Note: Descriptions are shown in the official language in which they were submitted.
SYSTEM FOR BUILDING-SPECIFIC MULTI-PERIL RISK
ASSESSMENT AND MITIGATION
BACKGROUND
Field
[0001] The disclosed subject matter relates to the field of risk
assessment. More
particularly, but not exclusively, the subject matter relates to asset-
specific multi-
peril risk assessment and mitigation.
Discussion of related field
[0002] Natural hazard risk analysis in real asset portfolios is a field
that is
currently dominated by the insurance industry who utilizes a class of
analytical
models called "catastrophe models" or CAT models for the determination of
portfolio losses and associated costs for offering coverage against natural
perils. In
general, all CAT models comprise of four different modules: hazard, exposure,
risk
and financial. The hazard module makes scientific predictions of intensity
measures
associated with the peril of interest given relevant geological and
meteorological
inputs; the exposure module contains the value and spatial distribution of
assets in
the region of interest; the risk module performs the calculation of loss given
hazard
and exposure using vulnerability functions; the financial module computes the
financial impact of the risk given insurance policy and coverage details. CAT
models are primarily oriented towards insurance and reinsurance users and the
methodology for evaluating natural hazard risk is suitable for insurance
portfolios
typically containing thousands to hundreds of thousands of spatially
distributed
assets. While the combination of hazard, exposure and risk modules can be
adopted
to perform loss analysis on an asset portfolio of arbitrary size, due to the
requirement for CAT models to handle large portfolios efficiently, the
evaluation
of loss at the individual asset level is extremely crude and has low accuracy
and
reliability when extrapolated to the individual asset level. This is not an
issue for
most insurers as high-resolution risk output at the asset level is not very
meaningful
compared to the aggregated risk metrics derived from the entire exposed
portfolio.
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However, the same cannot be said for owners of individual assets or
portfolios, who
may require high-resolution and reliable data to inform them on means of risk
management that go beyond property and casualty insurance.
[0003] High resolution risk analysis stems from the advancement in
performance-based engineering concepts first developed by earthquake engineers
in the early 2000's. The basic idea of performance-based earthquake
engineering
(PBEE) is to develop a set of theoretical tools that enables engineers to
derive
results that are directly relevant to the decision-making of owners of the
assets
exposed to seismic hazard. A direct consequence of this is that deliverables
of
performance-based engineering analysis can provide quantitative decision-
metrics
such as financial impacts, downtime and casualty, rather than technical
results that
address code adequacy, which has been the primary objective for engineers
prior to
PBEE. To do this, a probabilistic framework that integrates hazard and physics-
based damage and loss assessment of structures to such hazards has been
developed
and codified through various design standards.
[0004] By integrating methodologies employed in PBEE and in CAT models,
such concepts can be expanded to cover portfolio level analysis involving
multiple
spatially distributed assets for detailed risk, and mitigation analysis that
provides
accurate quantitative performance and cost-benefit metrics that are directly
relevant
for the management of risk in an asset-specific context. This has been
formulated
and implemented by Kinetica Risk in the past for earthquake risk.
[0005] However, there is presently no platform for accounting for
multiple
perils that can impact building portfolios, in a statistically consistent
meaningful
manner. In other words, owners of both public and private real asset
portfolios lack
a unified tool that enables them to examine multiple natural hazard risk
through
their own business lens, and make informed financial, operational and risk
management decisions for their assets, which is crucial for navigating the
physical
challenges that a changing climate, and increasingly urbanizing environment
poses.
[0006] In light of the above, it is apparent that there is a need for a
methodology
and implementation of a multi-hazard platform for evaluating risk and
resilience of
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real asset portfolios in a time and cost-efficient manner.
SUMMARY
[0007] In one embodiment a system for building-specific multi-peril
risk
assessment and mitigation is disclosed. The system comprises a multi-peril
hazard
module, a processor, a database and a probabilistic graphical network module.
