Entrepreneurial Innovation

ENTREPRENEURIAL INNOVATION:

PATENT RANK AND MARKETING SCIENCE

MONTE J. SHAFFER

A dissertation submitted in partial fulﬁllment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

WASHINGTON STATE UNIVERSITY College of Business

MAY 2011

The members of the Committee appointed to examine the dissertation of MONTE J. SHAFFER ﬁnd it satisfactory and recommend that it be accepted.

U.N. Umesh, Ph.D., Co-Chair

Gerard L. Tellis, Ph.D., Co-Chair

Len M. Jessup, Ph.D.

Babu John Mariadoss, Ph.D.

ii ACKNOWLEDGEMENT

I would like to begin by thanking those directly related to my successful completion of this dissertation. First and foremost, I would like to acknowledge and thank Umesh; without him, I would not have survived this Ph.D. program. Umesh has been a friend who has mentored me throughout this process of academic research utilizing principles of autonomous support (Deci and Ryan 1985). I would also like to thank his wife Opal and their children Ravi and Tara for allowing me to visit their home as Umesh and I worked on research in Vancouver. Next, I would like to thank Gerard Tellis for being receptive to my

‘patent pitch’ at the 2010 Sheth Consortium. Gerry has been very helpful in grounding my thinking in what can get published in top marketing journals. He has also been very helpful in providing meaningful writing strategies to implement in my essays. Next, I would like to thank Len Jessup who also has been supportive of my independent character. He has quietly offered suggestions to improve the positioning of the research in this dissertation and other research in the realm of entrepreneurship. His feedback has always been timely, responsive, and kind. Next, I would like to thank Babu John-Mariadoss for his collegiality. Babu has been someone I could exchange ideas with freely, as we tried to understand how we could develop the ratio concept into something more robust than the traditional measures; he was also supportive in the development of entrepreneurial innovation as an integration of the two competing Austrian theories of entrepreneurship: Schumpeterian/Kirznerian. Beyond the committee, I would next like to thank members of the Marketing Department in the development of my scholarship training. First, to Darrel and the search committee for offering me the opportunity to study at Washington State University. Next, to those who taught the seminars in my discipline: Dr. Jean Johnson (strategy), Yany (marketing theory), Traci Hess

(research methodology), and Jeff (consumer behavior). In addition, I would like to thank those who helped with my statistical training, most especially: Jan Dasgupta, Stephen Lee, Ron Mittelhammer, Jave Pascual,

Marc Evans, and Craig Parks. Finally, I would like to thank all of the faculty in the department for their personable encouragement, including: Kristine, Don, Elina, Patriya, Dave, Eric, Mauricio, Joe, Chris,

Ioannis, and Alberto. The Sheth Consortium was also a wonderful opportunity for me to meet with other scholars in the ﬁeld of marketing. I enjoyed the opportunity to learn invaluable gems of wisdom from top scholars, especially from: Gerry, Eric Bradlow, Chris Moorman, V. Kumar, Rajesh Chandy, and Don

iii Lehmann. I also acknowledge colleagues directly associated with my research as co-authors on several projects. Most important to my dissertation are the contributions of Gianna Del Corso and Francesco

Romani, who were responsive to my emails regarding the efﬁcient computations of eigenvector-centrality

(Del Corso, Gulli, and Romani 2005). Our interdisciplinary perspectives have made the research an enjoyable learning experience for me. In addition I would like to thank Jan Dasgupta for her strong quantitative focus of my statistical training, and Harry Khamis for patiently educating me on the

Multigraph procedure. I would like to thank the Marketing Science review team for their timely and encouraging feedback; in addition, I am indebted to Manual Trajtenberg, Alina Sorescu, and Natalie Mizik for input on improving these essays.

Solidarity among students has also been an important part of my development. I ﬁrst acknowledge students who were forerunners in the department: Rajiv, Scott, and Sanjay offered invaluable insights on how to be successful in working with Umesh. More recently, I acknowledge students such as Ronn, Ping,

David, and Kelly for establishing a tradition of excellent. Next I would like to acknowledge and thank students who have preceded me (Trent, Sanjay, David, Berna, Abdullah, Jeff, Kivilcim) for their encouragement and insights. Most especially, I would like to thank Sarah Varble, who entered the program with me, for her willingness to struggle through the ﬁrst-year coursework with me, encouraging me in a practical yet meaningful way. In addition, I would like to thank those who follow me (Mark, Brian, Hakil,

Manja, Sean, Brett, and Richie) for their friendship and interest in me and my research. I would also like to acknowledge and thank my peers in other disciplines that encouraged me in my coursework in economics, statistics and psychology: Heather, Miguel, Kevin, Casey, Josh, Bela, Erin, Georgina, Eric, Avi, Darin,

Abhi, Dane, Adam, Vugar, Letizia, Robin, Pablo, and many others. Speciﬁc to my research, I would like to thank them for research discourse which helped me further develop and reﬁne my ideas by considering their perspectives. I would like to also acknowledge and thank my students here at WSU; most especially:

Nikki, Josh, Haley, Jillian, Chris, Breeanne, Angela, Anne, Devon, Kayleen, Paige, Kayley, Nicole, Derek,

Ted, Jessica, Abby, John, Kevin, Renee, and many others. Additionally, I acknowledge and thank those assisting me with the logistics related to receiving this degree: Chris, Mary, Cheryl, Janel, Linda, Lael,

Lioudmila, Darlene, Christopher, Danielle, Serry, and John.

iv I next acknowledge those from my MBA days at Brigham Young University who encouraged and prepared me for the rigors of a Ph.D. program. Paul Godfrey for suggesting I consider pursuing a Ph.D.,

Scott Smith for market-research training and directing me towards a Ph.D. in marketing, Gary Rhoads for his gregarious mentoring, James McDonald for his kindness and econometric training, Bryan Sudweeks, and the MBA program directors and staff for the quality training I received. Finally, I would like to thank those who taught various graduate seminars at BYU: Dillon Inouye (principles of learning), Michael

Thompson (knowledge management), Andy Gibbons (instructional design), Dan Siebert (math learning), and my informal sessions on creativity with David Squires. In addition, I would like to thank my MBA-era friends: Brian, Eric, Leaa, Alisa, Lisa, Nick, Aisake, Rich, Marious, Burke, Anthony (and his wife

Rebekka and their boys), Yaninna, Adrienne, Monica, Rachel, Ryan, Armando, Derek, the Wilsons, and my basketball buddies. I would next like to thank those from Monterey, CA who were influential in my consulting development. I specifically thank Universal Internet for hiring me to do web-application development, my friends (Gabe, Todd, Robin, Mitch, Dale) at UI, and several clients who opened my eyes to the importance of marketing-related elements of entrepreneurial activities carried out on the web; specifically, Cindi Dodd and organicgirl, Larry Solow and selftestonline, and Tony Ciaverelli and the risk assessment survey software for ERAU, and so on. In addition, I want to express appreciation for many who were instrumental in given me leadership opportunities: Jeff Lemmon, Greg Robinson, Michael Leach,

Frank Packard, Kerry Varney, Reed Jacobs, Grant Oyler, Jack Wheatley, Firoz King Hussein, and many others; and to their families for making me feel welcome in their home. I would also like to thank my ward family here in Pullman, especially the Wadsworths, the Bells, Cotton, Travis, Jeffrey, Justin, Chad, Daman, the Owens, the Days, the Warners, and the Blauers.

Next I would like to thank those who were instrumental in my short-lived career as a high school math teacher. Most importantly, to Scott Hendrickson for hiring me, developing my potential, and helping me appreciate the importance of quality instruction of mathematics; to Jim Starr for allowing me to explore other interests in Internet communications; to my colleagues within the math department (Vicki, Dawn,

Mike, and others) who were interested in the principles of ‘access to breakthrough teaching’ and stretching ourselves; to my colleagues within the school, especially neighbors like Gary, Coach Morgan, Telford,

v Laurie and many others; to the staff that became dear friends, especially Valerie and Janae; and to my students for letting me challenge them and celebrate mathematics even on PI day—there are too many to name, and I am afraid I will forget several, but I acknowledge Ty for our chess games on the Utah Jazz table, Todd and Sean and Devin and Brian for geek-talk and others from the Internet Communications course, Wendy and Hillary for their positive outlook about a subject they did not enjoy, Annie and Megan for some classy ‘jock’ behavior, David for having a unique 9-9-9 name and attempting to build a countdown clock, the international students for bring diversity and culture to the school, and the other thousand students with whom I had the opportunity to interact. Next I would like to thank American Heritage School for giving me and my brother the opportunity to teach part-time as I ﬁnished my undergraduate degree.

Many of my strong convictions about the importance of our country’s roots in the development of our children were fortiﬁed as I taught at this school. I also acknowledge some dear friends from my undergrad days: Khrissy, Dave, Michael, Sarah, Kristi, Shelece, Christy, Travis, Adam, Alissa, and Candice.

I would like to also acknowledge and thank the many friends I made in my first international experience in Argentina. Between my freshman and sophomore year of college, I took a two-year hiatus from my studies to serve a religious mission in the northeast of Argentina, almost exclusively in the province of Misiones. I would like to express my gratitude for the opportunity to learn another language and learn about other worldviews. Specifically, I would like to thank my companions who had a positive influence on the development of my work ethic and leadership; most especially, I would like to thank

Jackson for the midnight ride to San Ignacio, McClellan for the sketches of ‘la ura,’ ‘Kitty-Kitty’ Blanco for the radio chats in Aristobulo, Bof for teaching me about efﬁciency, Buck and Anderson for the song

‘cual es su problema,’ and my many other companions and friends: Kelly, Sandin, Carrari, Steadman,

Camino, Muni, Alonso, Lopez, Petersen, Reid, Norwood, Burke, Gibbs, and so many others. In addition, I would like to thank the families who made me feel at home in many different ways, especially: the family

Froiz (San Javier), the family Dos Santos and Duarte (Campo Viera), the family Fortunato and Marchi

(Corrientes), and many others. Finally, I would like to thank the thousands of people I taught during this unique time in my life for being friendly to me as a person regardless of our differences in beliefs. I would also like to thank President Lopez for appropriately identifying my intentions with my initial response ’no’

vi to typing, and President Pincock for giving me the autonomy to lead as I found appropriate. I would next thank those who supported me to give me the opportunity to have diverse opportunities before the missionary experience. Most importantly, ﬁnancial support from relatives which gave me the opportunity to go to Washington D.C. for the Close-Up program and the University of Denver ‘Making of an Engineer’ program to meet others from around the West. I also appreciate the many opportunities through Babe Ruth baseball to travel and meet others throughout the Northwest. Such opportunities were invaluable in helping me appreciate the importance of respecting differing world views.

Next I would like to thank many friends who have become a part of my perceived family. Most especially, I would like to thank the Acersons for giving me an opportunity to learn within the safety of their family. I would like to thank Jeff/Karen and their children (Mark, Kimberly Anne, Lisa Marie, Scottie

Boy, Marianne, and Stephanie Jane) for letting me share a special time of their lives. I would also like to thank their extended family from Don/Birdie to Randy/Jennifer and the many others with whom I had opportunities to associate. I would also like to thank my other surrogate families throughout my life, the

Lemmons, the Robinsons, the Thuesons, the Robisons, the Kisers, the Roundys, the Bairs, the Wendts, the

Macdonalds, and many others. In addition, I would like to thank the community of Columbia Falls for a solid educational experience; especially, the Tuslers, Mr. Vincent, Mrs. Cockrell, Mr. Bartlett, my father,

Mr. Schmautz, Mr. Hemmer, Mr. McCleskey, Mrs. Vance, Mrs. Dunn, Mrs. Groschoff, Mr. Henderson,

Mr. Hjulstad, Mr. Snyder, Mr. Washburn, Mr. Teddy, Mrs. E., Mr. Dye, Mr. Anderson, and many others.

