Ganglmair, Bernhard

Helmers, Christian

Love, Brian

English

bepress Legal Repository

Patent Litigation: Empirical Analysis

Santa Clara University School of Law

Santa Clara Law Santa Clara Law Digital Commons Faculty Publications Faculty Scholarship 9-11-2019 Patent Litigation: Empirical Analysis Bernhard Ganglmair ZEW – Leibniz Centre for European Economic Research Christian Helmers Santa Clara University Brian Love Santa Clara University School of Law, blove@scu.edu Follow this and additional works at: https://digitalcommons.law.scu.edu/facpubs  Part of the Law Commons Automated Citation Bernhard Ganglmair, Christian Helmers, and Brian Love, Patent Litigation: Empirical Analysis (2019), Available at: https://digitalcommons.law.scu.edu/facpubs/984 This Conference Paper is brought to you for free and open access by the Faculty Scholarship at Santa Clara Law Digital Commons. It has been accepted for inclusion in Faculty Publications by an authorized administrator of Santa Clara Law Digital Commons. For more information, please contact sculawlibrarian@gmail.com, pamjadi@scu.edu. Patent Litigation: Empirical AnalysisBernhard Ganglmair Christian Helmers Brian J. Love11 September 2019Ganglmair, Helmers, Love Empirical Analysis 11 September 2019 1 / 33Overview• Policy interest• Empirical questions• Empirical challenges and ways to address themGanglmair, Helmers, Love Empirical Analysis 11 September 2019 2 / 33Policy interest• Strength of patent rights• Interplay with post-grant review systems (e.g. PTAB in U.S.)• Lots of litigation especially in information and communicationtechnology (ICT) industry• Litigation due to so-called patent assertion entities (PAE) aka patenttrolls• Patent litigation involving standard essential patents (SEPs)Ganglmair, Helmers, Love Empirical Analysis 11 September 2019 3 / 33How should the system be designed?• Allow patent owners to enforce a patent if infringement is detected• Allow defendants to challenge patent’s validity• But also:• Discourage strategic behavior• Deter plaintiffs from seeking ‘overly broad’ injunctions or ‘excessive’damages• Deter nuisance lawsuits• Discourage defendants from driving up enforcement costs to deterassertion or force settlements• System should strike balance between allowing patent ownersto enforce their rights and to obtain appropriate remedies whileavoiding incentives for excessive litigation• Is the litigation system achieving that objective?Ganglmair, Helmers, Love Empirical Analysis 11 September 2019 4 / 33Empirical questions• How much litigation is there?• Can we test validity of assumptions made by different theoreticalmodels of litigation (Shavell, 1996; Gelbach, 2018; Helland et al.,2018)?• Design of litigation system – specific aspects:• Bifurcation and sequential trials• Fee shifting• Forum shopping• Evaluate effect of specific litigation activity: NPEs, SEPs• Legal, institutional, legislative changes::• Frequent changes in the law and its application especially in commonlaw jurisdictions (e.g. in U.S. Mayo v. Prometheus 2012, CLS Bank v.Alice 2014)• Institutional changes (e.g. reform of IPEC in UK including SCT,introduction of opposition procedures in Japan and Korea in 2015 and2017 respectively)• Legislative changes (AIA in the U.S.)Ganglmair, Helmers, Love Empirical Analysis 11 September 2019 5 / 33Empirical analysis• Challenges for quantitative analysis of patent litigation:(1) Complexity of patent litigation (see Lecture 1)(2) Observability of information• Some information unobservable (private information exchangedbetween parties)• Information in principle observable, but unavailable (e.g. terms ofprivate settlement)• Information in principle observable, but missing at random or not (e.g.only judgments published, pre-trial motions are not)(3) Large heterogeneity among court cases (see Lecture 3)(4) Any observable information is the outcome of non-random choice:selectionGanglmair, Helmers, Love Empirical Analysis 11 September 2019 6 / 33Selection• Selection biggest problem in cause-effect analysis• Observed data outcome of optimizing behavior by the parties:(1) Selection into court filing(2) Selection conditional on claim filing(3) Selection into settlement/judgment(4) Selection into appeal• Why does it matter?