The
multi-peril hazard module is configured to obtain hazard information related
to one
or more buildings (a portfolio of buildings) for multiple perils, and create a
multi-
peril event catalogue for the multiple perils, wherein the multi-peril event
catalogue
is configured to generate intensity measures for each of the multiple perils
on a
event-by-event basis. The processor is configured to select a set of peril
vulnerability functions from a pre-generated database of vulnerability
functions for
each of the multiple perils based on representative archetypes of the
buildings in
the portfolio, wherein each peril vulnerability function relates to a
consequence of
interest corresponding to a building archetype. Further, the processor is
configured
to determine building-specific risk assessment results for each building based
on
the hazard information and the peril vulnerability functions by simulating
events in
the catalogue. The pre-generated vulnerability database stores the derived
plurality
of peril vulnerability functions for the multiple perils corresponding to each
of the
archetypes of buildings, which can be generated using performance-based
engineering analysis. The probabilistic graphical network module is configured
to
create probabilistic networks, wherein the probabilistic networks are
connected to
the determined multi-peril building-specific risk assessment results to model
impacts to processes that have strong interdependencies on different
consequences
in a single building, or between different consequences across multiple
buildings in
the portfolio, on an event-by-event basis.
BRIEF DESCRIPTION OF DRAWINGS
[0008] Embodiments are illustrated by way of example and not limitation
in the
figures of the accompanying drawings, in which like references indicate
similar
elements and in which:
[0009] FIG. 1 illustrates a conventional catastrophe (CAT) model 100,
in
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accordance with an embodiment.
[0010] FIG. 2 illustrates a system 200 for building-specific multi-
peril risk
assessment and mitigation, in accordance with an embodiment.
[0011] FIG. 3 illustrates compilation of multi-peril hazard data using
the multi-
peril hazard module 204, in accordance with an embodiment.
[0012] FIG. 4 is a flowchart 400 of the process of derivation of a
peril
vulnerability function for a building archetype, in accordance with an
embodiment.
[0013] FIG. 5 illustrates the database 206 comprising multi-peril
vulnerability
functions for each archetype of the building, in accordance with an
embodiment.
[0014] FIG. 6 illustrates a probabilistic network 600 created by the
graphical
network module 208, in accordance with an embodiment.
[0015] FIG. 7 illustrates a system 700 for building-specific multi-
peril risk
assessment and mitigation, in accordance with an embodiment.
DETAILED DESCRIPTION
[0016] The following detailed description includes references to the
accompanying drawings, which form a part of the detailed description. The
drawings show illustrations in accordance with example embodiments. These
example embodiments, which may be herein also referred to as "examples" are
described in enough detail to enable those skilled in the art to practice the
present
subject matter. However, it may be apparent to one with ordinary skill in the
art,
that the present invention may be practised without these specific details. In
other
instances, well-known methods, procedures and components have not been
described in detail so as not to unnecessarily obscure aspects of the
embodiments.
The embodiments can be combined, other embodiments can be utilized, or
structural, logical, and design changes can be made without departing from the
scope of the claims. The following detailed description is, therefore, not to
be taken
in a limiting sense, and the scope is defined by the appended claims and their
equivalents.
[0017] In this document, the terms "a" or "an" are used, as is common
in patent
documents, to include one or more than one. In this document, the term "or" is
used
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to refer to a nonexclusive "or," such that "A or B" includes "A but not B," "B
but
not A," and "A and B," unless otherwise indicated.
[0018] FIG. 1 illustrates a conventional catastrophe (CAT) model
process, in
accordance with an embodiment. Most CAT models have three modules that
supports the calculation of ground up losses: namely, a hazard module 102, an
exposure module 104 and a vulnerability module 106. Typically, the exposure
module 104 describes the portfolio of structures by their specific location,
building
attributes such as year built, type of construction, number of storeys, other
optional
modifiers such as whether the building meets code requirements associated with
the
peril of interest. In an assessment, a hazard intensity measure (IM) is
generated
either by a numerical model of the physical process giving rise to the peril
of interest
or based on historical data (hazard module 102), and the IM is used to
calculate the
likelihood of each building experiencing a certain level of loss
(vulnerability
module 106). This process is repeatedly performed, over many realizations and
many number of years until the desirable return period is achieved. For
instance, a
model may generate 10,000 one-year simulations of a hazard. For each one-year
simulation, there could be no events, a single event or multiple events with
different
losses. After 10,000 of such one-year simulations, the largest loss realized
would
correspond to a 1 in 10,000 year loss. Other types of risk metrics such as the
average
annualized loss and the tail value at risk can also be computed from the event
results.
