Appeal No. 2005-1644
Application 09/400,583
that allow them to refine merchandise placements. For
example, managers discovered that shoppers were more likely
to buy travel alarm clocks if they were placed in the
luggage department than in the jewelry department.
Furthermore, by being able to sort these patterns by age,
income and place of residence, the company can more
precisely target each store for the specific demographics of
the nearby population. Information derived from Wal-Mart's
co-branded credit card can also be used to develop
individualized customer profiles.
The examiner relies on the last two sentences for a teaching of
data mining by group and by individual.
The quotation above teaches using data mining to generate
data relationships "wherein the data relationships associate
individual customers with information related to the individual
customers." The teaching of "being able to sort these patterns
by age, income and place of residence" using data mining
techniques indicates the use of information related to individual
customers, i.e., "age, income and place of residence." In
addition, using information from the Wal-Mart credit card "to
develop individualized customer profiles" by data mining
techniques indicates the use of information related to individual
customers just as appellants' disclosure at page 27, lines 1-16,
of the specification that it was known in the art to data mine
based on the individual customer information obtained from use of
a preferred customer card. Toung also discloses data mining the
combination of spatial relationships (placement of products in
the store) and data relationships (association of product buying
patterns with individual customer information, such as age,
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