U.S. patent application number 14/290546 was filed with the patent office on 2014-09-18 for transforming data for rendering an insurability decision.
This patent application is currently assigned to RGA REINSURANCE COMPANY. The applicant listed for this patent is RGA Reinsurance Company. Invention is credited to J. David Burgoon, JR., David L. Snell, Susan L. Wehrman.
Application Number | 20140278588 14/290546 |
Document ID | / |
Family ID | 47175606 |
Filed Date | 2014-09-18 |
United States Patent
Application |
20140278588 |
Kind Code |
A1 |
Burgoon, JR.; J. David ; et
al. |
September 18, 2014 |
TRANSFORMING DATA FOR RENDERING AN INSURABILITY DECISION
Abstract
Transformation of disparate data for use in rendering a decision
involving a potentially insurable risk. An Extract, Transform, Load
(ETL) process extracts the data and converts it from a plurality of
formats into a standard format for processing. A heuristic engine
inferentially processes the converted data to identify information
relevant to the decision to be rendered. A consolidation and
presentation engine generates presentable knowledge from the
relevant information and then presents the knowledge to a
decision-making entity for rendering the decision. And an
optimization feedback process monitors one or more actions on the
presented knowledge by the decision-making entity and adjusts one
or more of the ETL process, the heuristic engine, and the
consolidation and presentation engine as a function of the
monitored actions.
Inventors: |
Burgoon, JR.; J. David;
(Weldon Spring, MO) ; Snell; David L.;
(Chesterfield, MO) ; Wehrman; Susan L.; (Ballwin,
MO) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
RGA Reinsurance Company |
Chesterfield |
MO |
US |
|
|
Assignee: |
RGA REINSURANCE COMPANY
Chesterfield
MO
|
Family ID: |
47175606 |
Appl. No.: |
14/290546 |
Filed: |
May 29, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13274869 |
Oct 17, 2011 |
8775218 |
|
|
14290546 |
|
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|
61487562 |
May 18, 2011 |
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Current U.S.
Class: |
705/4 |
Current CPC
Class: |
F04C 2270/041 20130101;
G06Q 40/08 20130101; G16H 70/00 20180101; G16H 10/60 20180101 |
Class at
Publication: |
705/4 |
International
Class: |
G06Q 40/08 20120101
G06Q040/08; G06Q 50/22 20060101 G06Q050/22 |
Claims
1. A computerized method of transforming disparate data for use in
rendering a decision involving a potentially insurable applicant,
said method comprising: receiving data relating to the applicant
from a plurality of sources, said data being stored in a memory in
a plurality of formats; accessing, by a computer, the received data
stored in the memory; and executing, by the computer,
computer-executable instructions for: extracting the received data
and converting the extracted data into one or more standard
formats, said converted data relating to insurability of the
applicant; filtering the converted data by one or more relevancy
factors assigned to the converted data, said relevancy factors
being a function of a decision to be rendered by an underwriter
regarding the insurability of the applicant; generating, from the
filtered data, presentable knowledge; presenting the knowledge to
the underwriter for rendering the decision; monitoring one or more
actions on the presented knowledge by the underwriter; and
adjusting, by the computer, one or more of said extracting,
converting, filtering, and generating presentable knowledge as a
function of the monitored actions.
2. The method of claim 1, wherein extracting the received data and
converting the extracted data into one or more standard formats
comprises executing a domain-specific Extract, Transform, Load
(ETL) process on the received data.
3. The method of claim 1, wherein the received data comprises one
or more of the following types of data: applicant-provided data,
electronic medical records data, prescription data, other medical
sources data, financial sources data, motor vehicle records data,
and other non-medical sources data.
4. The method of claim 1, wherein filtering the converted data by
one or more relevancy factors comprises executing a heuristic
engine for inferring risk assessment relationships among the
converted data.
5. The method of claim 4, further comprising storing the converted
data in a staging area repository, and wherein the heuristic engine
processes the data stored in the staging area repository.
6. The method of claim 1, wherein the received data comprises one
or more of the following types of complex data: social network data
and datamart data.
7. The method of claim 6, further comprising executing a data
mining process on the complex data to identify covariance
relationships among the data.
8. The method of claim 7, wherein the data mining process comprises
predictive modeling.
9. The method of claim 1, wherein adjusting one or more of said
extracting, converting, filtering, and generating presentable
knowledge as a function of the monitored actions of the decision
making entity as a component of the process comprises executing a
metaheuristic optimization algorithm to assist in refinement of the
presenting.
10. The method of claim 1, wherein presenting the knowledge to the
underwriter comprises executing a consolidation and presentation
engine to present a summary of relevant information to the
underwriter.
11. The method of claim 1, wherein one or more computer-readable
media have computer-executable instructions stored thereon for
performing the method of claim 1.
12. The method of claim 1, wherein generating presentable knowledge
further comprises executing at least one of: ant colony
optimization, a heuristic algorithm, network theory, predictive
modeling, deterministic chaos, behavioral economics, fractal
geometry, and cellular automata.
13. The method of claim 1, further comprising the mapping of data
to a database, wherein the adjusting further comprises a feedback
system based on the consumption or modification of the data that is
further used to refine and adjust the mapping of data to the
database.
14. A non-transitory computer-readable medium storing
computer-executable instructions that, when executed, transform
disparate data for use in rendering a decision involving a
potentially insurable applicant, said computer-readable medium
comprising: data from a plurality of sources and in a plurality of
formats; an Extract, Transform, Load (ETL) process for extracting
the data and converting the data from the plurality of formats into
one or more standard formats; a processing engine for inferentially
processing the converted data to identify information relevant to
the decision to be rendered, wherein the processing engine
implements at least one of: ant colony optimization, a heuristic
algorithm, network theory, predictive modeling, deterministic
chaos, behavioral economics, fractal geometry, and cellular
automata; a consolidation and presentation engine for generating
presentable knowledge comprising the identified relevant
information and presenting the knowledge to an underwriter for use
in rendering the decision; an optimization feedback process for
monitoring one or more actions on the presented knowledge by the
underwriter and adjusting one or more of the ETL process, the
processing engine, and the consolidation and presentation engine to
present an optimized update of the presented knowledge to the
underwriter as a function of the monitored actions.
