Exploring an essential patent through a GTM-based standard map

As competition for international standardization has intensified, many firms are now concentrating their capabilities on securing essential patents that claim one or more inventions that are required to practice a given industry standard. Despite the importance of such patents, recent critical debates tend to emphasize a hold-up problem, standard setting, and a strategy to link patents and standards. Thus, research on exploring an essential patent is still in an early stage. For the most part, development of essential patents depends on experts in the field to compare standard documents and patent documents. However, researchers rarely consider the relationship between standards and patents. As a remedy, this paper proposes a method that explores an essential patent through a generative topographic mapping (GTM)-based standard map. The proposed approach in this paper consists of three parts. First, text mining is performed to transform standard documents into keyword vectors. Second, generated keyword vectors are visualized by the GTM, enabling an analyst to find standard vacancy as well as keyword vectors of the vacancy. Finally, patents related to the standard vacancy are retrieved from patent databases with the extracted keywords. Moreover, candidates for essential patents are shortlisted by calculating a search relevancy that is an indicator to identify the relationship between the standard on vacancy and patent documents. The proposed approach will contribute to developing or finding promising essential patents, as well as provide critical opportunities to obtain strategic technology advantage.