Neural patent search systems often require examiner citation data for training, which we believe are potentially biased. Our work implements a self-supervised alternative by training BERT to match patent claims with their own description passages. We tested on 6,447 USPTO patents, and achieved 96% accuracy on claim-description matching without any examiner labels, demonstrating that document structure alone can teach models to recognize patent-relevant content.