15. The computer-readable medium of claim 14, wherein the received
data comprises one or more of the following types of data:
applicant-provided data, financial sources data, electronic medical
records data, electronic health records data, continuity of care
records data, prescription data, other medical sources data,
financial sources data, social network data, motor vehicle records
data, other non-medical sources data, and datamart data.
16. A system comprising: a memory storing disparate data relating
to rendering a decision involving a potentially insurable
applicant, said data being stored in a plurality of formats; a
computer executing a process for extracting at least a portion of
the stored data and transforming the extracted data from the
plurality of formats into a standardized format; wherein the memory
further stores the transformed data in the standardized format;
wherein the computer further executes a heuristic engine for
analyzing the transformed data for relevancy to a decision to be
rendered involving the potentially insurable applicant and
assigning one or more relevancy factors to the analyzed data as a
function of the decision to be rendered, said relevancy factors
providing an improved user experience by enabling adjustment by the
computer to presenting the transformed data to an underwriter as a
function of the decision to be rendered; and a display displaying
an output including the assigned relevancy factors to the
underwriter for use in rendering the decision and further
displaying, based on an adjustment by the computer, an updated
output to the underwriter.
17. The system of claim 16, wherein the data stored in the memory
area comprises one or more of the following types of data:
applicant-provided data, electronic medical records data,
electronic health records data, continuity of care records data,
prescription data, other medical sources data, financial sources
data, social network data, motor vehicle records data, other
non-medical sources data, and datamart data.
18. The system of claim 16, further wherein the heuristic engine
executes at least one of: ant colony optimization, a heuristic
algorithm, network theory, predictive modeling, deterministic
chaos, behavioral economics, fractal geometry, and cellular
automata.
Description
BACKGROUND
[0001] Insurance companies typically determine insurance premiums
and rates for applicants based on the process of underwriting. In
other words, underwriting involves measuring risk exposure and
determining the premium that needs to be charged to insure that
risk. For example, life insurance underwriting involves determining
an individual's relative mortality and health insurance
underwriting involves determining an individual's relative
morbidity. And as part of the underwriting process for life or
health insurance, medical underwriting and other factors (e.g., age
and occupation) are used to examine the applicant's health
status.
[0002] Several sources of medical and nonmedical data exist for use
in the underwriting process. For example, a life or health
insurance company often has internal records from previous
policies, application data for a currently proposed policy, and
data available from external sources such as hospital and physician
records, and prescription drug usage services. The hospital and
physician data can take the form of Electronic Medical Records
(EMR) or Patient Medical Information (PMI) files (including
Attending Physician Statements (APS)). And commercial inspection
companies make available to insurance companies a wide array of
information from banking or financial information to driving
history. To say this represents a river of data is an
understatement. The insurance underwriter is faced with the task of
drinking from the fire hose. Although most, but not all, of these
disparate sources are developing emerging standards for this data,
the standards for one source often vary widely from the standards
for another source because each source is focused on satisfying a
different business need.
[0003] Each insurance company has its own set of underwriting
guidelines to help an underwriter determine whether or not the
company should accept a risk and at what cost and with what
restrictions. Once an applicant for insurance authorizes the
company's access to various pieces of information, the underwriting
process uses the information to evaluate the risk of the applicant
for insurance based on the type of coverage involved. Insurance
companies sometimes use automated underwriting systems to deliver
an underwriting decision.
SUMMARY
[0004] Aspects of the invention translate and map data from a
medical record or the like into a structured database to enable the
data to be underwritten by either an electronic program or a human
underwriter.
[0005] A method embodying aspects of the invention transforms
disparate data for use in rendering a decision involving a
potentially insurable risk. The method includes receiving data,
which is in a plurality of formats, from a plurality of sources.
The data is extracted and converted into one or more standard
formats. The method also includes filtering the converted data by
relevancy to the decision to be rendered, generating presentable
knowledge from the converted data, and presenting the knowledge to
a decision-making entity for rendering the decision. By monitoring
one or more actions on the presented knowledge by the
decision-making entity, the method can adjust one or more of steps
as a function of the monitored actions.
[0006] In an aspect, a method of structuring and transforming
disparate data for use in rendering a decision involving a
potentially insurable risk includes retrieving data from a first
database and transforming the retrieved data into domain-specific
information. Once transformed, the information, which relates to
the potentially insurable risk, is stored in a second database. The
method includes defining one or more relevancy factors as a
function of the decision to be rendered and assigning at least one
of the relevancy factors to at least a portion of the information
stored in the second database. Additionally, the method includes
providing an output of the second database with the assigned
relevancy factors to a decision-making entity for rendering the
decision.
[0007] In another aspect, a computer-readable medium stores
computer-executable instructions that, when executed, transform
disparate data for use in rendering a decision involving a
potentially insurable risk. The computer-readable medium comprises,
data from a plurality of sources and in a plurality of formats, an
Extract, Transform, Load (ETL) process, a heuristic engine, a
consolidation and presentation engine, and an optimization feedback
process. The ETL process extracts the data and converts it from the
plurality of formats into one or more standard formats. The
heuristic engine inferentially processes the converted data to
identify information relevant to the decision to be rendered. The
consolidation and presentation engine generates presentable
knowledge from the relevant information and then presents the
knowledge to a decision-making entity for rendering the decision.
And the optimization feedback process monitors one or more actions
on the presented knowledge by the decision-making entity and
adjusts one or more of the ETL process, the heuristic engine, and
the consolidation and presentation engine as a function of the
monitored actions.
[0008] In yet another aspect, a system includes a memory storing
disparate data relating to a potentially insurable risk. A computer
executes a process for extracting at least a portion of the stored
data and transforming the extracted data from a plurality of
formats into a standardized format. The memory then stores the
transformed data in the standardized format. The computer executes
a heuristic engine for analyzing the transformed data for relevancy
to a decision to be rendered involving the potentially insurable
risk. Moreover, the heuristic engine assigns one or more relevancy
factors to the analyzed data. In addition, a display displays an
output including the assigned relevancy factors to a
decision-making entity for rendering the decision.
[0009] In an aspect of the invention, an automated system is
capable of interpreting medical conditions presented in a
structured medical record into one of a plurality of limited
underwriting impairments. The automated system is user-configurable
to include more or fewer underwriting impairments. And the
automated system is user-configurable to enable modification of the
medical condition mappings into underwriting impairments. The
automated system includes the capability to translate, interpret,
and map a known medical condition based on one or more factors
including, but not limited to: medical condition name; medical
condition code (e.g., CPT4, ICD9, ICD10, etc.); medications
assigned; treatment regimens; age; gender; and so forth.