In addition, I would like to acknowledge my childhood friends: Rod, Derek, Rubianna, Joelle, Mark, Sara,

Jason, Cory, Mike, Tara, Dagny, Mandi, Pat, Dan, Briar, Jeremy, Melanie, Michele, Cami, Renee, Jodi,

Kim, Kirsten, Shannon, Sharon, Malin, Lorena, Rich, Will, Big Tom, Eric, Scott, Lisa, Stephanie, and many others for wonderful memories. Additionally, I would like to thank my extended family; especially my Jim and Adelbert and their families, who, for very different reasons, have played important roles in my growth and development. To this, I also include some lifelong friends: Dave McClellan, Alatini Saulala

(and his wife Jennifer and their children: Fa, Cumorah, Beti, La Tai, Mosiah, and Anna) and Kevin

Cranney (and his wife Karsen and their beautiful daughters: Katelyn, Haley, Paige Christine, Emma, and

Jane; and his mom Diana, the memory of his father Garr, and his brother Daniel).

vii Finally, and most importantly, I would like to thank and acknowledge my immediate family. My parents, David and Carla, for endowing me with a unique set of gifts and training me to be a responsible adult who serves others. In addition, I would like to acknowledge and thank each of my siblings, who are some of the dearest friends in my life. To my siblings: Joey and Kimmy for a memorable summer experiences in my youth (and their spouses/children); to my brother Michael for teaching me to be both quick and resilient (and to his wife Claudia, his children: Zackary, Mikayla, Nikolas, and Rebekah for letting me enjoy ‘rovies’ and golf with them, even the inadvertent hole in one); to my sister Karey for sharing in entrepreneurial dreams (and to her husband Linz and her twins Neisha and Dylan; to my sister

Kathryn for sharing in entrepreneurial realities (and to her husband Darin and her children: ‘la llorona’

Tori, Katie-bug, Jackuah (Jack/Joshuah) and Pizza-Hut Wednesdays); to my sister Khristine for teaching me perseverance through her example (and to her husband Dan, and her children: Robert, Chandler, and

Elizabeth); to my sister Kandie Marie who has taught our families many things through her tragedy; to my sister Korrie for teaching me to laugh amidst adversity (and to her husband Jeremy and her children

Jessicah and Bobby Jones); to my brother Mark for his kind and playful disposition that is resurfacing thank in many ways to his bride Hilda; to my brother Matthew for meaningful conversations and a good wifﬂe-ball game (and his wife Jessica and their children: Weston, Lillian, and Benjamin); to my brother

Micah for his common sense, work ethic, and righteous practicality (and his wife Jessica and their boys:

Carson, Gabriel, and Nathaniel); to my brother Mason for his optimistic idealism (and his wife Tarsha and his beautiful girls: Miss Myra Mae and Kandie Fae).

viii ENTREPRENEURIAL INNOVATION: PATENT RANK AND MARKETING SCIENCE

Abstract

by Monte J. Shaffer, Ph.D. Washington State University May 2011 Co-Chair: U.N. Umesh Co-Chair: Gerard L. Tellis

This dissertation examines innovation from the Austrian-economic perspective of the entrepreneur utilizing patents as a proxy. For over 60 years, scholars in marketing, strategy, and economics have been looking for a way to link patents to radical innovation; I present Patent Rank as that link. Patent Rank defines a patent’s intrinsic value by considering its legally-defined relationships within the entire patent network; a patent’s value is a function of its ancestry (backward citations), its heritage (forward citations), and the dynamics of the patent network (it updates over time). Using over 5.6 million patents, 320 million annual Patent Rank scores, and 100 million nonlinear models, I examine patent innovations at the patent level, innovation level, firm level, industry level, and market level.

In the first essay, patents are defined as innovations using legal ‘first principles;’ Patent Rank is presented based on network theory and demonstrated to be superior to the forward-citation-count metric. In the second essay, the mathematics of Patent Rank is further explored; a generalized model is derived based on the sorting, augmenting, partitioning, and solving of a linear system. A marginal-structure (ms) model is utilized to demonstrate that the Austrian-defined market process of entrepreneurial innovation exists: both

Schumpeterian shocks and Kirznerian competition. In the third essay, a marginal-combined (mc) model is employed and a nonlinear model is derived to predict a patent’s expected lifetime value, and ultimately a

ﬁrm’s annual patent stock from its portfolio of patents. In the ﬁnal essay, Patent Rank is asserted to be an objective measure of radical innovation. This claim is validated using face validity (top patent innovations are radical), concurrent validity (top patent innovations align with Wharton experts), and predictive validity

(abnormal market portfolio returns correlate with different levels of patent stock).

ix TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS ...... viii

ABSTRACT ...... ix

LIST OF TABLES ...... xv

LIST OF FIGURES ...... xviii

DEDICATION ...... xxi

CHAPTER

1. INTRODUCTION ...... 1

2. PATENT RANK: AN IMPROVED VALUATION-METRIC ...... 14 2.1 Introduction ...... 14 2.2 A Patent’s Intrinsic Value: First Principles ...... 16 2.3 Patent Rank scores as Network Centrality ...... 20 2.3.1 Toy Example: Network with 10 patents and 14 citations...... 20 2.3.2 Network Centrality: Eigenvector Centrality measures importance of any patent...... 22 2.3.3 Advantages of Patent Rank scores ...... 26 2.4 Data Preparation and Computation of Patent Rank scores ...... 28 2.5 Patent Rank is Better ...... 29 2.5.1 Axiology ...... 29 2.5.2 Nomology ...... 29

x 2.5.3 Conceptual Convergent/Discriminant Test ...... 30 2.5.4 Empirical Convergent/Discriminant Test ...... 31 2.6 Discussion ...... 36 2.6.1 Limitations ...... 36 2.6.2 Future Research ...... 37 2.7 Conclusion ...... 38

3. ENTREPRENEURIAL INNOVATION: IDENTIFYING SCHUMPETERIAN SHOCKS AND KIRZNERIAN COMPETITION USING PATENT RANK ...... 39 3.1 Introduction ...... 39 3.2 Austrian Economics and Theory of Entrepreneurial Innovation ...... 40 3.2.1 Austrian economics ...... 41 3.2.2 Entrepreneurial Innovation ...... 43 3.3 Patent Innovations as an Observable Market Process ...... 44 3.3.1 Patent Innovations and the Entrepreneurial Phenomenon ...... 44 3.3.2 Proﬁtability from Monopoly Positions ...... 45 3.3.3 Overview of Patent Rank ...... 45 3.3.4 Hypotheses on the Margin ...... 46 3.4 Generalized Model Speciﬁcation of Patent Rank ...... 47 3.4.1 Toy Example computation of Patent Rank ...... 48 3.4.2 Linear Solution ...... 50 3.4.3 Generalized Model ...... 52 3.4.4 Computation of Generalized Model ...... 54 3.5 Application of (ms) Patent Rank model ...... 56 3.5.1 Data preparation ...... 56 3.5.2 Summary of Results ...... 57

xi 3.5.3 Hypothesis 1: Schumpeterian shocks ...... 58 3.5.4 Shock Patterns: Intensity, Duration, and Volume ...... 59 3.5.5 Hypothesis 2: Austrian economics market process ...... 61 3.5.6 Improving Normality with ClassMatch ...... 62 3.6 Case Study and Discussion: First generation CT-scanners ...... 64 3.7 Conclusion ...... 66

4. ASSESSING DIFFUSION OF RADICAL INNOVATION: LONGITUDINAL IDEN- TIFICATION OF A PATENT’S LIFETIME VALUE (PLV) AND A FIRM’S PATENT PORTFOLIO USING PATENT RANK ...... 67 4.1 Executive Summary ...... 67 4.2 Introduction ...... 68 4.2.1 Internal venturing ...... 69 4.2.2 External venturing ...... 70 4.2.3 Managing Radical Innovation ...... 70 4.3 Patent Rank ...... 71 4.3.1 The Problem ...... 72 4.3.2 Patent Rank as the Solution ...... 73 4.4 Patent Rank as an indicator of Schumpeterian shocks ...... 73 4.5 Diffusion Patterns and Trajectory Models ...... 74 4.6 Application of Methodology: IBM, the most proliﬁc patent producer ...... 75 4.6.1 Sample selection ...... 76 4.6.2 Introduction of a Random Sample ...... 76 4.6.3 Probability of Diffusion ...... 77 4.6.4 Patent Quantity ...... 77 4.6.5 Patent Quality ...... 78

xii 4.6.6 Modeled values to identify winners and losers ...... 79 4.6.7 IBM’s race horses, mules and show ponies ...... 79 4.7 Concluding Discussion ...... 80

5. PATENT RANK: AN OBJECTIVE MEASURE OF RADICAL INNOVATION . . . 82 5.1 Introduction ...... 82 5.2 Radical Innovation as better, faster, cheaper ...... 83 5.3 Patents ...... 85 5.3.1 Patent Data is Practically Messy ...... 85 5.3.2 Patent Data is Conceptually Useful ...... 87 5.3.3 Patent Rank as Solution ...... 87 5.4 Propositions: Patent Rank and Radical Innovation ...... 88 5.4.1 Getting to Financial Performance Using Patent Rank ...... 89 5.4.2 Information and Financial Markets ...... 91 5.5 Data Preparation and Computation of Patent Rank scores ...... 92 5.6 Proposition Validation and Discussion ...... 93 5.6.1 Face Validity: Top 10 Innovations by Model and Year ...... 93 5.6.2 Concurrent Validity: Expert Comparison ...... 96 5.6.3 Predictive Validity: Empirical Test of Abnormal Market Returns ...... 97 5.7 Conclusion ...... 101

6. CONCLUSION ...... 102 6.1 Summary of Contribution ...... 103 6.2 Does Patent Protection Beneﬁt Society? ...... 103 6.3 Patent Rank and Marketing Science ...... 105 6.4 Final remarks ...... 107

xiii APPENDIX

F A. RATIO DERIVATION B OF PATENT RANK ...... 109

B. GLOSSARY OF TERMS ...... 124

C. MATHEMATICS ...... 125

D. COMPUTATIONS OF PATENT RANK ...... 131

E. COMPUTATION OF CLASSMATCH ...... 134

F. SELECTION OF COMBINED MODEL ...... 137

G. GOLDEN RATIO φ AND PATENT RANK ...... 138

H. FIRM-PATENT MATCHING: RED LIGHT/GREEN LIGHT ...... 140

I. FIRM LISTS ...... 146

J. FINANCIAL PERFORMANCE: ALL MODELS ...... 154

xiv LIST OF TABLES

Page

1 Recent use of patent-related data in marketing science: For each article, we report the patent-valuation metric used and the patent-data impact on the ﬁndings. 211 2 Patent Network: Tabular Form of Toy Example ...... 212 3 Top-20 in 2000: We report the top-20 patent innovations with a description for the (1976–2000) patent network...... 213 4 Annual Top-10 Patent Innovations: For each year, we report the top-10 patents (by patent number) according to our computations of Patent Rank...... 214 5 Convergent Correlations: Diminishing Correlations between forward-citation counts (WPC) and Patent Rank scores over time...... 215 6 Cumulative Measures from Trajtenberg (1990a) with 3-year reverse lags: weighted

