U.S. patent application number 14/214636 was filed with the patent office on 2014-09-18 for methods and systems for growing and retaining the value of brand drugs by computer predictive model.
The applicant listed for this patent is Myrtle S. POTTER. Invention is credited to Myrtle S. POTTER.
Application Number | 20140278507 14/214636 |
Document ID | / |
Family ID | 51531900 |
Filed Date | 2014-09-18 |
United States Patent
Application |
20140278507 |
Kind Code |
A1 |
POTTER; Myrtle S. |
September 18, 2014 |
METHODS AND SYSTEMS FOR GROWING AND RETAINING THE VALUE OF BRAND
DRUGS BY COMPUTER PREDICTIVE MODEL
Abstract
The present invention is directed to a brand value growth and
retention system for brand drugs commercialized by brand drug
advertisers through a brand drug's lifecycle during patent
exclusivity and after loss of exclusivity. The brand value growth
and retention system iteratively analyzes combined computational
models of consumer, healthcare provider retailer and payor segment
data to produce brand drug promotional campaigns that are
predictive with modifying parameters that transform the promotional
campaigns over time. As a result, the brand drug promotional
campaign generates an increased number of brand drug purchases
while predicting the point where incremental promotional campaign
investments produce a diminishing number of incremental brand drug
purchases.
Inventors: |
POTTER; Myrtle S.;
(Dunwoody, GA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
POTTER; Myrtle S. |
Dunwoody |
GA |
US |
|
|
Family ID: |
51531900 |
Appl. No.: |
14/214636 |
Filed: |
March 14, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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61801978 |
Mar 15, 2013 |
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Current U.S.
Class: |
705/2 |
Current CPC
Class: |
G16H 20/10 20180101;
G16H 50/20 20180101; G06Q 30/0201 20130101; G06Q 30/0276 20130101;
G16H 70/40 20180101 |
Class at
Publication: |
705/2 |
International
Class: |
G06Q 50/22 20060101
G06Q050/22; G06Q 30/02 20060101 G06Q030/02 |
Claims
1. A computer-implemented method for generating a promotional
campaign in healthcare industry, comprising: executing a first
computational model on the consumer segment data to determine a
first substantially optimal brand drug promotional mix for
consumers who are candidates for a brand drug; executing a second
computational model on healthcare provider segment data to
determine a second substantially optimal brand drug promotional mix
for healthcare providers who treat the consumers that are
candidates for the brand drug; executing a third computational
model on a computer model on retail store segment data to determine
a substantially optimal product mix for retail stores that sell the
brand drug; executing a fourth computational model on a computer
model on payor segment data to determine a substantially optimal
contracting strategy for the brand drug; and generating a
promotional campaign for the brand drug by running a predictive
model of the consumer segment data, healthcare provider segment
data, retail store segment data and the payor segment data, based
on the combination of outputs from the first, second, third and
fourth computational models.
2. The method of claim 1, further comprising: generating a first
predictive element from the first computational model on the
consumer segment data; generating a second predictive element from
the second computational model on healthcare provider segment data;
and generating a third predictive element from the third
computational model on retail store segment data; generating a
fourth predictive element from the fourth computational model on
payor segment data; wherein the predictive model is generated based
on a quadripartite combination of the first predictive element, the
second predictive element, the third predictive element and the
fourth predictive element.
3. The method of claim 2, wherein each of the first, second, third
and fourth predictive elements partially affects the predictive
model in generating the promotional campaign.
4. The method of claim 1, wherein the promotional campaign
comprises a plurality of segment promotional plans, each
promotional plan including one or more tactic profiles, each tactic
profile being selected when a consumer segment in the consumer
segment data responds to a particular promotional tactic.
5. The method of claim 1, wherein the promotional campaign
comprises a plurality of segment promotional plans, each
promotional plan including one or more tactic profiles, each tactic
profile being created and selected when a consumer segment in the
consumer segment data responds to a particular promotional
tactic.
6. The method of claim 1, wherein the predictive model is adaptive
to a change in a market response, the market response being
affected by the first, second and third computational models.
7. The method of claim 6, wherein the predictive model is adapted
via the application of a learning machine that estimates parameters
thereby generating a transformed predictive model.
8. The method of claim 6, wherein the predictive model is adapted
via the application of a learning machine that modifies existing
parameters thereby generating a transformed predictive model.
9. The method in claim 1, wherein the predictive model combines
information from computational models in a linear manner, wherein
the combined information includes at least two of the consumer
segment data, healthcare provider segment data, retail sales data,
and the payor segment data.
10. The method in claim 9, wherein the combined information in the
predictive model provides explicit weights to one or more
components in the combined information.
11. The method of claim 1, wherein the predictive model is computed
from the following equation: SPP = i = 1 , m .alpha. ( t i ) .beta.
i T i ( F i , S j ) ##EQU00005## wherein the promotional campaign
comprises a plurality of segment promotional plans (SPP), the
symbol .alpha.(t.sub.i) representing temporal preferences including
order and weight, and the term .beta..sub.i*T.sub.i(F.sub.i,
S.sub.j) denoting one or more tactic profiles, coefficient
.beta..sub.i denoting weighted factors applying respectively each
corresponding tactic profile, the frequency F.sub.i applying to the
corresponding promotional tactic T.sub.i up to the m.sup.th tactic,
such that the symbol m represents the total number of tactics.
12. The method of claim 11, wherein the term .alpha.(t)'s denotes
any ordering or parallelizing temporal function.
13. The method in claim 1 wherein the promotional campaign is a
weighted combination of the segment promotional plans (SPP's).
14. The method of claim 13, wherein the promotional campaign (PC)
is represented by the following equation: PC = i = 1 , N .alpha. (
t i ) SPP i ##EQU00006##
15. The method of claim 1, wherein the promotional campaign
comprises explicit interaction terms among the actual or planned
segment promotional plans as well as individual segment promotional
plans (SPPs)
16. The method of claim 15, wherein the explicit interaction terms
are binary, comprising a planned or actual SPP interacting with a
second planned or actual SPP.
17. The method of claim 1, wherein the promotional campaign is
represented by the following equation, where j=1, M ranges over all
segment promotional plans in one or a plurality of promotional
campaigns, including active or planned promotion campaigns, and the
function G.sub.SPP(SPP.sub.i,SPP.sub.j) computes potential or
actual interactions among a plurality of segment promotional plans
contained in the promotional in the promotional campaign(s): PC = j
= 1 , M i = 1 , N .alpha. ( t i ) [ SPP i G spp ( SPP i , SPP j ) ]
##EQU00007##
18. The method of claim 2, wherein the initial promotional campaign
is modified to take into accounts interactions among the three
predictive elements.
19. The method of claim 18 wherein the interactions among the
predictive elements are binary comprising a first predictive
element interaction with a second predictive element.
20. The method of claim 19, wherein the interaction among the three
predictive elements (PEs) is governed by the following equation,
where the function G.sub.PE computes potential or actual
interactions among the three predictive elements: PC modified = PC
initial j = 1 , 4 i = 1 , 4 , i .noteq. j [ 1 + G pe ( PE i , PE j
) ] ##EQU00008##
21. The method of claim 1, further comprising selecting a different
segment promotional plan modifying the promotional campaign for a
particular consumer segment within the consumer segments if the
predictive model does not meet a predetermined substantially
optimal threshold.
22. The method of claim 21, further comprising determining the
benefit of a different promotional campaign and providing feedback
to a learning machine to re-estimate parameters and revise
corresponding predictions from one or more of the three predictive
elements.
23. The method of claim 1, further comprising training a learning
machine by invoking a machine learning method to estimate and
attempt to optimize parameters for prediction of one or more
computational models sourced from the executing step of the first
computational model, the executing step of the second computational
model, and executing step of the third computational model.
24. The method of claim 23, wherein the learning step comprises
learning from data sourced from the current campaign, data sourced
from at least one prior promotional campaign, and data sourced from
external market reception to the current promotional campaign, to
generalize learning from the collection of data.
25. The method of claim 23, wherein the machine learning method
comprises an active or proactive learning in which a new tactic may
attempt to jointly optimize both new knowledge gained about the
effectiveness of the new tactics and the immediate impact of the
selected campaigns and tactics.
26. The method of claim 4, wherein the machine learning method
comprises an active or proactive learning in which a new
promotional campaign attempts to jointly optimize both new
knowledge gained about the effectiveness of the new promotional
campaigns and the immediate impact of the selected promotional
campaigns and promotional tactics.
27. The method of claim 23, further comprising re-estimating one or
more computational models if not all results in the step of
estimating and attempting to optimize parameters for prediction of
one or more plurality of computational models are positive.
28. The method in claim 23, wherein the machine learning method
suggests multiple potential but mutually exclusive improvements
where one improvement is not positive and re-estimating the others
from the feedback of the first tested improvement.
29. A system for growing and retaining value in brand drugs,
comprising: a consumer segments module configured to execute a
first computational model on the consumer segment data to determine
a first substantially optimal brand drug promotional plan for
consumers who are candidates for a brand drug; a healthcare
provider module configured to execute a second computational model
on healthcare provider segment data to determine a second
substantially optimal prescription promotional plan for healthcare
providers who treat the consumers that are candidates for the brand
drug; a manufacturer payor module configured to execute a third
computational model on payor segment data to determine a
substantially optimal contracting strategy for the brand drug; and
a financial model simulator module, coupled to the consumer
segments module, the healthcare provider module, and the
manufacturer payor module, configured to generate a promotional
campaign for the brand-name drug by running a predictive model of
the consumer segment data, healthcare provider segment data, and
the payor segment data, based on the combination of outputs from
the first, second and third computational models.
30. A computer-implemented method for generating a promotional
campaign in healthcare industry, comprising: executing at least two
computational models for two segment data to determine a first
substantially optimal prescription promotional mix for a first
segment data the and a second substantially optimal prescription
promotional mix for a second segment data; and generating a
promotional campaign for the brand drug by running a predictive
model of the first segment data and the second segment data, based
on the combination of outputs from the first and second
computational models.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to U.S. Provisional
Application Ser. No. 61/801,978 entitled "Methods and Systems for
Growing and Retaining the Value of Brand Drugs by Computer
Predictive Modeling," filed on 15 Mar. 2013, the disclosure of
which is incorporated herein by reference in its entirety.
BACKGROUND
[0002] 1. Technical Field
[0003] The present invention relates generally to process and
optimization software, and more particularly to computer predictive
models of consumer, healthcare provider, retailer, pharmacy and
payor segment data to generate a flexible, responsive and adaptive
promotional campaign for growing and retaining the value of brand
drugs during a drug's lifecycle including its launch phase, growth
phase as well as the phase around the time of loss of exclusivity
(LOE).
[0004] 2. Related Art
[0005] Brand drugs marketed by most brand drug advertisers provide
the basis upon which many of these companies are able to meet
consumer medical needs and generate revenue and profits during a
brand drug's lifecycle. The brand drug lifecycle also overlaps with
the period of market exclusivity defined by the brand drug's
patents. This period of market exclusivity provides years of market
sales monopoly for the brand, but, at the same time, it imposes a
limited duration of revenue and profit due to the expiration of the
associated patents. As brand drug patents expire, brand drug
advertisers confront the inevitable risk of rapid and significant
loss of revenue and profits.
[0006] Each year, billions of dollars of brand drugs lose
exclusivity thereby opening the way for generic manufacturers to
enter the market with the same or similar drug at greatly
discounted prices. It is estimated that in the United States $267
billion of brand drugs will lose patent exclusivity from 2010 to
the end of 2016, and more than $50 of billion brands will become
generic within the following five years. A brand drug's sales
during its lifecycle often reach peak at the time of LOE; such was
the case for Singulair. The annual sales of Singulair at the time
of loss of exclusivity were approximately $3.3 billion, making it
the biggest selling prescription drug for its manufacturer, Merck.
After losing its patent exclusivity Singulair suffered a
precipitous and material decline in revenue and profit with sales
dropping 90% in just four weeks. It is estimated that brand drugs
typically retain significantly less than 10% brand share when
reaching the period of post-exclusivity.
[0007] Brand drug advertisers have long been known to invest
billions of dollars to bolster the sales of their brand drugs.
Second to drug research and development costs, the combined costs
of sales, marketing and promotion far exceed any other single
expense item for most Brand drug advertisers. A major and recurring
challenge for brand drug advertisers is the lack of predictive
methods that can be applied to the combined individual promotional
investment decisions for brand drugs to drive the highest sales of
brand drugs while at the same time predicting the point at which
massive investments in promotion no longer produce incremental
value. There is a need for a real-time, predictive system that
combines the correlations of various consumer, healthcare provider,
retailer and payor segment data for predictive value. The benefits
of such a system for brand drug advertisers are greater brand sales
at significantly lower costs. With the growing pressure to contain
healthcare system costs, methods such as the brand drug value
growth and retention system as described below can have a
meaningful and material impact on the industry.
[0008] Given the finite time that brand drug advertisers' patents
allow for the exclusive sale of the related brands, it is desirable
to grow and retain brand drug value during the brand's lifecycle,
which includes its launch phase, growth phase and the phase around
the loss of exclusivity phase. It is therefore desirable to have a
software predictive model to generate, apply, refine, modify,
transform, and improve, with a feedback mechanism through machine
learning, one or more promotional campaigns for growing and
retaining brand drug value.