[0010] In another aspect, the automated system receives its input
data from various sources such that the data received is in a
structured data format capable of being interpreted by an automated
system.
[0011] In yet another aspect, the automated system produces a
structured data output consisting of at least one of the following:
an underwriting medical condition; a severity indication; a
recommended action; or an indication that the medical condition is
referred to a human to correctly map the medical condition to an
underwriting impairment.
[0012] In yet another aspect of the present invention, the output
of the automated system is an input to an automated system or as
input to a human for the actual process of underwriting the
individual under consideration.
[0013] This Summary is provided to introduce a selection of
concepts in a simplified form that are further described below in
the Detailed Description. This Summary is not intended to identify
key features or essential features of the claimed subject matter,
nor is it intended to be used as an aid in determining the scope of
the claimed subject matter.
[0014] Other features will be in part apparent and in part pointed
out hereinafter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] FIG. 1 is an exemplary block diagram illustrating a system
for transforming medical and other data according to an embodiment
of the invention.
[0016] FIG. 2 is an exemplary block diagram illustrating a system
for transforming medical and other data according to another
embodiment of the invention.
[0017] FIG. 3 is an exemplary block diagram illustrating
alternative data sources to the system of FIGS. 1 and 2.
[0018] FIG. 4 is an exemplary flow diagram illustrating operation
of the system of FIGS. 1 and 2.
[0019] FIG. 5 is an exemplary flow diagram illustrating operation
of a consolidation and presentation engine of the system of FIGS. 1
and 2.
[0020] FIG. 6 is a block diagram illustrating an example of a
suitable computing system environment in which aspects of the
invention may be implemented.
[0021] Corresponding reference characters indicate corresponding
parts throughout the drawings.
DETAILED DESCRIPTION
[0022] Referring now to the figures, aspects of the present
invention translate and map information about an insurance
applicant into a structured database. This enables the information
to be more effectively and efficiently underwritten by either an
electronic program or a human underwriter. In one embodiment, a
computer system, generally indicated at 100, receives information,
such as data stored in an external data database 102, and creates
structured data that fits into major "underwritten" sections (e.g.,
cardiovascular disease). The structured data is preferably used for
further underwriting evaluation, either by an automated system or
by a human underwriter.
[0023] As an example, the data stored in the external data database
102 comprises data from electronic medical records (EMRs). This
external data can be from several sources and in varying formats.
The system 100 evaluates each EMR, for example, to identify
relevant information and to translate the identified information.
In this regard, system 100 uses industry-wide classifications,
performs lexical analysis, accesses open-source or propriety
databases (e.g., databases provided by a reinsurance company), or
the like. The EMR data input to system 100 often includes fields
such as medical condition name, medical condition code, medications
assigned, treatment regimens, age, gender, and so on.
[0024] As another example, a suitable source of information is a
continuity of care record (CCR). Those skilled in the art are
familiar with CCR standards for creation of electronic summaries of
patient health. The CCR provides a means for a healthcare
practitioner, system, or setting to aggregate pertinent data about
a patient and forward it to another practitioner, system, or
setting to support the patient's continuity of care. For example, a
typical CCR includes a summary of the patient's health status
(e.g., problems, medications, allergies, lab results, procedures)
and basic information about insurance, advance directives, care
documentation, and care plan recommendations. The CCR is not an EMR
or electronic health record (EHR) but it often contains some of the
same data as an EMR or EHR. A continuity of care document (CCD) is
a CCR created under the Clinical Document Architecture (CDA)
standard.
[0025] Aspects of the invention also relate to creating structured
data from non-traditional records sources such as data from social
networks and from internet datamarts instead of or in addition to
EMR, EHR, CCR, and/or CCD data or the like.
[0026] An underwriting impairment typically defines factors that
tend to increase an individual's risk above that which is normal.
Underwriting manuals define one or more underwriting impairments or
underwriting impairment groups. Information in the underwriting
impairment may define, for example, the individual's relative
mortality, morbidity, and/or longevity. Although described in the
context of life or health underwriting, it is to be understood that
aspects of the invention also apply to disability, long term care,
and other forms of insurance underwriting.
[0027] As shown in FIG. 1, computer system 100 permits selection
and mapping of translated external data from database 102 to a
structured database. The external data stored in database 102
includes, for example, applicant-provided data, financial sources
data, motor vehicle records data, other non-medical sources data,
electronic medical records data, electronic health records data,
continuity of care records or documents data, prescription data,
and other medical sources data.
[0028] The system 100 first extracts relevant information from the
external data and then converts the extracted data into standard
formats for processing. In one embodiment, system 100 weighs,
filters, or otherwise deems information to be more or less relevant
based on factors such as source, type, age of data, covariance with
other factors, etc. And the resulting structured data preferably
contains fields such as an underwriting medical condition, a
severity indication, a recommended action, and/or an indication
that further manual review is desired or required.
[0029] In one embodiment of system 100, the application programs 36
(see FIG. 6) include a plurality of processes that when executed by
system 100 filter the structured data by relevancy and mine the
data for valuable information. The processes further convert this
information into knowledge, namely, information that is
particularly useful in the underwriting process. FIG. 1 shows at
least one knowledge engineering process, generally indicated
process 104 for determining which of the relevant information is
actually usable in the underwriting process. Preferably, the
process 104 employs experience studies, feedback, etc. to create
and apply a knowledge model to the data. In addition, one or more
extract, transform, load (ETL) processes and one or more data
mining processes, generally indicated process 106, filter the
structured data by relevancy and mine the data for valuable
information. The result of these highly specialized processes 104,
106 is a relatively large staging area repository 108 of
potentially usable data concerning the applicant.
[0030] At least one heuristic engine 110 analyzes staged data
stored in the repository 108. In particular, the heuristic engine
110 compares the data against a proprietary database 112
representing a lexicon of phrases, synonyms, ICD 10 codes, etc. and
the covariances of the data items. Moreover, engine 110 assigns
relevancy weightings for life underwriting or for health
underwriting. The output of heuristic engine 110 is a refined,
filtered collection of information pertinent to the underwriting
process stored in an underwriting information database 114.