patent counts WPCt+3 and Patent Rank scores PRt+3...... 216 7 Comparison of Results: An application of principle 4 identiﬁes major improvement by appropriately comparing measures to social value cumulatively. Even within this improved comparison, Patent Rank has more information about the network, and is superior to the traditional measure...... 217 8 Top 10 (ms) Innovations: For each year, we report the top-10 patents (by patent number) according to our computations of Patent Rank...... 218 9 Summary of Basic Models: Temporal constraints (cumulative vs. marginal) and Network formation (structure vs. combined) as basic manifestations of the generalized Patent Rank model...... 219

xv 10 Descriptive Statistics for Random Sample: For each year, we report the number of patents in the random sample, the number of patents that have diffused (received at least one forward citation by the end of 2008), the percentage of patents that diffused. We also report descriptions of the patents (how long to get a ﬁled patent approved, in years; the number of backward citations; the number of inventors; the number of unique claims, the complexity deﬁned as the number of times a Table/Figure/Example was referenced in the patent)...... 220 11 Absolute Chance of Diffusion over Time: For each year, we report the probability as chance (e.g., 8.57% chance is a probability of 0.0857) that a patent has diffused...... 221 12 Conditional Chance of Diffusion over Time: For each year, we report the probability as chance (e.g., 8.57% chance is a probability of 0.0857) that a patent has diffused...... 222 13 Volume Parameter β: Percentiles observed in 2008 ...... 223 14 Growth Parameter δ: Percentiles observed in 2008 ...... 224 15 Speed Parameter τ : Percentiles observed in 2008 ...... 225 16 IBM’s 1986 Patent Inventory: IBM’s winners and losers for patents granted in 1986...... 226 17 IBM’s 1996 Patent Inventory: IBM’s winners and losers for patents granted in 1996...... 227 18 IBM’s 2006 Patent Inventory: IBM’s winners and losers for patents granted in 2006...... 228 19 Top 10 (cs) Innovations: For each year, we report the top-10 patents (by patent number) according to our computations of (cs) Patent Rank...... 229 20 Top 10 (cc) Innovations: For each year, we report the top-10 patents (by patent number) according to our computations of (cc) Patent Rank...... 230

xvi 21 Top 10 (ms) Innovations: For each year, we report the top-10 patents (by patent number) according to our computations of (ms) Patent Rank...... 231 22 Top 10 (mc) Innovations: For each year, we report the top-10 patents (by patent number) according to our computations of (mc) Patent Rank...... 232 23 Experts rankings compared to Patent Rank: “Top 30 Innovations of the Last 30 Years” as deﬁned by PBS’s viewing audience and ranked by a panel of Wharton experts ...... 233 24 Basic Portfolios: In each column, we report calendar-time portfolio results using the Fama-French/Carhart model. For each coefﬁcient, we subsequently report its

t-value below in parentheses. Since these portfolios have monthly returns m, we

also report the annualized abnormal return, calculated as follows: (1 + m)12 1.. 234 − 25 Decile Portfolios Compared to No Patents Portfolio: We report portfolio results using the Fama-French/Carhart model in each column. For each coefﬁcient, we subsequently report its t-value below in parentheses. Since these portfolios have

monthly returns m, we also report the annualized abnormal return, calculated as

follows: (1 + m)12 1...... 235 −

xvii LIST OF FIGURES

Page

1 Toy Example Patent Network. A very simple example of a directed patent graph to illustrate the patent network. Nodes represent patents, links represent citations between patents. The direction of the arrows defines the nature of the link—from incrementalness to radicalness. The temporal constraints of the network are represented in the timeline...... 180 2 Toy Example Patent Network with Super-node: We define all patents to have a full association with the super-node, the U.S. Patent Office...... 181 3 Comparison of Patent Rank to Google’s Page Rank: The introduction of the U.S. Patent Office as a super-node is a more intuitive technique which simplifies the computation using linear algebra...... 182 4 Diffusion patterns of the top CT-scanner innovations from Trajtenberg (1990a):

Rankings are based on WPC1981; corresponding rankings for PR2009 are 1 (101.93), 4 (32.60), 3 (32.89), 2 (35.16) with respective scores in parentheses...... 183

5 WPCt as a Proxy for Adoption: We report the number of adopters of the CT- scanner and the number of weighted patent counts, cumulative over time...... 184 6 Entrepreneurial Innovation: Two competing views on entrepreneurial activity from the Austrian school; the Kirznerian Entrepreneur and the Schumpeterian En- trepreneur...... 185 7 Continuum of Entrepreneurial Innovation: Schumpeterian entrepreneurial activity occurs less frequently (y-axis as frequency) and inherently represents changes that are more radical (x-axis as impact of innovation on society)...... 186

xviii 8 Patent Rank: Using patent-citation analysis, we define the patent network and ascertain every patent’s value based on its citation ancestry (backward citations) and heritage (forward citations)...... 187 9 Schumpeterian shocks: A shock can be uniquely described based on its intensity, time of intensity, duration, and total volume...... 188 10 Comparison of Patent Rank to Google’s Page Rank: The introduction of the U.S. Patent Office as a super-node is a more intuitive technique which simplifies the computation using linear algebra...... 189 11 Summary of Data Inputs: We report the size of the network over time and the structure of the adjacency matrix...... 190 12 Shock Patterns for Top Entrepreneurial Innovations: Using marginal Patent Rank scores, we identify Schumpeterian shocks for exception patent innovations. . 191 13 Nontrivial Patent Rank scores: Summaries of the portion of the sample that has some minimal level of diffusion...... 192 14 Distribution Patterns: Histogram of nontrivial Patent Rank scores and transfor- mations...... 193 15 Competitive Landscape: We report the firms who secured patent protection for CT-scanner technologies and the total value of these firms’ portfolios over time. . . 194 16 Patent Rank: Using patent-citation analysis, we define the patent network and ascertain every patent’s value based on its citation ancestry (backward citations) and heritage (forward citations)...... 195 17 Schumpeterian shocks: A shock can be uniquely described based on its intensity, time of intensity, duration, and total volume...... 196 18 Shock Patterns for Top Entrepreneurial Innovations: Using marginal Patent Rank scores, we identify Schumpeterian shocks for exception patent innovations. . 197

xix 19 Overview of Process: Measuring a patent’s lifetime value is a function of its Patent Rank score, its actual diffusion pattern, and its modeled diffusion pattern...... 198 20 Filmstrip Shock/Growth: An example how the diffusion model for a patent innovation updates as more information about the patent’s intrinsic value is available. . 199 21 Top Patenting Firms (by Quantity): From our sample, we report the top-15 patenting firms for 1976–2006...... 200 22 Top Patenting Firms (by Quality): From our sample, we report the top-15 patenting firms for 1976–2006...... 201 23 Top Patenting Firms (by Quality, Changes): From our sample, we report the top-15 patenting firms for 1976–2006...... 202 24 Top Patenting Firms (by Quality, Changes Per Patent): From our sample, we report the top-15 patenting firms for 1976–2006...... 203 25 Continuum of Entrepreneurial Innovation: Schumpeterian entrepreneurial activity occurs less frequently and inherently represents changes that are more radical. 204 26 Theoretical conceptualizations of Radical Innovation: Product/Process Innova- tion in relation to the marketing concept ...... 205 27 Schumpeterian shocks: A shock can be uniquely described based on its intensity, time of intensity, duration, and total volume...... 206 28 Patent Rank: Using patent-citation analysis, we define the patent network and ascertain every patent’s value based on its citation ancestry (backward citations) and heritage (forward citations)...... 207 29 Overview of Process: Measuring a patent’s lifetime value is a function of its Patent Rank score, its actual diffusion pattern, and its modeled diffusion pattern...... 208 30 Filmstrip Shock/Growth: An example how the diffusion model for a patent innovation updates as more information about the patent’s intrinsic value is available. . 209

xx DEDICATION

This is dedicated to all of those who have inﬂuenced the development of my character, representing a gratitude that lies ‘too deep for tears.’

– To my God, the Eternal Father, and His Son, Jesus Christ for this mortal experience, the gift of agency, and the spiritual gifts and promises endowed upon me. – To my grandfather, Robert Andrew Shaffer, for my namesake ‘Monte from Montana’ and for trace of Native American blood that runs deep within my soul. – To my grandparents, especially Velma Schouten, for teaching me by example the importance of tenacity and endurance amidst adversity. – To my father, David, for his logical/mathematical acumen and his disposition to be true. – To my mother, Carla, for her visual/conceptual acumen and her disposition to serve others. – To my siblings, who taught me how to compete, to collaborate, to negotiate, and to validate. – To my extended families, for their mentoring, love and support. – To my friends, for their acceptance of me and my faults. – To my students, for allowing me to inﬂuence their growth and development. – To the current love of my life, Miss Myra Mae (who likes to dance all day) for letting me share the ﬁrst two years of her beautiful life. – To the future love of my life, wherever she may be, for hope deferred that may one day become a tree of life. – To this great country, the land of opportunity, which, despite its shortcomings, represents principles dear to me: democracy, free markets, entrepreneurial proclivity.

Finally, I dedicate this to all those who, whether by faith or science, seek truth.

xxi CHAPTER 1

INTRODUCTION

With the advancement of computing technology in the 1960s, “machine-readable” patent data looked very promising as an objective source to measure the innovativeness of firms and industries (Arrow 1962; Scherer 1965; Schmookler 1966). With the advent of the personal computer, academics were even more optimistic about the influence of patent data on the study of innovation (Griliches 1984, 1990; Trajtenberg 1990b). Some important contributions to economics, strategy, marketing and law been addressed (Trajtenberg 1990a; Jaffe, Trajtenberg, and Henderson 1993; Hall, Jaffe, and Trajtenberg 2005); however, the ability to study innovation using patent data is downright disappointing (Tellis, Prabhu, and Chandy 2009). Within the field of marketing, patent data have been relegated to be little more than a control variable (Sorescu, Chandy, and Prabhu 2003; Wuyts, Dutta, and Stremersch 2004; Prabhu, Chandy, and Ellis 2005; Chandy, Hopstaken, Narasimhan, and Prabhu 2006; Sorescu, Chandy, and Prabhu 2007; Aboulnasr, Narasimhan, Blair, and Chandy 2008; Rao, Chandy, and Prabhu 2008). A few scholars have made efforts to consider patent data more comprehensively (Narasimhan, Rajiv, and Dutta 2006); however, such efforts are few and far between. Today, we own hand-held smartphones that have more computing power than the fastest computers of a generation ago, yet we still have not developed a comprehensive measure of innovation using patent data. In this dissertation, I attack this problem. For over 18 months, I programmatically harvested all patents from the U.S. Patent Office website. For each patent, I have extracted key variables using a parsing algorithm I developed. Using over 5.6 million patents, I examine patent innovations at the patent level, innovation level, firm level, industry level, and market level. In this dissertation, I revisit how others historically tried to utilize patent data (Schmookler 1966; Trajtenberg 1990b). I re-examine what we know about the patent system. In this retro- spection, I identify some key ‘first principles’ that seem to have been forgotten: (1) all approved

1 patents are innovation in a basic form, (2) a patent’s ancestry (backward citations) must be considered when assessing a patent’s intrinsic value, (3) a patent’s heritage (forward citations) must be considered when assessing a patent’s intrinsic value, (4) a patent’s intrinsic value changes over time. Using these fundamental principles of patents, I present Patent Rank as an objective measure to precisely assess a patent’s intrinsic value within the patent system. Patent Rank is grounded in linear algebra and graph theory—defined using the concept of eigenvector network-centrality. I coin my network metric Patent Rank in analogy to Google’s PageRankTMalgorithm although my computations and patent-network assumptions vary greatly from Google’s algorithm used to ‘bring order to the web’ (Page, Brin, Motwani, and Winograd 1999). Albeit unique, Patent Rank also has an analogous purpose: ‘bringing order to the patent network in the study of innovation.’ Patent Rank scores are computed annually which provide (for every patent in the network) a time-variant, continuous, and objective value in relationship to the entire network. I demonstrate the utility of this new patent data, and how it can help us finally achieve what has been proposed over fifty years ago: an unbiased, consistent, objective measure of every patent’s value in relationship to every other patent. By the end of this dissertation, I extend the preliminary findings and conclude that Patent Rank represents an objective measure of radical innovation.

