Conceptual design is an important, but difficult, phase of systems development. Analysis patterns can greatly benefit this phase because they capture abstractions of situations that occur frequently in conceptual modeling. Naïve approaches to automate conceptual design with reuse of analysis patterns have had limited success because they do not emulate the learning that occurs over time. This research develops learning mechanisms for improving analysis pattern reuse in conceptual design. The learning mechanisms employ supervised learning techniques to support the generic reuse tasks of retrieval, adaptation, and integration, and emulate expert behaviors of analogy making and designing by assembly. They are added to a naïve approach and the augmented methodology implemented as an intelligent assistant to a designer for generating an initial conceptual design that a developer may refine. To assess the potential of the methodology to benefit practice, empirical testing is carried out on multiple domains and tasks of different sizes. The results suggest that the methodology has the potential to benefit practice.