[0031] In one embodiment, heuristic engine 110 executes a Markov
Chaining Monte Carlo (MCMC) algorithm. Those skilled in the art are
familiar with algorithms of this type for use in predictive
modeling. Aspects of the present invention utilize the MCMC
methodologies to infer risk assessment relationships in seemingly
unrelated data from disparate sources.
[0032] At least one consolidation and presentation engine 116
presents the structured output of heuristic engine 110 in a form
more directly usable for underwriting (either manual or automated
or both). Moreover, the consolidation and presentation engine 116
offers a drill-down capability, described below, to further
underwriting information stored in a database 114. In this manner,
engine 116 outputs scenario and applicant-specific information as
well as reference statistics particularly useful in the
underwriting process.
[0033] Referring further to FIG. 1, system 100 includes a visual
tool that enables a user, such as an underwriter 118, to view the
information output from heuristic engine 110 as well as the
information's underlying factors. Moreover, the visual tool enables
the underwriter 118 access to the information in the underwriting
information database 114. In one embodiment, the visual tool
comprises a dashboard of consolidated summary information displayed
on a display of a computer 120. The underwriter 118, generally
considered the decision maker in underwriting scenarios, renders
his or her decision based on the summary information. Typically,
underwriter 118 is a trained professional who evaluates the
presented data and makes a decision to approve the application at a
specific rating for the policy, to decline the application, or to
request more information. In an alternative embodiment, the
computer 120 executes automated underwriting processes in addition
to or instead of manual underwriting by underwriter 118. In the
absence of a human underwriter, computer 120 constitutes the
underwriter in this alternative embodiment.
[0034] In an embodiment, a feedback system based on the consumption
or modification of the structured data is used to refine and adjust
the selection, translation, and/or mapping of data to the
structured database. Moreover, the feedback process monitors
underwriter actions and results and alters previous operations via
feedback loops. For example, the actions of each individual
underwriter 118 are closely observed using an optimization
technique, such as an "Ant Colony Optimization" technique executed
at process 122. The process 122 infers collective information from
the repeated and combined actions of independent individuals and
adjusts the dashboard of summary information displayed at computer
120 accordingly.
[0035] FIG. 2 illustrates an alternative embodiment of the
invention. As shown in FIG. 2, computer system 100 permits
selection and mapping of translated external data stored in
database 102 to a structured database. The external data 102
includes, for example, applicant-provided data 202, financial
sources data 204, electronic medical records data 206, prescription
data 208, and other medical sources data 210 (including but not
limited to, for example, continuity of care records data). In
addition, external data database 102 includes complex data from
non-EMR sources such as social network data 212 and internet
datamart data 214. The different types of external data included in
the external data database 102 can be stored in one or more
database structures.
[0036] Advantageously, extracting information from multiple data
sources provides the benefit of network theory. In this regard, the
strength of a network is the usual fault tolerance (e.g., random
hits can take out as many as 80% of the locations while retaining
functionality). But the weakness of a network is the vulnerability
to catastrophe (e.g., targeted hits take out very few locations but
cause chaos). The government sponsored movement towards more
integration of medical and related information into personal
medical records is countered to some extent by another regulatory
initiative concerning privacy issues. The goals are at times in
conflict and the posture regarding what information is fair game
for risk assessments is in a state of flux. Embodiments of the
invention use network theory to adjust processing centers for high
efficiency of data processing and embracing of data deemed
relevant, ethical, and legal to use, yet reduce the vulnerability
to any specific data source or selection criterion as perspectives
change.
[0037] The system 100 preferably uses inferential analysis to
extract useful information from the external data. Those skilled in
the art are familiar with computational methods such as predictive
modeling, Bayesian inference, genetic algorithms, and the like for
performing inferential analysis. The system 100 first extracts
relevant information from external data stored in database 102 and
then converts the extracted data into a standard format for
processing. In one embodiment, system 100 weighs, filters, or
otherwise deems information to be more or less relevant based on
factors such as source, type, age of data, covariance with other
factors, etc. And the resulting structured data preferably contains
fields such as an underwriting medical condition, a severity
indication, a recommended action, and/or an indication that further
manual review is desired or required.
[0038] Similar to the embodiment of FIG. 1, application programs 36
(see FIG. 6) include a plurality of processes that when executed by
system 100 filter the structured data by relevancy and mine the
data for valuable information. The processes further convert this
information into knowledge, namely, information that is
particularly useful in the underwriting process. FIG. 2 shows a
plurality of processes, such as knowledge engineering process 104,
heuristic engine 110, and consolidation and presentation engine
116. Moreover, FIG. 2 illustrates process 106 as one or more ETL
processes 218 and one or more data mining processes 220. The
processes 104, 106 (including 218, 220), 110, 116 are collectively
referred to as inference engines.
[0039] The engine 116 transforms information from various sources
into a form more directly usable for underwriting (either manual or
automated or both). Traditional information sources include
applicant-provided data 202, financial sources data 204, electronic
medical records data 206, prescription data 208, and other medical
sources data 210. The traditional sources of data, although
different from each other in many respects, share a general
perspective on the health or financial state of the applicant.
[0040] A person who recently underwent major surgery, or who is in
financial distress, for example, is more likely to have a greater
mortality or health insurance risk than another person with a
secure, comfortably high income, low debt, good family history of
longevity, lower (but not too low) blood pressure and cholesterol
levels, and a body mass index (BMI) and other physical
characteristics in the more desirable ranges.
[0041] The consolidation and presentation engine 116 generates
succinct, high usable information from the transformed data stored
in underwriting information database 114. For example, engine 116
summarizes data representing years of biometric levels into a
moving weighted average. In another embodiment, engine 116 presents
a chart of the metrics superimposed on a background chart of those
metrics for the normal range of individuals of similar age, gender,
smoker status, and other key underwriting criteria. Similarly,
instead of data representing years of prescriptions, engine 116
presents a listing of the distinct prescriptions, and an indication
of dosage levels (and increasing or decreasing trends), periods of
noncompliance, and other key indicators to flag possible
interactions between prescriptions or possible misuse of them.
