Abstract: Rapid increase in global competition demands increased protection of intellectual property rights and underlines the importance of patents as major intellectual property documents. Prior art patent search is the task of identifying related patents for a given patent file, and is an essential step in judging the validity of a patent application. This article proposes an automated query generation and postprocessing method for prior art patent search. The proposed approach first constructs structured queries by combining terms extracted from different fields of a query patent and then reranks the retrieved patents by utilizing the International Patent Classification (IPC) code similarities between the query patent and the retrieved patents along with the retrieval score. An extensive set of empirical results carried out on a large-scale, real-world dataset shows that utilizing 20 or 30 query terms extracted from all fields of an original query patent according to their log(tf)idf values helps form a representative search query out of the query patent and is found to be more effective than is using any number of query terms from any single field. It is shown that combining terms extracted from different fields of the query patent by giving higher importance to terms extracted from the abstract, claims, and description fields than to terms extracted from the title field is more effective than treating all extracted terms equally while forming the search query. Finally, utilizing the similarities between the IPC codes of the query patent and retrieved patents is shown to be beneficial to improve the effectiveness of the prior art search.