine 200. For
example, if the machine-learning analytics model is being trained,
then the output of the text-based analysis may be passed to the
particular model as part of the training process. Using the
aforementioned neural network as an example, the neurons comprising
a first input layer of the neural network may be trained such that
each neuron receives particular input(s) that may correspond, in
one aspect, to one or more pieces of information from policy
information 610a and loss information 610b. Similarly, one or more
input neurons may be configured to receive particular input(s) from
links 620a-620g.
[0173] In various aspects, the data inputs provided by the
electronic claim record 600 and/or other information used to train
and apply the machine-learning analytics model may be useful to
make various predictions associated with insurance claims (e.g.,
life and health insurance claims). For example, the total cost of a
new claim may be predicted by applying the machine-learning
analytics model trained using historical electronic claim data from
the dynamic data set, of which electronic claim record 600 may be
one data point.
[0174] In other words, by correlating similar claims, users,
polices, diagnoses, etc., machine-learning analytics engine 200 may
predict, with a particular probability, the total payout on new
claims. Continuing this example, the trained model may be
configured so that inputting sample parameters, such as those in
the example electronic claim record 600, may accurately predict,
for example, the estimate of total costs ($12,214) and the settled
amount ($9,500). In this case, random weights may be chosen for all
input parameters. Certain aspects may include the machine-learning
analytics model being dynamically re-trained as additional
electronic claim data is collected, such that the predicted dollar
values and the correct dollar values converge.
[0175] Moreover, certain aspects may include the machine-learning
analytics engine 200 performing certain actions in response to
various predictions being made about the claims. To provide an
illustrative example, assume that a particular electronic claim
includes a set of information that correlates to other claims that
have a high rate of being flagged, rejected, and/or manually
reviewed before they are paid. In one aspect, the machine-learning
analytics engine 200 may automatically route, flag, or otherwise
manage the claim handling process to ensure that a particular claim
is expedited, taking into account the likelihood of the claim
requiring further processing.
[0176] In one aspect, the machine-learning analytics engine 200 may
also modify the information available within an electronic claim
record. For example, the machine-learning analytics engine 200 may
predict a series of labels (i.e., text) as described above that
pertain to a given claim. The labels may then be appropriately
weighted in accordance with their relevant, or contribution,
towards claim loss value. Next, the labels and corresponding
weights, in one embodiment, may be used in conjunction with base
rate information to predict a claim loss value. In any event, once
the claim loss value is computed, it may be associated with the
claim by, for example, writing the amount to the loss information
section of the electronic claim record (e.g., to the loss
information section 610b of FIG. 6).
[0177] Exemplary Computer-Implemented Methods
[0178] FIG. 7 illustrates an exemplary computer-implemented method
flow 700, in accordance with certain aspects of the present
disclosure. In the present aspects, one or more portions of method
700 (or the entire method 700) may be implemented by any suitable
device, and one or more portions of method 700 may be performed by
more than one suitable device in combination with one another. For
example, one or more portions of method 700 may be performed by
machine-learning analytics engine 200, as shown in FIG. 2. In one
aspect, method 700 may be performed by any suitable combination of
one or more processors, instructions, applications, programs,
algorithms, routines, etc. In one embodiment, method 700 may be
performed via processing unit 222 executing instructions stored in
memory unit 226, as shown in FIG. 2, in conjunction with data
collected, received, and/or generated via one or more health
institutions (e.g., one or more health institutions 150), one or
more back-end computing devices (e.g., one or more back-end
computing devices 120), and/or one or more client devices 102
(e.g., client device 102).
[0179] Method 700 may start when one or more processors access
(block 702) a dynamic data set. This dynamic data set may include,
for instance, the dynamic data set shown and discussed herein with
reference to FIG. 3, which may include electronic medical records,
demographic information, insurance records, lifestyle information,
family medical history information, etc. Again, the information
accessed from the dynamic data set may include any suitable type of
information that is relevant to determine the level or risk
associated with insuring a user for a particular type of insurance
product.
