The goal of this project was to study automated methods for finding related patents based on a combination of many features extracted from the target patent and, thus, allow to address the task of finding prior art for a patent (application). In this setting, the patent document itself was used as the query for finding prior art. Retrieval was accomplished by building feature sets including languages models, n-grams, etc. of both the query patent and the patents in a large patent corpus. The approach employed ranking algorithms such as RankNet to discover highly associated resources.