[0019] A major shortcoming of CAT models is that the individual asset
vulnerabilities are inherently simple in order to facilitate large portfolio
sizes. These
vulnerabilities are typically described by a single loss metric (usually the
loss ratio
corresponding to the ratio of replacement value) which are functions of a set
of
general attributes, such as building type and year of construction. Hence, CAT
models are usually unable to identify loss drivers, or resolve any details of
the loss
at the asset specific level to support risk mitigation and financial decisions
relating
to maintenance and capital investment. Furthermore, the asset-specific results
from
CAT models are also highly uncertain as they are intended to describe a much
larger
Date Recue/Date Received 2021-10-01
categories of assets corresponding to the general attributes associated with
them.
[0020] More detailed loss assessments for earthquake risk is well
documented
through the PBEE standards such as FEMA P-58. This framework has also been
extended to cover cost-benefit assessments and portfolio level analysis
through an
existing patent application. Recently, analogous procedures are developed
sporadically in the literature for other type natural hazards, utilizing
engineering
methods suitable for capturing damage and losses from these hazards [flood,
ARA
wind]. However, a unified methodological framework is not formed that allows
the
integration of multiple natural hazards in risk assessment.
[0021] FIG. 2 illustrates a system 200 for building-specific multi-
peril risk
assessment and mitigation, in accordance with an embodiment. The system may
comprise a processor 202, a multi-peril hazard module 204, a database 206 and
a
probabilistic graphical network module 208. The system 200 may facilitate high-
resolution risk analysis that provides portfolio-specific performance metrics
for
most portfolios without the need for detailed engineering analysis, which will
be
reserved for highly unique building portfolios.
[0022] The processor 202 may be implemented in the form of one or more
processors 202 and may be implemented as appropriate in hardware, computer-
executable instructions, firmware, or combinations thereof. Computer-
executable
instruction or firmware implementations of the processor 202 may include
computer-executable or machine-executable instructions written in any suitable
programming language to perform the various functions described.
[0023] The database 206 may include a permanent memory such as hard
disk
drive, may be configured to store data, and executable program instructions
that are
implemented by the processor 202. The database 206 may be implemented in the
form of a primary and a secondary memory. The database 206 may store
additional
data and program instructions that are loadable and executable on the
processor 202
module 204, as well as data generated during the execution of these programs.
Further, the database 206 may be volatile memory, such as random-access memory
and/or a disk drive, or non-volatile memory. The database 206 may comprise of
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removable memory such as a Compact Flash card, Memory Stick, Smart Media,
Multimedia Card, Secure Digital memory, or any other memory storage that
exists
currently or may exist in the future.
[0024] FIG. 3 illustrates compilation of multi-peril hazard data using
the multi-
peril hazard module 204, in accordance with an embodiment. The multi-peril
hazard
module 204 may be configured to obtain hazard information related to at least
a
building for multiple perils and create a multi-peril event catalogue for the
multiple
perils, wherein the events catalogue is configured to generate intensity
measures for
each of the multiple perils.
[0025] In one embodiment, the multi-peril hazard module 204 may obtain
probabilistic hazard data across multiple natural perils. At step 302, the
asset/building location information of the target portfolio is provided to the
multi-
peril hazard module 204.
[0026] In one embodiment, the multi-peril hazard module 204 may obtain
the
hazard information based on the received location information.
[0027] In one embodiment, the multi-peril hazard module 204 may be
configured to obtain hazard information using one of the many commercial
hosting
platforms (e.g., NASDAQ risk modelling, QOMPLEX, EMEA). These platforms
offer a unifying interface to access both hazard and risk models developed by
traditional catastrophe model vendors. While the form of information may
differ
from one peril to another, and from one CAT model vendor to another, the
hazard
information can typically be extracted either through subscription to hazard
maps,
stochastic event catalogues, or to event-based risk models which can be
converted
to hazard information.
[0028] At step 304, the multi-peril hazard module 204 determines
whether
spatial correlation between assets/buildings is important for a gives hazard.
If the
spatial correlation between assets is not important, then at step 306, hazard
maps at
different intensities (specified by its return period) may be used to extract
the
required intensity measures by sampling hazard curve at each location
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independently at step 312.
