2005 Special Issue: A systems approach to appraisal mechanisms in emotion
Neural Networks - Special issue: Emotion and brain
Label ranking by learning pairwise preferences
Artificial Intelligence
Learning from measurements in exponential families
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
COA: finding novel patents through text analysis
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding nuggets in IP portfolios: core patent mining through textual temporal analysis
Proceedings of the 21st ACM international conference on Information and knowledge management
Patent partner recommendation in enterprise social networks
Proceedings of the sixth ACM international conference on Web search and data mining
| Hi-index | 0.00 |
The number of patents filed each year has increased dramatically in recent years, raising concerns that patents of questionable validity are restricting the issuance of truly innovative patents. For this reason, there is a strong demand to develop an objective model to quantify patent quality and characterize the attributes that lead to higher-quality patents. In this paper, we develop a latent graphical model to infer patent quality from related measurements. In addition, we extract advanced lexical features via natural language processing techniques to capture the quality measures such as clarity of claims, originality, and importance of cited prior art. We demonstrate the effectiveness of our approach by validating its predictions with previous court decisions of litigated patents.