Predicting Institution Decisions of Patent Litigation: A Study on Inter Partes Review
| Autor: | Yuh-Harn Yang, 楊喻涵 |
|---|---|
| Rok vydání: | 2017 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 105 Patent is an agreement between the government and patent owner. At a certain time, the patent owner has the right to exclude others from implementing the invention. As an exchange, the patent owner needs to reveal the invention to public. When the right expired, it can be public property used by the community. A patent litigation is very professional and technical, so it costs huge amount of money and time. The litigation not only causes heavy economic pressures, and the uncertainty of the results also bring disadvantages for business operations of both sides. If we can predict the results precisely by information technology before the litigation begins or the results come out, it would be key effect on designing business strategies and save huge money and time for both sides. The time of the right to exclude others for patent owner usually lasts for 20 years, so it’s very powerful. To prevent the government from giving the right improperly and obstructing progresses of technologies, there are plenty of laws for the community to challenge the rights in every country. In USA, a new law process called inter partes review(IPR) was announced by United States Patent and Trademark Office(USPTO) in 2012. As of the end of 2016 statistics by USPTO, once the IPR instituted and get the final written decision, the probability of at least one claim of the challenge patent being invalidated up to 83%. That’s why decision of institution is so important for both petitioners and patent owners, however, the previous works which study on prediction and analysis of institution decisions for IPR are very few. In this thesis, we design and analyze three predicting models for institution decision of IPR. We extracted three different kinds of features from documents of IPRs as bases to construct a predicting model. The first one is text-based features and we define a formula to find representative and discriminate terms in the petition. In graph-based features, the model uses the concept of a social network and learns continuous representation of influential entities from IPRs by the relations between each other. The last features follow “Valuable Patent”[1], a very influential paper in patent field, such as number of claims. In the end, our model can predict the decision with a strong accuracy and AUC. The accuracy of text-based feature is the best. The idea of using graph-based feature in patent prediction is novel and it get the excellent performance of AUC. Although the performance of the third feature is not so well, we find that the length of grant lag is important for decision of institution. The performance of time-series is getting better and better with time and its AUC surpasses 0.81which means our model can implement in the real world. Our aim is to help formulate strategies for both petitioners and patent owners, so we analyze the results and infer some summaries about the reasons of the prediction. Our results represent an important advance for the science of quantitative legal prediction and are practical with high commercial value. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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