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| Content Provider | Springer Nature Link |
|---|---|
| Author | Zhang, Yi Qian, Yue Huang, Ying Guo, Ying Zhang, Guangquan Lu, Jie |
| Copyright Year | 2017 |
| Abstract | How to evaluate the value of a patent in technological innovation quantitatively and systematically challenges bibliometrics. Traditional indicator systems and weighting approaches mostly lead to “moderation” results; that is, patents ranked to a top list can have only good-looking values on all indicators rather than distinctive performances in certain individual indicators. Orienting patents authorized by the United States Patent and Trademark Office (USPTO), this paper constructs an entropy-based indicator system to measure their potential in technological innovation. Shannon’s entropy is introduced to quantitatively weight indicators and a collaborative filtering technique is used to iteratively remove negative patents. What remains is a small set of positive patents with potential in technological innovation as the output. A case study with 28,509 USPTO-authorized patents with Chinese assignees, covering the period from 1976 to 2014, demonstrates the feasibility and reliability of this method. |
| Starting Page | 1925 |
| Ending Page | 1946 |
| Page Count | 22 |
| File Format | |
| ISSN | 01389130 |
| Journal | Scientometrics |
| Volume Number | 111 |
| Issue Number | 3 |
| e-ISSN | 15882861 |
| Language | English |
| Publisher | Springer Netherlands |
| Publisher Date | 2017-03-07 |
| Publisher Place | Dordrecht |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Patent analysis Indicator system Bibliometrics Technological innovation Entropy Information Storage and Retrieval Library Science |
| Content Type | Text |
| Resource Type | Article |
| Subject | Library and Information Sciences Computer Science Applications |
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