Mihai Surdeanu
Christopher D. Manning
ABSTRACT
We introduce the problem of risk analysis for Intellectual Property (IP) lawsuits. More specifically, we focus on estimating the risk for participating parties using solely prior factors, i. e., historical and concurrent behavior of the entities involved in the case. This work represents a first step towards building a comprehensive legal risk assessment system for parties involved in litigation. This technology will allow parties to optimize their case parameters to minimize their own risk, or to settle disputes out of court and thereby ease the burden on the judicial system. In addition, it will also help U.S. courts detect and fix any inherent biases in the system.
We model risk estimation as a relational classification problem using conditional random fields [6] to jointly estimate the risks of concurrent cases. We evaluate our model on data collected by the Stanford Intellectual Property Litigation Clearinghouse, which consists of over 4,200 IP lawsuits filed across 88 U.S. federal districts and ranging over 8 years, probably the largest legal data set reported in data mining research. Despite being agnostic to the merits of the case, our best model achieves a classification accuracy of 64%, 22% (relative) higher than the majority-class baseline.
- C. Andrieu, N. De Freitas, A. Doucet, and M. I. Jordan, An introduction to MCMC for Machine Learning, Machine Learning, 2003.Google Scholar
- D. Arditi, F. E. Oksay, and O. B. Tokdemir, Predicting the Outcome of Construction Litigation Using Neural Networks, Computer-Aided Civil and Structural Engineering, 13, 1998.Google Scholar
- J. Besag, Statistical Analysis of Non-lattice Data, The Statistician, 24, pp. 179--195, 1975.Google ScholarCross Ref
- K. W. Chau, Prediction of Construction Litigation Outcome Using Particle Swarm Optimization, Proc. of the International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 2005. Google ScholarDigital Library
- J. Finkel, T. Grenager, and C. Manning, Incorporating Non-local Information into Information Extraction Systems through Gibbs Sampling, Proc. of the Annual Meeting of the Association for Computational Linguistics, 2005. Google ScholarDigital Library
- J. Lafferty, A. McCallum, and F. Pereira, Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, Proc. of International Conference on Machine Learning, 2001. Google ScholarDigital Library
- A. Stranieri and J. Zeleznikow. Knowledge Discovery from Legal Databases, Springer, ISBN 978-1-4020-3036-9, 2005. Google ScholarDigital Library
Index Terms
- Risk analysis for intellectual property litigation
Recommendations
A Comparative Analysis of Intellectual Property Rights: A case of Developed versus Developing Countries
AbstractThis paper aims to elaborate the concept of the intellectual property rights and comparatively analyse intellectual property laws in the UK and Turkey. The first reason of selecting one developed and one developing country for this analysis is ...
Request for information as an auxiliary measure in intellectual property litigation concerning electronic works and the protection of personal data
AbstractThe purpose of introducing a new procedure on intellectual property into the Code of Civil Procedure was to ensure the efficiency of the procedure and to develop uniform practices. At the same time, the solutions adopted so far in Directive 2004/...
Comments