Topics in prior-art patent search are typically full patent applications and relevant items are patents often taken from sources in different languages. Cross language patent retrieval (CLPR) technologies support searching for relevant patents across multiple languages. As such, CLPR requires a translation process between topic and document languages. The most popular method for crossing the language barrier in cross language information retrieval (CLIR) in general is machine translation (MT). High quality MT systems are becoming widely available for many language pairs and generally have higher effectiveness for CLIR than dictionary based methods. However for patent search, using MT for translation of the very long search queries requires significant time and computational resources. We present a novel MT approach specifically designed for CLIR in general and CLPR in particular. In this method information retrieval (IR) text pre-processing in the form of stop word removal and stemming are applied to the MT training corpus prior to the training phase of the MT system. Applying this step leads to a significant decrease in the MT computational and resource requirements in both the training and translation phases. Experiments on the CLEF-IP 2010 CLPR task show the new technique to be 5 to 23 times faster than standard MT for query translation, while maintaining statistically indistinguishable IR effectiveness. Furthermore the new method is significantly better than standard MT when only limited translation training resources are available.