SUMMARY
[0009] Methods, computer program products, and computer systems are
described for growing and retaining the value of brand drugs by
predictive computational modeling of consumer segment, healthcare
provider segment, retailer segment, and payor segment data to
generate a promotional campaign during a brand drug launch phase,
growth phase, or around the loss of exclusivity phase. A brand drug
value growth and retention engine comprises a financial model
simulator module, a consumer segments module, a healthcare segments
module, a retailer segments module, a promotional campaign module,
a manufacturer copay card pricing module, a manufacturer brand
execution module, and other modules. The consumer segments module
is configured to provide a computational modeling of consumer
segments to determine an optimal promotional plan for a directed
consumer segment for a brand drug. The healthcare provider module
is configured to provide a computational modeling on healthcare
provider segments for the brand drug. The manufacturer PBM/payor
strategy module and the manufacturer PBM/payor execution module are
configured to provide a computational model of payor segments. The
financial model simulator module is configured to receive the
computational model consumer segment data, the computational model
healthcare provider segment data, and the computational model payor
segment data, and executes a predictive model of promotional
tactics to segments of the consumers, healthcare providers and
payors to produce an optimal promotional campaign for the specified
brand drug.
[0010] A promotional campaign represents a combination of segment
promotional plans. A first set of segment promotional plans is for
rolling out to consumer segments, where each segment promotional
plan has one or more tactic profiles. A tactic profile is
applicable when a particular consumer segment responds well to the
directed promotional tactic. A second set of segment promotional
plans rolls out to healthcare provider segments, where each segment
promotional plan has one or more tactic profiles. A tactic profile
is applicable when a particular healthcare provider segment, which
can be grouped by behavior of individual healthcare providers,
responds well to the directed promotional tactic. A third set of
segment promotional plans rolls out to payor segments, where each
segment promotional plan has one or more tactic profiles. A tactic
profile is applicable when a particular healthcare provider
segment, which can be grouped by behavior of individual healthcare
providers, responds well to the directed promotional tactic.
[0011] The brand drug value growth and retention engine includes a
dashboard interface module configured to provide a user interface
to communicate data and control information between the brand drug
value growth and retention engine and a master dashboard located at
the computer device of the brand strategist. The dashboard is
partitioned into different sections for displaying the consumer
segment data, healthcare provider data and payor segment data, as
well as the predictive modeling result of a recommended promotional
campaign. Alternative segment promotional plans are also provided
when the current promotional campaign is less than optimal as
determined by the financial model simulator module.
[0012] Broadly stated, a method for selecting a promotional
campaign in the healthcare industry, comprising executing a first
computational model on the consumer segments data to determine a
first substantially optimal brand drug promotional mix for
consumers who are candidates for a brand-name drug; executing a
second computational model on healthcare provider segments data to
determine a second substantially optimal brand drug promotional mix
for healthcare providers who treat the consumers who are candidates
for the brand-name drug; executing a third computational model on
payor segment data to determine a substantially optimal contracting
strategy for the brand-name drug; and generating a promotional
campaign for the brand-name drug by running a predictive model of
the consumer segments data, healthcare provider segments data and
payor segment data, based on the combination of outputs from the
first, second and third computational models.
[0013] Advantageously, the present invention is an effective
predictive model for generating and optimizing a promotional
campaign for brand drug advertisers to grow and retain the value of
brand drugs when launching or growing a product, or around the time
of patent expiration.
[0014] The structures and methods of the present invention are
disclosed in the detailed description below. This summary does not
purport to define the invention. The invention is defined by the
claims. These and other embodiments, features, aspects, and
advantages of the invention will become better understood with
regard to the following description, appended claims and
accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The invention will be described with respect to one or more
various embodiments thereof, and reference will be made to the
drawings. The drawings are provided for purposes of illustration
and merely depict typical or example embodiments of the invention.
These drawings are provided to facilitate the reader's
understanding of the invention and shall not be considered limiting
of the breadth, scope, or applicability of the invention. It should
be noted that for clarity and ease of illustration these drawings
are not necessarily made to scale.
[0016] FIG. 1 is a high-level block diagram illustrating a brand
drug value growth and retention system in a cloud computing
environment in accordance with the present invention.
[0017] FIG. 2 is a block diagram illustrating one embodiment of a
brand drug value growth and retention engine in accordance with the
present invention.
[0018] FIG. 3 is a pictorial representation of a brand drug
lifecycle with seven key strategies in accordance with the present
invention.
[0019] FIG. 4A is a flow diagram illustrating a predictive model of
brand drug value growth and retention in pre-LOE and post-LOE
phases in accordance with the present invention; and FIG. 4B is a
flow diagram illustrating a predictive model of brand drug value
growth and retention in the launch phase of a brand drug in
accordance with the present invention
[0020] FIG. 5 is a flow diagram illustrating a first embodiment of
a predictive model method in brand drug value growth and retention
in accordance with the present invention.
[0021] FIG. 6A is a table illustrating a promotional campaign from
a collection of segment promotional plans in accordance with the
present invention; FIG. 6B is a graphical curve illustrating
different promotional tactics in accordance with the present
invention; FIG. 6C is a flow diagram providing one illustration of
promotional campaign at time t.sub.o with multiple promotional
tactics that are applied to multiple segments of consumers; FIG. 6D
is a flow diagram providing one illustration of promotional
campaign during a first time period with multiple promotional
tactics that are applied to multiple segments of consumers; and
FIG. 6E is a flow diagram providing one illustration of promotional
campaign during the first time period and a second time period with
multiple promotional tactics that are applied to multiple segments
of consumers.
[0022] FIG. 7 is a flow diagram illustrating a second embodiment of
a predictive model method in brand drug value growth and retention
in accordance with the present invention.
[0023] FIG. 8 is a graphical curve illustrating the impact of the
incremental value to the brand drugs by the promotional campaign
launched by the brand strategist relative to a conventional
approach.
[0024] FIG. 9A is a flow diagram illustrating the communications
between the brand strategist dashboard, the drug manufacture
dashboard and the payor dashboard in accordance with the present
invention; FIG. 9B is a pictorial diagram illustrating one
embodiment of the brand strategist dashboard in accordance with the
present invention; and FIGS. 9C-9I are exemplary graphs that may be
displayed on the brand strategist dashboard in accordance with the
present invention.
[0025] FIG. 10 is a block diagram illustrating an example of a
computer device on which computer-executable instructions to
perform the methodologies discussed herein may be installed and
run.
DETAILED DESCRIPTION
[0026] A description of structural embodiments and methods of the
present invention is provided with reference to FIGS. 1-10. It is
to be understood that there is no intention to limit the invention
to the specifically disclosed embodiments but that the invention
may be practiced using other features, elements, methods, and
embodiments. Like elements in various embodiments are commonly
referred to with like reference numerals.
[0027] The following definitions may apply to some of the elements
described with regard to some embodiments of the invention. These
terms may likewise be expanded upon herein.
[0028] Brand Drug--refers to a medication, including prescription
drugs, over the counter (OTC) drugs, and supplements that are
associated with a proprietary trade name, often have a trade mark,
and that in many cases have patents that provide monopolistic
market protections for a finite amount of time and/or intellectual
property protections.
[0029] Brand Drug Advertisers--refers to manufacturers and
retailers.
[0030] Brand Samples--refers to small quantities of free brand
drugs provided by a brand drug advertiser for distribution by
healthcare providers to consumers or directly to consumers.
[0031] Brand Strategist--refers to a person who monitors or directs
the monitoring of market and brand drug trends and oversees the
planning, management and execution of brand drug analyses,
planning, pricing, contracting, advertising and promotional
campaigns and insures that tactics are being delivered to
consumers, healthcare providers and payors to achieve desired
outcomes. The brand strategists may or may not be an employee of
the manufacturer.
[0032] Computational Model (also referred to as "computational
modeling," "computer model," or "computer modeling"--refers to any
software that models an external process (such as a promotional
campaign).
[0033] Consumer Segmentation--refers to the process of defining and
subdividing a large homogenous group of consumers who are currently
using or who are candidates for using brand drugs into clearly
identifiable groups having similar demographics, needs, wants,
demand characteristics or behaviors for the purpose of designing a
segment promotional plan that matches the expectations of consumers
in the segments. In one embodiment, which is not intended to limit
the various constructions of a consumer segmentation, within this
identified population the entire pool of consumers is subdivided
based on sub-regions or segments that make up the whole geography.
For example, in California, a state may be divided into 25
segments, such as San Francisco, Oakland, San Diego, Los Angeles,
Santa Barbara, etc.
[0034] Copay Card--refers a multiple use or single use tool through
which rebates and purchase discounts are offered to a consumer who
uses, or is a candidate for, a particular brand drug. Copay cards
come in many forms including plastic, paper or any electronic
equivalent on a computer, smartphone, tablet or wearable device.
Copay cards are most often financed by brand drug advertisers.
[0035] Coupon--refers to a voucher entitling the holder to rebates
and purchase. Coupons come in many forms including plastic, paper
or any electronic equivalent on a computer, smartphone, tablet or
wearable device. Coupons are most often financed by brand drug
advertisers
[0036] Direct to Consumer (DTC)--refers to a form of brand drug
advertising that is directed toward consumers, rather than
healthcare professionals.
[0037] Distribution Channels--refers to networks of organizations,
including manufacturers, brand drug advertisers, wholesalers,
retailers and pharmacies supply brand drugs to consumers.
[0038] Elasticity Curve--refers to a measure used to show the
responsiveness, or elasticity, of the ratio of the percentage
change in at least one variable to the percentage change in another
variable.
[0039] Finite Post-LOE Phase--refers to a predetermined period of
time as set forth by U.S. Patent Law that allows for certain brand
drugs to exist on the market with a restricted number of generic
competitors.
[0040] Formulary--refers to a list or database of brand drugs. The
main function of a formulary is to specify the drugs that are
approved to be prescribed by healthcare providers under a
particular contract with a payor who provides a drug benefit plan
to consumers. Consumers pay varying portions of the cost of the
drug (known as a copay) for prescription drugs that are on
formulary based on which drugs are preferred by the payor. For
drugs that are not on formulary, consumers must pay a larger
percentage of the cost of the drug, sometimes 100%. Formularies
vary between drug plans and differ in the breadth of drugs covered
and costs of copay and the drug insurance benefit premium. Most
formularies encourage generic substitution.
[0041] Healthcare Provider--refers to physicians, doctors, nurses,
physician assistants, dentists, optometrists, podiatrists,
osteopaths, or any individual who has the state or federal
government authority to prescribe drugs as well as those whose
industry stature and influence constitute them as being brand drug
thought leaders.
[0042] Healthcare Provider Segmentation--refers to the process of
defining and subdividing a large homogeneous group of healthcare
providers. One embodiment of the physician segmentation is to
divide the entire population of physicians into clearly
identifiable groups having similar demographics, needs, wants,
demand characteristics or behaviors for the purpose of designing a
segment promotional plan that matches the expectations of
physicians in the segments. For example, within a given population
of physicians, physicians who are surgeons may be segmented as one
group, and family doctors may be part of a different group or
segmentation.
[0043] Key parameter--refers to a parameter, typically
numerically-valued in a predictive element or model or learning
machine whose value affects the quality of the prediction. For
instance, a key parameter can be associated with each factor of a
drug--cost, availability, generic competition (if any), and side
effects--that a predictive model would use in order to generate a
promotional tactic or segment promotional plan or promotional
campaign. Key parameters may be set manually from experience or may
be estimated by a learning machine.
[0044] Learning Machine--refers to a software system that creates a
predictive model or more typically infers, refines and adapts the
parameters of a predictive model based on past or current training
data. Examples of learning machines include decision trees, random
forests, Bayesian classifiers, neural networks, support vector
machines, and logistic regression.
[0045] Loss of exclusivity (LOE)--refers to the expiration of
patents granted by the U.S. Patent and Trademark Office, which is
calculated by standard duration of a patent plus any applicable
Patent Term Adjustment (PTA).
[0046] Loyalty Cards (also known as Affinity Cards)--a plastic or
paper card, visually similar to a credit card or debit card, or
digital card that identifies the cardholder as a member in a
loyalty or affinity program. By presenting the card, the purchaser
is typically entitled to either a discount on purchases, or points,
credits, rewards, rebates or credits that can be used for current
or future purchases or for merchandise rewards.
[0047] Manufacturers--refers to companies that research, develop,
produce, and/or market drugs licensed for use as medications
including but not limited to pharmaceutical companies, biotech
companies and consumer packaged goods companies.
[0048] Optimal--inclusive of both the mathematical meaning of the
best or highest-valued outcome, and a looser general meaning of
producing an outcome better than others considered given a limited
level of computation or effort. It is often the case that
optimality can be approximated closer with increasing effort or
computation (such as in submodular functions,
http://en.wikipedia.org/wiki/Submodular_set_function), but for
practical reasons, such as diminishing returns, the computation is
halted with the best results so far, and that result is labeled
"optimal," or "optimal" for the effort expended. This is also
called "near optimal" or "approximately optimal" in the art.
[0049] Payor--refers to entities other than the consumer that
finance or reimburse the cost of brand drugs. This term refers to
PBMS, health insurance companies, other third-party payors or
health plan sponsors (e.g. employers or unions).
[0050] Pharmacy Benefit Manager (PBM)--refers to third-party
administrators of prescription drug insurance benefit programs who
are primarily responsible for processing and paying prescription
drug insurance claims and supplying prescription drugs via mail
distribution channels to consumers. PBMs also develop and maintain
drug formularies http://en.wikipedia.org/wiki/Formulary, enter
contract arrangements with pharmacies, and negotiate discounts and
rebates with drug manufacturers. Currently a majority of Americans
receive prescription drug benefits administered by PBMs.