[0042] In an alternative embodiment, engine 116 may be configured
to operate on non-traditional information, such as social network
data 212 and internet datamart data 214. Vast amounts of data on
our personal lifestyle habits have been collected and stored in
various datamarts. And people contribute to the collective
knowledge by voluntary participation in social networks. Referring
further to FIG. 2, if the traditional sources form a river, the
social networks data 212 and associated datamarts data 214 (e.g.,
specialty companies that harvest data about us from myriad sources)
form a sea of data. If processed effectively, this lifestyle data
can be a useful prognosticator of future, rather than just current
morbidity and mortality concerns. And this data could add
significantly to the total picture of insurability.
[0043] For example, assume person X lives in a neighborhood where
the crime rate is very low, jogging trails are plentiful, and the
local culture encourages walking rather than driving. Further, X
has high equity in her home, a graduate degree in a high paying but
relatively low stress profession, and does not subscribe to the
premium cable television package (thus, is not a couch potato).
Instead, she subscribes to a popular magazine for serious runners,
writes a blog on organic foods, buys mostly whole grains and
vegetables on her loyalty card at the grocery chain, wrote a review
of her cardiac monitoring wristwatch on an online retailer's
website, regularly attends a yoga class at her local fitness
center, and recently posted pictures to her social network profile
showing her grandfather's 100th birthday celebration. This mix of
data could provide a favorable indicator of X living for a longer
time than an otherwise similar individual who posts, for example,
pictures from a party at a local tavern, blogs about the taste
differences of cigar A versus cigar B, and comments about recently
buying a new muscle car to race at the local stock car track.
[0044] Today's life or health insurance underwriter is a
magnificent human inference engine capable of assimilating
information about an applicant and assigning appropriate risk
classifications that drive the issuance of profitable, yet
equitable, rates for insurance coverage. But it is no longer
humanly possible (and certainly not cost effective) for an
underwriter to study all of the data available for an applicant for
a life or health insurance policy. Aspects of this invention embody
a transformation from vast amounts of data to usable nuggets of
information.
[0045] Referring further to FIG. 2, some data, especially data from
the more traditional sources, are run through tailored ETL
processes 218 to consolidate them into the common repository 108
for further study. In one embodiment, a tailored ETL process 218
corresponds to each source of external data 102. In other words,
each ETL process 218 is specific to the domain, or source, of the
data. The ETL process extracts information from its corresponding
data source without regard to each data organization/format and
transforms, or converts, the extracted data to a standard format.
This permits consolidation and loading of the data into repository
108.
[0046] Other data, such as social networks data 212 and datamarts
data 214, can be so voluminous as to make this more direct type of
mapping process unfeasible in realistic timeframes. This other data
212, 214 is processed by, for example, advanced statistical
methodologies, i.e., data mining processes 220. In one embodiment,
data mining processes 220 comprise predictive modeling and similar
techniques to "follow the bread crumbs" and detect covariance
relationships between seemingly independent pieces of data.
[0047] The system 100 also operates on internal information stored
in a database 222 and converts the raw data into a form more
directly usable for underwriting. For example, a reinsurance
company has a perspective on underwriting practices and mortality
results across many companies and maintains its own repository of
extensive data, indicated generally as internal data database 222.
The knowledge engineering process 104 with expert human
underwriters, actuaries, and other insurance professionals
continually refines this valuable source of proprietary
information.
[0048] Embodiments of the invention involve the storage of vast
amounts of data, such as external data in database 102 (both
traditional and non-traditional sources), internal data in database
222, lexicon and relevancy weights data in database 112, staged
data in repository 108, and underwriting information in database
114. Although referred to as stored in databases or repositories,
it is to be understood that the data can be stored, organized, and
maintained in myriad forms.
[0049] In the embodiment of FIG. 2, heuristic engine 110 analyzes
the staged data in repository 108. In particular, heuristic engine
110 compares the data against the proprietary database 112
representing a lexicon of phrases, synonyms, ICD 10 codes, etc. and
the covariances of the data items. Moreover, engine 110 assigns
relevancy weightings for life underwriting or for health
underwriting.
[0050] For example, the relevancy of an item such as back pain
might be of little consequence for a life application but of much
higher relevance for health underwriting. And in another example, a
hearing loss might be unimportant for most life applicants, yet
rise in importance considerably if the applicant is employed as a
traffic guard.
[0051] The result of this proprietary filtering process is a
refined collection of information pertinent to life (or health, if
that is the coverage sought) underwriting. Even this may be too
much information for an underwriter to efficiently absorb. For
example, BMI and blood pressure and cholesterol levels for the past
30 years is likely to be more information than underwriter 118 can
effectively process. Similarly, information about monthly
prescription medications for the past 15 years is likely too much
data to be usable. The consolidation and presentation engine 116
transforms this information into a form more directly usable by the
underwriter.
[0052] Referring further to FIG. 2, system 100 includes a visual
tool that enables underwriter 118 access to the information in the
underwriting information database 114. And in an embodiment, a
feedback system based on the consumption or modification of the
structured data is used to refine and adjust the selection,
translation, and/or mapping of data to the structured database.
Moreover, the feedback process monitors underwriter actions and
results and alters previous operations via feedback loops. For
example, the actions of each individual underwriter 118 are closely
observed using an optimization technique, such as an "Ant Colony
Optimization" technique executed at process 122. The process 122
infers collective information from the repeated and combined
actions of independent individuals and adjusts the dashboard of
summary information displayed at computer 120 accordingly.
[0053] For example, if multiple underwriters 118 tend to drill down
on the medications and consult a dictionary for potential drug
interactions, this becomes part of the collective knowledge of the
inference engines 104, 218, 220, 110, and/or 116. Future summary
dashboards reflect this feedback by including this specific
information, which saves underwriting time on future applications.
Likewise, the information value is quickly scored by underwriter
118 and information used less frequently loses prominence, or real
estate, on the summary screen. In this manner, aspects of the
invention improve at providing the information wanted and not
providing the extraneous data that obscures a cost and time
effective decision on the part of the human expert. Likewise, if
the information in the refined repository of underwriting
information 114 is not sufficient, the inference engines 104, 218,
220, 110, and/or 116 may be adjusted accordingly.
[0054] Aspects of the invention provide all that is necessary and
sufficient without the distraction of that which is superfluous.
And, in one embodiment, the invention comprises an underwriting
appliance that has several alternative physical forms. Referring
now to FIG. 3, a ceding company can choose a stand-alone,
proprietary terminal linked to a reinsurer for maximum efficiency
of this operation, or one of various other options that permit a
balance of functionality and ease-of-use versus ceding company
internal data security concerns.
