Pattern recognition-informed (PRI) feedback using channel current cheminformatics (CCC) software is shown to be possible in "real-time" experimental efforts. The accuracy of the PRI classification is shown to inherit the high accuracy of our offline classifier: 99.9% accuracy in distinguishing between terminal base pairs of two DNA hairpins. The pattern recognition software consists of hidden Markov model (HMM) feature extraction software, and support vector machine (SVM) classification/ clustering software that is optimized for data acquired on a nanopore channel detection system. For general nanopore detection, the distributed HMM and SVM processing used here provides a processing speedup that allows pattern recognition to complete within the time frame of the signal acquisition - where the sampling is halted if the blockade signal is identified as not in the desired subset of events (or once recognized as nondiagnostic in general). We demonstrate that Nanopore Detection with PRI offers significant advantage when applied to data acquisition on antibody-antigen system, or other complex biomolecular mixtures, due to the reduction in wasted observation time on eventually rejected "junk" (nondiagnostic) signals.