[0051] Predictive--refers to generating an expectation of a future
outcome based on presently available information.
[0052] Predictive Element--refers to a computer model or program
that, given a set of inputs, uses one or more methods internally to
predict a tactic and/or outcome optionally with a weight or
confidence score. For instance, given attributes of a certain
consumer segment, such as cost-sensitivity, medial needs etc., a
predictive element would generate a tactic (e.g. a way to reach
best reach this targeted consumer segment) and optionally a measure
of estimated effectiveness.
[0053] Predictive Model--refers to an automated or semi-automated
process of generating a prediction based on a model, typically
combining software and data. The model may be programmed in
software and its parameters (e.g. weights) modified or optimized by
a learning machine or by a domain expert.
[0054] Promotional Campaign--refers to a combination of segment
promotional plans which consumer, healthcare provider, retailer,
pharmacy or payor segments are responsive to specific promotional
tactic profiles deployed during a brand drug product launch phase,
growth phase, or around the brand drug's loss of exclusivity
phase.
[0055] Promotional Channels--refers to the physical distribution
network or electronic distribution networks (meaning computer
communication media or handheld devices) through which brand drug
promotional campaigns are distributed to patients, consumers,
healthcare providers and/or payors.
[0056] Promotional Mix--refers to specific combination of
promotional methods d for a brand drug. Elements of a promotion mix
may include print advertising, DTC advertising, digital advertising
or other means of advertising.
[0057] Promotional Tactics--refers to a collection of tactics
deployed to a particular segment of consumers, healthcare providers
or payors pertaining to a brand drug. Some popular promotional
tactics include TV advertisement, Internet advertisement, social
networking advertisement, direct mail advertisement, presentations
by sales representatives either by telephone, computer, mobile
device or in person, and copay cards.
[0058] Retail (retail stores, retailer)--refers to pharmacies,
supermarkets, grocery stores, big box stores and other retail
outlets.
[0059] Sales Presentation--refers to detailed information about a
product or product-line that is presented by a sales person or
sales team face to face or electronically to a healthcare provider,
PBM or payor for the purpose of convincing the healthcare provider,
PBM or payor to use or allow the use of a brand drug or set of
brand drugs.
[0060] Segment Promotional Plans--refers to each segment
promotional plan comprised of a collection of tactic profiles that
have been determined to be effective and responsive with a
particular consumer segment, healthcare provider segment, retailer
or payor segment.
[0061] Switch Data--refers to consumer and payor prescription
transaction data created by certain systems technology companies
for the purpose of managing and monitoring the processing of
prescription drug claims and claims payment cycles. These data are
often provided to or sold to healthcare providers, pharmacies,
wholesalers, retailers, PBMs, payers or others.
[0062] Tactic Profile refers to a promotional tactic to which a
particular consumer segment, healthcare provider segment, retailer
segment or payor segment responds, either positively, negatively or
neutrally.
[0063] Quadripartite Model--refers to a computer-implemented
combination of four different models or predictive elements that
generates a joint or combined prediction. For instance, a
combination of a healthcare provider element, a payor (e.g.
insurance) element, and a consumer or consumer group element.
System Architecture
[0064] FIG. 1 is a high-level block diagram illustrating one
embodiment of a brand drug value growth and retention system 1 in a
cloud computing environment 2 for conducting a predictive model for
growing and retaining the value of brand drugs. The brand drug
value growth and retention system 1 is coupled to a computer device
3a of a brand strategist 4 and through a network 5 to a computer
device 3 of brand drug manufacturers 6, a computer device 3 of
consumers 7, and other intermediaries between the drug
manufacturers 6 and the consumers 7. Each of the intermediaries,
PBMs 8, pharmacies 9, retailers stores 10, doctors and hospitals
11, special pharmacies 12, wholesalers 13, and direct mail
prescription providers 14 has an associated respective computer
device 3d, 3e, 3f, 3g, 3h, 3i and 3j. Collectively, the brand drug
manufacturers 6, the consumers 7, and the intermediaries operate as
cloud clients 15. The cloud clients 15 communicate with the brand
value growth and retention system 1 through the network 5, either
wirelessly or via a wired connection. The retail stores 10 include
supermarkets, groceries, pharmacies and other retail segments.
[0065] The brand value growth and retention system 1 includes a
computer processor 16 for executing a cloud operating system 17 and
a brand drug value growth and retention engine 18, which are
configured on a random access memory (RAM) 19. An authentication
module 20 is also part of the brand drug value growth and retention
system 1 for authenticating a cloud client. The brand drug value
growth and retention system 1 also includes a virtual storage 21,
which includes a virtual consumer database 22 for storing consumer
data, a virtual healthcare provider (HCP) database 23 for storing
healthcare provider data, a virtual payor database 24 for storing
PBM/payor segment data, and a virtual retail database 24b for
storing retail data. The computer device 3a has a master dashboard
25, which displays data for viewing and assessing by the brand
strategist 4. The brand drug manufacturers 6, the consumer 7, and
the intermediaries also have a dashboard 25 at their disposal
located in their respective computer devices.
[0066] The cloud system 2 is also referred to as web/Hypertext
Transfer Protocol (HTTP) server. Alternatively, the authentication
module 20 can be a separate server, which may employ a variety of
authentication protocols to authenticate the user, such as a
Transport Layer Security (TLS) or Secure Socket Layer (SSL), which
are cryptographic protocols that provide security for
communications over networks like the Internet. The protocols
described herein are merely exemplary, and embodiments of the
present invention include other emerging and new protocols.
[0067] In one embodiment, the cloud computer system 2 is a
browser-based operating system communicating through an
Internet-based computing network that involves the provision of
dynamically scalable, and often virtualized, resources as a service
over the Internet, such as iCloud.RTM. available from Apple Inc. of
Cupertino, Calif., Amazon Web Services (IaaS) and Elastic Compute
Cloud (EC2) available from Amazon.com, Inc. of Seattle, Wash., SaaS
and PaaS available from Google Inc. of Mountain View, Calif.,
Microsoft Azure Service Platform (Paas) available from Microsoft
Corporation of Redmond, Wash., Sun Open Cloud Platform available
from Oracle Corporation of Redwood City, Calif., and other cloud
computing service providers.
[0068] The web browser is a software application for retrieving,
presenting and traversing a Uniform Resource Identifier (URI) on
the World Wide Web provided by the cloud computer 2 or web servers.
One common type of URI begins with HTTP and identifies a resource
to be retrieved over the HTTP. A web browser may include, but is
not limited to, browsers running on personal computer operating
systems and browsers running on mobile phone platforms. The first
type of web browsers may include Microsoft's Internet Explorer,
Apple's Safari, Google's Chrome, and Mozilla's Firefox. The second
type of web browsers may include the iPhone OS, Google Android,
Nokia S60 and Palm WebOS. Examples of a URI include a web page, an
image, a video, or other type of content.
[0069] The network 5 can be implemented as a wireless network, a
wired network protocol or any suitable communication protocol, such
as 3G (third-generation mobile telecommunications), 4G
(fourth-generation cellular wireless standards), long-term
evolution (LTE), 5G, a wide area network (WAN), Wi-Fi.TM. like
wireless local area network (WLAN) 802.11n, or a local area network
(LAN) connection (internetwork--connected to either WAN or LAN),
Ethernet, Bluebooth.TM., high frequency systems (e.g., 900 MHz, 2.4
GHz and 5.6 GHz communication systems), infrared, transmission
control protocol/internet protocol (TCP/IP) (e.g., any of the
protocols used in each of the TCP/IP layers), hypertext transfer
protocol (HTTP), BitTorrent.TM., file transfer protocol (FTP),
real-time transport protocol (RTP), real-time streaming protocol
(RTSP), secure shell protocol (SSH), any other communications
protocol and other types of networks like a satellite, a cable
network, or optical network set-top boxes (STBs).
[0070] The brand drug manufacturers 6 have various distribution
channels to distribute brand drugs to the consumers 7. FIG. 1 shows
one embodiment of such distribution channels, but the present
invention is not limited to this embodiment. Various distribution
channels are also applicable. In one embodiment, after the pharmacy
benefit manager (PBM) 8 receives brand drugs from manufacturers 6,
the PBM 8 distributes brand drugs to the pharmacy 9, which then
sells the drugs to the consumers 7. The PBM 8 may also deliver the
drugs via the mail to consumers on behalf of health plan
providers.
[0071] A consumer obtains their brand drugs from a variety of
sources, of which six exemplary sources are provided herein. The
first source from which consumers can get their brand drugs is from
a hospital 11 or prescribed by a doctor directly because the doctor
office sometimes operates as a point of sale location. The second
source of distribution channel is a pharmacy 9, such as CVS,
Walgreens, independent pharmacies etc., where the consumer can
purchase brand drugs. A third source from which the consumer may
obtain brand drugs is from a PBM 8. A fourth channel of
distribution is a wholesaler 13, which buys large quantities of
brand drugs to resell to retail stores, PBMs, physician offices,
hospitals, consumers and others. A special pharmacy 12 provides a
fifth source for drug distribution. A sixth source of is retail
stores 10 such as grocery stores, big box stores etc. Overall,
manufacturers 6 have numerous channels to distribute brand drugs to
consumers and to ensure that consumers use the drugs as
prescribed.
[0072] The distribution channels of drugs are becoming more
integrated, offering brand drug advertisers, manufacturers 6 and
consumers 7 more active and direct interactions. The majority of
the consumers who have an insurance drug benefit get their drugs
through a PBM. The PBM 8 operates on behalf of payors and
distributes the prescription drugs to pharmacies 9, retail stores
10, hospitals 11 or directly to consumers 7. As a distribution
channel, the PBMs 8 are an integrated delivery network in which
synchronized consumer information flows across different entities
enabling a more direct communication between manufacturers 6 and
consumers 7. Additionally, PBM companies have the capability to
distribute prescription infusible drugs because PBM companies may
also have special distribution channels, such as specialty
pharmacies 12 that carry infusible drugs and other specialty
pharmaceutical products for certain patients such as those with
cancer, hemophilia, cystic fibrosis, organ transplant, etc. The
PBMs 8 are highly automated and are able to offer efficient service
to consumers, including mailing order prescriptions. Specialty
pharmacies 12 also exist as independent entities distinct and
separate from PBMs.
[0073] In addition to being key components in the distribution
infrastructure, drug manufacturers 6 are often able to control the
pricing of the brand drugs sold to consumers 7 during the
exclusivity period. The brand value growth and retention system is
used as a software tool to maintain a prescription drug's pricing
for the drug manufacturers 6 during the period preceding and after
the loss of exclusivity date. The brand drug value growth and
retention engine also produces a promotional campaign after the
exclusivity period ends to retain the value of a branded drug for
the brand drug advertisers and manufacturers 6. During the
distribution process from the manufacturers 6 to the consumers 7, a
brand drug value growth and retention system can be put in place,
which increases the likelihood that a consumer will stay with a
specific brand drug for a period of time, even after the brand drug
has lost its exclusivity. Essentially, the brand drug value growth
and retention engine creates a new period of commercialization, and
its software facilitates the retention of brand value for brand
drugs after the exclusivity period has ended. Additionally, the
brand drug value growth and retention engine software allows the
storage of a massive amount of consumer information in the virtual
storage 21. The virtual consumer database that can also be linked
to healthcare provider, retailer and payor virtual databases 22,
23, 24, 25. In some embodiments, a consumer would make a choice to
opt-in to the consumer virtual database. In turn, in one embodiment
the consumer virtual database and the HCP virtual database are
systems used to create retrospective analysis and the construction
of a predictive model. In the HCP database, the data gathered from
the physicians, third-party audited data aggregators of physician
prescribing like IMS, NPA and others and third-party aggregators of
audited data of brand drug advertiser promotion activity is used to
assess the brand drug use by consumers and, in turn, affect
promotional tactic profiles. The data is evaluated by focusing on
the different kinds of promotional activities that physicians
report as having been directed to them, which allows the brand drug
value growth and retention engine software to build a predictive
model around the information to assess future behavior of
physicians, prescribing changes and prescribing patterns. In the
consumer system, the consumer information can be drawn from a wide
variety of sources including but not limited to data inputs from
consumer behavioral databases like Acxiom, consumer medical record
databases, consumer record databases of retail stores, pharmacies,
PBMs, wholesalers and switch data companies, among others, which
permit the user to plot not only the behavior of consumers but also
the optimization of a predictive consumer model, revealing how
consumers use or are likely to use a particular brand drug.
[0074] One skilled in the art will recognize that the brand value
growth and retention system 1 implemented in the cloud computing
environment 2 illustrated in FIG. 1 is merely exemplary, and that
the embodiments described herein may be practiced and implemented
using many other architectures and environments, such as a
client-server platform.
[0075] Optionally, network security can be added to the cloud
system (brand drug value growth and retention system) 1 in the
cloud computing environment 2 to make the cloud system 1 secure and
compliant. Network security can be enhanced placing a firewall
system between the processor/server 16 and the cloud clients 15.
Additional network security can also be enhanced using a
client-side firewall system on the cloud clients 15. Moreover, the
cloud system 1 can employ a backup method in compliance with SAS 70
and HIPAA requirements.
