Recently some algorithms have been proposed to clean post-training rule populations evolved by XCS, a state of the art Learning Classifier System (LCS). We present an algorithm to extract optimal rules, which we refer to as signatures, during the operation of UCS, a recent variant of XCS. In a benchmark binary valued dataset our method seconds the generalization and optimality hypotheses for UCS and provide mechanisms for retrieving all maximally general rules in real time. In real valued problems, where precise realization of decision boundaries is often not possible, our algorithm is able to retrieve near optimal representations with the help of a modified subsumption operator. The algorithm is able to reduce the processing time asymptotically and provides a mechanism for early stopping of the learning process.