Method and system for recognizing end-user transactions

Data processing: artificial intelligence – Neural network – Learning task

Reexamination Certificate

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C706S012000

Reexamination Certificate

active

06925452

ABSTRACT:
A method and system are described for end-user transaction recognition based on server data such as sequences of remote procedure calls (RPCs). The method may comprise machine-learning techniques for pattern recognition such as Bayesian classification, feature extraction mechanisms, and a dynamic-programming approach to segmentation of RPC sequences. The method preferably combines information-theoretic and machine-learning approaches. The system preferably includes a learning engine and an operation engine. A learning engine may comprise a data preparation subsystem (feature extraction) and a Bayes Net learning subsystem (model construction). The operation engine may comprise transaction segmentation and transaction classification subsystems.

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T.M. Mitchell, “Machine Learning,” Chapter 6, Bayesian Learning, pp. 176-185.
D. Beeferman et al., “Statistical Models for Text Segmentation,” School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA, pp. 1-37.

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