Software Architecture
[0076] FIG. 2 is a block diagram illustrating one embodiment of a
brand drug value growth and retention engine 18. The brand drug
value growth and retention engine 18 is a comprehensive software
tool for optimizing a promotional campaign for a particular brand
drug by considering a multitude of data inputs, including
consumers, healthcare providers, payors, at least one predictive
tool, and a feedback mechanism through a learning machine. The
brand drug value growth and retention engine 18 comprises several
modules, including the financial model simulator module (and
optional dashboard) 26, a consumer segments module 28a, a
healthcare segments module 28b, a retailer segments module 28c, a
payor segments module 28d, the manufacturer copay card pricing
module (optional report and optional dashboard) 29, the
manufacturer brand execution module 30, the manufacturer PBM/payor
strategy module 31, the manufacturer PBM/payors execution module
32, the promotional campaign module 33, the sales presentations
brand samples module 34, the media medical meetings module 35, the
dashboard interface module 36, and a bus 41. The payor segments
module 28d is configured to provide a predictive module that is
used to predict the behavior of consumer and, especially, the
predictive behavior around the propensity or likelihood that
consumers use a copay card as a secondary source for paying for
their medication. The predictive model in the consumer segments
module 28a operates to predict when patients desire to use their
drugs. For patients who are using the brand drug, the predictive
model computes the promotional spending mix in the actual dollar
amount of what patients are spending and how patients most
effectively spend money around a copay card and other related
factors. The healthcare provider segments module 28b is configured
to provide a predictive model of promotional activities directed to
healthcare providers, which include physicians, nurse practitioners
and physician assistants. The manufacturer PBM/payor strategy
module 31 is configured to provide a predictive model of behaviors
of PBMs, insurance companies, and other payors including government
payors. The term "copay card" refers generally and broadly to a
consumer loyalty card, which can come in various forms, including
digital coupons via a smartphone or a computer, a digital copay
card, a traditional copay card, etc.
[0077] The financial model simulator module 26 is configured to
receive one or more predictive measures from the consumer segments
module 28a, healthcare provider segments module 28b, and
manufacturer PBM/payor strategy module 31, as well as knowledge
from investment decisions that are made by experts to predict brand
profitability and cash flows. The output from the tool expresses
the incremental brand drug units generated, increment brand drug
revenue generated and the incremental profit generated. The
financial model simulator module 26 continuously and interactively
observes the incoming data associated with a particular copay card
and matched control groups of another company's copay card to
reveal the promotional output data. The output report will show the
profile of a consumer who is most likely to use a copay card. The
output report also shows the profile of healthcare providers,
specifically those which healthcare providers who use copay cards.
The output report will also show which consumers are already on a
brand drug or which types of consumers are already on that
company's drug, so that a drug manufacturer 6 will not spend money
to formulate an advertisement campaign to get these consumers on a
copay card because these consumers are already using an identified
prescription drug.
Process Flow
[0078] FIG. 3 is a pictorial representation of a brand drug
lifecycle with seven key strategies. In one embodiment, a
successful pharmaceutical company advertiser typically attempts to
grow a brand's revenue and profit by applying seven strategies
during its lifecycle, with effectiveness measured by revenue and
profitability along time dimensions. Initially, a prescription drug
manufacturer creates a target product profile at time t.sub.1. Once
a target product profile has been established, clinical development
and market development activities ensue at time t.sub.2.
Subsequently, at time t.sub.3, launch activities are started, which
allows the segmentation of potential target consumers. The regional
launch of a brand drug positions the brand drug in the launch
market based on a selected promotional strategy and execution
tactics. Launch of the drug in other regions around the globe
generally occurs at time t.sub.4 once the clinical development and
market development plans for those regions have been completed and
the necessary regulatory approvals have been garnered. At time
t.sub.5, the duration during the launch and growth phases of a
brand drug, that consumer experience and engagement with the drug
provides valuable data to create more consumer strategies for
growing the product. Finally, to grow a brand drug even further, a
pharmaceutical company often seeks new claims, indications,
formulations and uses through additional clinical trials and new
regional regulatory filings at time t.sub.6. From target product
profile through market development, launch and growth, a brand
drug's lifecycle generally reaches its peak sales and later
declines during a period in which either a competing drug entry
takes market share from the established brand or the patent
protection of a brand drug expires leading to its Drug Industry
Patent Cliff and its eventual loss of exclusivity in the market at
time t.sub.7.
[0079] FIG. 4A is a flow diagram illustrating the effect of a
predictive model of the brand drug growth, value, a retention
engine in pre-LOE period 37 and a post-LOE period 38. The brand
strategist 4 formulates a pre-LOE promotional campaign(s) and a
post-LOE promotional campaign(s) relative to the patent expiration
of a brand drug to generate and retain sales of the brand drug used
by consumers prior to and leading up to the period around patent
expiration. The application of the brand value growth and retention
engine 18 produces the greatest amount of drug brand volume for the
least number of promotional campaign dollars and takes into account
the following promotional tactics: sales electronic presentations,
sales face-to-face presentations, formulary positions, DTC
advertising, print advertising, mobile advertising (including
smartphones, tablets, and wearable sensors), social network
advertising, medical meetings for healthcare professionals,
celebrity blogging, samples, reimbursement rates, rebates,
discounts, secondary insurance in the form of copay cards, loyalty
cards, coupons, vouchers to name just a few.
[0080] One embodiment of the overall brand value growth and
retention engine 18 comprises a number of key steps as illustrated
in FIG. 4 of the present invention. At time t.sub.1, which in one
example is about 18 to 24 months prior to the brand drug loss of
exclusivity, the brand drug value growth and retention engine 18 is
configured to generate a predictive model that produces a
promotional campaign(s) prior to loss of exclusivity. Time t.sub.1
is set by the brand strategist 4.
[0081] The predictive model processes a combination of the
following data: the computational model of consumer segment data,
the computational model of healthcare provider segment data and the
computational model of payor segment data. The predictive model
identifies correlations that indicate that certain combinations of
consumer, healthcare and payor data are predictive. Simultaneously,
the brand strategist 4 begins promotional planning at t.sub.2 to
assure that resources, including dollars, people and processes, are
secured to support the implementation of a promotional campaign
designed from the output of the predictive model.
[0082] If the predictive model indicates the combined data are
predictive, the brand strategist 4 then deploys a promotional
campaign at time t.sub.3 that is comprised of consumer segment
promotional plans, healthcare provider segment promotional plans
and payor segment promotional plans that are the product of the
computational models for consumers, healthcare providers and
payors.
[0083] In one embodiment, if the predictive model indicates the
combined data are not predictive, the output of the predictive
model is used to select a different segment promotional plan
modified for a specified segment within a target consumer group for
feeding back iteratively to the computational model of consumer
segment data, the computational model of healthcare provider
segment data and the computational model of payor segment data for
further optimization until a combination of data is deemed by the
predictive model to be predictive.
[0084] Given that consumers, healthcare professionals and payors
have changing needs, wants, demands and behaviors, in the
embodiment in FIG. 4 the application of the brand value growth and
retention engine 18 continuously operates and optimizes promotional
campaigns until the brand strategist 4 determines that he or she no
longer desires to promote the brand drug in the market. In the
embodiment as shown in FIG. 4, the application of the brand value
growth and retention engine 18 continues for the finite post-LOE
phase 38 of the brand drug. In this embodiment the predictive model
produces various promotional campaigns that are generated based on
segment promotional plans that are optimized from t.sub.1 through
the period when multiple generics enter the market during the
finite post-LOE phase of the brand drug in the market.
[0085] The predictive model determines segment promotional plans
and which promotional tactic profiles require adjustment to yield a
higher response rate at a certain investment level over time and
therefore a promotional campaign relies on the learning machine in
the predictive model to reveal which segment promotional plans are
optimized or not optimized.
[0086] The brand drug value growth and retention engine 18 is
configured to generate an optimal segment promotional plan(s) from
the computation model segment data that are combined and processed
through the predictive model to generate a promotional campaign at
time t.sub.3 prior to the loss of exclusivity based on the
predictive model produced at time t.sub.1.
[0087] One objective is to find correlations between a promotional
tactic profile and prescribing levels of a brand drug by physician
segments. Promotional tactics can include the number of brand sales
presentations made to a doctor, the number of medical meetings that
the physician attended, the number of brand samples that were
provided to a doctor, and the number of copay cards provided to a
doctor, among others. The optimal segment promotional plan(s) in a
promotional campaign have tactic profiles that are directed to
consumers, healthcare providers, and payors. In some instances, the
computational models are run iteratively until there is sufficient
data, and the predictive model is sufficiently developed to deem
the output predictive. After the predictive model is deemed
predictive, the overall promotional campaign will most likely have
the highest impact, which provides the highest brand drug volume
for the least amount of promotional dollars.
[0088] Even after the promotional campaign has been launched, the
predictive model continues to operate, continues to receive new
data, and continues to refine and modify the parameters of the
predictive models. A curve 39 represents the iterative and
continuous running of predictive models to refine, modify,
transform and improve an optimal promotional campaign, which over
time is intended to increase the sales of brand drugs used by the
consumers, as shown in a first population of consumers using brand
drug 40a, a second population of consumers using brand drug 40b
that is larger than the first population size, and a third
population of consumers using brand drug 40c that is larger than
the second population size.
[0089] To better select a tactic profile, to which a consumer 7 may
be more responsive, the brand strategist 4 attempts to understand
the segments of consumers including their needs, wants, demands and
behaviors. Depending on what a particular segment of consumers will
respond to, the brand strategist 4 selects effective consumer
segment promotional plans, which, combined with healthcare provider
segment promotional plans and payor segment promotional plans,
constitute a brand drug promotional campaign by operating through a
predictive model at 42. Similarly, the brand drug value growth and
retention engine 18 is configured to collect computational model
data from physician segments, analyzing physician prescribing
behavior, and analyzing the data relative to the promotional
tactics a drug manufacturer 6 has deployed against a particular
physician. Additional considerations can include sales calls from
sales representatives and the number of educational programs that
physicians attend. Similarly, the brand drug value growth and
retention engine 18 is configured to collect computational model
data from payor segments, analyzing payor behavior, and analyzing
the data relative to the promotional tactics (including pricing and
discounting) a drug manufacturer 6 has deployed against a
particular payor.
[0090] Modified promotional tactics producing different segment
promotion plans begin at time t.sub.3, which in one embodiment of
the present invention is a time duration closer to the loss of
exclusivity relative to t.sub.1 and t.sub.2. A promotional campaign
comprises a plurality of segment promotional plans. Each segment
promotional plan is directed to a particular segment of consumers,
a particular segment of healthcare providers and/or a particular
segment of payors with specific tactics to which a respective
segment responds. Different segments of consumers, segments of
healthcare providers and segments of payors may have the same or
different sets of promotional tactics that are applied in order to
have the predictive model produce an effective promotional
campaign.
[0091] In one embodiment, the brand value and retention engine 18
is configured to optimize promotion at the time of loss of
exclusivity. A brand drug company may significantly increase its
brand drug direct to consumer advertising including but not limited
to TV advertising, print advertising, copay cards, loyalty cards,
coupons etc. This is done through several different promotional
channels, including, but not limited to, electronic mail, physical
mail, video push, mobile device advertising and the use of copay
cards. The aim in this embodiment is to encourage consumers to
speak to their doctors about starting therapy on a brand drug or to
remain loyal to brand drugs that they have been using. The increase
in direct to consumer advertising is intended to retain more
consumers on the brand drug and thus increase and retain brand drug
volume before the loss of exclusivity, after the loss of
exclusivity even months after the loss of exclusivity.
[0092] While not available to all drugs, sometimes a brand drug
will be designated as one that is granted the legal rights to have
only one competing single source generic drug at the time of loss
of exclusivity of the brand. This designation would have occurred
early in the brand's life.
[0093] A brand drug is typically price discounted immediately after
the drug's loss of exclusivity 43 so the brand drug can remain
price competitive with the single or other available generic drugs.
Therefore, if a consumer 7 views the brand drug and the generic
drug to be comparable in price and effectiveness and safety, they
are more likely to be open to remaining on the branded drug after
loss of exclusivity.
[0094] The negotiation for brand formulary position and pricing 44
with PBMs 8 is another key factor to ensure patients can get access
to the branded drug before and after the loss of exclusivity. To
ensure the PBM 8 patients have access to the brand, manufacturers
negotiate arrangements that provide brand drugs at discounted
prices to remain price competitive with generic drugs. Without such
negotiated arrangements, a PBM may automatically switch patients
from the brand drug to a generic drug upon the expiration of the
exclusivity period. For example, the price of a brand drug could
decrease by 20% to 30% relative to the price before the loss of
exclusivity period in order to remain price competitive with a
generic drug at 45, which the population size of the consumers is
reduced relative to the third population size of consumers using
the brand drug.
[0095] Often market share of the brand drug will fall after the
loss of exclusivity. A drug manufacturer aims to retain significant
market share up to and post loss of exclusivity. In time, multiple
sources of generic drugs will often come to one market, which will
place growing price pressure on expired brands s and can cause the
manufacturer to continue to reduce the price of the brand drug to
remain competitive with generics.