[0180] Method 700 may include one or more processors training
(block 704) a machine-learning analytics model. This may include,
for example, training any suitable number and type of
machine-learning analytics models based upon the specific type of
insurance product that is sought. For example, the machine-learning
analytics model may include neural networks (e.g., as shown in
FIGS. 4-5) that are trained in accordance with specific inputs
accessed via the dynamic data set (block 704), Training the
machine-learning analytics model may include defining the sample
inputs, the importance (e.g., weighting) of the various inputs, and
defining the outputs that are determined using the weighted inputs
(block 704).
[0181] Method 700 may include one or more processors receiving
(block 706) user data. This may include, for example, receiving
data entered by a user in response to various prompts (e.g., via
client device 102). In various aspects, the user data may be
received from one or more sources, and may include data extracted
from the dynamic data set that is correlated to a particular user
once data is received from that user identifying him or her (block
706). For instance, the user data may represent any suitable number
and/or type of information that is useful or otherwise relevant to
calculate health and/or life insurance policies for one or more
users (block 706).
[0182] Method 700 may include one or more processors applying
(block 708) the trained machine-learning analytics model to the
user data to predict one or more medical-related conditions. This
may include, for instance, identifying similar users within the
dynamic data set compared to the user represented by the user data,
identifying correlations among the user data and/or among the
information contained within the dynamic data set, etc., to
formulate various predictions regarding the likelihood of various
medical-related conditions occurring within some future time period
(block 708).
[0183] Method 700 may include one or more processors determining
(block 710) an initial level or risk associated with a risk of
insuring the user. This may include, for instance, assigning a
weight or importance to each predicted risk-based variable (e.g.,
medical-related condition) output by the trained machine-learning
analytics model. This may additionally include, for instance,
calculating an overall risk level associated with insuring a user
for a specific type of insurance policy (e.g., a health of life
insurance policy), assuming that the predicted risk-based variables
will occur over a particular future time horizon in accordance with
a specific probability.
[0184] Method 700 may include one or more processors identifying
(block 712) one or more loss-mitigating variables (or
loss-prevention variables) that reduce the initial determined
(block 710) level of risk. This may include, for instance, the
identification of various actions that, when performed by a user,
may reduce the likelihood of the various risk-based variables
occurring within a future time period. Again, these actions may be
determined, for example, in accordance with a re-trained or
alternate machine-learning analytics model (compared to the model
that was applied to determine the risk-based variables) to
determine based upon a correlation to other similar actions and/or
other similar users such that actions that have been
previously-known to work for a particular user are selected.
[0185] Method 700 may include one or more processors calculating
(block 714) an insurance premium corresponding to the initially
determined (block 710) level of risk and/or a reduced level of risk
that is associated with an assumption that the user will perform
the one or more identified (block 712) actions. This may include,
for example, executing another trained machine-learning analytics
model to calculate pricing and/or correlating each calculated level
of risk to an insurance premium, as discussed herein.
[0186] Method 700 may include one or more processors transmitting
(block 716) an insurance premium corresponding to the initially
determined (block 710) level of risk and/or a reduced level of risk
that is associated with an assumption that the user will perform
the one or more identified (block 712) actions. Additionally or
alternatively, this may include transmitting the one or more
loss-mitigating variables (e.g., one or more actions to be taken by
the user). In any event, the calculated insurance premiums and/or
one or more loss-mitigating variables, upon being transmitted, may
be received by a suitable computing device (e.g., client device
102), and presented to a user on a suitable display (e.g., display
110). The method may include additional, less, or alternate
functionality or actions, including those discussed elsewhere
herein.
[0187] Technical Advantages
[0188] The aspects described herein may be implemented as part of
one or more computer components such as a client device and/or one
or more back-end components, such as one or more machine-learning
analytics engines 120.1 and/or machine-learning analytics engine
200, for example. Furthermore, the aspects described herein may be
implemented as part of a computer network architecture that
facilitates communications between various other devices and/or
components. Thus, the aspects described herein address and solve
issues of a technical nature that are necessarily rooted in
computer technology.
[0189] For instance, aspects include analyzing various sources of
data to train a machine-learning analytics model and to execute the
trained model to make various predictions. In doing so, the aspects
overcome issues associated with the inconvenience of manual and/or
unnecessary monitoring of such data. Moreover, because of the
nature of machine-learning systems, juxtapositions of data, and/or
correlations of information may be made, which would not be
possible within the confines of traditional insurance underwriting.
