A Novel Patent Similarity Measurement Methodology: Semantic Distance and Technological Distance

TL;DR

This paper introduces a hybrid patent similarity measurement approach combining semantic, technical, and bibliographic data, leveraging NLP and expert evaluation to improve accuracy over existing methods.

Contribution

It proposes a novel hybrid methodology that integrates multiple similarity measures with weighted importance, enhancing patent similarity analysis accuracy.

Findings

Outperforms recent NLP-based similarity methods.

Demonstrates effectiveness with 420 manually evaluated patent pairs.

Integrates semantic, technical, and bibliographic similarities for comprehensive analysis.

Abstract

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.