[0096] FIG. 4B is a flow diagram illustrating the effect of a
predictive model of the brand drug growth, value, a retention
engine in launch phase of a brand drug. The brand strategist 4
formulates a launch promotional campaign(s) to generate and retain
brand drug use at launch and post launch and prior to a brand's
growth phase. The application of the brand value growth and
retention engine 18 produces the greatest amount of brand drug
sales for the least amount of promotional campaign dollars and
takes into account the following promotional tactics: sales
electronic presentations, sales face-to-face presentations,
formulary positions, DTC advertising, print advertising, mobile
advertising (including smartphones, tablets, and wearable devices),
social network advertising, medical meetings for healthcare
professionals, celebrity blogging, samples, reimbursement rates,
rebates, discounts, secondary insurance in the form of copay cards,
loyalty cards, coupons to name just a few.
[0097] One embodiment of the overall brand value growth and
retention engine 18 comprises a number of key steps as illustrated
in FIG. 4B of the present invention. At time t.sub.1, which in one
example is about 3 to 9 months prior to the brand drug, the brand
drug value growth, and retention engine 18 is configured to
generate a predictive model that produces a promotional campaign(s)
for use at the time of brand launch. Time t.sub.1 is set by the
brand strategist 4.
[0098] The predictive model processes a combination of the
following data: computational model of consumer segment data, the
computational model of healthcare provider segment data and the
computational model of payor segment data. The predictive model
identifies correlations that indicate that certain combinations of
consumer, healthcare, and payor data are predictive.
Simultaneously, the brand strategist 4 has begins promotional
planning at t.sub.2 to assure that resources, including dollars,
people, and processes, are secured to support the implementation of
a promotional campaign designed from the output of the predictive
model.
[0099] If the predictive model indicates the combined data are
predictive, the brand strategist 4 then deploys a promotional
campaign at time t.sub.3 that is comprised of consumer segment
promotional plans, healthcare provider segment promotional plans
and payor segment promotional plans that are the product of the
computational models for consumers, healthcare providers and
payors.
[0100] In one embodiment, if the predictive model indicates the
combined data are not predictive, the output of the predictive
model is used to select a different segment promotional plan
modified for a specified segment within a target consumer group for
feeding back iteratively to the computational model of consumer
segment data, the computational model of healthcare provider
segment data and the computational model of payor segment data for
further optimization until a combination of data is deemed by the
predictive model to be predictive.
[0101] Given that consumers, healthcare professionals and payors
have changing needs, wants, demands and behaviors, in the
embodiment in FIG. 4B the application of the brand value growth and
retention engine 18 continuously operates and optimizes promotional
campaigns until the brand strategist 4 determines that he or she no
longer desires to promote the brand drug in the market. In the
embodiment as shown in FIG. 4B, the application of the brand value
growth and retention engine 18 for the brand drug launch phase
continues for the launch phase 37b of the brand drug. The launch
phase is defined by the brand strategist. In this embodiment the
predictive model produces various promotional campaigns that are
generated based on segment promotional plans that are optimized
from t.sub.1 through the period when the brand strategist
determines that the launch phase of the brand drug has
concluded.
[0102] The predictive model determines segment promotional plans
and which promotional tactic profiles require adjustment to yield a
higher response rate at a certain investment level over time and
therefore a promotional campaign relies on the learning machine in
the predictive model to reveal which segment promotional plans are
optimized or not optimized.
[0103] The brand drug value growth and retention engine 18 is
configured to generate an optimal segment promotional plan(s) from
the computation model segment data that are combined and processed
through the predictive model to generate a promotional campaign at
time t.sub.3 launch phase based on the predictive model produced at
time t.sub.1.
[0104] One objective is to find correlations between a promotional
tactic profile and prescribing levels of a brand drug by physician
segments. Promotional tactics can include the number of brand sales
presentations made to a doctor, the number of medical meetings that
the physician attended, the number of brand samples that were
provided to a doctor, and the number of copay cards provided to a
doctor, and among others. The optimal segment promotional plan(s)
in a promotional campaign have tactic profiles that are directed to
consumers, healthcare providers, and payors. In some instances, the
computational models are run iteratively until there is sufficient
data, and the predictive model is sufficiently developed to deem
the output predictive. After the predictive model is deemed
predictive, the overall promotional campaign will most likely have
the highest impact, which provides the highest brand drug sales for
the least amount of promotional dollars. Even after the promotional
campaign has been launched, the predictive model continues to
operate, continues to receive new data, and continues to refine and
modify the parameters of the predictive models. A curve 39
represents the iterative and continuous running of predictive
models to refine, modify, transform and improve an optimal
promotional campaign, which over time is intended to increase the
volume brand drugs used by the consumers, as shown in consumers in
first population size using brand drug 40a, consumers in a second
populations size using the brand drug 40b with a second population
size that is larger than the first population size, and consumers
in a third population size using brand drug 40c with a third
population size that is larger than the second population size.
[0105] To better select a tactic profile, which a consumer 7 may be
more responsive, the brand strategist 4 attempts to understand the
segments of consumers including their needs, wants, demands and
behaviors. Depending on what a particular segment of consumers will
respond to, the brand strategist 4 selects effective consumer
segment promotional plans, which combined with healthcare provider
segment promotional plans and payor segment promotional plans,
constitute a brand drug promotional campaign by operating through a
predictive model at 42. Similarly, the brand drug value growth and
retention engine 18 is configured to collect computational model
data from HCP segments, analyzing HCP prescribing behavior, and
analyzing the data relative to the promotional tactics, a drug
manufacturer 6 has deployed against a particular physician.
Additional considerations can include sales calls from sales
representatives and the number of educational programs that
physicians attend among other promotional tactics. Similarly, the
brand drug value growth and retention engine 18 is configured to
collect computational model data from payor segments, analyzing
payor behavior, and analyzing the data relative to the promotional
tactics (including pricing and discounting) a drug manufacturer 6
has deployed against a particular payor.
[0106] Modified promotional tactics producing different segment
promotion plans. A promotional campaign comprises a plurality of
segment promotional plans. Each segment promotional plan is
directed to a particular segment of consumers, a particular segment
of healthcare providers, and/or a particular segment of payors with
specific tactics to which a respective segment responds. Different
segments of consumers, segments of healthcare providers, and
segments of payors may have the same or different sets of
promotional tactics that are applied in order to have the
predictive model produce an effective promotional campaign. In one
embodiment, the brand value and retention engine 18 is configured
to optimize promotion at the time launch. A brand drug company may
launch a brand drug with very high spending aimed at direct to
consumer advertising including but not limited to TV advertising,
print advertising, copay cards, loyalty cards, coupons etc. This is
done through several different promotional channels, including but
not limited to, electronic mail, physical mail, video push, mobile
device advertising and the use of copay cards. The aim in this
embodiment is to encourage consumers to speak to their doctors
about starting therapy on a brand drug or switching from another
brand drug that they have been using. The increase in direct to
consumer advertising is intended to capture more consumers on the
brand drug and thus increase brand drug volume during the launch
phase.
[0107] The negotiation for brand formulary position and pricing 44
with PBMs 8 is another key factor to ensure patients can get access
to the branded drug at launch. To assure the PBM 8 patients have
access to the brand, manufacturers negotiate arrangements that
provide branded drug at discounted prices to remain price
competitive with other branded drugs. Often without such negotiated
arrangements, a PBM may automatically deny its patients access to
the brand drug at launch, instead requiring patients to use cheaper
brand drugs or generics.
[0108] FIG. 5 is a diagram illustrating one embodiment of a
predictive model method executed by the brand drug value growth and
retention engine 18. At step 46, the brand drug value growth and
retention engine 18 is configured to determine a predefined or
computed threshold for a prospective brand. The threshold for a
prospective brand engagement, in one embodiment, can be dictated by
time to LOE, a competitive market, brand revenue, brand marketing
spending, and brand profit. If the prospective brand does not meet
the threshold, the process remains at step 46. Upon meeting the
threshold for a brand engagement, the brand drug value growth and
retention engine 18 proceeds to execute a computational model on
consumer segments in step 47, execute a computational model on
healthcare provider segments in step 48, and execute a
computational model on payor segments in step 50. Steps 47, 48, 49,
50 can occur in parallel or in some combination in this embodiment
of the present invention. At step 47, the consumer segments module
28a is configured to run a computational model on consumer segments
to determine an optimal promotional plan for consumers who are
candidates for a particular brand drug. The computational models on
consumer segments are built based on various attributes of
individual consumers or subgroups including but not limited to the
consumer's use of copay cards, their medical conditions, prior
prescription brand drug purchases, prior OTC brand drug purchases,
insurance carriers, preferred retail stores, their buying patterns,
shopping patterns, income levels, gender, race, educational levels,
among others. The number and types of attributes in a computational
model of consumer segments are dependent and can be adjusted based
on several factors including but not limited to a set of
predetermined attributes at the outset of a promotional campaign,
modifying the attributes from a source during a promotional
campaign, the result of a predictive model, or the output from the
learning machine.
[0109] At step 48, the healthcare provider segments module 28b is
configured to run a computational model on healthcare providers who
treat consumers are users or who are candidates using the
particular brand drug. In this embodiment, the term healthcare
provider segments refers to optimizing individual healthcare
providers or subgroups within segments as a segment, rather than by
geographical segmentation. At step 49, the healthcare provider
segments module 28b is configured to run a computational model on
individual or subgroups of retail segments to determine an optimal
promotional mix for retailers that provide the brand drug (or drug
brand X) to consumers.
[0110] At step 50, the manufacturer PBM/payor strategy module 31
and manufacturer PBM/payors execution module 32 are configured to
run a computational model on individual or subgroups of payor
segments, such as PBMs and insurers, to determine optimal
contracting strategies for the particular brand drug. Data
intelligence for selecting a particular promotion campaign can be
sourced from the computational model on consumer segments at step
47, the computational model on healthcare provider segments at step
48, and the computational model of payor segments at step 50. The
promotional campaign is not only based on the costs of advertising,
but it also considers formulary position and the related pricing
associated with a brand's formulary position, pricing for a brand
manufacturer and the optimal pricing for patients by considering
the different promotional mixes for consumers and individual
healthcare providers and by matching that to an optimizing rebating
and discounting strategy.
[0111] In one embodiment, pertaining to formulary positions, a PBM
may make available a number of prescription drugs intended to treat
a specific disease or disorder. The PBM may allow one of the drugs
to be widely available to their patients because of the negotiated
rebates and discounts extended by the manufacturer and the broad
benefits determined by the PBM medical authorities. In this
embodiment, the PBM may require no insurance copay for this drug as
a way to encourage physicians and patients to use this preferred
drug. As an alternative to the preferred drug, alternative drugs
may be made available with different rebates and discounts. For
these drugs, patients may be charged an insurance copay or may have
to pay for the full cost of the drug with no contribution from
their insurance company. The different levels of rebates,
discounts, and copay pricing provide different variables to produce
an optimal brand drug formulary position.
[0112] At step 51, the financial model simulator module 26 in the
brand value and retention engine 18 is configured to compute and
generate a combination predictive model in this embodiment. The
financial model simulator module 26 receives the computational
model on consumer segments from step 47, the computational model on
healthcare providers segments from step 48, the computational model
on the retail store segments from step 49, and the computational
model on payor segments in step 50 to run a predictive model of
these input data to generate an optimal promotional campaign for
the specified brand drugs. Data received by the financial model
simulator module 26 should be sufficiently large to make the
predictive model meaningful. Continuous streams of consumer segment
data, healthcare provider data and payor segment data are fed into
the predictive model. In some embodiments, the predictive model is
computed based on receiving two or more computational models from
among the four possible computational models, i.e., the consumer
segments model in step 47, the healthcare provider segments
computational model in step 48, the retail store segments
computational model in step 49, and the payor segments
computational model in step 50. In other embodiments, the
predictive model is computed based on receiving one or more
computational models from among the four possible computational
models in steps 47, 48, 49, 50.
[0113] In some embodiments, the predictive model combines various
computational models are executed in a way that is complaint with
present government regulations, like HIPPA, or future government
amendments or legislations.
[0114] At step 52, the brand value growth and retention 18 stores,
accumulates and retrieves prior promotional campaigns and their
results, for both promotions of different earlier products and any
prior campaigns and their results for the current product.
[0115] At step 53, the brand drug value growth and retention engine
18 is configured to invoke machine learning methods in real time or
recent data to estimate and attempt to optimize the parameters for
prediction of one or a plurality of computational models such as
those in steps 47, 48, 49, 50. The specifics of the implementation
of machine learning methods employed are known in the literature.
For additional information on the machine learning methods, see
Michalski, R., J. Carbonell, and T. Mitchell (1986), Machine
Learning: An Artificial Intelligence Approach, Volume II, Morgan
Kaufman Publishers: Los Angeles; Bishop, C. M. (2006), Pattern
Recognition and Machine Learning, Springer; Singh, Y., P. K.
Bhatia, O. Sangwan (2007), A Review of Studies of Machine Learning
Techniques, International Journal of Computer Science and Security,
Volume (1): Issue (1), 70-84, which are incorporated by reference
as if fully set forth herein. Machine learning methods may include,
for example, logistic regression, support vector machines, decision
trees, random forests, max-entropy classifiers, re-enforcement
learning, genetic algorithms, neural networks or other known or new
methods. Most of these methods are based on Bayesian statistics and
use prior data (e.g. prior campaigns) and prior results (e.g.
failure, success, degree of partial success) of said prior data
(e.g. campaigns or individual tactics within the campaigns) to
improve the weights and other parameters in the predictive models.