[0055] For example, in FIG. 3, an underwriting appliance 302 (i.e.,
a hardware arrangement) comprises a dedicated terminal to the
reinsurer, such as computer 120, with a specialized keyboard and
hot keys to most common functions. This has no connection to ceding
company IT operations and, thus, is ideal for situations where
security is a prime concern of the ceding company. In an
alternative underwriting appliance 304, the ceding company
underwriter 118 uses a personal computer, such as computer 120,
with a reinsurer specialized keypad 306 attached via the USB port
or the like. This permits normal access to the ceding company
network and peripherals. Moreover, the appliance 304 is convenient
for a large underwriting department and for situations involving
remote underwriters. Another alternative underwriting appliance 308
includes a specialized tablet 310 (e.g., an iPad) for use by a
highly mobile underwriter 118. In yet another alternative
underwriting appliance 312, the ceding company underwriter 118 uses
a personal computer, such as computer 120, with no attached
hardware. A relatively small, on-screen keyboard 314 is available
to provide the hot key operations. This permits normal access to
the ceding company network and peripherals. Similar to the
underwriting appliance 304, the appliance 312 is convenient for a
large underwriting department and for situations involving remote
underwriters. Preferred hot keys on the specialized input device
include an automatic login to the reinsurer's underwriting
appliance via a secure internet site, and various views
(arrangements of content and form) for differing benefit
underwriting perspectives such as Life, Health, Disability Income,
Long Term Care, etc. as well as direct access to the reinsurer's
underwriting manual. Additional features include the ability to
submit the application to the reinsurer.
[0056] FIG. 4 illustrates an exemplary, non-limiting process in
accordance with an embodiment of the invention. In operation,
computer system 100 receives external data 102 at 402 for selection
and mapping to a structured database. As set forth above, external
data 102 includes data from multiple sources in a variety of
formats, such as applicant-provided data, financial sources data,
electronic medical records data, prescription data, and other
medical sources data. At 406, system 100 first extracts relevant
information from external data 102 and then converts the extracted
data into standard formats for processing. In one embodiment,
system 100 executes process 104 and/or process 106 to perform the
data extraction and conversion. The system 100 stores the extracted
data in staging area repository 108.
[0057] Proceeding to 408, system 100 executes heuristic engine 110
to weigh, filter, or otherwise deem information to be more or less
relevant based on factors such as source, type, age of data,
covariance with other factors, etc. And the resulting structured
data preferably contains fields such as an underwriting medical
condition, a severity indication, a recommended action, and/or an
indication that further manual review is desired or required.
Moreover, engine 110 assigns relevancy weightings for life
underwriting or for health underwriting. The output of heuristic
engine 110 is a refined, filtered collection of information
pertinent to the underwriting process stored in underwriting
information database 114.
[0058] At 410, the consolidation and presentation engine 116 of
system 100 converts this information into knowledge, namely,
information that is particularly useful in the underwriting
process. As a result, engine 116 presents the structured output of
heuristic engine 110, i.e., the underwriting information 114, in a
form more directly usable for underwriting (either manual or
automated or both). The system 100 includes a visual tool that
enables underwriter 118 to view the summary information output from
heuristic engine 110 as well as the information's underlying
factors. For example, computer 120 displays a dashboard of
consolidated summary information to underwriter 118.
[0059] Feedback at 412 based on the consumption or modification of
the structured data refines and adjusts the selection, translation,
and/or mapping of data to the structured database. Moreover, the
feedback process monitors underwriter actions and results and
alters previous operations via feedback loops 414.
[0060] FIG. 5 provides a logical overview of the operation of
consolidation and presentation engine 116 at step 410 of FIG. 4
according to an embodiment of the invention. Beginning at 502,
engine 116 receives the extracted information stored in
underwriting information database 114. At 504, engine 116 executes
a decision operation to determine whether the received information
has a relatively high degree of relevance to the particular
underwriting scenario. If so, engine 116 proceeds to 506 for a
determination of whether the information is already in a concise,
usable form. And if the information is relevant and concise, engine
116 determines at 508 whether the information is suitable for top
level display.
[0061] On the other hand, if engine 116 determines at 504 that the
received information does not have a sufficiently high degree of
relevance to the particular underwriting scenario, operation
proceeds to 510. At 510, engine 116 determines whether the
information would have a relatively high degree of relevance if
combined with other data. If not, the information engine 116
disregards the data at 512. But if the information would be
sufficiently relevant if combined, engine 116 combines the data at
514 and proceeds to 506.
[0062] If engine 116 determines at 506 that the relevant
information is not already in a concise, usable form, operation
proceeds to 516. The engine 116 builds a summary at 516 such that
the information is more usable in the underwriting process and then
proceeds to 508 for a decision on whether the summarized
information is suitable for top level display.
[0063] The engine 116 causes information suitable for top level
display to be displayed at 518 and otherwise stores the information
at 520 so that it is available for display when underwriter 118
drills down for further detail. The consolidation and presentation
engine 116 offers the drill-down capability to permit underwriter
118 to access further underwriting information stored in a database
114. In other words, the relevance and nature of certain
information may not warrant top immediate display but underwriter
118 can access the information if he or she deems it of importance
to the underwriting decision. In this manner, engine 116 outputs
scenario and applicant-specific information particularly useful in
the underwriting process and provides the ability to drill down on
additional underwriting information.
[0064] As described above, system 100 preferably uses inferential
analysis to extract useful information from external data 102. The
system 100 first extracts relevant information from external data
102 and then converts the extracted data into a standard format for
processing. In one embodiment, system 100 weighs, filters, or
otherwise deems information to be more or less relevant based on
factors such as source, type, age of data, covariance with other
factors, etc.
[0065] Those skilled in the art are familiar with computational
methods such as predictive modeling, Bayesian inference, genetic
algorithms, nature-inspired metaheuristic algorithms and the like
suitable for performing inferential analysis in the form of
knowledge engineering process 218, data mining process 220,
heuristic engine 110, consolidation and presentation engine 116,
and/or optimization process 122. Advantageously, system 100
according to an embodiment of the invention utilizes a combination
of processes to weigh, filter, or otherwise deems information to be
more or less relevant and to optimize the processes. This
combination of processes permits system 100 to identify ways in
which the processes are vulnerable to minute changes in data
granularity, starting assumptions or on covariances between major
and obscure variables, and adjust accordingly.