• How would you answer the following research question:What was the impact of a specific legal/institutional change onlitigation behavior (claims filed, plaintiff win rate, etc.)?Ganglmair, Helmers, Love Empirical Analysis 11 September 2019 7 / 33Reminder: Selection• Define:Di ={1 if i files claim0 otherwise.• 2 “potential” outcomes for individual i (only 1 outcome realized)• Outcome if does not file claim: Y0i• Outcome if files claim: Y1i• Causal effect of filing claim:κ = Y1i − Y0i (1)• Rewrite:Y1i = Y0i + κ (2)Ganglmair, Helmers, Love Empirical Analysis 11 September 2019 8 / 33Reminder: Selection• This means we can write:Avgn[Y1i|Di = 1] = κ+Avgn[Y0i|Di = 1] (3)• Subtract Avgn[Y0i|Di = 0]:{Avgn[Y1i|Di = 1]−Avgn[Y0i|Di = 0]}︸ ︷︷ ︸Difference in group means=xxxxxxxxxxxxxxxxxxxxκ+ {Avgn[Y0i|Di = 1]−Avgn[Y0i|Di = 0]}︸ ︷︷ ︸Selection Bias• This means:difference in group means = average causal effect + selection biasGanglmair, Helmers, Love Empirical Analysis 11 September 2019 9 / 331: Selection into court filing• Only complaints filed with a court areobserved• Disputes resolved or dropped beforeplaintiff files complaint unobservable• Survey results for the U.S. suggest70% of patent infringement claimsnever reach a court (Lemely et al.,2017)• Unclear how to account for this typeof selectionGanglmair, Helmers, Love Empirical Analysis 11 September 2019 10 / 331: Selection into court filing: implication• Assume some legal or institutional change• Observable: increase in the number of cases litigated in courtOut Before After In court In court Ganglmair, Helmers, Love Empirical Analysis 11 September 2019 11 / 331: Selection into court filing: implication• Assume some legal or institutional change• Observable: increase in the number of cases litigated in courtOut Before Outside of court In court Ganglmair, Helmers, Love Empirical Analysis 11 September 2019 12 / 331: Selection into court filing: implication• Assume some legal or institutional change• Observable: increase in the number of cases litigated in courtOut Before After Outside of court In court In court Outside of court Ganglmair, Helmers, Love Empirical Analysis 11 September 2019 13 / 331: Selection into court filing: implication• Assume some legal or institutional change• Observable: increase in the number of cases litigated in courtOut Before After Outside of court In court In court Outside of court Ganglmair, Helmers, Love Empirical Analysis 11 September 2019 14 / 332: Selection conditional on court filing• Some cases dropped after claim filing(claim form only document)• Defendant acknowledges service andfiles response (counterclaim)• If case proceeds, parties interact andmake series of decisions (litigantcontrolled motions that forceexchange of information)• Amount of information availabledepends on these decisionsStartendDon’t sueTrialendSettleJudgmentDon’t settleSueGanglmair, Helmers, Love Empirical Analysis 11 September 2019 15 / 332: Selection conditional on court filing• Court may dismiss the case• Parties may settle at any point –decision to settle depends on set offactors• If case settled, usually no informationrevealed about terms of settlement(e.g. potential payments, licensingagreements etc.)