For additional information on specifics of Bayesian statistics, see
Spiegelhalter D. and K. Rice (2009), Bayesian statistics,
Scholarpedia; Bolstad, W. (2007), Introduction to Bayesian
Statistics, 2nd Ed., John Wiley & Sons: New Jersey; Bishop, C.
M. (2006), Pattern Recognition and Machine Learning, Springer,
which are incorporated by reference as if fully set forth
herein.
[0116] Step 53 may also include active or proactive learning where
a new tactic or new campaign (e.g. advertising via social media
such as Facebook, or via mobile apps on smartphones) may be tried
in order to jointly optimize both new knowledge gained about the
effectiveness of the new campaigns or tactic and the immediate
impact of the selected campaigns and tactics. The former may be
viewed as longer-term or amortized benefit, whereas the latter is
the current benefit predicted by the computational model(s).
Proactive learning takes into account the cost and risk of
experimental campaigns, and this would be a novel application area
for such machine learning systems. For additional information on
specifics of active or proactive learning, see B. Settles (2012),
Active Learning: Synthesis Lectures on Artificial Intelligence and
Machine Learning, Morgan & Claypool; Donmez, P., Carbonell, J.
(2008), "Proactive Learning: Cost-Sensitive Active Learning with
Multiple Imperfect Oracle," in Proceedings of the 17th ACM
Conference on Information and Knowledge Management (CIKM '08), Napa
Valley; Donmez, P. and Carbonell, J. (2008), "Optimizing Estimated
Loss Reduction for Active Sampling in Rank Learning," in
Proceedings of the International Conference in Machine Learning,
which are incorporated by reference as if fully set forth
herein.
[0117] At step 54, the brand drug value growth and retention engine
18 is configured to allow for re-estimating one or more
computational models if their results were not positive. For
instance, if the machine learning method suggested two potential
but mutually exclusive improvements, one of which was attempted
without positive results, the computational model may then be
re-estimated with the second improvement and fed back into the
overall system.
[0118] At step 55, the brand drug value growth and retention engine
18 is configured to determine the benefit (e.g. improvement in
sales) of the promotional campaign and feeds back to the learning
machine (step 53) directly if positive to re-enforce, or indirectly
via step 54 (re-estimation), and re-runs the computational models
to inform the learning machine that certain predictions need
revision.
[0119] As an example of a promotional campaign, FIG. 6A is a table
illustrating a promotional campaign from a collection of segment
promotional plans, represented in this embodiment as a table 59
stored in a database. The table 59 comprises vertical columns on
different segments 60 of consumers (or segments of healthcare
providers). The horizontal rows denote different promotional
tactics 61 directed to the consumer segments 60. For the first
consumer segment 62, among the different promotional tactics 61
from 1 to n as applied to the first consumer segment 62, the first
consumer segment 62 responds well to the second tactic profile 63b,
the third tactic profile 63b, and the seventh tactic profile 63c.
The identified promotional tactics 63a, 63b, 63c, which have been
determined to be effective promotional tactics directed to the
first consumer segment 62, are collectively referred to as a first
segment promotional plan SPP1 64. The brand drug value growth and
retention engine 18 is configured to determine the number of tactic
profiles that are effective against a particular consumer segment,
resulting in a series of segment promotional plans: SPP1 64 for the
first consumer segment, SPP2 66 for the second consumer segment,
SPP3 67 for the third consumer segment, and SPPn 68 for the n
consumer segment. A promotional campaign 65 comprises a plurality
of segment promotional plans, represented by the following equation
where timing of segments is regulated by the function
.alpha.(t.sub.i), where in one embodiment the function can be a
sequence, in another all in parallel, and in other embodiments any
partially or fully sequential or parallel segments:
PC = i = 1 , N .alpha. ( t i ) SPP i ##EQU00001##
[0120] In one embodiment, the promotional campaign may include
explicit interaction terms among the actual or planned segment
promotional plans as well as individual segment promotional plans
(SPPs). The explicit interaction terms can be binary, comprising
any planned or actual SPP interacting with any other planned or
actual SPPs. The terminology "explicit interaction terms" may refer
to a term that combines two or more variables in a potentially
non-linear way, e.g. G(X.sub.1, X.sub.2), especially if used inside
another equation, such as an otherwise linear equation on X.sub.1
and X.sub.2 is an explicit interaction term between X.sub.1 and
X.sub.2. The function G can be anything meaningful in the
application area. For instance, G can be a product X.sub.1*X.sub.2
or a ratio X.sub.1/X.sub.2, or something such as a transformed sum,
e.g. LOG(X.sub.1)+LOG(X.sub.2). For example, the magnitude of one
campaign element may be three times larger than that of a different
campaign element, e.g. G(C1, C2)=Cost(C1)/Cost(C2)=3, without
having to specify the actual value of either Cost(C1) of Cost(C2),
just their relative magnitudes.
[0121] Returning to step 51 in FIG. 5, the financial model
simulator module 26 is configured to compute a segment promotional
plan by considering several key variables, including coefficients,
promotional tactics, segments and frequency. In one formulation,
each tactic profile in a particular segment promotional plan is
determined by the weighted factors of the coefficients multiplied
by the frequency in which the tactic is displayed for a particular
segment as represented by the following equation:
SPP = i = 1 , m .alpha. ( t i ) .beta. i T i ( F i , S j )
##EQU00002##
where .beta..sub.1*T.sub.1(F.sub.1, S.sub.j) denotes the first
tactic profile, .beta..sub.2*T.sub.2(F.sub.2, S.sub.j) denotes the
second tactic profile, .beta..sub.3*T.sub.3(F.sub.3, S.sub.j)
denotes the third tactic profile, and so on up to the n.sup.th
tactic profile, where n is the total number of profiles. The
coefficients .beta..sub.i are weighted factors applying
respectively to the first tactic profile, the second tactic profile
and the third tactic profile. The first term,
.beta..sub.i*T.sub.1(F.sub.1 S.sub.j), represents the frequency
F.sub.1 in applying the first promotional tactic T.sub.1 to a first
consumer segment S.sub.j. The second term,
.beta..sub.2*T.sub.2(F.sub.2, S.sub.j), represents the frequency
F.sub.2 in applying the second promotional tactic T.sub.2 to first
consumer segment S.sub.i and so on up to the m.sup.th tactic, where
m is the total number of tactics. The .alpha.(t)'s again denote any
ordering or parallelizing temporal function. Although, for
simplicity, the equation is presented as a summation, which is only
one embodiment of the invention; the summation may be replaced by
any other constructive combination function. Tactic profiles may
indicate any manner of promotion, including but not limited to
traditional media, social media, consumer-direct and payor direct
programs.
[0122] If the result of the predictive model is negative, the
process returns to steps 47, 48, and 50 for conducting real time or
recent data predictive analysis. If the financial model simulator
module 26 determines that the data is considered predictive, then
at step 56 the brand drug value growth and retention engine 18 is
configured to determine if a change in promotional campaign is
desired at this time. The current promotional campaign continues if
there are no changes in the promotional campaign, as is shown in
step 57. However, if a change in a promotional campaign is desired,
the brand drug value growth and retention engine 18 is configured
to select a different promotional campaign for a specified segment
within a targeted consumer-based group at step 58. At step 53, the
brand drug value growth and retention engine 18 continuously trains
a learning machine as a feedback mechanism to improve and optimize
the predictive model of promotional campaigns.
[0123] In an alternative embodiment as shown in FIG. 7, the
predictive model method can be a cumulative predictive model at
step 51 based on an individual predictive model, which the
financial model simulator module 26 is configured to determine
whether the consumer data is predictive at step 101, whether the
healthcare provider data is predictive at step 102, and whether the
payor segment data is predictive at step 103. This formulation can
be represented mathematically by the following equation:
Cumulative Predictive
model=F(P(M.sub.C),P(M.sub.HCP),P(M.sub.P),P(M.sub.R))
where P(M.sub.C) denotes the predictive model consumer segment data
in step 101, P(M.sub.HCP) denotes the predictive model of
healthcare provider segment data in step 102, P(M.sub.P) denotes
the predictive model of payor segment data in step 103, and
P(M.sub.R) denotes the retail-sales-based predictive model in step
YYY. For example, the predictive model would evaluate a run on 30
segments of patients, 30 segments of physicians, and 15 possible
rebating structures. The function F is any combination function and
in its simplest embodiment would be additive, e.g. a weighted
sum.
[0124] In one embodiment the predictive model uses data from retail
sales of pharmaceutical products gathered from consumers with
affinity cards at pharmacies or other retail outlets. This data is
typically normalized to account for the fraction of the population
that uses affinity cards in each major geographical region or with
each large-scale retailer, and for other factors (age differences,
health plans, etc.). This predictive model based on actual sales is
then used to estimate trends (e.g. increased sales of certain
products or product categories, seasonal variations, etc.), and
these trends plus current values are used to estimate the expected
sales. A more complex embodiment would include the effects of
marketing efforts combined with baseline trend prediction. The
prediction in such an embodiment may be estimated by the following
function or by other methods of combining similar information:
P ( M R ) = X i .di-elect cons. R N ( X i ) Sales ( X i ) ( 1 + (
Sales ( X ) ) t ) ##EQU00003##
In the above function we have a set of retail products R={X.sub.1,
X.sub.2, . . . X.sub.n}, measured sales volume of each X.sub.i,
normalization N(X.sub.i) to account for the expected fraction of
sales actually recorded (N=the inverse of that fraction), and
adjusting for increasing or decreasing trends as measured over a
time interval t.
[0125] In one embodiment, the predictive model combines information
from computational models of the consumer segment data, healthcare
provider segment data, the retail segment data and the payor
segment data in a linear manner or a substantially linear manner.
The combined information in the predictive model provides explicit
weights to one or more components in the combined information. The
term "linear" is commonly used in the art and may refer to the
elements (variables, components, sub-components) that are combined
via an additive process, possibly with weights. The term
"substantially linear" refers to a linear equation that could still
have a small corrective factor that may be non-linear. For
instance, a linear combination of variables X.sub.1 X.sub.2,
X.sub.3, and X.sub.4 can be represented mathematically by
Y=A.sub.1X.sub.1+A.sub.2X.sub.2+A.sub.3X.sub.3+A.sub.4X.sub.4,
where A.sub.1 is a coefficient (a weight) assigned to X.sub.1,
A.sub.2 is a coefficient for X.sub.2, A.sub.3 is a coefficient for
X.sub.3, and A.sub.4 is a coefficient for X.sub.4. Explicit weights
are the A1, A2, A3, A4 above, i.e. the coefficients.
[0126] In some embodiments, the predictive model in step 51 is
computed based on receiving two or more computational models from
among the four possible computational models, i.e., the consumer
segments model in step 47, the healthcare provider segments
computational model in step 48, the retail store segments
computational model in step 49, and the payor segments
computational model in step 50. To phrase it in another way, the
predictive model in step 51 is computed based on receiving two or
more predictive elements from among the four possible predictive
elements, i.e., the predictive element for the consumer segments
model in step 101, the predictive element for the healthcare
provider segments computational model in step 102, the predictive
element for the retail store segments computational model in step
145, and the predictive element for the payor segments
computational model in step 103. In other embodiments, the
predictive model is computed based on receiving one or more
computational models from among the four possible computational
models in steps 47, 48, 49, 50, or receiving one or more predictive
elements among the four predictive elements in steps 101, 102, 145,
103.
[0127] Embodiments of the present invention include a promotion
campaign that is represented by the following augmented equation,
where j=1, M ranges over all segment promotional plans, including
those of other active or planned promotion campaigns and the
function g(SPP.sub.i,SPP.sub.j) computes interactions among the
campaign plan portions if any:
PC = j = 1 , M i = 1 , N .alpha. ( t i ) [ SPP i + g ( SPP i , SPP
j ) ] ##EQU00004##
[0128] For example, two promotional campaigns for the same
medication may interact. For instance, their message should
normally be consistent, e.g. "more effective treatment" (vs
"cheaper" vs "easier to take"). Alternately, two concurrent
campaigns for different medications may be optimized by combining
promotion plan segments. For instance, the same mailing may contain
two fliers for treating age-related ailments and state that
medications may be taken together--e.g. an anti-inflammatory and a
skin-rejuvenation ointment. Alternatively, two drugs for the same
ailment may confuse the market, and therefore they should not be
promoted simultaneously. These examples are meant to be
illustrative and not limiting as to the range of possible positive
and negative interactions among promotion campaigns (PCs) and their
segment promotional plans (SPPs). The function g(SPPi,SPPj)
computes the interaction and can have a positive value (e.g. cost
savings by combining mailings) or a negative value (e.g. two statin
drugs promoted in parallel campaigns targeted at the same
consumers, confusing them). Thus, g modulates the campaigns in
order to optimize their combination, not just each campaign
independently. In some embodiments, the promotional campaign PC is
a weighted combination of the segment promotional plans (SPPs). The
term "weighted combination" may refer to "linear" and implies that
the weights (coefficients) for each variable are normally different
from each other.
[0129] Optionally, the computational model on consumer segments,
the computational model on healthcare provider segments, and the
computational model on payor segments can also be modified based on
other variables including to changes in reimbursement, changes in
distribution, and consumer health dynamics. Embodiments of the
present invention also include an online promotional campaign over
a web browser or a mobile device that is personalized and directed
to an individual consumer, rather than a consumer segment.
Furthermore, embodiments of the present invention also include
mechanisms for consumers to provide feedback on the brand drug
using online and mobile tools and devices linked to social
platforms like YouTube, Facebook, Twitter and Pinterest.
