PREDICTING THE UNPREDICTABLE : AN EMPIRICAL ANALYSIS OF U.S. PATENT INFRINGEMENT AWARDS

Transcription

1 PREDICTING THE UNPREDICTABLE : AN EMPIRICAL ANALYSIS OF U.S. PATENT INFRINGEMENT AWARDS 12 TH Annual Intellectual Property Scholars Conference Stanford University Law School 9 August 2012 Michael J. Mazzeo Kellogg School of Management, Northwestern University Jonathan Hillel Skadden, Arps, Slate, Meagher & Flom LLP Samantha Zyontz Harvard University

2 Focus: PredicDng Damage Awards Widespread concern exists about the unpredictability of patent damage awards and its effect on everything from lidgadon strategy to incendves for innovadve acdvity FTC Report highlights losery Dcket mentality regarding lidgadon outcomes in some circles. Our approach: assemble comprehensive data on damage awards and run straighuorward regressions that use readily available, reasonable factors to predict award size. Findings: Infringement damages are highly predictable overall and are correlated with factors associated with economic value of patents, lidgant size and case complexity.

3 Focus: PredicDng Damage Awards Widespread concern exists about the unpredictability of patent damage awards and its effect on everything from lidgadon strategy to incendves for innovadve acdvity FTC Report highlights losery Dcket mentality regarding lidgadon outcomes in some circles. Our approach: assemble comprehensive data on damage awards and run straighuorward regressions that use readily available, reasonable factors to predict award size. Findings: Infringement damages are highly predictable overall and are correlated with factors associated with economic value of patents, lidgant size and case complexity.

4 Focus: PredicDng Damage Awards Widespread concern exists about the unpredictability of patent damage awards and its effect on everything from lidgadon strategy to incendves for innovadve acdvity FTC Report highlights losery Dcket mentality regarding lidgadon outcomes in some circles. Our approach: assemble comprehensive data on damage awards and run straighuorward regressions that use readily available factors to predict award size. Findings: Infringement damages are highly predictable overall and are correlated with factors associated with economic value of patents, lidgant size and case complexity.

5 Prior Literature Studies by Lanjouw & Schankerman ( ) described the predictors of patent lidgadon. Studies by consuldng firm PwC ( ) described the data (and caused considerable alarm). Lemley & Shapiro (2007) demonstrated heterogeneity across industries in reasonable royalty rates. Allison, Lemley & Walker (2009) described the characterisdcs of the most lidgated patents. Operdeck (2009) finds no overriding paserns when trying to explain the size of awards stadsdcally.

6 Analysis Dataset: comprehensive informadon from 340 cases decided in US federal courts between 1995 and Controls: characterisdcs that correlate with economic value such as patent citadons, firm size and ownership, industry as well as case informadon. Findings: a straighuorward regression analysis establishes that our controls explain more than 74 percent of the variadon in patent damage awards.

7 Evolving the PwC Dataset

8 Dataset: Size distribudon of damage awards in patent infringement cases,

9 Almost the EnDre Iceberg: the top eight cases represent 47.6 percent of collecdve damages

10 Analysis Dataset: comprehensive informadon from 340 cases decided in US federal courts between 1995 and Controls: assembled a detailed set of case characterisdcs, matched to the damage award levels, to act as potendal explanatory variables.

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12 Analysis Dataset: comprehensive informadon from 340 cases decided in US federal courts between 1995 and Controls: assembled a detailed set of case characterisdcs, matched to the damage award levels, to act as potendal explanatory variables. Regressions: 1. Overall predictability of damage award amounts. 2. Analysis of explanatory power of pardcular significant factors.

13 Regressions (1): Overall predictability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

14 Regressions (1): Overall predictability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

15 Regressions (2): What masers? Focus the analysis on exactly which cridcal factors help to explain the size of awarded damages: Underlying value of the patents in the case: Number of patents Number of claims Forward citadons Patent Age LiDgant informadon: Status of patent holders as pracdcing enddes Proxies for size/income of defendants Case strategy informadon: Judge vs. Jury Time to trial

16 Dependent = Regressions (2): What masers? Log of patent damage awards in 2008 dollars Coef. Robust Std. Error t P>t Number of obs 240 F( 10, 229) Prob > F R squared Root MSE [95% Conf. Interval] Average Number of Patent Claims Number of Patents Average Number of Forward Cita\ons Average Age of Patent Dummy for Prac\cing Patent Holder Defendant is a Fortune 500 Comp. (or sub) Defendant is a Public Comp. (or sub) Dummy for Trial by Jury Time to Trial (days) Year of Decision (\me trend) Constant

17 Dependent = Regressions (2): What masers? Log of patent damage awards in 2008 dollars Coef. Robust Std. Error t P>t Number of obs 240 F( 10, 229) Prob > F R squared Root MSE [95% Conf. Interval] Average Number of Patent Claims Number of Patents Average Number of Forward Cita\ons Average Age of Patent Dummy for Prac\cing Patent Holder Defendant is a Fortune 500 Comp. (or sub) Defendant is a Public Comp. (or sub) Dummy for Trial by Jury Time to Trial (days) Year of Decision (\me trend) Constant

18 Dependent = Regressions (2): What masers? Log of patent damage awards in 2008 dollars Coef. Robust Std. Error t P>t Number of obs 240 F( 10, 229) Prob > F R squared Root MSE [95% Conf. Interval] Average Number of Patent Claims Number of Patents Average Number of Forward Cita\ons Average Age of Patent Dummy for Prac\cing Patent Holder Defendant is a Fortune 500 Comp. (or sub) Defendant is a Public Comp. (or sub) Dummy for Trial by Jury Time to Trial (days) Year of Decision (\me trend) Constant

19 Dependent = Regressions (2): What masers? Log of patent damage awards in 2008 dollars Coef. Robust Std. Error t P>t Number of obs 240 F( 10, 229) Prob > F R squared Root MSE [95% Conf. Interval] Average Number of Patent Claims Number of Patents Average Number of Forward Cita\ons Average Age of Patent Dummy for Prac\cing Patent Holder Defendant is a Fortune 500 Comp. (or sub) Defendant is a Public Comp. (or sub) Dummy for Trial by Jury Time to Trial (days) Year of Decision (\me trend) Constant

20 Dependent = Regressions (2): What masers? Log of patent damage awards in 2008 dollars Coef. Robust Std. Error t P>t Number of obs 240 F( 10, 229) Prob > F R squared Root MSE [95% Conf. Interval] Average Number of Patent Claims Number of Patents Average Number of Forward Cita\ons Average Age of Patent Dummy for Prac\cing Patent Holder Defendant is a Fortune 500 Comp. (or sub) Defendant is a Public Comp. (or sub) Dummy for Trial by Jury Time to Trial (days) Year of Decision (\me trend) Constant

21 ApplicaDons & Extensions Model that explains awards can also be used to predict damage award levels based on available data (case, lidgant and patent at issue informadon). Expand dataset to include informadon about: More nuanced details regarding potendal non pracdcing enddes Cases lost at trial Cases sesled between infringement decision and damage awards

22 Summary SystemaDc empirical evidence suggests that the wellpublicized, very large patent infringement damage awards are infrequent. Constructed regression model with detailed control variables explains considerable pordon of the variadon in observed damage awards. More targeted regressions suggest that patent value, lidgant size and case strategy affect the level of damage awards (in predictable ways). Future research: expanding the dataset on damage awards and exploring other datasets on patent value.