Entrepreneurial Innovation

Innovation is the introduction of something new. Who determines what is new? How is new determined? These two questions are inherently subjective. Is it new to you? Is it new to your town? Is it new to your industry? Is it new to your country? Is it new to the world? Subjectivity is an important driver of innovation, because if people perceive that they are engaging in innovation, they will consequently perform various entrepreneurial activities: starting a new business, creating a new product, seeking patent protection, and so on. Entrepreneurial innovation, whether subjectively perceived or objectively realized, stimulates economic development and growth.

2 Etymologically, the word1 entrepreneurship is derived from the French word entreprendre meaning to undertake, as in, to undertake an enterprise. I define entrepreneurial innovation within the context of Austrian economics. I utilize the Austrian definition of the entrepreneur, attributed to Friedrich von Wieser, as the ‘the heroic intervention of individual men who appear as leaders toward new economic shores.’ This definition inherently places the entrepreneur subjectively within a market process. Information is imperfect, the market never reaches a true equilibrium, and entrepreneurial activity represents decisions made by individuals based on Austrian fundaments: opportunity costs, marginal utility, imperfect rationalism, and unknowable subjectivism. Within this view, there are two competing theories of entrepreneurship. The first, the Schum- peterian entrepreneur, was economically characterized by Joseph Schumpeter immediately after receiving his Ph.D. from the Austrian School in 1906. He introduced the entrepreneur as Un- ternehmergeist, a fiery spirit, who through ‘creative gales’ disrupted the dynamic equilibrium, shocking the market (Schumpeter 1911). These creative destructions are commonly coined Schum- peterian shocks. Contrasted with Adam Smith’s labor theory of value, Schumpeter argues that only two things are needed to explain the market process: entrepreneurs and capitalists, roles he defines to be mutually exclusive. A contemporary and antagonist of Schumpeter was Ludwig von Mises, who received the same economic training from von Wieser and Eugen von Bohm-Bawerk.¨ He had a more practical view of the entrepreneur, homo agens, a man who is merely ‘chasing it.’ This man is competitive by nature, alert to market conditions, and exploitative. Mises’ student, Israel Kirzner, adopted this praxeological view of the alert entrepreneur in contrast to Schumpeter’s creative hero (Kirzner 1973). Kirzner’s entrepreneur is competitive and performs arbitrage based on alertness to disequilibrium in the market. 1There is some debate regarding who initially defined the term economically (Drucker 1984), was it Richard Cantillon or Jean-Baptiste Say?

3 Kirznerian Innovation Schumpeterian Innovation

Incremental Radical

Although some have said these two competing views cannot be merged into an integrated framework (Holcombe 1998, 2003; Glancey and McQuaid 2000; Fu-Lai Yu 2001), and others in- terpret one or both views to try and develop entrepreneurship theory (Shane and Venkataraman 2000; Gartner 2001; Woods 2002; Ucbasaran, Westhead, and Wright 2001; McMullen and Shep- herd 2006), I posit that these contrasting views synergistically work in harmony—a Texas two-step. Schumpeterian activity disrupts the market, and Kirznerian activity equilibriates. These opposing forces, a Yin and Yang as the creator and arbitrageur, define entrepreneurial innovation (Kirzner 1979, p. 115): “Development is initiated by innovators who are generating new opportunities. The Schumpeterian innovators stir the economy from its sluggish stationariness. The imitators compete away the innovational profits, restoring the stationary lethargy of a new circular flow, until a new spurt of innovational activity emerges to spark development once again.” As outlined above, Schumpeterian innovation happens less frequently (y-axis as frequency), but has a more drastic impact on society (x-axis as impact of innovation). Whether a firm engages in Kirznerian entrepreneurial innovation, Schumpeterian entrepreneurial innovation, or a combination of the two, it is still imperative for firms to ‘get innovative or get dead’ (Peters 1990, 1991).

4 Objectivity to measure innovation

Subjective perceptions motivate individuals to engage in entrepreneurial activity. As a marketing scientist, my goal is to be able to separate this subjectivity from an objective benchmark. Patent Rank serves as a benchmark of objectivity, from which further research can progress. How important is the innovation? Is it drastic/radical? Or is it minor/incremental? Perceptions will prompt individuals to actions (Zappia 2000; Yadav et al. 2007); however, the benefit or value of the innovation needs to be measured objectively. Differences between an objective measure of radical innovation and subjective counterparts will help marketing science further appreciate the importance of intangible marketing factors such as brand power. Disentangling the subjective from the objective is not a trivial task. Consider the iPod? Is it a radical innovation? What makes it radical? A strong brand presence or an innovative marketing strategy? Certainly these marketing intangibles are important for the firm’s financial success, but can we separate them from an objective measure of innovation? As moral agents with unique experiences, we each have subjective biases. Consider the following questions:

1. Who was more innovative, Thomas Edison or Kia Silverbrook?

2. Who created the radio innovation, Guglielmo Marconi or Nikola Tesla?

3. Who created the CT-scanner innovation, Allan M. Cormack or Godfrey N. Hounsﬁeld?

4. Which ﬁrm is (was) more innovative, General Electric or Hitachi?

5. Which ﬁrm is (was) more innovative, Microsoft or IBM?

6. Which ﬁrm is (was) more innovative, Apple or XEROX PARC?

7. Who was more innovative, Isaac Newton or Albert Einstein?

8. Who was more innovative, Isaac Newton or Galileo Galilei?

9. Who was more innovative, Isaac Newton or Robert Hooke?

5 Answering these questions deterministically is not simple because subjectivity is embedded in our personal perceptions of innovation. Now compound this subjectivity by the amount of competing information that is available. In the age of ‘too much information’ how can we ﬁlter and discern subjectivity from objectivity? Surveying top executives, or managers of business units are invaluable tools, but an external metric needs to be considered to isolate the subjective from objective.

Pursuit of objectivity with patents

Patents are a good candidate to pursue objectivity. Patents are publicly disclosed. Patents are universal. Patents are discrete, atomic units of intellectual property from which a firm can do things better, faster, cheaper. Although a single patent may be utilized by a firm from multiple, possibly unknowable reasons, there are two things we can conclude about patents: (1) the intellectual property is a resource available to a firm in determining its product offerings (Penrose 1959); (2) managers within the firm have fiduciary responsibility to initiate and manage projects (such as securing patents) that have nonnegative net-present value (Sorescu, Chandy, and Prabhu 2003; Roberts 2007; Sorescu and Spanjol 2008). In no way does this mean the firm has the capabilities to utilize the resource (Moorman and Slotegraaf 1999), nor can assumptions be made about its appropriability (Ceccagnoli 2009; Mizik and Jacobson 2003). Minimally, a patent has value as intellectual property. As a discrete resource, a patent can be sold, traded, or licensed (Atuahene-Gima 1993; Serrano 2010). Based on the Austrian principle of opportunity costs, if a firm cannot generate value from the patent internally, it would be in its best interest financially to sell it. Regardless of the firm’s ability to extract Schumpeterian profits, society benefits generally from the disclosure of the innovation. In economic terms, the relationship between a patent’s value can be summarized as (Trajtenberg 1990b, p. 185):

IPV FV SV (1.1) ⊂ ⊂

6 where IPV represents the value of the intellectual property rights, FV represents the value of the innovation to the firm, and SV represents the value of the innovation to society, and the subset symbol denotes that IPV is contained in FV which is contained in SV . ⊂ In theory, this valuation makes sense; however, in practice, how can you objectively assess the market value of a single patent? Of a portfolio of patents? Both venturing and research firms exist and use metrics to valuate a single patent, a portfolio of patents, or a firm’s patent stock. In addition, secondary markets exist where patents are transferred and licensed (Serrano 2010). This is further evidence of entrepreneurial innovation—an alert entrepreneur, the Kirznerian competitor (Sood and DuBois 1995), sees opportunity in an undervalued patent. Some firms have been accused of being patent trolls, exploiting intellectual property in legal leverage without ever intending to directly create products from the patents. For example, Kodak reported $838 million in non- recurring intellectual property income for 2009 (Ratings 2011) and currently is in the process of suing Apple for patent infringement which could result in a billion-dollar payout (Shetty 2011).

Patent Rank: A network approach

Patents are traditionally valuated using the number of subsequent citations it receives, commonly called forward citations (Trajtenberg 1990a). Using Patent Rank, I assess a patent considering more information from the patent network. Every patent and its citations (forward and backward) are evaluated simultaneously to deﬁne a patent’s value within the network.

F My idea for Patent Rank began as a ratio B where F represents the count of forward citations and B represents the count of backward citations for a given patent X. The initial strategy was to develop a decision rule of radical innovation utilizing distributional properties of this ratio. This

strategy was analogous to R.A. Fisher’s F -test which is a ratio of two independent χ2 distributions. Could I similarly, create a new statistical test that was based on a ratio of two Poisson distributions? As I pursued this strategy, I realized that these two Poisson distributions were far from independent; in fact, they were deﬁnitionally endogenous. To appreciate the endogeneity, I began considering

7 recursive ratios (see Appendix A). Mathematically, this recursive property is deﬁned as a Markov process, and I replace the ratio strategy with the network-theory approach as it is more robust and efﬁcient.

P1 P2 P3 P4 P5 P6 P7 P8 P9 P10

The fundaments of Patent Rank are grounded within the domain of network graph theory and its corresponding linear algebra. Patent Rank scores are computed2 by solving the following:

π = P T π where P = diag(d)−1M and d = Me. where π represent the Patent Rank scores, and M represents a matrix that contains all of the patents and their citations. Several models are presented to identify the formation of the network and the associations between patents within the network. A generalized model is also defined to allow future research to consider other specifications for Patent Rank. In this dissertation, I consider four Patent Rank models in my analysis of patent innovation, based on two association definitions (structure or combined) and two network formations (cumulative or marginal). Structure accounts for the presence of a patent citation whereas combined accounts for the presence and strength of its association based on a technology overlap; cumulative considers the entire network since data is available (1976) whereas marginal considers a temporally-constrained, moving-window model, which helps identify the marginal utility of a patent; that is, what value does the patent have recently? To summarize: 2I denote the vector e as a unitary vector of length n with all elements equal to one. A matrix that is row-stochastic and row-normalized is scaled so the sum of every row is equal to one (that is, j pi,j = 1). The scaling factors di = hi,j define the vector d = (di), which satisfies the equation d = Me. I denote the diagonal matrix Dn×n j P as diag(d) with diagonal entries composed of vector d = (d ) for i = 1, 2, . . . , n. P T is the transpose of matrix P . P i

8 (cs) This is the most basic model, a cumulative-structure model, and is useful in identifying the originating innovation (Golder, Shacham, and Mitra 2009).

(cc) This model, cumulative-combined, is also useful in identifying the originating innovation while accounting for the technological overlap of a patent and its citation.

(ms) This model, marginal-structure, is useful in identifying a patent’s marginal utility, a fundamental principle of Austrian economics.

(mc) This model, marginal-combined, is also useful in identifying a patent’s marginal utility while accounting for the technological overlap of a patent and its citation.

Utilizing these network models, each patent’s intrinsic value can be assessed at a given point of time. As I demonstrate in the ﬁrst essay, a patent’s intrinsic value correlates strongly with the the value of the innovation to society (SV ).