[0029] If the spatial correlation between assets is important, then at
step 308 the
information pertaining to perils where by-event loss can be generated from
user-
defined invertible vulnerabilities or at step 310 the information pertaining
to perils
where stochastic event catalogue is available is used.
[0030] At step 314, the intensity measures may be extracted by applying
inverse
vulnerability function to the by-event risk information obtained from the
platform.
[0031] At step 316, the intensity measures may be extracted from the
stochastic
events catalogue.
[0032] At step 318, the multi-peril hazard module 204 may be configured
to
create a multi-peril event catalogue configured to generate intensity measures
for
multiple perils.
[0033] In one embodiment, the multi-peril hazard module 204 may
generate the
intensity measures for different perils on a by-event basis.
[0034] In one embodiment, the multi-peril hazard module 204 may
generate the
intensity measures for different perils on a by-peril basis.
[0035] Thus, the multi-peril hazard module 204 may create the multi-
peril event
catalogue that describes the relevant intensity measures for each event, for
each
type of peril considered at each location of interest for multi-peril risk
assessment.
[0036] FIG. 4 is a flowchart 400 of the process of derivation of a
peril
vulnerability function for a building archetype, in accordance with an
embodiment.
This process is peril-dependent and is based on engineering analysis of
individual
building archetypes.
[0037] At step 402, a full range of intensity measures corresponding to
a peril
of interest are defined for an archetype. The peril of interest may be wind,
flood,
earth quake so on and so forth. As an example, the intensity measure for flood
may
be water depth and intensity measure for wind may be wind speed.
[0038] At step 404, engineering demand parameters of the building
archetype
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based on the defined range of intensity measures are generated. This may be
done
by performing engineering analysis to quantify the engineering demand
parameters
which are the building response quantities that lead to component damage.
[0039] Based on this, load cases which represent physical manifestation
of the
hazard for each of the respective peril can be defined using engineering
standards
relating to the hazards of interest. Some hazards, such as fluvial flood, may
not
require detailed structural analysis as the building component damage is
directly
correlated to the water depth and content elevation. Other hazards, such as
tornado,
hurricanes and tsunami, may involve dynamic analysis of the building using
finite
element models and computational fluid dynamic models to determine the
engineering demand parameters, which are response quantities that are related
to
the damage of different building components. This process will result in a
probabilistic distribution of engineering demand parameters due to inherent
uncertainties in the hazard input for a given intensity.
[0040] At step 406, damage analysis on each component of the building
archetype based on the determined engineering demand parameters can be
performed.
[0041] In one embodiment, the processor 202 may process the engineering
demand parameters data to derive smooth joint engineering demand parameters
distributions using principal component analysis. The smooth joint
distributions
may then be used by the processor 202 to generate individual realizations of
engineering demand parameters directly correlated to the component damage in
the
building using peril-specific fragility and consequence functions. This
process
results in a high-resolution building-specific loss evaluation applicable to
multiple
different natural perils.
[0042] In one embodiment, the processor 202 may perform damage analysis
through Monte Carlo sampling using engineering demand parameters on each
individual component in the building of interest using the component-specific
fragility.
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[0043] At step 408, the processor 202 may determine the consequences of
interest and aggregate all the loss across all the components to arrive at a
final loss
distribution for a given peril and a given hazard intensity.
[0044] The consequences of interest may be downtime, casualty and other
functional processes that may be impacted by component damage.
[0045] At step 410, the processor 202 may derive a peril vulnerability
function
of each of the consequences of interest corresponding to the peril of
interest.
[0046] In one embodiment, the processor 202 may repeat steps 406 to 410
for
multiple hazard intensities to derive the peril-specific vulnerability
functions (loss
versus intensity input) for a single building archetype.
[0047] FIG. 5 is an elaborated process for generating peril-specific
vulnerability functions for each building archetype that are stored in
database 206,
in accordance with an embodiment. Engineering demand parameters generated
from dynamic simulations and peril-specific component fragility and
consequence
databases may be used by processor 202 to perform damage and risk simulations
of
to the building structure, building roof and exterior closure, in order to
define the
peril-specific vulnerabilities for each building archetype.
[0048] In one embodiment, the dynamic analysis 504 may be performed
using
finite element analysis, computational fluid dynamic simulation or the like.
