  Patent similarity analysis plays a crucial role in evaluating the risk of
patent infringement. Nonetheless, this analysis is predominantly conducted
manually by legal experts, often resulting in a time-consuming process. Recent
advances in natural language processing technology offer a promising avenue for
automating this process. However, methods for measuring similarity between
patents still rely on experts manually classifying patents. Due to the recent
development of artificial intelligence technology, a lot of research is being
conducted focusing on the semantic similarity of patents using natural language
processing technology. However, it is difficult to accurately analyze patent
data, which are legal documents representing complex technologies, using
existing natural language processing technologies. To address these
limitations, we propose a hybrid methodology that takes into account
bibliographic similarity, measures the similarity between patents by
considering the semantic similarity of patents, the technical similarity
between patents, and the bibliographic information of patents. Using natural
language processing techniques, we measure semantic similarity based on patent
text and calculate technical similarity through the degree of coexistence of
International patent classification (IPC) codes. The similarity of
bibliographic information of a patent is calculated using the special
characteristics of the patent: citation information, inventor information, and
assignee information. We propose a model that assigns reasonable weights to
each similarity method considered. With the help of experts, we performed
manual similarity evaluations on 420 pairs and evaluated the performance of our
model based on this data. We have empirically shown that our method outperforms
recent natural language processing techniques.

Information Retrieval;Artificial Intelligence;Social and Information Networks