Furthermore, because the machine-learning analytics model may be
re-trained as additional information is added to the dynamic data
set, the accuracy and efficiency of the system is improved over
time given the inherent nature of machine learning systems. Without
the improvements suggested herein, additional time, processing
resources, and memory usage would be required to achieve these
results and, in some instances, the results would be otherwise
unachievable.
[0190] Furthermore, the machine-learning techniques described
herein improve upon existing technologies by more accurately
forecasting and mitigating conditions representative of future risk
to an insurer, and allow for large data sets to be monitored from a
larger number of sources than would otherwise be feasible or
practical. Due to these improvements, the aspects address
computer-related issues regarding efficiency over the traditional
amount of processing power and models used to assess risk and/or
price insurance in a manner that accurately reflects insurer risk
and mitigates the loss borne by an insurer in the event that a
claim is made. Still further, the improvements discussed herein
leverage machine-learning techniques to streamline the electronic
claim process by accessing a history of claims. These improvements
further increase the speed in which an insurer may process
insurance claim data, as well as increasing the overall insurance
claim process as compared to traditional claims handling.
[0191] Thus, the aspects may also improve upon computer technology
by requiring fewer calculations due to the increased efficiency
provided, for example, via the combination of processes, steps,
elements, and/or components described herein. In other words, the
specific combination of elements and/or components working in
conjunction with one another (e.g., via networked communications)
and of itself represent a significant improvement to the overall
technology involved.
[0192] Exemplary Computer-Implemented Method for Implementing
MACHINE LEARNING TO CALCULATE AND MITIGATE INSURER RISK
[0193] In one aspect, a computer-implemented method for
implementing a machine-learning analytics model to calculate a
level of risk of insuring a user, and/or how to reduce this risk
may be provided. The method may include one or more processors
and/or associated transceivers (1) accessing a dynamic data set
associated with one or more users including electronic medical
records, demographic information, insurance records, and/or
lifestyle information; (2) training a machine-learning analytics
model (or other artificial intelligence model) using the dynamic
data set as training data to generate a trained machine-learning
analytics model; (3) receiving user data associated with a user;
(4) applying the trained machine-learning analytics model to the
user data to predict one or more medical-related conditions
associated with the user based upon the user data; (5) determining,
in accordance with the trained machine-learning analytics model, a
first level of risk associated with insuring the user based upon
the one or more predicted medical-related conditions; (6)
identifying, in accordance with the trained machine-learning
analytics model, one or more intervening actions that, when
executed by the user within a future time period, reduce the first
level of risk associated with insuring the user to a second level
of risk; and/or (7) transmitting the one or more intervening
actions to a computing device to be presented to the user. The
method may include additional, less, or alternate actions,
including those discussed elsewhere herein.
[0194] For instance, in various aspects, the first and second
levels of risk associated with insuring the user may represent
insuring the user for a health insurance or a life insurance
policy. Moreover, the machine-learning analytics model may include
a neural net, such that training the machine-learning analytics
model includes training a neural net.
[0195] In some aspects, premiums may be calculated for each of the
first and the second level of risk, and these premium calculations
may additionally be transmitted to the computing device for
presentation to the user. In some instances, the first and second
levels of risk, and their respective calculated premiums, may be
associated with a risk of insuring a user for a health or life
insurance product. In such aspects, upon insuring the user for the
health or the life insurance policy in accordance with the
calculated health or life insurance premium, the method may
additionally include accessing the dynamic data set to collect user
activity monitoring data and/or applying the trained
machine-learning analytics model to the user activity monitoring
data to determine a likelihood of whether the user will continue to
execute the one or more intervening actions during the future time
period. Still further, when the insurance product is a life or
health insurance product, the future time period may correspond to
a period of insurance coverage for a health or a life insurance
policy.
[0196] Additionally, certain aspects may include the intervening
actions including various suggestions that, when executed by the
user, may reduce the initial level of risk borne by the insurer.
These intervening actions may include, for instance, suggestions
regarding a type and/or frequency of exercise, daily nutrition,
lifestyle habits, etc.
[0197] Exemplary Computing Device for Implementing Machine Learning
TO CALCULATE AND MITIGATE INSURER RISK
[0198] In another aspect, a computing device for implementing a
machine-learning analytics model to calculate a level of risk of
insuring a user, and/or how to reduce this risk may be provided.
