Patents have provided a legal framework to protect intellectual property rights. There are also traded commodities. In recent years, patents originating from China have seen phenomenal growth along with bilingual patent processing, which includes translation between Chinese and other languages with heightened expectations on quality. As part of AI, Machine Translation (MT) is also evolving rapidly. It is now used for many translation tasks, including preliminary patent translation, which, like other high-value documents, requires human post-editing to ensure top final quality. With the arrival of Generative-AI, fluency of MT output has significantly improved. However, fidelity or accuracy in translation remains a problem, especially with scientific and technological terminology and between Chinese and English. Thus, an important problem is to provide tools to help the post-editor to (1) evaluate the quality of the MT and (2) to improve final accuracy. In this presentation, we compare the accuracy of MT results between some top MT suppliers and ChatGPT based on expert human evaluation as well as some objective metrics, including one based on terminological usage among a specially cultivated corpus of 300,000 comparable Chinese-English patents. We shall explore some major challenges facing any system to optimize the output of the final quality assurance process through AI-mediated post-editing.