S-curves: Modeling a patent’s expected lifetime value

Each patent’s intrinsic value, as determined by Patent Rank, changes over time. Using Patent Rank, the longitudinal value of a patent can be monitored and modeled which allows for a prediction of a patent’s expected lifetime value; a patent’s trajectory of diffusion can be estimated. This modeling procedure is invaluable as it allows patent data to be used ex ante rather than ex post (Trajtenberg 1990a); evaluation can become prediction (Eyring 1963). As outlined below, the marginal utility of a patent is evaluated using the (mc) Patent Rank model, at a speciﬁc point in time. Any radical disruption in the network can be observed as a measurable Schumpeterian shock. The nature of the Schumpeterian shock can be estimated using a nonlinear growth curve. In the estimation, a patent’s trajectory can classiﬁed based on its growth rate, its velocity, and its total volume.

9 ● ● ● ● ● ● ● ● ● ● ● ● Intensity ● ● ^ ● ● β = 101.644 ● ● ●

●

● maturation stage * ^ growth stage δ = 0.6644 Volume ● Patent Lifetime Value (PLV) Value Lifetime Patent

P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 ●

● ● ● Equilibrium ^τ = 1984.85

Duration

Time Network is formed at time t using (mc) Patent Rank scores. Radical innovation as Schumpeterian shock is observed up Diffusion pattern of total volume is modeled to identify the

to and including time t. growth δ, time of maximum growth τ and the expected

patent lifetime value β at time t. To model a patent’s diffusion trajectory, I use a three-parameter form of the Richards’ curve (Richards 1959):

βit Yit = f(Xit;Θit) = f(Xit; βit, δit, τit) = (1 + e−δit(Xit−τit)) where Y represents the total volume of the Schumpeterian shock for patent i measured in year it e

Xit utilizing information up-to, and including time t. The selected three-parameter model helps identify: (1) the maximum growth rate δ, (2) the time at which maximum growth τ occurs, and (3) the ceiling value β which represents the expected total volume of the Schumpeterian shock. The parameters are conceptually meaningful in the assessment of a patent from a venturing perspective. Utilizing this technique, the parameters from the modeled trajectory can help discern winner patents (race horses and mules) from loser patents (show ponies) where winners (losers) represent patents that have high (low) expectations in terms of intrinsic value which ultimately have a radical (an incremental) impact on society. From this social value (SV ) defined by a patent’s Schumpeterian shock, a firm has potential to extract monopolistic profits (FV ).

Patent Rank and abnormal ﬁnancial returns

At the firm level, a portfolio can be identified as patent stock. A firm’s patent stock changes over time, and these changes should influence financial performance. As outlined in Equation (1.1),

10 the value of the patent innovation to society (SV ) is very different from the value of the patent innovation to the firm (FV ). So although radical innovation benefits society, the innovating firm may not directly see these profits (Golder, Shacham, and Mitra 2009). Regardless, changes in the value of patent innovations a firm possesses should be observable in the financial markets. That is, investors can utilize the information of a firm’s patent stock (as defined by Patent Rank) to create a calendar-time portfolio strategy that will generate abnormal market returns. Anchored to the assumption of efficient markets, changes in a firm’s patent stock should be observable during the year the changes are occurring. Using annual patent stocks and monthly financial returns for a firm, I test whether patent innovations lead to abnormal returns using a portfolio approach. A portfolio is modeled using the Fama-French/Carhart four-factor model (Fama and French 1993; Carhart 1997; Core and Guay 1999; Barber and Odean 2001; Fama and French 2006):

Rjt Rft = αj + βj(Rmt Rft) + sj(SMBt) + hj(HMLt) + uj(UMDt) + jt − − where j represent a portfolio, t is a month in years 1980–2009, Rft is the risk-free rate for year t, Rmt is the market return for t, βj is the classical CAPM β for portfolio j, sj is the coefficient associated with size of market capitalization (SMB as small minus big) for portfolio j, sj is the coefficient associated with value/growth (HML as high minus low book-to-market ratio) for portfolio j, uj is the coefficient associated with momentum (UMD as up minus down) for portfolio j, jt is the disturbance (residuals from unobservables) for portfolio j at time t, and αj + jt is defined as the abnormal return for portfolio j. Using a sample of 381 firms from the S&P 500, I create two basic portfolios using median returns: monthly returns from firms with patents and monthly returns from firms without patents. A difference portfolio (SOME patents less NO patents) is created and modeled; this portfolio represents a financial equity long-short strategy. There is some evidence from this basic difference

11 portfolio that firms with patents have higher abnormal returns than firms without patents. To further explore the abnormal returns for firms with patents, I create decile portfolios based on the amount of patent stock a firm possesses where I define a firm’s patent stock from each patent’s discernible Schumpeterian shock using Patent Rank. Every year, changes in a firm’s patent stock is compared to those of other firm’s also possessing patents. Deciles of patent stock changes are defined, and

each year a ﬁrm’s monthly returns are placed into a decile portfolio. Decile1 would represent a

firm with little or no change in patent stock for that year whereas Decile10 would represent a firm with significant change in patent stock for that year. Difference portfolios (Decilei less No patents) identify abnormal returns for certain patent-innovation deciles.

Summary

Succinctly, I apply the principles of network theory to the patent-citation network to define a patent’s intrinsic value at any point in time using Patent Rank. I demonstrate that Patent Rank is superior to the traditional forward-citation-count metric. I describe the phenomena of entrepreneurial innovation—Schumpeterian shocks and Kirznerian competition. I model a patent’s ‘Schumpete- rian shock’ over time using the generalized logistic function (nonlinear S-curve) to predict the expected lifetime value of each patent. I demonstrate that, at the firm level, changes in expectation are observable in the financial markets.

12 Organization of the dissertation

This dissertation consists of four essays comprising Chapters 2-5. In Chapter 2, I demonstrate that Patent Rank is a more consistent, less biased, and more precise measure than forward-citation counts. In Chapter 3, I define a generalized model for Patent Rank and anchoring to Austrian economics, demonstrate the presence of entrepreneurial activity among patent innovations; changes in a patent’s value (a Schumpeterian shock) can be observed and measured. In Chapter 4, I use the data associated with a specific patent’s Schumpeterian shock to estimate and predict the patent’s overall value, its speed and growth. Using this information, a firm’s portfolio of patents can be computed. In Chapter 5, I synthesize what was learned previously in the dissertation to make the claim that Patent Rank is an objective measure of radical innovation. This claim is validated using rhetoric, case studies, and empirics. I demonstrate the firms with patent stock have higher abnormal returns than firms that have no patents. In Chapter 6, I conclude by summarizing the contributions of this dissertation, and considering where these findings leave us in our study of innovation. Additionally, there are several appendices: (a) the derivation of Patent Rank from a ratio, (b) a glossary of terms, (c) mathematics, (d) example on the computation of Patent Rank, (e) description of ClassMatch , (f) rationale for the combined-model specification, (g) the natural phenomenon of the Golden Ratio in Patent Rank and the unique double natural-logarithmic transformation, (h) the methodology ‘red light/green light’ to carefully review patents and match them appropriately to firms, (i) lists of firms used in Chapters 4 and 5, (j) additional decile-models used to link patent stocks and abnormal financial returns based on various patent-stock definitions.

13 CHAPTER 2

PATENT RANK: AN IMPROVED VALUATION-METRIC Innovation is the most fundamental entrepreneurial activity. Researchers in marketing, strategy, economics, and law make attempts to ascertain the substance of innovation - is it radical or incremental? The purpose of this chapter is to present Patent Rank (which has correspondence to the famed PageRank approach used by Google) as a continuous, objective measure to valuate a patent innovation. That is, we endogenously evaluate a patent’s quality based on its entire legally-deﬁned genealogy of forward and backward citations. From 1976-2009, we compute annual Patent Rank scores for all 5.6 million utility patents. With 98 million longitudinal values, we can analyze any patent innovation relative to the comprehensive network, on a continuum from incremental to radical, at any point of time. We compare Patent Rank to the traditional metric (forward-citation counts) and demonstrate Patent Rank is a more precise measure of a patent’s intrinsic value.

2.1 Introduction

We perform an EBSCO Business Source Complete search for the term patent within our top- marketing journals (Journal of Marketing, Journal of Marketing Research, Marketing Science, and Journal of Consumer Research). We find nearly six hundred articles that reference patents. Early in the development of our field, patents were an important part of the legal developments reported in the Journal of Marketing—regulatory updates on distribution, product characteristics, and monopolistic methods such as price fixing, antitrust investigations, unfair competition, patent infringement, false advertising (e.g., patent-protection or patent-pending), and other ethical concerns related to patents as innovations (Curran 1958; Journal of Marketing 1965; Statman and Tyebjee 1981; Werner, Griffiths, and Kirk-Duggan 1991, and so on). Consumer studies in marketing also utilize patents. Friedman (1985) describes the importance of patents and trademarks on brand; Belk (1988) illustrates how consumers’ identity and the extended self relate to patent innovations; Bearden and Mason (1980) describe how consumer confidence of generic pharmaceuticals following expiration of patented brands depends on the physicians; and Ratneshwar and Chaiken

14 (1991) define how patent experts are relied upon to persuade when a novel product is incompre- hensible. Most recently, studies in marketing science utilize patents to examine different aspects of innovation: to understand knowledge flow within and across firms, to describe how knowledge flow influences the success of innovation, and to identify antecedents and outcomes of radical and incremental product innovation. This research (see Table 1) requires a metric to valuate patents.

[Table 1 about here.]

How valuable are patents to a firm? To a nation? To society as a whole? Simply counting the number of patents a firm possesses is not sufficient, as each patent may have a different value (Allison, Lemley, Moore, and Trunkey 2004; Moore 2005); not all patents are created equal. How many patents a firm has (its simple patent count) correlates with R&D and represents the (Trajten- berg 1990a, p. 173) “input side of the innovative process.” How important an individual patent is ex post can be valuated by counting subsequent patents that are legally-bound to cite it as prior art. We define these subsequent citations as forward citations. These forward-citation counts represent, among patents, the inherent diffusion and adoption of the originating patent innovation; they represent an output measure of the innovative process (Trajtenberg 1990a; Hall, Jaffe, and Trajtenberg 2005). We broaden the logic of the Trajtenberg (1990a) metric by considering the uniqueness of both forward and backward citations: simply counting the number of forward citations a patent possesses is not sufficient, as each citation may have a different value; not all forward citations are created equal. Similarly, not all backward citations are created equal. Therefore, we define a comprehensive, graph-based patent network using both types of citations: a patent’s ancestry (backward citations) and a patent’s heritage (forward citations). To assess the value of every patent in the network, we consider all patent-citation pairs utilizing the mathematics of eigenvector-centrality— a procedure that is endogenous, simultaneous, comprehensive, and universal. This technique considers each patent-citation association and accounts for the importance of each association relative

15 to the entire network. We present the resulting scores as Patent Rank, in analogy to Google’s PageRankTMalgorithm: as PageRank has brought order to the web (Page, Brin, Motwani, and Winograd 1999), we propose to bring order to the patent network in the study of innovation. Both algorithms rely on the same fundamental principles of linear algebra yet we establish herein that our technique is indeed different from Google’s approach. Our contribution is: (1) the refined logic to valuate patents more precisely than forward-citation counts (Trajtenberg 1990a), (2) the comprehensive patent dataset to implement the logic, (3) an intuitive, network methodology to execute the logic, and (4) validation that the refined logic is indeed more precise than the Trajtenberg (1990a) metric. In sum, Patent Rank serves as an improved valuation-metric for patent innovations. We define diffusion as (Rogers 1995, p. 5) “the process by which an innovation is commu- nicated through certain channels over time among the members of a social system.” We define adoption as the act of legally citing a prior patent innovation. We define a patent’s intrinsic value as its computed importance within the patent network using forward-citation counts (Trajtenberg 1990a) or Patent Rank. We economically define the value of a patent innovation in three ways: the value of the intellectual property rights (IPV ), the value of the innovation to the firm (FV ), and the value of the innovation to society (SV ); the economic argument necessarily implies that IPV FV SV (Trajtenberg 1990b, p. 185). We compare the Trajtenberg (1990a) metric to ⊂ ⊂ Patent Rank metric using two social-value (SV ) measures (TW and ∆W ) that Trajtenberg (1989) defined based on discrete-choice and hedonic-pricing models.

