Ranking Structured Documents: A Large Margin Based Approach for Patent Prior Art Search

We propose an approach for automatically ranking structured documents applied to patent prior art search. Our model, SVM Patent Ranking (SVMPR) incorporates margin constraints that directly capture the specificities of patent citation ranking. Our approach combines patent domain knowledge features with meta-score features from several different general Information Retrieval methods. The training algorithm is an extension of the Pegasos algorithm with performance guarantees, effectively handling hundreds of thousands of patent-pair judgements in a high dimensional feature space. Experiments on a homogeneous essential wireless patent dataset show that SVMPR performs on average 30%-40% better than many other state-of-the-art general-purpose Information Retrieval methods in terms of the NDCG measure at different cut-off positions.