District of Columbia and the Eastern District of Virginia seem to have developed experience in patent law cases, as least as measured by the number of appeals in the short time frame under study. As such, I created two alternative measures of In the first measure, I added the “experience.” judges from the District of Delaware to the list of pilot program judges. Since over 20% of the cases in the database hail from Delaware, this increased the size of our dummy variable. Alternatively, I also created a variable where I coded as any judge in the dataset who had at least three pending cases on “1” appeal. Of course, the three case cutoff is somewhat arbitrary, but a review of the data indicates that most judges in the dataset have only one case under appeal. Thus, if a judge has multiple cases under appeal, it is more likely that they have more experience in patent law. This measures may undercount experience as well. A particular judge, for instance, may have a lot of experience in patent litigation, but for whatever reason, many of the cases from his or her courtroom end up settling. The nature of the judge’s experience is also somewhat unaccounted for. A judge could have experience in infringement analysis but not in validity determinations or in holding Markman hearings. The Federal Judicial Center contends, for instance, that while Markman hearings are only held in 4% of all patent cases, over 60% of all total Markman hearings occur before pilot judges. I also controlled for the technology at issue, using two alternative measures. It may be the case that judges in certain districts hear cases having more complex technology than in other districts, and as such, failure to control for complexity could obscure the results as reversals may simply be more common in complex cases. The technology at issue in the case may in fact impact case duration as well as case complexity. First, I coded each patent case according to one of eleven broad subject matters: 1) medical; 2) computers/online; 3) electronics; 4) chemical; 5) consumer products (other than electronics); 6) manufacturing/mechanical/other; 7) telecommunications; 8) transportation; 9) electrical; 10) energy; and 11) design patents. As detailed in Figure 5, the distribution of patents in dispute across technology category differed among the district courts, reflecting either differences in known expertise of the
District of Columbia and the Eastern District of Virginia seem to have developed experience in patent law cases, as least as measured by the number of appeals in the short time frame under study. As such, I created two alternative measures of “experience.” In the first measure, I added the judges from the District of Delaware to the list of pilot program judges. Since over 20% of the cases in the database hail from Delaware, this increased the size of our dummy variable. Alternatively, I also created a variable where I coded as “1” any judge in the dataset who had at least three pending cases on appeal. Of course, the three case cutoff is somewhat arbitrary, but a review of the data indicates that most judges in the dataset have only one case under appeal. Thus, if a judge has multiple cases under appeal, it is more likely that they have more experience in patent law. This measures may undercount experience as well. A particular judge, for instance, may have a lot of experience in patent litigation, but for whatever reason, many of the cases from his or her courtroom end up settling. The nature of the judge’s experience is also somewhat unaccounted for. A judge could have experience in infringement analysis but not in validity determinations or in holding Markman hearings. The Federal Judicial Center contends, for instance, that while Markman hearings are only held in 4% of all patent cases, over 60% of all total Markman hearings occur before pilot judges. I also controlled for the technology at issue, using two alternative measures. It may be the case that judges in certain districts hear cases having more complex technology than in other districts, and as such, failure to control for complexity could obscure the results as reversals may simply be more common in complex cases. The technology at issue in the case may in fact impact case duration as well as case complexity. First, I coded each patent case according to one of eleven broad subject matters: 1) medical; 2) computers/online; 3) electronics; 4) chemical; 5) consumer products (other than electronics); 6) manufacturing/mechanical/other; 7) telecommunications; 8) transportation; 9) electrical; 10) energy; and 11) design patents. As detailed in Figure 5, the distribution of patents in dispute across technology category differed among the district courts, reflecting either differences in known expertise of the
semet_amy_patent_paper.docx