[0066] In the past, underwriters, actuaries, economists, and
computer scientists built sophisticated mathematical models based
upon prevailing reductionist theory, and expected the world to
conform to them. They were dismayed when the world did not adhere
and behave the way it was "supposed" to behave. In contrast,
aspects of the present invention add the power of inductive
reasoning techniques, which learn from the data and the way it is
utilized. These adaptive aspects of the invention provide a unique
advantage for the increasingly dynamic nature of risk assessment
for life, health, disability income, long term care, and other
types of insurance applications.
[0067] Aspects of the invention utilize complexity science tools
and techniques, including predictive modeling, network theory,
deterministic chaos, behavioral economics, fractal geometry,
genetic algorithms, and cellular automata. These aspects represent
a marked departure from the classical, more deterministic approach
to risk assessment.
[0068] For example, embodiments of the invention involve the
storage of vast amounts of data, such as external data in database
102 (both traditional and non-traditional sources), internal data
in database 222, lexicon and relevancy weights data in database
112, staged data in repository 108, and underwriting information in
database 114. Although vast, the data is readily accessible when
needed, and the data models are highly scalable. In an embodiment,
fractal geometry techniques help achieve scalability of
interrelationship inferences beyond currently popular methods by
taking advantage of self-similarities in the data.
[0069] In another example, genetic algorithms, namely,
nature-inspired metaheuristic algorithms and the like, provide
solutions to optimization and search problems in inferential
analysis processes. Many risk assessment problems have no clear
deterministic solution, and an exhaustive search is beyond
computational capabilities. In a situation in which the number of
variables (e.g., gender, age, height, weight, systolic and
diastolic blood pressure readings, low and high density cholesterol
readings, etc.) is large and the covariances of variables (such as
diabetes plus high blood pressure plus obesity) can lead to complex
interactions, system 100 in one embodiment uses one or more genetic
algorithms to simulate emergent phenomena from the interactions of
simpler, complex adaptive agents. An example of very simple agents
interacting in complex ways would be the operation of an ant
colony. An ant placed on a tabletop moves aimlessly but an ant
colony is capable of complex behaviors even without a designated
leader. In an analogous manner, ant colony optimizations, bee
colony algorithms, and other modeling techniques based on the
complex interactions of simple agents to solve problems not
solvable with classic deterministic methods.
[0070] These nature-inspired metaheuristic algorithms are suited to
observe the human actions of the underwriters as they utilize
system 100. The dashboard output generated on computer 120 by
consolidation and presentation engine 116 presents the information
generally thought to be of the most interest to the human
underwriter 118, with drill-down capability to get more granular or
detailed information as desired. The feedback process monitors how
often the various primary items are clicked for more information,
and which items are ignored, or used less frequently. It will then
spawn simulations to infer how the future dashboard arrangement can
be changed to improve the user experience. The drill-down process
also provides feedback to the collection and filtering routines
(e.g., processes 104, 106) to ensure that desired information is
collected and made more prominent. In a similar manner, ignored
information no longer takes up valuable screen real estate (or in
an extreme case, is no longer collected). It is contemplated that
processes can evolve; and the continual application of scoring
mechanisms to determine the "fittest" aspects of the process,
coupled with the deliberately induced element of mutations
(experimental features) can help system 100 to adapt to the
changing scene of risk assessment in a manner superior to
classical, more static, processes.
[0071] Moreover, it is contemplated that cellular automata
principles can add a new dimension to genetic algorithm simulations
for feedback and self-adjustment of the collection, filtering,
relevancy, and presentation engine processes.
[0072] Embodiments of the present invention may comprise a special
purpose or general purpose computer including a variety of computer
hardware, as described in greater detail below.
[0073] Embodiments within the scope of the present invention also
include computer-readable media for carrying or having
computer-executable instructions or data structures stored thereon.
Such computer-readable media can be any available media that can be
accessed by a general purpose or special purpose computer. By way
of example, and not limitation, such computer-readable media can
comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage,
magnetic disk storage, or other magnetic storage devices, or any
other medium that can be used to carry or store desired program
code means in the form of computer-executable instructions or data
structures and that can be accessed by a general purpose or special
purpose computer. When information is transferred or provided over
a network or another communications connection (either hardwired,
wireless, or a combination of hardwired or wireless) to a computer,
the computer properly views the connection as a computer-readable
medium. Thus, any such a connection is properly termed a
computer-readable medium. Combinations of the above should also be
included within the scope of computer-readable media.
Computer-executable instructions comprise, for example,
instructions and data which cause a general purpose computer,
special purpose computer, or special purpose processing device to
perform a certain function or group of functions.
[0074] FIG. 6 and the following discussion are intended to provide
a brief, general description of a suitable computing environment in
which aspects of the invention may be implemented. Although not
required, aspects of the invention will be described in the general
context of computer-executable instructions, such as program
modules, being executed by computers in network environments.
Generally, program modules include routines, programs, objects,
components, data structures, etc. that perform particular tasks or
implement particular abstract data types. Computer-executable
instructions, associated data structures, and program modules
represent examples of the program code means for executing steps of
the methods disclosed herein. The particular sequence of such
executable instructions or associated data structures represent
examples of corresponding acts for implementing the functions
described in such steps.
[0075] Those skilled in the art will appreciate that aspects of the
invention may be practiced in network computing environments with
many types of computer system configurations, including personal
computers, hand-held devices, multi-processor systems,
microprocessor-based or programmable consumer electronics, network
PCs, minicomputers, mainframe computers, and the like. Aspects of
the invention may also be practiced in distributed computing
environments where tasks are performed by local and remote
processing devices that are linked (either by hardwired links,
wireless links, or by a combination of hardwired or wireless links)
through a communications network. In a distributed computing
environment, program modules may be located in both local and
remote memory storage devices.
[0076] With reference to FIG. 6, an exemplary system for
implementing aspects of the invention includes a general purpose
computing device in the form of a conventional computer 20,
including a processing unit 21, a system memory 22, and a system
bus 23 that couples various system components including the system
memory 22 to the processing unit 21. The system bus 23 may be any
of several types of bus structures including a memory bus or memory
controller, a peripheral bus, and a local bus using any of a
variety of bus architectures. The system memory includes read only
memory (ROM) 24 and random access memory (RAM) 25. A basic
input/output system (BIOS) 26, containing the basic routines that
help transfer information between elements within the computer 20,
such as during start-up, may be stored in ROM 24.
