Name
Abstract | ||
|---|---|---|
Patents are of crucial importance for businesses, because they provide legal protection for the invented techniques, processes or products. A patent can be held for up to 20 years. However, large maintenance fees need to be paid to keep it enforceable. If the patent is deemed not valuable, the owner may decide to abandon it by stopping paying the maintenance fees to reduce the cost. For large companies or organizations, making such decisions is difficult because too many patents need to be investigated. In this paper, we introduce the new patent mining problem of automatic patent maintenance prediction, and propose a systematic solution to analyze patents for recommending patent maintenance decision. We model the patents as a heterogeneous time-evolving information network and propose new patent features to build model for a ranked prediction on whether to maintain or abandon a patent. In addition, a network-based refinement approach is proposed to further improve the performance. We have conducted experiments on the large scale United States Patent and Trademark Office (USPTO) database which contains over four million granted patents. The results show that our technique can achieve high performance. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICDM.2011.116 | ICDM |
Keywords | Field | DocType |
ranking,new patent mining problem,high performance,patents,large scale,patent mining,businesses,patent maintenance recommendation,new patent feature,patent information network model,prediction,recommender systems,organisational aspects,patent mining problem,maintenance fee,automatic patent maintenance prediction,decision support systems,united states patent and trademark office database,large maintenance fee,data mining,legal protection,patent maintenance decision,organizations,invented techniques,patent information network,large company,patent maintenance,maintenance fees,million granted patent | Recommender system,Data science,Data mining,Ranking,Computer science,Decision support system,Trademark,Patent mining,Network model | Conference |
ISSN | ISBN | Citations |
1550-4786 | 978-1-4577-2075-8 | 13 |
PageRank | References | Authors |
0.67 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Jin | 1 | 70 | 19.16 |
| Scott Spangler | 2 | 162 | 27.58 |
| ying chen | 3 | 134 | 17.07 |
| Keke Cai | 4 | 243 | 15.36 |
| Rui Ma | 5 | 24 | 3.95 |
| Li Zhang | 6 | 392 | 20.72 |
| Xian Wu | 7 | 495 | 36.50 |
| Jiawei Han | 8 | 43085 | 3824.48 |