US 7,096,209 B2Granted

Generalized segmentation method for estimation/optimization problem

AssigneeHewlett-Packard Development Company, L.P.

InventorsTongwei Liu, Dirk M. Beyer

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Abstract

A method for action selection based upon an objective of an outcome relative to a subject. In one embodiment, a training set is obtained that contains attributes of a subject. In the present embodiment, a best behavioral model for predicting an outcome when a subject has an action applied is calculated. The training set is mapped to the best behavioral model. The mapping provides a base from which a random sub-sample is acquired. In the present embodiment, a random sub-sample of the training set and the best behavioral model is then selected. This random sub-sample reduces the computational requirements when determining an optimized strategy. The optimized strategy provides an optimal action relative to the subject for the objective of the outcome. In one embodiment, the subject is a customer of a business entity, enabled to interact with the customer, and an action is a promotion offered by the business entity.

Claims16

Dependent claims

A computer system in a computer network, said computer system comprising:

a bus;
a memory unit coupled to said bus;
and at least one processor coupled to said bus, said at least one processor for executing a method for action selection based upon an objective of an outcome relative to a subject, said method comprising:
a) acquiring and storing a training set, said training set an existing database of information, said information are attributes of said subject, wherein said training set is to provide a base of data for said method;
b) calculating and storing a best behavioral model for predicting said outcome, provided an action is applied to said subject;
c) mapping of said training set to said best behavioral model within a business metric space, wherein said mapping is subsequently stored;
d) selecting and storing a random sub-sample of said training set mapped to said best behavioral model, said random sub-sample for reducing computational requirements when determining an optimized strategy;
and
e) determining and storing said optimized strategy for said random sub-sample, said optimized strategy for providing an optimal action relative to said subject for said objective of said outcome.

6 more elements

Dependencies are inferred from the claim text.