[0130] One embodiment of the overall brand value growth and
retention engine 18 comprises a number of key steps as illustrated
in FIG. 7 of the present invention. In this embodiment, a grocery
store chain desires to launch a promotional campaign intended to
increase the sales of their store chain brand of baby aspirin.
[0131] The overall brand value growth and retention engine 18
processes a combination of the following data: the computational
model of consumer segment data at step 47 to identify individual
segments or sub-segments of consumers who have been diagnosed or
are at risk of developing heart attacks and strokes; the
computation model of payor segment data at step 50 to identify
individual segments or sub-segments of consumer baby aspirin users
or candidates for baby aspirin by analyzing medical claims data
diagnosis codes for diseases typically found in consumers who have
had a prior heart attack or stroke or who are at risk of having a
heart attack or stroke, in addition to other data sources in a
HIPPA compliant fashion; the computation model of healthcare
segment data at step 48 to identify individual segments or
sub-segments HCPs who treat patients with or at risk of developing
heart attacks or strokes by analyzing data on the prescribing
history of individual segments or sub segments of healthcare
providers using multiple sources including, but not limited to
switch data, prescriber audit data and other data sources; the
computational model of retail segment data at step 49 aiming to
identify individual segments or sub-segments consumers who have
made prior purchases of the store chain brand baby aspirin and/or
other brands of baby aspirin.
[0132] The brand drug value growth and retention engine is
configured to analyze customer segments and sub-segments data to
identify correlations that indicate that certain combinations of
customer segment data are predictive. The brand drug value growth
and retention engine 18 is configure to separately analyze
healthcare provider segments and sub-segments data to identify
correlations that indicate that certain combinations of healthcare
provider segment data are predictive. The brand drug value growth
and retention engine separately analyzes retail store segments and
sub-segments data to identify correlations that indicate that
certain combinations of retail segment data are predictive. The
brand drug value growth and retention engine 18 is configured to
separately analyze payor segments and sub-segments data to identify
correlations that indicate that certain combinations of payor
segment data are predictive.
[0133] The brand drug value growth and retention engine 18 then is
configured to combine the predictive outputs 101, 102, 145, 103 of
the separate predictive computational models for consumer segment
data, healthcare provider segment data, retails store segment data
and payor segment data.
[0134] Simultaneously, the brand strategist 4 begins promotional
planning at to assure that resources, including dollars, people and
processes, are secured to support the implementation of a
promotional campaign designed from the output of the predictive
model.
[0135] If the predictive model at step 51 indicates the combined
data are predictive, the brand strategist 4, through the computer
device 3a, then deploys a promotional campaign that is comprised of
consumer segment promotional plans, healthcare provider segment
promotional plans, payor segment promotional plans and retailer
segment promotional plans that are the product of the respective
computational models for consumers, healthcare providers, payors
and retailers.
[0136] In one embodiment, if the predictive model at step 51
indicates the combined data are not predictive, the output of the
predictive model is used to select a different segment promotional
plan modified for a specified segment within the target consumer
group, a specified segment within the target HCP group, a specified
segment within the target retail group and a specified segment
within the target payor group for feeding back iteratively to the
computational model of consumer segment data, the computational
model of healthcare provider segment data and the computational
model of payor segment data and the computational model of retail
data for further optimization until a combination of data is deemed
by the predictive model to be predictive. The computation model
specifies which segments are not predictive.
[0137] In one embodiment, the brand drug value growth and retention
engine 18 output might call for a promotional campaign the includes
consumer segment promotional plans that include push digital
advertising of the grocery store brand of baby aspirin to Facebook,
Twitter, YouTube or other social media outlets. The promotional
campaign might also include a retail segment promotional plan that
calls for providing electronic coupons to the grocery store chain
loyalty card holders distributed to the card holder's smart phones
via SMS, their tablets or on their computers as an incentive for
them to purchase the grocery store chain brand of baby aspirin. In
the same embodiment the predictive output might call for the
promotional campaign to include an HCP segment promotional plan
that includes distributing paper grocery store brand baby aspirin
coupons and samples to certain HCPs for redistribution to their
respective patients. Given that consumers, healthcare professionals
and payors have changing needs, wants, demands and behaviors, in
this embodiment the application of the brand value growth and
retention engine 18 continuously operates and optimizes the grocery
store chain's promotional campaigns for their grocery store brand
of baby aspirin until the brand strategist 4 determines that he or
she no longer desires to change promotional campaign or no longer
desires to continue the promotional campaign.
[0138] The predictive model determines segment promotional plans
for the grocery store chain's brand of baby aspirin and determines
which promotional tactic profiles require adjustment to yield a
higher response rate at a certain investment level over time and
therefore a promotional campaign relies on the learning machine in
the predictive model to reveal which segment promotional plans are
optimized or not optimized.
[0139] The brand drug value growth and retention engine 18 is
configured to generate optimal segment promotional plans from the
four computation models' segment data that are combined and
processed through the predictive model to generate a promotional
campaign based on the predictive model produced.
[0140] A promotional campaign for the grocery store chain's brand
of baby aspirin comprises a plurality of segment promotional plans.
Each segment promotional plan is directed to a particular segment
of consumers, a particular segment of healthcare providers, a
particular segment of payors and a specific segment of retailers
with specific tactics to which a respective segment responds.
Different segments of consumers, segments of healthcare providers,
segments of retailers and segments of payors may have the same or
different sets of promotional tactics that are applied in order to
have the predictive model produce an effective promotional
campaign.
[0141] FIG. 6B is a graphical elasticity curve illustrating
different promotional tactics 69, 70, 71 and 72. The x-axis on the
curve denotes promotional pressure 73 and the y-axis on the curve
denotes the brand drug volume 74. Each of the promotional tactics
69, 70, 71 and 72 has an optimal point or region in considering the
promotional pressure 73 relative to the brand drug volume 74, as
indicated by a rectangular point 75 depicted on the promotional
tactic curve 69, a rectangular point 76 depicted on the promotional
tactic curve 70, a rectangular point 77 on the promotional tactic
curve 71, and a rectangular point 78 on the promotional tactic
curve 72.
[0142] FIG. 6C is a flow diagram providing one illustration of
promotional campaign at time t.sub.o with multiple promotional
tactics that are applied to multiple segments of consumers. The
promotional campaign is directed to a first consumer segment 79, a
second consumer segment 80, a third consumer segment 81, and a
fourth consumer segment 82. Each of the first, second, third, and
fourth consumer segments may involve different types of promotional
tactics over time as a mechanism to adjust and achieve optimal
effectiveness. At time t.sub.0 as shown in FIG. 6C, four consumer
segments have been identified that are associated with this
promotional campaign; however, the promotional tactics have yet to
be deployed during the first time period 83 and the second time
period 84.
[0143] Various types of promotional tactics that are deployed for
the four consumer segments during the first time period 83 are
illustrated in FIG. 6D. In this illustration, four exemplary
promotional tactics are selected, which are represented by a
circular symbol for the first promotional tactic, a rectangular
symbol for the second promotional tactic, a triangular symbol for
the third promotional tactic, and a trapezoid symbol for the fourth
promotional tactic. The symbol sizes of the circle, rectangle,
triangle and trapezoid represent the amount of budget allocated for
that particular promotional tactic at that point in time. For the
first consumer segment 79 during the first time period 83, the
promotional tactics involve the first promotional tactic 85, which
in this example is a coupon/copay card for a portion during the
first time period 83. The second promotional tactic 86 is then
launched for a portion of time during the first time period 83 to
the first consumer segment 79, which in this instance is television
advertisement of the brand drug. The promotional tactic then
changes to the third type of promotional tactic 87, which in this
example involves providing samples of a brand drug to the first
consumer segment 79 for a portion of time during the first time
period 83. After the first three promotional tactics have been
deployed, the fourth promotional tactic 88 supplements the previous
promotional tactics by working with a payor to advertise to the
first consumer segment 79. Similar types of promotional tactics and
sequences are executed for the second consumer segment 80, the
third consumer segment 81 and the fourth consumer segment 82 during
the first time period 83. One significant difference between the
promotional tactics directed to the first, second, third and fourth
consumer segments is that the amount of budget allocated for a
specific promotional tactic may differ depending on the suitability
for that particular consumer segment. For example, the allocated
budget for television advertisement 86 for the first consumer
segment 79 and the television advertisement 92 for the fourth
consumer segment 82 is larger than the television advertisement
budget 89 for the second consumer segment 80. The amount of
complementary samples 90 provided by the second consumer segment 80
is larger than the amount of samples 87 provided to the first
consumer segment 79 and the amount of samples 93 provided to the
fourth consumer segment 82. As for the third consumer segment 81,
there is no promotional tactic deployed during the initial portion
of the first time period 83, but, instead, a larger television
advertisement budget 91 is allocated right after the initial
period.
[0144] Carefully selected promotional tactics deployed during the
second time period 84 for the first, second, third and fourth
consumer segments are illustrated in FIG. 6E. In part, based on the
effectiveness of the previous promotional tactics deployed during
the first time period 83, adjustments are made to the types of
promotional tactics and budget sizes during the second time period
84. For example, the circle symbol representing a coupon/copay card
94 is expanded for the first consumer segment 79 during the second
time period 84 relative to the first time period 83. The
rectangular symbol representing television advertisement 95 is
reduced for the first consumer segment 79 in the second time period
84 compared to the first time period 83. Both the triangle symbol
representing samples 96 and the trapezoid symbol representing payor
letter 97 are enlarged for the first consumer segment 79 in the
second time period 84 in relation to the first time period 83.
Significant adjustments can be made to the size of the budget
allocations as shown in the third consumer segment 81; the triangle
symbol representing television advertisement 98 is drastically
reduced during the second time period 84 relative to the first time
period 83, which may signify that the television advertisement was
ineffective as a promotional tactic to the consumers in the third
consumer segment 81. Instead, the amount of samples 99 and payor
letter 100 are increased substantially during the second time
period 84 relative to the first time period 83, perhaps an
indication that the brand drug samples and advertisement through
payors work quite effective during the first time period 83 for the
third consumer segment 81.
[0145] Promotional tactics can vary depending on the advancement of
technology as applied to segments of a population, where popular
promotional tactics may include TV advertisement, Internet
advertisement, social networking advertisement, direct mail
advertisement, presentations by sales representatives either by
telephone, computer or in person, and copay cards. Social media
advertising and mobile adverting have become increasingly
attractive as promotional tactics due to the large number of users.
Feedbacks from these social media companies and mobile devices can
serve as the basis to determine the effectiveness of a particular
promotional tactic. For example, a predictive model can derive in
part by "like button" on social media companies like Yelp,
Facebook, and LinkedIn, or compilation of unstructured data from
Twitter, Yelp, WhatsApp, Line, WeChat as well as other social media
sources on consumer users' comments about brand drugs. The
unstructured data from social media that have been analyzed can
provide meaningful feedback into the predictive model for tracking
chatters on social media or mobile devices to drug consumption
behavior. Our data platform can help to uncover the online
behaviors of consumers, map the promotional tactics that influence
their behavior and provide insights into their engagement and
buying patterns. In addition, the analysis of the unstructured data
can further contribute in personalizing a promotional messaging,
improving targeting and even designing new tactics to increase the
effectiveness of the promotional campaigns.
[0146] FIG. 8 is a graphical curve illustrating the volume of a
brand drug produced by a conventional approach and compares it to
the volume of the same brand drug using the promotional campaign
generated by the brand value growth and retention engine 18. The
curve has an x-axis 104 representing time, and a y-axis 105
denoting the percentage of brand drug volume. A dash line 108 in
the center denotes when the brand drug loses exclusivity of patent.
A first curve 107 represents the brand volume of the conventional
approach, and a second curve 106 represents the brand volume with
the impact of the promotional campaign executed by the brand drug
value growth and retention engine 18. Prior to the loss of
exclusivity, there in a first area 109 between the two curves 106
and 107, which shows the incremental brand drug volume produced by
the promotional campaign as applied to the brand drug. After the
loss of exclusivity around the time of dash line 108, there is a
second area 110 between the two curves 106 and 107, which also
shows the incremental value produced by the promotional campaign as
applied to the brand drug. The comparison of the two exemplary
curves, 106, 107, illustrates in particular that the first curve
has data points without a promotional campaign, leading to loss in
value both before and after LOE.
[0147] FIG. 9A is a flow diagram illustrating the communications
between the brand strategist dashboard 25, the drug manufacture
dashboard 25 and the payor dashboard 25. The brand drug value
growth and retention engine 18 is configured to supply data
periodically, such as on a daily basis, to the brand strategist
dashboard 25. The brand strategist dashboard 25, through the brand
drug value growth and retention engine 18, pushes filtered and
customized information to customize the manufacture dashboard 25
and the payor dashboard 25. The manufacture dashboard 25 furnishes
information to and receives information from a C-suit manager 111,
a brand management manager 112, a sales/payor operations manager
113, a sales force manager 114 and a finance manager 115. The payor
dashboard 25 furnishes information to and receives information from
the PBMs 8, the commercial manager 116, a compliance manager 117, a
copay utilization manager 118, a patient trends manager 119 and a
finance manager 120. The communications between the brand
strategist dashboard 25, the drug manufacture dashboard 25 and the
payor dashboard 25 ensures that checks and balances are
maintained.