The
processor 202 may use the engineering demand parameters 506 based on the
dynamic analysis in conjunction with component fragility and consequences to
derive peril-specific vulnerabilities.
[0049] In one embodiment, the peril-specific vulnerabilities in
database 206
may be generated by processor 202 using a component fragility database 510
comprising fragility functions and a consequence database 508 comprising
consequence functions.
[0050] The processor 202 may determine multi-peril component based
damage
and impact 512 based on the engineering demand parameters, consequence
functions and peril-specific fragility information. The processor 202 may
determine
Date Recue/Date Received 2021-10-01
the consequences of interest for the peril of interest and may derive a peril
vulnerability functions 514 for each of the consequences of interest for the
peril of
interest.
[0051] The database 206 may store the multi-peril vulnerability
functions for
multiple perils corresponding to a building archetype. Similarly, the database
206
may store the multi-peril vulnerability functions 516 for multiple building
archetypes.
[0052] The processor 202 may be configured to determine building-
specific
risk assessment results based on the hazard information and the peril
vulnerability
function obtained from database 206.
[0053] FIG. 6 illustrates a probabilistic network 600 created by the
probabilistic
graphical network module 208, in accordance with an embodiment. The
probabilistic graphical network may be integrated with the building loss
metrics to
predict the impact to interdependent processes. The probabilistic networks are
directly connected to the building-specific risk assessment results (asset
performance metrics) generated from the high-resolution peril vulnerability
functions of each asset to model processes that have strong interdependencies.
[0054] In one embodiment, multiple networks may be defined to capture
different decision-metrics of interest 610 represented by an output node 606.
This
node may be connected via a directed edge to a number of independent variables
604. Independent variables 604 can be direct outputs of the asset-specific
analysis
or can be external portfolio variables that have been predefined or pre-
computed
prior to the network analysis.
[0055] In one embodiment, the output node 606 may also be connected to
other
nodes defined previously via either a directed or undirected edge. Each edge
may
have a corresponding conditional probability function that provides the
complete
probabilistic description of the output of the node it connects to.
Deterministic
dependencies may also be modelled by setting the conditional probabilities to
binary values. Hence, three types of basic dependencies may be captured by
this
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network structure, namely: fault-tree, Bayesian network, Markov network. These
network models represent directed deterministic dependence, directed
probabilistic
dependence and undirected probabilistic dependence, respectively. This network
structure allows the modelling of strong interdependent processes and the
impacts
of natural peril while preserving the causal relationships between process
impacts
and damage of the portfolio as well as the capability to explicitly propagate
uncertainties in the portfolio risk analysis. This type of simulation can be
done at a
by-realization basis, and is therefore capable of generating probabilistically
consistent descriptions of damage, loss and impacts to interdependent
processes in
a multi-peril context.
[0056] FIG. 7 illustrates a system 700 for building-specific multi-
peril risk
assessment and mitigation, in accordance with an embodiment. The system may
comprise an exposure module 708, a multi-peril catastrophe model platform 712,
a
multi-peril hazard module 204, a database 206 and a graphical network module
208.
[0057] The exposure module 708 may receive data pertaining to asset
list and
identifying attributes to match up with vulnerability database 702, data
pertaining
to description of interdependent processes and relevant variables 704, and
building
and site inspection data 706. The exposure module 708 may comprise data
related
to portfolio asset and process interdependency definition.
[0058] Typically, the exposure module 708 describes the portfolio of
structures
by their specific location, building attributes such as year built, type of
construction,
number of storeys, other optional modifiers such as whether the building meets
code
requirements associated with the peril of interest.
[0059] Further, the exposure module 708 may map each of the asset to a
building archetype.
[0060] The multi-peril catastrophe model platform 712 may comprise
hazard
information for multiple perils.
[0061] The multi-peril hazard module 204 may obtain the hazard
information
to create a multi-peril event catalogue configured to generate event hazard
intensity
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measures 716.
[0062] The database 206 may comprise high-resolution multi-peril
vulnerabilities functions 710. The processor 202 may determine building-
specific
risk assessment results 718 based on the event hazard intensity measures 716
and
vulnerabilities functions 710.
