透過文字及引文分析預測評估專利新穎性

Abstract

With the rapid advancement of technologies over the last two decades, IP (intellectual property), especially patents, represents the most valuable and essential assets to firms. When leveraging effectively, it can serve as a powerful strategic and competitive tool. Identifying the technological novelty of patents has such a profound effect on competition and firm survival since it can help managers and decision makers detect drastic technological changes in the concerning technological fields, thus allowing them to plan responses at an early stage. However, identifying the technological novelty of patents is knowledge-intensive, time-consuming, and costly. Therefore, we apply data mining techniques to build an automatic system to accomplish this task. Specifically, we propose a TRIZ-based approach to evaluate the technological novelty of patents. Then, we integrate this approach with those proposed in prior studies (i.e., citation-based and text-based approaches) to form a more comprehensive framework for automatically classifying patents into different degrees of novelty. We conduct several experiments using an expert assessment dataset to evaluate the effectiveness of our proposed system.