Data processing: financial – business practice – management – or co – Automated electrical financial or business practice or... – Discount or incentive
Reexamination Certificate
2000-11-01
2004-11-30
Hafiz, Tariq R. (Department: 3623)
Data processing: financial, business practice, management, or co
Automated electrical financial or business practice or...
Discount or incentive
C705S001100, C705S014270, C705S035000, C705S03600T, C705S002000, C705S004000, C702S081000, C702S082000, C702S083000, C702S084000, C702S179000, C702S180000, C702S181000, C702S182000, C702S183000, C702S184000, C702S185000, C702S186000, C702S188000
Reexamination Certificate
active
06826541
ABSTRACT:
TECHNICAL FIELD
The present invention relates to using a proven social science statistical technique called conjoint analysis to facilitate choices among complex alternatives. More particularly, the present invention relates to methods, systems and computer program products for facilitating individual user choices among complex alternatives using a unique adaptation of conjoint analysis.
BACKGROUND ART
As a research methodology, conjoint analysis has been in use in the academic and commercial research community for many years (since the mid-1970's), and has been commonly used for marketing research purposes to assess consumer preferences among competing products or services.
Generally, conjoint analysis is a tool that researchers use to estimate the relative importance of the attributes that comprise the “alternatives” in the “choice set” and how much utility each “setting” of each “attribute” has for individuals. Results are often used to simulate the effect on market share that various changes in the “attribute settings” have and thus to fine tune “alternatives” (e.g. identify the optimal price for a product) and to forecast market share. While many forms of conjoint analysis exist, there are two general defining properties of any conjoint process: 1) each at some point gathers data from individuals by asking each individual to consider (the “con” in conjoint) two or more variables simultaneously or jointly (the “joint” in conjoint) and 2) each uses the gathered data (responses) to estimate how much utility or value each “attribute setting.” Typically, conjoint data is gathered from a sample of users and then analyzed with no flow of information back to the user. Thus, there exists a long-felt need for applications that use conjoint analysis to facilitate individual user choices among complex decisions by providing conjoint analysis results back to the user.
For example, this need is particularly acute in the area of employer-sponsored health plans. Many large- and medium-sized employers offer a number of health plan options for employees. Each health plan includes various features, such as monthly premium, annual deductible, prescription drug coverage, etc. Due to the number of plans and the number of different features of each plan, the choice between plans becomes difficult for the individual employee. Moreover, the employer typically cannot advise an employee to choose one plan over the other because the employer can be held liable if the employer advises an employee to choose a plan that does not pay for some of the employee's medical expenses. Accordingly, in the employer-sponsored health plan selection process, there exists a long-felt need for methods and systems for facilitating employee choices among health plans.
DISCLOSURE OF THE INVENTION
According to one aspect, the present invention includes a software tool that embodies a “conjoint” model decision process permitting the simplification of difficult choices among complex alternatives into a sequence of short, simpler decisions. “Alternatives” in this context can be products (such as automobiles), services (such as health plans), combinations of complementary services and products, or virtually anything else individuals must decide to choose or not choose. Complex “alternatives” are those defined in terms of many “variables” such that in the decision process a lot of information must be considered. Complex “alternatives” often create difficult decisions that demand that the chooser trade-off the good and bad in each “alternative.” For example, the choice between a high-quality bicycle versus a low-quality bicycle, given quality is the only criterion used in the selection, is an easy one. However, as the alternatives become more complex, the choice becomes more difficult and trade-offs must be made. The choice between a high-quality, $500 bicycle that comes in pink only versus a low-quality, $100 bicycle that comes in either green, black, or blue is a more difficult decision than that based on quality only.
The present invention uses, at its core, an adaptation of the conjoint model decision process. The use of the conjoint exercise allows to the tool to assist users in making difficult decisions less complex. By going through the exercise, unique profiles of what is important to the user are developed by the application.
In addition to developing user profiles, the present invention, at the end of the exercise, provides users with a “quality of fit” measure of how well each product or service available to them meets their unique profile.
In order to facilitate user choices among complex alternatives, the present invention includes computer software that requires an individual user to go through a series of less complex choices. The software first presents the user with a list of features. The user selects features which are of importance to the user. The software then presents the user with a first series of choices requiring the user to input or select first values indicating the relative importance of a best setting and a worst setting of each of the selected features. The user is then presented with a second series of choices requiring the user to input or select second values indicating the relative importance of the user's preference between first and second pairings of the selected attributes. Each pairing includes a best setting of one attribute and a worst setting of another attribute. The values input by the user in the second series of choices are interpreted as the mathematical difference equal to the relative importance of a best and worst setting of one attribute minus the relative importance of a best and a worst setting for the other attribute in the pairing. A final importance value is calculated for each of the attributes based on the initial relative importance values in the first series of choices and the mathematical difference values. Products and services available to the user are rated based on the final importance values. The user is then presented with data indicating the relative utility to the user of each of the products or services.
Terminology
Before proceeding, a review of keywords and key phrases and their definitions used in this document is warranted. These keywords are placed in double quotes throughout the document to indicate their use may be somewhat different from common use.
Keyword or Key Phrase
Definition
“user”
A person going through the software exercise
to gain help in making a choice
“alternative”
A single product or service (among a set of
products or services) the “user” can potentially
choose
“choice set”
All the “alternatives” the “user” is eligible to
choose from
“attribute”
One of numerous variables, each defined as
the continuum between its worst “setting” and
best “setting,” used to define the “alternatives”
“setting” (or “attribute
The value a particular hypothetical or actual
setting”)
“alternative” has for a particular “attribute”; the
hypothetical “alternatives” studied during the
data-gathering phase of the algorithm are all
specified in terms of the worst “setting” vs the
best “setting” for each “attribute,” whereas
actual “alternatives” available to the “user”
may be specified by “settings” anywhere
along each “attribute's” continuum
“importance”
A measure that the user gives directly via the
importance-of-the-difference (between worst
and best “settings”) screens of the relative
importance of a single “attribute”
“difference in
A measure that the user gives directly via the
importance”
trade-off screens of the (mathematical)
difference in the “importance” of two
“attributes”
“final computed
A final estimate of the true relative importance
importance”
of an “attribute” to a “user”
“setting utility”
The relative (relative to all “attribute settings”)
(or “attribute
worth or utility of a particular
setting utility”)
“attribute setting”
(anywhere along the “attribute” continuum) to
a particular “user”
“total utility”
The total relative (relative to all “alternatives”
available to that user) worth or utility of a
particular “alternativ
Johnston Jeffrey M.
Richman Adam B.
Sheeks, III David L.
Shopmyer Richard R.
Decision Innovations, Inc.
Doren Beth Van
Hafiz Tariq R.
Jenkins & Wilson & Taylor, P.A.
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