The computing device may include a communication unit configured to
access a dynamic data set associated with one or more users
including electronic medical records, demographic information,
insurance records, and lifestyle information, and to receive user
data associated with a user. Additionally, the computing device may
include a processing unit that is configured to (1) train a
machine-learning analytics model using the dynamic data set as
training data to generate a trained machine-learning analytics
model; (2) apply the trained machine-learning analytics model to
the user data to predict a set of one or more medical-related
conditions associated with the user; (3) determine a first level of
risk associated with insuring the user based upon the one or more
predicted medical-related conditions in accordance with the trained
machine-learning analytics model; and/or (4) identify one or more
intervening actions in accordance with the trained machine-learning
analytics model that, when executed by the user within a future
time period, reduce the first level of risk associated with
insuring the user to a second level of risk. Moreover, the
communication unit may be further configured to transmit the one or
more intervening actions to a computing device to be presented to
the user. The computing device may include additional, less, or
alternate components, including those discussed elsewhere
herein.
[0199] For instance, in various aspects, the first and second
levels of risk associated with insuring the user may represent
insuring the user for a health insurance or a life insurance
policy. Moreover, the machine-learning analytics model may include
a neural net, such that training the machine-learning analytics
model includes training a neural net.
[0200] In some aspects, the processing unit may be configured to
calculate the premiums for each of the first and the second level
of risk, and the communication unit may be further configured to
transmit these premium calculations to the computing device for
presentation to the user. In some instances, the first and second
levels of risk, and their respective calculated premiums, may be
associated with a risk of insuring a user for a health or life
insurance product. In such aspects, upon insuring the user for the
health or the life insurance policy in accordance with the
calculated health or life insurance premium, the processing unit
may be further configured to access the dynamic data set to collect
user activity monitoring data and/or apply the trained
machine-learning analytics model to the user activity monitoring
data to determine a likelihood of whether the user will continue to
execute the one or more intervening actions during the future time
period. Still further, when the insurance product is a life or
health insurance product, the future time period may correspond to
a period of insurance coverage for a health or a life insurance
policy.
[0201] Additionally, in some aspects, the intervening actions
including various suggestions that, when executed by the user, may
reduce the initial level of risk borne by the insurer. These
intervening actions may include, for instance, suggestions
regarding a type and/or frequency of exercise, daily nutrition,
lifestyle habits, etc.
[0202] Exemplary Computer-Readable Media for Implementing Machine
LEARNING TO CALCULATE AND MITIGATE INSURER RISK
[0203] In yet another aspect, a non-transitory computer readable
media may be provided to calculate a level of risk of insuring a
user and/or how to reduce this risk. The instructions stored on the
non-transitory computer readable may, when executed by one or more
processors, cause the one or more processors to: (1) access a
dynamic data set associated with one or more users including
electronic medical records, demographic information, insurance
records, and/or lifestyle information; (2) train a machine-learning
analytics model using the dynamic data set as training data to
generate a trained machine-learning analytics model; (3) receive
user data associated with a user; (4) apply the trained
machine-learning analytics model to the user data to predict one or
more medical-related conditions associated with the user based upon
the user data; (5) determine, in accordance with the trained
machine-learning analytics model, a first level of risk associated
with insuring the user based upon the one or more predicted
medical-related conditions; (6) identify, in accordance with the
trained machine-learning analytics model, one or more intervening
actions that, when executed by the user within a future time
period, reduce the first level of risk associated with insuring the
user to a second level of risk; and/or (7) transmit the one or more
intervening actions to a computing device to be presented to the
user. The non-transitory computer readable media device may include
additional, less, or alternate instructions stored thereon,
including those discussed elsewhere herein.
[0204] For instance, in various aspects, the instructions may cause
the one or more processors to calculate the first and second levels
of risk as those associated with a risk of insuring the user for
health insurance or life insurance. Moreover, the machine-learning
analytics model may include a neural net, and the instructions may
cause the one or more processors to train the machine-learning
analytics model by training a neural net.