• Difficult which party prevailed insettlementStartendDon’t sueTrialendSettleJudgmentDon’t settleSueGanglmair, Helmers, Love Empirical Analysis 11 September 2019 16 / 333: Selection into judgment – no settlement• Theory showed thatsettlement process acts as a“filter” on filed cases• Empirically this means thatsmall and non-randomsubset of cases not settled(although ultimately empiricalquestion)• Decided cases are notrepresentative of all patentdisputes filed with court, evenless so of all patent disputesthat never reach a courtStartendDon’t sueTrialendSettleJudgmentWin?Lose?Don’t settleSueGanglmair, Helmers, Love Empirical Analysis 11 September 2019 17 / 333: Selection into judgment – no settlementPatentCopyrightTrademarkDesign.5.6.7.8.91Plaintiff win rate.5 .6 .7 .8 .9 1Settlement rateUK IPEC/PHC 2007-2013PatentDesignTrademarkCopyright.5.6.7.8.91Plaintiff win rate.5 .6 .7 .8 .9 1Plaintiff favourable settlementUK IPEC/PHC 2007-2013Ganglmair, Helmers, Love Empirical Analysis 11 September 2019 18 / 333: Selection into judgment – no settlement• Important implication (Shavell,1996; Gelbach, 2018):• Any plaintiff’s win rate can beobserved among litigated cases• Cannot infer anything aboutunderlying causes from observed winrates• Interpreting win rates requires theoryGanglmair, Helmers, Love Empirical Analysis 11 September 2019 19 / 334: Selection into appeal• Cases decided on appeal evenmore highly selected subset ofpatent cases and in no wayrepresentative of patentdisputes more generallyStartendDon’t sueTrialendSettleJudgmentWin?LoseendDon’t appealTrialAppeal?Don’t settleSueGanglmair, Helmers, Love Empirical Analysis 11 September 2019 20 / 33Selection• How to address selection?• In practice often simply ignored (“[W]e do not control for selection.Rather, we ask, given any selection that occurs, is there anyremaining association between patent and patentee characteristicsand the outcomes?” (Lanjouw and Schankerman, 2004))• Ways to address selection:• Theory• Diff-in-diff• Regression discontinuity• Instrumental variableGanglmair, Helmers, Love Empirical Analysis 11 September 2019 21 / 33Reminder: Differences-in-differences• Differences-in-differences (diff-in-diff) method works when there isselection• Need ‘treatment’ and ‘control’ groups (e.g. one type of cases affectedby Supreme Court decision, another is not)• But treatment and control groups can differ for many reasons• Main assumption: treatment and control outcomes move inparallel in the absence of treatment• Effect obtained from divergence between treatment and control grouppost-treatmentGanglmair, Helmers, Love Empirical Analysis 11 September 2019 22 / 33Reminder: Differences-in-differences• Diff-in-diff has 3 ingredients (assuming 1 treatment and 1 controlgroup):Yit = α+ βTi + γPt + δrDD(Ti × Pt) + eit (4)(1) A dummy for the treatment Ti that varies across treatment and controlgroups – inclusion of Td controls for fixed differences between the unitsbeing compared(2) A dummy for post-treatment periods Pt that varies over time –inclusion of Pt controls for the fact that conditions change over timefor everyone, whether treated or not(3) Interaction term Td × Pt – the coefficient on this term is the diff-in-diffcausal effect.Ganglmair, Helmers, Love Empirical Analysis 11 September 2019 23 / 33Differences-in-differencesIPECPHCIntroduction of costs cap01234ln number of court cases2007q1 2008q1 2009q1 2010q1 2011q1 2012q1 2013q1 2014q1Case filing dateIntroduction of costs cap-1-.50.511.5Coefficient2008q3 2009q4 2011q1 2012q2 2013q3Case filing dateGanglmair, Helmers, Love Empirical Analysis 11 September 2019 24 / 33Reminder: Regression discontinuity• Often changes in legal system occur on a specific date or decisionshappen within fixed time periods (e.g. institutional/legal changetakes effect on specific date; institution decision at PTAB)• This means that treatment is a deterministic function of time• If change generates a discontinuity in the data, can use RegressionDiscontinuity Design (RDD)Ganglmair, Helmers, Love Empirical Analysis 11 September 2019 25 / 33Reminder: Regression discontinuity• Define treatment asDi ={1 if time t ≥ t∗0 otherwise.