Due to the increasing number of patents being published every day, patent citation recommendations have become one of the challenging tasks. Since patent citations may lead to legal and economic consequences, patent recommendations are even more challenging as compared to scientific article citations. One of the crucial components of the patent citation algorithm is negative sampling which is also a part of many other tasks such as text classification, knowledge graph completion, etc. This paper, particularly focuses on proposing a transformer-based ranking model for patent recommendations. It further experimentally compares the performance of patent recommendations based on various state-of-the-art negative sampling approaches to measure and compare the effectiveness of these approaches to aid future developments. These experiments are performed on a newly collected dataset of US patents from Google patents.