Citation counts have been widely used in a digital library for purposes such as ranking scientific publications and evaluating patents. This paper demonstrates that distinguishing different types of citations could rank better for these purposes. We differentiate patent citations along two dimensions (assignees and technologies) into four types, and propose a weighted citation approach for assessing and ranking patents. We investigate five weight learning methods and compare their performance. Our weighted citation method performs consistently better than simple citation counts, in terms of rank correlations with patent renewal status. The estimated weights on different citations are consistent with economic insights on patent citations. Our study points to an interesting and promising research line on patent citation and network analysis that has not been explored.