2.2 A Patent’s Intrinsic Value: First Principles

Patents represent exclusionary “intellectual property rights” granted by the government to the ﬁrm for a speciﬁc period of time in exchange for public disclosure. Article I, Section 8 of the Consti- tution of the United States states that “Congress shall have power [...] to promote the progress of

16 science and useful arts by securing for limited times to inventors the exclusive right to their respective discoveries.” This contractual agreement of exclusionary rights for public disclosure allows for universal studies of innovation among patents.

Principle 1: Approved patents inherently represent innovation in a basic form.

Innovation is a noun of action, derived etymologically from the Latin verb innovatus (“to renew or change”), and means to introduce a new idea, method or device. Innovation is generally considered the actualization of an invention, succinctly summarized by Thomas Edison when he said “Anything that won’t sell, I don’t want to invent. Its sale is proof of utility, and utility is success.” Utility represents the distinct difference between innovation and invention. Albeit important, inventors generally spend their fortunes on their ideas whereas innovators create fortunes from their ideas. The filing of an patent application is in essence the introduction of a new idea, method or device; it embodies a basic form of innovation. In the United States, the Patent Office has several criteria1 to approve a patent. Fundamentally, these criteria can be summarized by answering three questions: (1) is it novel, (2) is it non-obvious, (3) does it have utility. The third question can be decomposed into two components: (a) is it useful, and (b) is it appropriable (i.e., can it make money). Legal precedence2 for these patentability conditions delineates that obviousness and novelty are intertwined with utility and appropriability predicated upon the marketing concept. That is, customer need fulfillment and commercial success has legal precedence to determine whether a

1Within U.S. Tax Code, Title 35 (Patents), Part II (Patentability of Inventions and Grant of Patents), Chapters 10 (Patentability of Inventions) and 11 (Application for Patent), see 101, 102, 103, and 112. The appropriability requirement has a low threshold in the United States, and is related to the§ European concept of industrial applicability. 2“Under Section 103 the scope and content of the prior art are determined; differences between the prior art and the claims at issue are to be determined [... and] ascertained; and the level of ordinary skill in the pertinent art resolved. Against this background, the obviousness or nonobviousness of the subject matter is determined. Such secondary considerations as commercial success, long felt but unsolved needs, failures of others, etc., might be utilized to give light to the circumstances surrounding the origin of the subject matter sought to be patented. As indicia of obviousness or nonobviousness, these inquiries may have relevancy.” — Graham v. John Deere Co., 148 U.S.P.Q. 459 [S. Ct. 1966]

17 patent is granted or not. Patent approval represents the ﬁrst demonstration of the patent’s utility; that is, when the legal review deems the patent to have utility, it further substantiates the claim that the patent invention is a basic innovation. Therefore, we posit approved patents represent a basic form of innovation.

Principle 2: A patent’s ancestry (its backward citations) contributes ex ante to the degree of its radicalness.

Prior to approval, during the patent application review process, the applicant has a legal duty to provide relevant information related to the field/background of the innovation that delineates its unique claims (e.g., describe its radicalness) in context of the patent’s subject matter to demonstrate principle 1. This relevant “prior art” (such as U.S. patent documents, scholarly articles, and foreign patents) must be acknowledged and cited within the patent. Once a patent is approved, these backward antecedents become fixed and time-invariant. Prior art is carefully considered during the prosecution of a patent application as it determines the monopolistic scope of the innovation which, in turn, delineates the intellectual property claims. Throughout the patent review process, interested parties may contest the inclusion and/or omission of these citations. If an applicant fails to include important prior art during the application stage, an alleged infringer has a legal basis to challenge the patent’s validity (Carr 1995, p. 40). Citations to historic patents, which we define as backward citations, are utilized in this research. These backward citations discount the current patent’s radicalness (e.g., the patent is incremental). This adoption represents a borrowing of past innovation in the current innovation (Golder, Shacham, and Mitra 2009). Lots of backward borrowing constitutes incrementalness whereas little backward borrowing typifies radicalness. Therefore, we posit that backward citations must be included in the assessment of a patent’s intrinsic value.

18 Principle 3: A patent’s heritage (its forward citations) contributes ex post to the degree of its radicalness.

In juxtaposition, one patent’s backward citation represents another patent’s forward citation. If in the future a new patent subsequently cites the current patent, that future patent is borrowing from the current patent which increases the current patent’s radicalness. Chandy, Hopstaken, Narasimhan, and Prabhu (2006, p. 7) succinctly characterize this as follows: “the greater the number of forward citations, the higher is the importance.” We emphasize the forward-citations counts, introduced by Trajtenberg (1990a) and most commonly utilized by researchers in marketing science, are expressions of this third principle. For a subsequent patent to cite the current patent, it necessarily implies that the current patent has prior art that is a basis for the newer innovation to build upon—standing on the shoulder of giants (John of Salisbury 1159). The newer innovation delineates unique claims in context of the current innovation, regardless of the patent-expiry status. Therefore, we posit that forward citations must be included in the assessment of a patent’s intrinsic value.

Principle 4: A patent’s intrinsic value changes over time.

Taken together (that is, applying both principles 2 and 3), any patent X can be appraised at any point in time based on both its backward and forward citations. Backward citations represent a borrowing of radicalness to X. Forward citations represent a lending of radicalness from X. The direction of the temporal dependency of the citations—are the citations forward-looking or backward-looking from X (Trajtenberg, Henderson, and Jaffe 1997)—deﬁne the inﬂuence of these citations on a patent’s radicalness. By considering both backward and forward citations simultaneously and endogenously, we can assess any patent X based on its entire genealogy—its upstream antecedents and its downstream descendants at a particular moment in time. Consequently, when

19 additional patents join the network, every citation’s value may also change. Therefore, we posit that as the patent network updates, so should the patent’s intrinsic value.

2.3 Patent Rank scores as Network Centrality

In this section, we delineate how we can use the patent-citation network to define Patent Rank. We begin by reviewing the fundamental principles of network theory. Utilizing a toy example, we demonstrate how the patent-citation network is formed into a graph. We discuss network centrality, and delineate a novel network formation and computation that accounts for the uniqueness of the patent system. Network theory is a type of graph theory that maps a network structure based on a defined association (link) between objects (node). In our case, we define our objects to be the patents, and we define the forward and backward citations as associations. Our patent network can then be described as a directed graph; that is, the direction of the association defines whether the citation is a forward or backward citation. The arrows are drawn in a directed way (McKee and McMorris 1999); the links point to radicalness and away from incrementalness. The resulting directed patent graph identifies the genealogy of each patent innovation.

[Figure 1 about here.]

2.3.1 Toy Example: Network with 10 patents and 14 citations.

To illustrate, we introduce a toy example of a simple network of ten patents having fourteen associations (links). Figure 1 graphically displays both the temporal constraints and citation association of 10 patents (P1, P2, P3, P4, P5, P6, P7, P8, P9, and P10). The U.S. Patent Ofﬁce assigns an incremental number to each patent once it is granted so Patent P1 is necessarily older than (or the same age as) patent P2. Information regarding citations is also limited by the data available from the U.S. Patent Ofﬁce. Prior to 1976, limited patent information is available, so necessarily there are many older patents that do not have backward-citation information.

20 Forward citations for any patent X represent inbound links and backward citations represent

outbound links. Patents P1, P2, and P6 each have 3 forward citations, providing some support

for their radicalness. Patent P7 has 4 backward citations, suggesting its incrementalness. Table 2 summarizes the patent graph. The rows and columns of the table represent the nodes (patents) of the graph. The elements within the table indicate an associations between the patents. Since the table consists of 100 elements (10 10), yet non-zero values are found in only 14 cells, we deﬁne × this to be sparse table.

[Table 2 about here.]

Patent P5 we deﬁne as a core patent as it has both forward and backward citations (P4 and P8

are also of this type); patent P6 we deﬁne as a dangling node as it has forward citations, yet no

3 backward citations (P1, P2, and P3 are also of this type); and patent P7 we deﬁne as a dud patent

as it has no forward citations (P9 and P10 are also of this type). Any elemental cell (r, c) in Table 2 is a binary response that deﬁnes the link from the patent

in the row (r) to the patent in the column (c). For example, (P5,P1) equals one as it represents a link from P P ; P borrows radicalness from P or said conversely, P lends radicalness to 5 → 1 5 1 1

P5. This deﬁnes a directional association; the reverse direction, (P1,P5) equals zero because the association P P is not possible due to the temporal assignment of patents in chronological 1 → 5 order (i.e., P5 was granted after P1 and hence P1 could not possible backward-cite P5). Therefore, the rows represent backward citations and the columns represent forward citations. For example,

row P5 identiﬁes two backward citations P1 and P2; column P5 identiﬁes one forward citation P7.

Since, by deﬁnition, a node does not cite itself, cell (P5,P5) is equal to zero. In Equation (2.1), we derive a matrix M from the directed associations of this network described in Table 2:

3 Patents P1, P2, and P3 are dangling nodes because backward citation information is unavailable for them (e.g., granted prior to 1976) whereas patent P6 is a dangling node because no prior art was identiﬁed during the patent’s legal review.

21 0 0 0 0 0 0 0 0 0 0   0 0 0 0 0 0 0 0 0 0      0 0 0 0 0 0 0 0 0 0       0 1 1 0 0 0 0 0 0 0       1 1 0 0 0 0 0 0 0 0    M =   . (2.1)    0 0 0 0 0 0 0 0 0 0       1 1 0 0 1 1 0 0 0 0       1 0 0 1 0 1 0 0 0 0         0 0 1 0 0 0 0 0 0 0       0 0 0 0 0 1 0 1 0 0      2.3.2 Network Centrality: Eigenvector Centrality measures importance of any patent.

There are several centrality measures for a network (Bonacich 1972; Friedkin 1991; Faust 1997; Opsahl, Agneessens, and Skvoretz 2010). Degree centrality is the most basic, expressing the number of links associated with a node. In context of the patent network, degree centrality directly corresponds to the number of backward (outdegree) or forward (indegree) citations. With an intent to capture the the importance of every patent in context of the entire network of patents, we deﬁne Patent Rank scores using a more robust network centrality measure. We utilize eigenvector centrality as it considers all associations simultaneously and thereby captures a “total effects centrality” (Faust 1997, p. 169). In essence, it considers information about both forward and backward citations simultaneously and endogenously which necessarily removes any bias from considering forward or backward citations individually. Considering the two-dimensional form from Equa- tion (2.1), we emphasize that the importance of a patent is not only measured by the number of forward and backward citations it has but also by the relative importance of these cites, as measured by their respective forward and backward citations, and in turn, these forward and backward citations are measured by their respective forward and backward citations. This endogenous and

22 recursive consideration is mathematically deﬁned as a Markov process and can be computed using eigenvector centrality.

