Towards Comprehensive Patent Approval Predictions:Beyond Traditional Document Classification

Anonymous

Abstract: Predicting the approval chance of a patent application is a challenging problem involving multiple facets. The most crucial facet is arguably the novelty --- \emph{35 U.S. Code § 102} rejects more recent applications that have very similar prior arts. Such novelty evaluations differ the patent approval prediction from conventional document classification --- Successful patent applications may share similar writing patterns; however, too-similar newer applications would receive the opposite label, thus confusing standard document classifiers (e.g., BERT). To address this issue, we propose a novel framework \our that unifies the document classifier with handcrafted features, particularly time-dependent novelty scores. Specifically, we formulate the novelty scores by comparing each application with millions of prior arts using a hybrid of efficient filters and a neural bi-encoder. Moreover, we impose a new regularization term into the classification objective to enforce the monotonic change of approval prediction w.r.t. novelty scores. From extensive experiments on the large-scale USPTO dataset, we find that our time-dependent novelty features offer a boost on top of the document classifier. Also, our monotonic regularization, while shrinking the search space, can drive the optimizer to better local optima, yielding empirical performance gains. Ex-post analysis of prediction scores further confirms that the document classifier and handcrafted features capture distinct sets of learning information.