[0205] In some aspects, the instructions may cause the one or more
processors to calculate the premiums for each of the first and the
second level of risk, and these premium calculations may
additionally be transmitted to the computing device for
presentation to the user. In some instances, the first and second
levels of risk, and their respective calculated premiums, may be
associated with a risk of insuring a user for a health or life
insurance product. In such aspects, upon insuring the user for the
health or the life insurance policy in accordance with the
calculated health or life insurance premium, the instructions may
cause the one or more processors to access the dynamic data set to
collect user activity monitoring data and/or apply the trained
machine-learning analytics model to the user activity monitoring
data to determine a likelihood of whether the user will continue to
execute the one or more intervening actions during the future time
period. Still further, when the insurance product is a life or
health insurance product, the future time period may correspond to
a period of insurance coverage for a health or a life insurance
policy.
[0206] Furthermore, certain aspects may include the instructions
causing the one or more processors to determine the intervening
actions including various suggestions that, when executed by the
user, may reduce the initial level of risk borne by the insurer.
These intervening actions may include, for instance, suggestions
regarding a type and/or frequency of exercise, daily nutrition,
lifestyle habits, etc.
[0207] Additional Considerations
[0208] As discussed herein, data may be collected from various
sourced to generate, update, and/or modify a dynamic data set that
is used to train and apply a machine-learning analytics model. As
described herein, the collection of data may be performed after the
user provides their affirmative consent or permission, in some
aspects. Furthermore, there are several references herein to
re-training the machine-learning analytics models to perform
different calculations, make different types of predictions,
utilize different types of information as inputs, use different
weightings, etc. In such cases, it will be understood that, as an
alternative to re-training a machine-learning analytics model, more
than one machine-learning analytics model may be implemented, each
with a particular function and/or structure. These machine-learning
analytics models may be of the same type or different types
depending on the available information and/or the particular
objective that is sought to be achieved by each one.
[0209] Moreover, in many instances throughout the present
disclosure the example insurance types are life or health insurance
policies. However, the aspects described herein are applicable to
any suitable type of insurance policy that may implement risk
assessment as part of the pricing structure.
[0210] This detailed description is to be construed as exemplary
only and does not describe every possible embodiment, as describing
every possible embodiment would be impractical, if not impossible.
One may be implement numerous alternate embodiments, using either
current technology or technology developed after the filing date of
this application.
[0211] Furthermore, although the present disclosure sets forth a
detailed description of numerous different embodiments, it should
be understood that the legal scope of the description is defined by
the words of the claims set forth at the end of this patent and
equivalents. The detailed description is to be construed as
exemplary only and does not describe every possible embodiment
since describing every possible embodiment would be impractical.
Numerous alternative embodiments may be implemented, using either
current technology or technology developed after the filing date of
this patent, which would still fall within the scope of the claims.
Although the following text sets forth a detailed description of
numerous different embodiments, it should be understood that the
legal scope of the description is defined by the words of the
claims set forth at the end of this patent and equivalents.
[0212] The detailed description is to be construed as exemplary
only and does not describe every possible embodiment since
describing every possible embodiment would be impractical. Numerous
alternative embodiments may be implemented, using either current
technology or technology developed after the filing date of this
patent, which would still fall within the scope of the claims.
[0213] The following additional considerations apply to the
foregoing discussion. 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.
[0214] 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 in a transmission signal)
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 exemplary 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.
[0215] 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.
[0216] 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.
[0217] Hardware modules may 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 device to which it is
communicatively coupled. A further hardware module may then, at a
later time, access the memory device to retrieve and process the
stored output. Hardware modules may also initiate communications
with input or output devices, and may operate on a resource (e.g.,
a collection of information).
[0218] 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.
[0219] 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 home environment, an office environment or
as a server farm), while in other embodiments the processors may be
distributed across a number of locations.
[0220] 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 exemplary embodiments, the one or more processors or
processor-implemented modules may be located in a single geographic
location (e.g., within a vehicle, within a home 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.
[0221] 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.
[0222] 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.
[0223] 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.
[0224] 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 13 are true (or present).
[0225] 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.
[0226] 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).
[0227] The various systems and methods described herein are
directed to an improvement to computer functionality, and improve
the functioning of conventional computers, as described, for
example, in the "Technical Advantages" Section and elsewhere
herein.
* * * * *