[0077] The computer 20 may also include a magnetic hard disk drive
27 for reading from and writing to a magnetic hard disk 39, a
magnetic disk drive 28 for reading from or writing to a removable
magnetic disk 29, and an optical disk drive 30 for reading from or
writing to removable optical disk 31 such as a CD-ROM or other
optical media. The magnetic hard disk drive 27, magnetic disk drive
28, and optical disk drive 30 are connected to the system bus 23 by
a hard disk drive interface 32, a magnetic disk drive-interface 33,
and an optical drive interface 34, respectively. The drives and
their associated computer-readable media provide nonvolatile
storage of computer-executable instructions, data structures,
program modules, and other data for the computer 20. Although the
exemplary environment described herein employs a magnetic hard disk
39, a removable magnetic disk 29, and a removable optical disk 31,
other types of computer readable media for storing data can be
used, including magnetic cassettes, flash memory cards, digital
video disks, Bernoulli cartridges, RAMs, ROMs, and the like.
[0078] Program code means comprising one or more program modules
may be stored on the hard disk 39, magnetic disk 29, optical disk
31, ROM 24, and/or RAM 25, including an operating system 35, one or
more application programs 36, other program modules 37, and program
data 38. A user may enter commands and information into the
computer 20 through keyboard 40, pointing device 42, or other input
devices (not shown), such as a microphone, joy stick, game pad,
satellite dish, scanner, or the like. These and other input devices
are often connected to the processing unit 21 through a serial port
interface 46 coupled to system bus 23. Alternatively, the input
devices may be connected by other interfaces, such as a parallel
port, a game port, or a universal serial bus (USB). A monitor 47 or
another display device is also connected to system bus 23 via an
interface, such as video adapter 48. In addition to the monitor,
personal computers typically include other peripheral output
devices (not shown), such as speakers and printers.
[0079] The computer 20 may operate in a networked environment using
logical connections to one or more remote computers, such as remote
computers 49a and 49b. Remote computers 49a and 49b may each be
another personal computer, a server, a router, a network PC, a peer
device or other common network node, and typically include many or
all of the elements described above relative to the computer 20,
although only memory storage devices 50a and 50b and their
associated application programs 36a and 36b have been illustrated
in FIG. 6. The logical connections depicted in FIG. 6 include a
local area network (LAN) 51 and a wide area network (WAN) 52 that
are presented here by way of example and not limitation. Such
networking environments are commonplace in office-wide or
enterprise-wide computer networks, intranets and the Internet.
[0080] When used in a LAN networking environment, the computer 20
is connected to the local network 51 through a network interface or
adapter 53. When used in a WAN networking environment, the computer
20 may include a modem 54, a wireless link, or other means for
establishing communications over the wide area network 52, such as
the Internet. The modem 54, which may be internal or external, is
connected to the system bus 23 via the serial port interface 46. In
a networked environment, program modules depicted relative to the
computer 20, or portions thereof, may be stored in the remote
memory storage device. It will be appreciated that the network
connections shown are exemplary and other means of establishing
communications over wide area network 52 may be used.
[0081] Preferably, computer-executable instructions stored in a
memory, such as hard disk drive 27, and executed by computer 120
embody the illustrated inference engines, including processes 104,
106 (including processes 218, 220) and engines 110, 116. Moreover,
computer 20 is suitably embodies computer 120.
[0082] In operation, system 100 transforms disparate data for use
in rendering an underwriting decision involving a potentially
insurable risk. The processes 104, 106, for example, receive data,
which is in a plurality of formats, from a plurality of sources
(i.e., external data 102). At least process 106 extracts the data
and converts it into one or more standard formats. The heuristic
engine 110 then filters the converted data by relevancy to the
underwriting decision to be rendered. The consolidation and
presentation engine 116 generates presentable knowledge from the
converted data, and presents the knowledge to a decision-making
entity for rendering the underwriting decision. By monitoring one
or more actions on the presented knowledge by the decision-making
entity, optimization process 122 can adjust one or more of steps as
a function of the monitored actions.
[0083] Alternatively, in operation, system 100 structures and
transforms disparate data for use in rendering an underwriting
decision involving a potentially insurable risk. The processes 104,
106, for example, retrieve data from a first database, such as
database 102, and transform the retrieved data into domain-specific
information. Once transformed, the information, which relates to
the potentially insurable risk, is stored in a second database,
such as staging area repository 108. The heuristic engine 110
defines one or more relevancy factors as a function of the
underwriting decision to be rendered and assigns at least one of
the relevancy factors to at least a portion of the information
stored in the second database. Additionally, consolidation and
presentation engine 116 providing an output of the second database
with the assigned relevancy factors to a decision-making entity for
rendering the underwriting decision.
[0084] The order of execution or performance of the operations in
embodiments of the invention illustrated and described herein is
not essential, unless otherwise specified. That is, the operations
may be performed in any order, unless otherwise specified, and
embodiments of the invention may include additional or fewer
operations than those disclosed herein. For example, it is
contemplated that executing or performing a particular operation
before, contemporaneously with, or after another operation is
within the scope of aspects of the invention.
[0085] Embodiments of the invention may be implemented with
computer-executable instructions. The computer-executable
instructions may be organized into one or more computer-executable
components or modules. Aspects of the invention may be implemented
with any number and organization of such components or modules. For
example, aspects of the invention are not limited to the specific
computer-executable instructions or the specific components or
modules illustrated in the figures and described herein. Other
embodiments of the invention may include different
computer-executable instructions or components having more or less
functionality than illustrated and described herein.
[0086] When introducing elements of aspects of the invention or the
embodiments thereof, the articles "a," "an," "the," and "said" are
intended to mean that there are one or more of the elements. The
terms "comprising," "including," and "having" are intended to be
inclusive and mean that there may be additional elements other than
the listed elements.
[0087] Having described aspects of the invention in detail, it will
be apparent that modifications and variations are possible without
departing from the scope of aspects of the invention as defined in
the appended claims. As various changes could be made in the above
constructions, products, and methods without departing from the
scope of aspects of the invention, it is intended that all matter
contained in the above description and shown in the accompanying
drawings shall be interpreted as illustrative and not in a limiting
sense.
* * * * *