Abstract--This paper describes an automatic process for combining patterns and features, to guide a search process and reason about it. It is based on the functionality that a human brain might have, which is a highly distributed network of simple neuronal components that can apply some level of matching and cross-referencing over retrieved patterns. The process uses memory in a more dynamic way and it can realise results using a shallow hierarchy, which is a recognised brain-like construct. The paper gives one example of the process, using computer chess as a case study. The second half of the paper then presents a formal language for describing the global pattern sequences and transitions. These pattern ensembles are created from the same techniques that the search and prediction processes require and they define an outer framework that a distributed setup can try to learn. They can also be created automatically, resulting in further functionality for the generic cognitive model.