Mathematics of Eigenvector Centrality

In order to compute the eigenvector centrality of a network, certain mathematical properties must exist. A fundamental theorem in linear algebra (the Perron-Frobenius Theorem) states that if a matrix is irreducible and non-negative, we can identify a unique eigenvector for the matrix. This

means we can uniquely collapse the network structure of size n n (from Equation (3.2) or Ta- × ble 2) into a vector of n unique scores (the eigenvalues). Essentially, this theorem allows for the computation of a Patent Rank score for each patent in the network and assures a converged, unique solution. To be able to apply the Perron-Frobenius Theorem, we ﬁrst note that, by construction, matrix

M is non-negative; that is, every element (mij) in the matrix is greater than or equal to zero. Utilizing principles of linear algebra, we need to transform matrix M into irreducible matrix P . Once matrix P is appropriately speciﬁed, the computation of the eigenvector π will deﬁne4 the Patent Rank scores:

π = P T π where P = diag(d)−1M and d = Me. (2.2)

To achieve our objective, two keys need to be addressed. First, the inverse of the diagonal matrix

must be defined which means that di = 0 i. Since di represents a row sum, this constraint 6 ∀ means that each patent must have at least one backward citation. If this constraint is satisfied, by performing the row-normalization technique described as D, we can construct a row-stochastic matrix P . If this constraint is not satisfied (e.g., a patent is a dangling node), the row sum is 0

4We denote the vector e as a unitary vector of length n with all elements equal to one. A matrix that is row- stochastic and row-normalized is scaled so the sum of every row is equal to one (that is, j pi,j = 1). The scaling factors di = hi,j deﬁne the vector d = (di), which satisﬁes the equation d = Me. We denote the diagonal j P matrix D as diag(d) with diagonal entries composed of vector d = (d ) for i = 1, 2, . . . , n. P T is the transpose n×n P i of matrix P .

23 (division cannot occur), and the diagonal matrix D = diag(d) is not invertible, so P cannot be constructed. Second, matrix P must be irreducible. An irreducible graph has a closed form which implies it is strongly connected—from any node in the graph every other node can be reached by following the directed links in the graph. To understand irreducibility, reconsider the toy example of patents and its directed graph presented in Figure 1. Starting at node P1, can the directed links be followed

to arrive at P5? The answer is no. In fact, the entire directed patent graph (as currently deﬁned) is weakly connected because of its chronological constraint, which does not allow certain patents to be connected to others. Therefore, it does not have a closed form, and the matrix P is not irreducible (it is reducible).

Google’s PageRankTM: Example of Link Analysis of the World Wide Web

To understand conceptually how link analysis works using eigenvector centrality, how matrix assumptions are made, and how eigenvalues are computed, we review Google’s PageRank algorithm. We discuss the PageRank algorithm here because: (1) though our actual approaches are different, it is easier to understand our concept for those who are familiar with the Google web-search process;

and (2) Google has had spectacular success with this algorithm, helping it to achieve a 2/3 share of the US online search usage market (Letzing 2010), and it is therefore important to understand this major algorithm. This algorithm was developed by Google’s two founders, Sergey Brin and Lawrence Page, to provide a score of importance for each node (web page) in the network (World Wide Web) that then can be ranked (Brin and Page 1998; Page, Brin, Motwani, and Winograd 1999). Google’s search engine ranks relevant web page results based on link analysis—the links between web pages. Google’s PageRank5 mathematically weights the importance of each web page, then the

5PageRank is commonly believed to be derived from its inventor, one of Google’s founders, Larry Page; others argue that it refers to ranking webpages. Patent #6,285,999 refers to Larry Page as the inventor, and Stanford University as the assignee of the PageRank algorithm.

24 results are ranked in context of a search term provided. Originally, this algorithm was described as follows (Google Answers 2002):

PageRank relies on the uniquely democratic nature of the web by using its vast link structure as an indicator of an individual page’s value. In essence, Google interprets a link from page A to page B as a vote, by page A, for page B. But, Google looks at more than the sheer volume of votes, or links a page receives; it also analyzes the page that casts the vote. Votes cast by pages that are themselves ‘important’ weigh more heavily and help to make other pages ‘important’.

Google’s concept of PageRank is useful to understand our concept of Patent Rank. Google considers all the web pages of the World Wide Web; we consider all the utility patents of the U.S. Patent system (and these two are different). However, our forward citations can be understood in the context of Google’s inbound links and our backward citations can be understood in the context of outbound links. The nature of the patent network and its radicalness is clearly different from the importance of a web page in the World Wide Web. The web is democratic in terms of who can decide to link to which page; in contrast, inclusion in the patent network is regulated through a legal review orchestrated by the U.S. Patent Ofﬁce. A patent application is carefully screened to determine the innovation’s merit: (1) is it novel, (2) is it non-obvious, (3) does it have utility. In addition, during patent’s prosecution, the backward citations are carefully considered and scru- tinized. This regulated-temporal constraint on inbound (forward) and outbound (backward) links makes the patent network different from the World Wide Web. Finally, the computations from the PageRank algorithm requires a strategy to address invertibility (the problem of dangling nodes) and irreducibility (the problem of connectedness) which also differs from our approach. Google’s PageRank algorithm addresses the problem of dangling nodes and irreducibility of the World Wide Web by performing a rank-one perturbation of the initial adjacency matrix M and introducing a “damping factor” (Brin and Page 1998). Simply stated, the ﬁrst condition implies that any null row (a row of zeroes which implies that the patent has no backward citations; it is a dangling node) is replaced by a row containing 1/n elements where n represents the total nodes in the network. Irreducibility is then enforced by adding to each element in this new, row-normalized

25 matrix 1/n scaled by a damping factor (0.85 generally). In essence, matrix M is added to two other

matrices with miniscule variations. The former enforces each di = 0 so the inverse of D can be 6 computed; the latter guarantees irreducibility—both at the cost of losing sparsity in the matrix. We address these two issues—dangling nodes (will the matrix invert) and irreducibility (is the matrix strongly connected)—in a different way. Proverbially, we kill two birds with one stone.

[Figure 2 about here.]

Irreducibility via a Super-node — the U.S. Patent Ofﬁce.

Bini, Del Corso, and Romani (2008) discuss how augmenting a matrix can address the problem of dangling nodes and irreducibility. We apply this to the patent system by introducing the U.S.

Patent Office as a super-node (P0) into the patent network. Patents represent nodes, and the the overarching structure of the network system, the U.S. Patent Office, we define as a super-node. The Patent Office grants inclusion in the network and inclusion in the network implies an association with the Patent Office. Therefore, we introduce the node P0 to represent the Patent Office in the patent network in such a way as to describe its full, bi-directional association; that is, P0 is cited by

all patents in the network and cites all patents in the network. The ﬁrst association (P0 is cited by all patents) addresses the problem of dangling nodes by providing a backward citation. The second association (P0 cites all patents) in conjunction with the ﬁrst association addresses the problem of irreducibility; the super-node serves as a bridge6 between any pair of nodes in the network. Figure

2 updates the toy example to demonstrate the inclusion of the Patent Ofﬁce P0 as the super-node.

2.3.3 Advantages of Patent Rank scores

To summarize, Patent Rank scores represents an eigenvector centrality measure from network theory. Such scores simultaneously consider all citations in the valuation of any speciﬁc patent in

6 The super-node serves as a mediator between any two nodes. Adding the super-node P0 to the graph strengthens its connectedness. Now, for each pair of nodes (Pi,Pj), there exists a connected path from Pi to Pj through P0, that is Pi P0 and P0 Pj. This mediating and intercessory role fairly represents the sanctioned obligations and ﬁduciary responsibilities→ of→ the U.S. Patent Ofﬁce which becomes prominent during legal disputes (e.g., patent infringement).

26 the network. Inherent to the selection of this centrality measure, we emphasize that the nature of the entire network must be defined and considered. Although similar to Google’s PageRank algorithm, our Patent Rank algorithm addresses the mathematical constraints imposed by the Perron- Frobenius Theorem in a completely different way. Specifically, we include the Patent Office as the super-node to conceptually account for the legal rigor required for a patent to become of member of the patent network. In addition, we compute the the Perron vector using a very efficient technique. Though there are many methods that can be used to compute the dominant eigenvector of a matrix (Golub and Loan 1996), the most commonly used is the power method, especially when working with large and sparse matrices. Though not the most efficient, Google still uses this computational technique to calculate PageRank (Langville and Meyer 2006, pp. 40-41). Computationally, this power method is a simple iterative procedure. This computation is mathematically equivalent to repeatedly mul- tiplying the matrix P by itself, and identifying any row as the centrality eigenvector. Recently, Del Corso, Gulli, and Romani (2005) propose an alternative technique to compute the Perron vector utilizing linear algebra. For a web graph with 24 million nodes and 100 million links, they demonstrate a 92% improvement in time, and a 65% improvement in CPU computing cost compared to the power method. We modify this technique to include the super-node (Bini, Del Corso, and Ro- mani 2008) and apply it to the patent network. In doing so, we reorganize the matrix to simplify the linear system through a partitioning schema—we group patents based on their link structure: core patents (patents having both forward and backward citations), dangling nodes (patents having forward citations but not having any backward citations), and dud patents (patents having no forward citations). Solving this partitioned linear system produces Patent Rank scores π that are mathematically equivalent to the power method, yet is much more efficient. In Figure 3, we summarize the differences between our Patent Rank approach and Google’s PageRank approach.

[Figure 3 about here.]

27 Finally, we normalize our results, so the minimum score assigned to a patent in the network is one. This aligns directly with traditional count measures (Trajtenberg 1990a). A simple patent count gives each patent a score of one, and forward-citation counts (generally referred to as weighted patent counts) gives each patent a minimum score of one if no forward citations exist: WPCt = 1 + Ft; that is, at any time t, the forward citations F can be counted which deﬁnes the weighted patent count.

2.4 Data Preparation and Computation of Patent Rank scores

In December 2008, we began collecting patent data, programmatically7 harvesting all utility patents from the USPTO website. Once all the patents were collected, we parsed all utility patents granted in a year to extract their backward citations, as one patent’s backward citation represents another patent’s forward citation. From this information, we construct a directed graph in matrix form (M). We partition this matrix based on an ordering schema which classifies each patent into one of three types: dangling nodes, core patents, and duds. We augment the matrix to include the super-node, and finally, we row-normalize as defined in Equation (3.1). After this preparation, we solve the partitioned linear system to identify the unique Patent Rank scores (Del Corso, Gulli, and Romani 2005; Bini, Del Corso, and Romani 2008). With an intent to understand the patent’s intrinsic value over time, we annually8 perform these calculations. Starting with approximately 70,000 patents and 345,000 citations in 1976 and finish- ing with approximately 5.6 million patents and 40 million citations in 2009, we compute the annual Patent Rank scores; e.g., 1976–1976, 1976–1977, 1976–1978, . . . , 1976–2007, 1976–2008, 1976– 2009. In total, we compute over 98 million Patent Rank scores. For example, the final cumulative model (1976–2009) has 5,608,070 patents and 39,811,892 patent-citation links therefore 5,608,070

7A robot script was written that saved each patent webpage to a local machine. A parsing algorithm was created to extract the appropriate data from each patent. This process took approximately 18 months. 8Although newly granted patents are publicly available every-other Tuesday, we choose to update the patent network every year.

28 Patent Rank scores are computed. Of these patents, 35.8% are dangling9 nodes, 50.5% are core patents, and 13.7% are dud patents.

2.5 Patent Rank is Better

Validity is deﬁned as the “degree to which the measures or observations are appropriate or meaningful in the way they claim to be” (Rosenthal and Rosnow 2008, p. 763). We claim that these Patent Rank scores represent a better measure of patent’s intrinsic value than the forward-citation metric (Trajtenberg 1990a). To verify this claim, we utilize axiology, nomology, and two convergent/discriminant tests.