[0063] The graphical network module 208 may generate probabilistic
networks
720 for wherein the probabilistic networks are connected to the determined
building-specific risk assessment results 718 of the peril vulnerability
functions that
have strong interdependencies. Further, the system may determine the multi-
peril
risk 722 associated with the building.
[0064] In one embodiment, the processor 202 may be further configured
to
generate a mitigation solution for the determined multi-peril risk for the
building.
[0065] The system may cost-efficiently and quickly generate high-
resolution
multi-peril risk assessment that are probabilistically consistent and are
suitable for
decision-support for owners of individual assets.
[0066] The system may enable integration of asset interdependency maps
as
part of portfolio impact assessment.
[0067] The system may enable multi-peril event-based risk analysis for
portfolios of assets that have the capability to preserve inter-peril
correlation and
intra-event correlation.
[0068] The system may cause rapid generation of mitigation solutions
for
multiple perils and presents quantitative cost and benefit of all generated
risk
mitigation options.
[0069] The system may enable explicit evaluation and optimization of
portfolio
risk mitigation plans against multiple perils implemented over time
(incremental
risk mitigation).
[0070] The system may compactly represent the performance of large
numbers
of multi-peril natural hazard risk mitigation solutions in graphical form for
decision
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makers.
[0071] The system may enable stakeholders to accurately assess the
quantifiable property-specific financial, downtime, safety and other
operational
impacts due to multiple natural peril exposure for long-term maintenance and
risk
management plans.
[0072] The system may enable stakeholders of portfolio containing
similar
properties to perform rapid and high accuracy multi-peril risk assessment and
mitigation analysis.
[0073] The system may enable stakeholders of general multi-property
portfolios to quantitatively assess and optimize practical options of
incremental
multi-peril risk mitigation over a period of time.
[0074] The system may enable stakeholders to identify the breakdown of
components that contribute to the overall risk to identify cost-effective
solutions
that may be different from other existing guidelines/engineering standards
based
only on achieving code-based life-safety performance.
[0075] The system may enable portfolio owners to identify the breakdown
of
building properties that contribute to the overall portfolio seismic risk.
[0076] The system may enable portfolio owners and managers to properly
prioritize and allocate limited temporal, financial and human resources to
manage
seismic risk based on a multifaceted natural hazard impact analysis.
[0077] The system may enable stakeholders to identify critical elements
in
interdependent network of assets and the quantitative contribution to risk for
targeting mitigation.
[0078] The system may enable the construction of rational, and
quantitative
arguments for justifying various degrees of natural hazard risk mitigation and
resilience interventions.
[0079] The system may promote the wide-spread use of cost-effective
resilience planning by enabling the building of business cases based on
rational and
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quantitative cost-benefit analysis.
[0080] The system may enable owners and developers to realize upfront
cost-
savings relative to existing prescriptive code-based approach that relies
heavily on
conventional engineering code approaches.
[0081] The system may enable owners and stakeholders to obtain better
and
more rationally developed insurance policies for multiple-perils.
[0082] The system may enable owners and stakeholders to actively
natural
disaster direct and indirect losses.
[0083] The system may promote a safer and more resilient building
environment and society that resists damage and quickly recovers from
catastrophic
natural hazard events.
[0084] The processes described above is described as a sequence of
steps, this
was done solely for the sake of illustration. Accordingly, it is contemplated
that
some steps may be added, some steps may be omitted, the order of the steps may
be re-arranged, or some steps may be performed simultaneously.
[0085] The example embodiments described herein may be implemented in
an
operating environment comprising software installed on a computer, in
hardware,
or in a combination of software and hardware.
[0086] Although embodiments have been described with reference to
specific
example embodiments, it will be evident that various modifications and changes
may be made to these embodiments without departing from the broader spirit and
scope of the system and method described herein. Accordingly, the
specification
and drawings are to be regarded in an illustrative rather than a restrictive
sense.
[0087] Many alterations and modifications of the present invention will
no
doubt become apparent to a person of ordinary skill in the art after having
read the
foregoing description. It is to be understood that the phraseology or
terminology
employed herein is for the purpose of description and not of limitation. It is
to be
understood that the description above contains many specifications, these
should
not be construed as limiting the scope of the invention but as merely
providing
Date Recue/Date Received 2021-10-01
illustrations of some of the personally preferred embodiments of this
invention.
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