Case Based Reasoning (CBR) provides a framework to capture past problems and their solutions to solve future problems. Problem cases are typically complete; however, it is not always possible to have a complete problem case due to complexity, lack of data, or availability of human expertise. The limitations of existing approaches for handling incomplete cases include a reliance upon manual input, such as Conversational CBR (CCBR) and Incremental CBR (ICBR), or a rigid structure of relationships maintained using a semantic ontology, to infer the missing feature values. Using the case base to infer feature values increases the efficiency and likelihood of identifying a relevant solution compared with manual interactions because the case base is based upon proven problem to solution correlation. Therefore, in this work-in-progress paper, we propose 'Nested CBR' as an approach for the automated completion of partial problem cases, and the subsequent solution identification, thereby avoiding manual input and improving solution efficiency and meaning.