[0148] FIG. 9B is a pictorial diagram illustrating one embodiment
of the brand value growth and retention dashboard (also referred to
as "brand strategist dashboard") 25. The brand strategist dashboard
25 displays data received from the brand drug value growth and
retention engine 18 for the brand strategist 4 to manage various
components of a promotional campaign. The brand strategist
dashboard 25 in FIG. 9B illustrates an exemplary embodiment, and
other variations and modifications of the brand strategist
dashboard 25 can be implemented without departing from the spirit
of the present invention. The brand strategist dashboard 25 is
divided into several sections, including a brand drug section 121,
a promotional campaign 65, a promotional tactics 122, a consumer
segments computational model 123, a healthcare provider segments
computational model 124, a retail store segments model 125, a payor
segment computational model 126, a particular brand drugs X
incremental sales 127, a DTC dollars spent on a particular brand
drug X 128, incremental revenue generated through incremental brand
drug X volume by promotional tactics 129, and an alternate
promotional campaign 130. With the brand strategist dashboard 25,
(clarify the dashboard) the consumer segments computational model
dashboard section 123 receives data from and transmits data to step
47 which executes a computational model on consumer segments, a
healthcare provider segments computational model dashboard section
124 receives data from and transmit data to step 48 which conducts
a computational model on healthcare provider segments, the
dashboard portion of a retail store segments model section 125
receives data from and transmit data to step 49 which executes a
computational model on retail store segments, a payor segment
computational model 126 receives data from and transmit data to
step 50 which conducts a computational model simulation on payor
segments. The financial model simulator module 26 is configured to
supply the promotional campaign information to the promotional
tactics 122. The consumer segments module 28c is configured to
supply data to the consumer segments model section 123. The
healthcare provider segments module 28b is configured to supply
data to the HCP segments model section 124. The manufacturer
PBM/payor strategy module 31 and the manufacturer PBM/payor
execution module 32 are configured to supply to the PBM/payor
computational model 126 and the retailer segments module 28c is
configured to supply data to the retail store segments model
section 125. The manufacturer PBM/payor strategy module 31 and the
manufacturer PBM/payor execution module 32 are configured to supply
to the PBM/payor computational model 126.
[0149] FIGS. 9C-9I are exemplary graphs that may be displayed on
different sections of the brand strategist dashboard. FIG. 9C is a
bar graph illustrating a particular brand drug Incremental Sales:
budget, year-to-date (YTD) target, and YTD actual. FIG. 9D is a
graph showing DTC dollars spent on a particular brand drug. A bar
graph in FIG. 9E depicts dollars generated through incremental
brand drug volume for specified months during a particular year.
FIG. 9F shows PBM data for a particular brand drug copay cards
redeemed by month. A sample of the copay cards distributed by a
particular month for the brand drug is illustrated in FIG. 9G. FIG.
9H illustrates the amount of sales of a particular brand drug for a
particular month during the current year versus the particular
month from last year. A sampling of DTC exposures for a particular
brand drug is depicted in FIG. 9I.
[0150] FIG. 10 is a block diagram illustrating an example of a
computer device, as shown in 3a, 3b, 3c, 3d, 3e, 3f, 3g, 3h, 3i and
3j, on which computer-executable instructions to perform the
methodologies discussed herein may be installed and run. As alluded
to above, the various computer-based devices discussed in
connection with the present invention may share similar attributes.
Each of the computer devices in 3a, 3b, 3c, 3d, 3e, 3f, 3g, 3h, 3i
and 3j is capable of executing a set of instructions to cause the
computer device to perform any one or more of the methodologies
discussed herein. The computer devices may represent any or all of
the 3a, 3b, 3c, 3d, 3e, 3f, 3g, 3h, 3i, and 3j server 10, or any
network intermediary devices. Further, while only a single machine
is illustrated, the term "machine" shall also be taken to include
any collection of machines that individually or jointly execute a
set (or multiple sets) of instructions to perform any one or more
of the methodologies discussed herein. The exemplary computer
system 131 includes a processor 132 (e.g., a central processing
unit (CPU), a graphics processing unit (GPU), or both), a main
memory 133 and a static memory 134, which communicate with each
other via a bus 135 and project onto a display 136. The computer
system 131 may further include a video display unit 137 (e.g., a
liquid crystal display (LCD)). The computer system 131 also
includes an alphanumeric input device 138 (e.g., a keyboard), a
cursor control device 139 (e.g., a mouse), a disk drive unit 140, a
signal generation device 141 (e.g., a speaker), and a network
interface device 144.
[0151] The disk drive unit 140 includes a machine-readable medium
142 on which is stored one or more sets of instructions (e.g.,
software 143) embodying any one or more of the methodologies or
functions described herein. The software 143 may also reside,
completely or at least partially, within the main memory 133 and/or
within the processor 132 during execution thereof, the computer
system 131, the main memory 133, and the instruction-storing
portions of the processor 132 also constituting machine-readable
media. The software 143 may further be transmitted or received over
a network 5 via the network interface device 144.
[0152] While the machine-readable medium 142 is shown in an
exemplary embodiment to be a single medium, the term
"machine-readable medium" should be taken to include a single
medium or multiple media (e.g., a centralized or distributed
database, and/or associated caches and servers) that store one or
more sets of instructions. The term "machine-readable medium" shall
also be taken to include any tangible medium that is capable of
storing a set of instructions for execution by the machine and that
cause the machine to perform any one or more of the methodologies
of the present invention. The term "machine-readable medium" shall
accordingly be taken to include: not be limited to, solid-state
memories, and optical and magnetic media.
[0153] The present invention has been described in particular
detail with respect to possible embodiments. Those skilled in the
art will appreciate that the invention may be practiced in other
embodiments. The particular naming of the components,
capitalization of terms, the attributes, data structures, or any
other programming or structural aspect is not mandatory or
significant, and the mechanisms that implement the invention or its
features may have different names, formats or protocols. The system
may be implemented via a combination of hardware and software, as
described, or entirely in hardware elements, or entirely in
software elements. The particular division of functionality between
the various system components described herein is merely exemplary
and not mandatory; functions performed by a single system component
may instead be performed by multiple components, and functions
performed by multiple components may instead be performed by a
single component.
[0154] In various embodiments, the present invention can be
implemented as a system or a method for performing the
above-described techniques, either singly or in any combination. In
another embodiment, the present invention can be implemented as a
computer program product comprising a computer-readable storage
medium and computer program code, encoded on the medium, for
causing a processor in a computing device or other electronic
device to perform the above-described techniques.
[0155] As used herein, any reference to "one embodiment" or to "an
embodiment" means that a particular feature, structure or
characteristic described in connection with the embodiments is
included in at least one embodiment of the invention. The
appearances of the phrase "in one embodiment" in various places in
the specification are not necessarily all referring to the same
embodiment.
[0156] Some portions of the above are presented in terms of
algorithms and symbolic representations of operations on data bits
within a computer memory. These algorithmic descriptions and
representations are the means used by those skilled in the data
processing arts to most effectively convey the substance of their
work to others skilled in the art. An algorithm is generally
perceived to be a self-consistent sequence of steps (instructions)
leading to a desired result. The steps are those requiring physical
manipulations of physical quantities. Usually, though not
necessarily, these quantities take the form of electrical, magnetic
or optical signals capable of being stored, transferred, combined,
compared, transformed, and otherwise manipulated. It is convenient
at times, principally for reasons of common usage, to refer to
these signals as bits, values, elements, symbols, characters,
terms, numbers, or the like. Furthermore, it is also convenient at
times to refer to certain arrangements of steps requiring physical
manipulations of physical quantities as modules or code devices,
without loss of generality.
[0157] It should be borne in mind, however, that all of these and
similar terms are to be associated with the appropriate physical
quantities and are merely convenient labels applied to these
quantities. Unless specifically stated otherwise as apparent from
the following discussion, it is appreciated that, throughout the
description, discussions utilizing terms such as "processing" or
"computing" or "calculating" or "displaying" or "determining" or
the like refer to the action and processes of a computer system, or
similar electronic computing module and/or device, that manipulates
and transforms data represented as physical (electronic) quantities
within the computer system memories or registers or other such
information storage, transmission or display devices.
[0158] Certain aspects of the present invention include process
steps and instructions described herein in the form of an
algorithm. It should be noted that the process steps and
instructions of the present invention could be embodied in
software, firmware and/or hardware, and, when embodied in software,
can be downloaded to reside on and be operated from different
platforms used by a variety of operating systems.
[0159] The present invention also relates to an apparatus for
performing the operations herein. This apparatus may be specially
constructed for the required purposes, or it may comprise a
general-purpose computer selectively activated or reconfigured by a
computer program stored in the computer. Such a computer program
may be stored in a computer readable storage medium, such as, but
not limited to, any type of disk including floppy disks, optical
disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs),
random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical
cards, application specific integrated circuits (ASICs), or any
type of media suitable for storing electronic instructions, and
each coupled to a computer system bus. Furthermore, the computers
and/or other electronic devices referred to in the specification
may include a single processor or may be architectures employing
multiple processor designs for increased computing capability.
[0160] The algorithms and displays presented herein are not
inherently related to any particular computer, virtualized system,
or other apparatus. Various general-purpose systems may also be
used with programs in accordance with the teachings herein, or it
may prove convenient to construct more specialized apparatus to
perform the required method steps. The required structure for a
variety of these systems will be apparent from the description
provided herein. In addition, the present invention is not
described with reference to any particular programming language. It
will be appreciated that a variety of programming languages may be
used to implement the teachings of the present invention as
described herein, and any references above to specific languages
are provided for disclosure of enablement and best mode of the
present invention.
[0161] In various embodiments, the present invention can be
implemented as software, hardware, and/or other elements for
controlling a computer system, computing device, or other
electronic device, or any combination or plurality thereof. Such an
electronic device can include, for example, a processor, an input
device (such as a keyboard, mouse, touchpad, trackpad, joystick,
trackball, microphone, and/or any combination thereof), an output
device (such as a screen, speaker, and/or the like), memory,
long-term storage (such as magnetic storage, optical storage,
and/or the like), and/or network connectivity, according to
techniques that are well known in the art. Such an electronic
device may be portable or non-portable. Examples of electronic
devices that may be used for implementing the invention include
mobile phones, personal digital assistants, smartphones, kiosks,
desktop computers, laptop computers, tablets, wearable devices,
wearable sensors, consumer electronic devices, televisions, set-top
boxes or the like. An electronic device for implementing the
present invention may use an operating system such as, for example,
iOS available from Apple Inc. of Cupertino, Calif., Android
available from Google Inc. of Mountain View, Calif., Microsoft
Windows 7 available from Microsoft Corporation of Redmond, Wash.,
webOS available from Palm, Inc. of Sunnyvale, Calif., or any other
operating system that is adapted for use on the device. In some
embodiments, the electronic device for implementing the present
invention includes functionality for communication over one or more
networks, including for example a cellular telephone network,
wireless network, and/or computer network such as the Internet.
[0162] Some embodiments may be described using the expression
"coupled" and "connected" along with their derivatives. It should
be understood that these terms are not intended as synonyms for
each other. For example, some embodiments may be described using
the term "connected" to indicate that two or more elements are in
direct physical or electrical contact with each other. In another
example, some embodiments may be described using the term "coupled"
to indicate that two or more elements are in direct physical or
electrical contact. The term "coupled," however, may also mean that
two or more elements are not in direct contact with each other, but
yet still co-operate or interact with each other. The embodiments
are not limited in this context.
[0163] As used herein, the terms "comprises," "comprising,"
"includes," "including," "has," "having" or any other variation
thereof are intended to cover a non-exclusive inclusion. For
example, a process, method, article or apparatus that comprises a
list of elements is not necessarily limited to only those elements
but may include other elements not expressly listed or inherent to
such process, method, article or apparatus. Further, unless
expressly stated to the contrary, "or" refers to an inclusive or
and not to an exclusive or. For example, a condition A or B is
satisfied by any one of the following: A is true (or present) and B
is false (or not present), A is false (or not present) and B is
true (or present), and both A and B are true (or present).
[0164] The terms "a" or "an," as used herein, are defined as one or
more than one. The term "plurality," as used herein, is defined as
two or more than two. The term "another," as used herein, is
defined as at least a second or more.
[0165] The term "or" is inclusive and not exclusive, unless
expressly indicated otherwise or indicated otherwise by context.
Therefore, "A or B" means "A, B, or both," unless expressly
indicated otherwise or indicated otherwise by context. Moreover,
the term "and" is both joint and several, unless expressly
indicated otherwise or indicated otherwise by context. Therefore,
"A and B" means "A and B, jointly or severally," unless expressly
indicated otherwise or indicated otherwise by context.
[0166] An ordinary artisan should require no additional explanation
in developing the methods and systems described herein but may
nevertheless find some possibly helpful guidance in the preparation
of these methods and systems by examining standard reference works
in the relevant art.
[0167] While the invention has been described with respect to a
limited number of embodiments, those skilled in the art, having
benefit of the above description, will appreciate that other
embodiments may be devised which do not depart from the scope of
the present invention as described herein. It should be noted that
the language used in the specification has been principally
selected for readability and instructional purposes, and may not
have been selected to delineate or circumscribe the inventive
subject matter. The terms used should not be construed to limit the
invention to the specific embodiments disclosed in the
specification and the claims but should be construed to include all
methods and systems that operate under the claims set forth herein
below. Accordingly, the invention is not limited by the disclosure,
but instead its scope is to be determined entirely by the following
claims.
* * * * *
References