(5)• Treatment status is a deterministic function of t• Treatment status is a discontinuous function of t, no matter how closet gets to cutoff t∗, Di remains unchanged until cutoff is reached• Sharp v fuzzy RDDGanglmair, Helmers, Love Empirical Analysis 11 September 2019 26 / 33Regression discontinuity1-year limit.02.04.06.08.1.12Probability of filing of parallel PTAB petition-100 -50 0 50 100Difference in days between filing of court case and 1-year limitGanglmair, Helmers, Love Empirical Analysis 11 September 2019 27 / 33Instrumental Variable (IV): Judge Fixed Effects• Individual judges affect outcomes• Heterogeneity among judges• Key institutional feature: random assignment of cases to judges(exclusion restriction)• Key idea: binary outcome of cases i 6= j valid IV for outcome of casei if same judge in i and j• Widely used in analysis of court decisions for a long time• Application to patent litigation: Galasso and Schankerman (2015)Ganglmair, Helmers, Love Empirical Analysis 11 September 2019 28 / 33Reminder: Instrumental Variable• IV requires:• IV has a causal effect in first-stage (direct effect of IV ontreatment)• IV is unrelated to the omitted variables (independence assumption)• Single channel through which the IV affects outcomes (exclusionrestriction)• Instrument pushes treatment only in one direction (monotonicity)Ganglmair, Helmers, Love Empirical Analysis 11 September 2019 29 / 33Reminder: Instrumental VariableFirst stage:Di = α1 + φZi + γ1Xi + e1i (6)where Di is the endogenous variable, Zi is the IVFrom the first stage we get:D̂i = α1 + φZi + γ1Xi (7)Second stage (which includes Xi):Yi = α2 + λSLSD̂i + γ2Xi + e2i (8)Ganglmair, Helmers, Love Empirical Analysis 11 September 2019 30 / 33Judge Fixed Effects• Example: impact of invalidity on forward citationscitesi = β0 + β1invalidi + β2Xi + εi (9)where citesi forward cites for litigated patent i, invalidatedi equal toone if patent i was invalidated, and Xi are patent characteristics• OLS estimate of β1 biased if E(β1εi) 6= 0• Use IV: leave-one-out mean of case outcomesZij =∑nj−1k 6=i invalidknj − 1(10)• where nj is the total number of cases decided by judge jGanglmair, Helmers, Love Empirical Analysis 11 September 2019 31 / 33Summary• Lots of interesting questions (testing theory, policy, etc.)• Selection poses fundamental problems to any type of analysis ofpatent litigation data• Good idea to combine empirical analysis with theory• But you can still use standard empirical tool set to address selectionGanglmair, Helmers, Love Empirical Analysis 11 September 2019 32 / 33References• Galasso Alberto and Mark Schankerman (2015): Patents and CumulativeInnovation: Causal Evidence from the Courts, Quarterly Journal ofEconomics, Vol. 130, No. 1, pp. 317-369.• Gelbach Jonah B. (2018): The Reduced Form of Litigation Models and thePlaintiff’s Win Rate, Journal of Law and Economics, Vol. 61, No. 1, pp.125-157• Helland Eric, Daniel Klerman, and Alex Lee Yoon-Ho (2018): Maybe thereIs No Bias in the Selection of Disputes for Litigation, Journal of Institutionaland Theoretical Economics, Vol 174, No. 1, pp. 143-170• Lanjouw Jean O. and Mark Schankerman (2004): Protecting IntellectualProperty Rights: Are Small Firms Handicapped?, Journal of Law andEconomics, Vol. 47, No. 1, pp. 45-74.• Shavell Steven (1996): Any Frequency of Plaintiff Victory at Trial IsPossible, Journal of Legal Studies, Vol. 25, pp. 493-501Ganglmair, Helmers, Love Empirical Analysis 11 September 2019 33 / 33

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