中文摘要：

Due to the importance of patent, many studies have been done in patent analysis. However, the problem of finding the hotspots of competitors is seldom considered. Although there exist some hotspot discovery methods in Micro-blog and online public opinion, it is difficult to be directly applied because of the particularity of the patent text. In this paper, we proposed a text-clustering-based patent hotspot discovery method to find the hotspots of competitors. We first measure the similarity between patents by both semantic association and IPC association. After that, we use a hierarchical clustering algorithm to find the research topics and name for them. Then, we calculate the hotness of the technical phrases in order to find the hotspots. Finally, we use a case study of Huawei company to show the effectiveness of the proposed method.

英文摘要：

Due to the importance of patent, many studies have been done in patent analysis. However, the problem of finding the hotspots of competitors is seldom considered. Although there exist some hotspot discovery methods in Micro-blog and online public opinion, it is difficult to be directly applied because of the particularity of the patent text. In this paper, we proposed a text-clustering-based patent hotspot discovery method to find the hotspots of competitors. We first measure the similarity between patents by both semantic association and IPC association. After that, we use a hierarchical clustering algorithm to find the research topics and name for them. Then, we calculate the hotness of the technical phrases in order to find the hotspots. Finally, we use a case study of Huawei company to show the effectiveness of the proposed method.