2.5.1 Axiology

How should we value a patent? This question is the foundation from which to compare the two metrics using axiology, the study of value judgment. Using first principles, we have logically argued that more information (specifically the inclusion of backward citations and the endogenous value of all citations in the network) should improve our measure of value. Using mathematics and network theory, we have described how the eigenvector centrality is more informative than degree centrality as it accounts for both forward and backward citations whereas degree centrality only accounts for one. Based on first principles and the mathematics of network theory, we claim Patent Rank is logically a better valuation metric than forward-citation counts.

[Table 3 about here.]

[Table 4 about here.]

2.5.2 Nomology

How should the two measures compare? This question is the foundation from which to compare the two metrics using nomology, the study of reasoning. That is, do the results of the two different

9We note that about 1% of the total patents (58,521) are dangling nodes (post-1976) without any speciﬁed prior art.

29 measures behave as expected? In Table 3, we report the top-20 patent innovations from 1976– 2000, defined by Patent Rank scores. We report the Patent Ranks, the Patent Rank scores, the title of the patent innovation, the comparable forward-citation counts (1976–2000), and updated forward-citation counts (1976–2010). Generally, we see that forward-citation counts overstate the patent’s intrinsic value as it treats each forward citation equally. There are a few exceptions, which are worth noting. A few important patent innovations have forward-citation counts that understate the patent’s intrinsic value. Conceptually, these differences represent false-positives and true-negatives. From first principles, we argue that these differences represent bias in the forward-citation-count metric, as it considers each forward citation equally. Finally, we note that the forward-citation counts change over time, as we expect. In Table 4, we highlight three10 top patents, by number (4,683,195; 4,237,224; and 3,778,614), to emphasize the change in Patent Rank over time. Obviously, forward-citation counts will also change over time; however, we emphasize that Patent Rank scores change over time because the entire patent network changes whereas forward-citation counts change only based on a specific patent’s subsequent citations.

2.5.3 Conceptual Convergent/Discriminant Test

Convergent validity is the degree to which concepts that should be related theoretically are interrelated in reality (Campbell and Fiske 1959). By construction, the computation of Patent Rank includes all of the information from forward-citation counts plus information from backward- citation counts and each citation’s intrinsic value. As such, Patent Rank should correlate with forward-citation counts. In Table 5 we report the correlation between forward-citation counts (called weighted patent counts as WPC) and Patent Rank scores (PR) for all patents over time. We note that the two measures are correlated demonstrating convergent validity. Discriminant validity is the degree to which concepts that should not be related theoretically

10The three patents are bolded, italicized, and underlined to make it easier for the reader to follow, as each patent number appears repeatedly over time to demonstrate the trending Patent Ranks.

30 are, in fact, not interrelated in reality (Campbell and Fiske 1959). Because Patent Rank more precisely evaluates a patent’s intrinsic value, we expect there to be differences between the scores. In Table 5, we note that as the size of the network grows, the correlations between Patent Rank and forward-citation counts are weakening over time. This trend supports discriminant validity conceptually, although it is insufﬁcient to verify that our proposed measure is indeed more precise. We need to demonstrate our measure has more explanatory power, which requires external data.

[Table 5 about here.]

2.5.4 Empirical Convergent/Discriminant Test

In our estimation, the best approach to empirically demonstrate improved explanatory power is to compare our measure to forward-citation counts using the original data from Trajtenberg (1990a). In this section, we formally deﬁne the forward-citation metric as weighted patent count (WPC):

WPCt = 1 + Ft; that is, at any time t, the forward citations F determine each patent’s value. In addition, each patent can be simply counted; we deﬁne this as simple patent count (SPC): SPC = 1.

Patents in Computed Tomography: another look (Trajtenberg 1990a)

Trajtenberg (1990a) considers all11 first-generation patent innovations (400+) related to the X-ray computed tomography (CT) scanners—scanners that use multiple X-ray measurements to create three-dimensional images. Although CT-scanners were introduced in the 70’s, they are still commonly used today; in fact, there has been a recent surge in their use (Mozes 2010). The original term for ‘a CAT scan’ or a ‘CT scan’ in hospitals was ‘an EMI scan’ since a British firm by that name was the assignee for this first patent. The inventor, Sir Godfrey N. Hounsfield

11This seminal paper has over 900 citations on Google Scholar, and his work on innovation using patent data has laid the foundation for an estimated 13,053 different research papers including Jaffe, Trajtenberg, and Henderson (1993) and Hall, Jaffe, and Trajtenberg (2005). Based on IDEAS impact factor, he is among the top 5% of authors based on his research on innovation using patent data (http://ideas.repec.org/e/ptr35.html); according to Google Scholar, he has an h-index of 34 and a g-index of 112 (http://interaction.lille.inria.fr/ roussel/projects/scholarindex/index.cgi). In this section, we utilize the results that can be directly sourced from Trajtenberg (1990a). The ﬁrst author contacted Manual Trajtenberg who was kind enough to provide the list of patents from his research. With this list, it is now to evaluate our Patent Rank measure in relation to Trajtenberg’s original data and measure. Truly, we are standing on the shoulders of a giant (John of Salisbury 1159).

31 (Nobel Prize 1979), worked for EMI in Research and Development. He had an idea to combine slices of X-ray images to create 3-D imaging. Patent # 3,778,614 which was ﬁled on December 1971 and granted on December 1972 is the originating innovation (see 3,778,614 in Table 4). This patent cites two theoretical papers by American Physicist Allan M. Cormack as prior art, who mathematically deﬁned how such slices could be merged into a 3-D image. Based on Patent Rank scores, this patent innovation was top-ranked in 1976–1980, and remained in the top-ten through 1976-1999 (see Table 4), so we agree with the Nobel Laureate and others (Leifer et al. 2000, p. 6) that this patent innovation represents a radical innovation.

Longitudinal Diffusion and Adoption

In Figure 4[a], we provide12 the longitudinal diffusion pattern of this radical patent innovation in terms of three comparative measures: the time-invariant simple patent count (s), the annual weighted patent count (w), and the annual Patent Rank score (*). We note that this particular pattern of the weighted patent count (w) is double-sigmoidal in nature. It grows rapidly and levels off around 1991 when the exclusionary rights13 for the patent expired, then it takes off again and levels off about 30 years after it was originally ﬁled which corresponds closely to ﬁndings from Hall, Jaffe, and Trajtenberg (2001). Thus, patents have an impact on new innovations even after their exclusionary rights have expired.

[Figure 4 about here.]

We also note that for this originating patent innovation, the Patent Rank scores intuitively ﬁt between the simple patent count (s) and the weighted patent count (w). By construction, the simple patent count (s) is a time-invariant lower bound. Generally, the weighted patent count (w) is a time-variant upper bound. This implies that equal treatment of every forward-citation generally

12We include vertical lines in 1981 (when forward-citation counts [approximately] were measured for analysis in Trajtenberg (1990a)), 1991 (when the patent’s exclusionary rights expired), and 2001 (arbitrary, yet consistent 10-year window). 13Prior to June 8, 1995, a patent had 17-year life from the date it was granted; afterward, a patent has a 20-year life from the date it was ﬁled (assuming maintenance fees are paid at 31/2, 71/2, and 111/2 years).

32 overstates a patent’s intrinsic value within the patent network. In a few rare cases, as identiﬁed in Table 3, weighted patent count (w) will understate a patent’s intrinsic value. Therefore, based on the underlying network theory, we contend that Patent Rank removes (over and under) bias in the estimation of a patent’s intrinsic value. In Figure 4[b,c,d], we report the longitudinal adoption patterns of three other top patent innovations. We rank them according to their Patent Rank scores computed in 1981 (from 1976–1981); these rankings are exactly the same using weighted patent counts in 1981. However, if we were to rank them today, the Stanford patent innovation would be ranked second to the originating innovation by EMI (based on both Patent Rank and weighted patent counts). We conjecture that based on the trajectory of the diffusion of the Stanford patent using the weighted patent count, this patent’s intrinsic value will soon surpass the originating radical innovation. Using the Patent Rank scores, it does not seem plausible that this Stanford patent will every surpass the originating patent innovation. From this we surmise that the (over) bias of the traditional measure (WPC) may cause us to arrive at inaccurate conclusions in our study of innovation.

The Social Value of Patent Innovations (SV)

Trajtenberg (1990a) developed two economic value-measures (∆W and TW ) that define the value of the innovation to society (SV) which he described as Trajtenberg (1989, p. 445) “the social benefits stemming from improvements in the quality of available products and from the appearance of new ones.” These measures were formed by calling hospitals through the U.S. and collecting information about the specific CT-scanner product in the hospital with its corresponding features and benefits. Changes in social surplus, ∆W , is calculated as a function of the price for a product and a vector of product characteristics using a discrete-choice model specification and a hedonic price function. This variable represents a consumer’s willingness to pay for an innovation. From this variable, the total gains and diffusion is computed as TW where an ‘appropriate’ carry-over discount rate is included in the computation (Trajtenberg 1989). The basic argument is that firms

33 generally are able to extract proﬁts from this social value (e.g., consumer surplus), which deﬁnes

the firm’s value (FV ). Grouping patent innovations into years (based on filing dates), Trajtenberg (1990a) shows significant correlations between social value and WPC. We intend to demonstrate that Patent Rank has more explanatory power than weighted patent counts (WPC). In the process, we also identify the methodological importance of measuring WPC longitudinally.

[Table 6 about here.]

To begin, we report a correlation of 0.8324 between SPC and R&D, grouping the patents temporally based on the year ﬁled. Since SPC is time-invariant, we emphasize that there are no longitudinal concerns in this computation. Next, since WPC and Patent Rank scores are tabulated annually based on the year forward citations are granted, not ﬁled, we proceed to create comparable correlations. To do this, from (Trajtenberg 1990a, Table 1), we identify when the originating patent temporally grouped in 1972

would have a WPC of 73; its 72nd patent was granted in June 1981. Third, using this speciﬁc

timeframe, we measure WPC at the end of 1981, WPC1981. Using WPC1981, we report correlations of 0.6075 and 0.7200 for TW and ∆W , respectively. Fourth, although imperfect, these correlations are conservatively comparable to the within-industry correlations from Trajtenberg (1990a, Table

3): 0.685 and 0.755 for TW and ∆W , respectively. What would happen to these correlations if we measure WPC (or for that matter, Patent Rank scores) at a different point of time? Say 1991? Or 2001? Since we previously noted that each patent has a unique diffusion pattern, we would assume that, based on principle 4, the correlations would also change over time. So to proceed, we must consider WPC longitudinally: WPCt.

In addition, a longitudinal form of WPCt inherently represents a cumulative measure of the patent’s intrinsic value up to, and including, time t whereas annual measures of R&D, TW , and

∆W are noncumulative, annual measures. In essence, we are temporally comparing apples to oranges using these correlations. In 1981, this comparison happened to work for this data; however,

34 we emphasize that insigniﬁcant results using WPC (see Table 1) may be the consequence of taking a temporal snapshot of a measure that is inherently cumulative.

Comparing Apples to Apples

Since weighted patent counts WPCt and Patent Rank scores PRt vary over time, we proceed to consider how to empirically demonstrate discriminant validity by comparing apples to apples. First, from (Trajtenberg 1990a, Table 2), we note that these values represent yearly measures of economic benefit and numbers describing the diffusion environment (number of firms, number of new brands, and number of new adopters [hospitals]). Second, we emphasize the cumulative nature of both the patent network and its corresponding patents. The primal patent filed in 1972 should have carry-over effect into subsequent years, most especially if it is a radical innovation. Without this original innovation, how would society benefit? This implies that the original patent

contributes to the total social value (SV ) in all subsequent years. Therefore, we update Table 2 from Trajtenberg (1990a) and report cumulative14 measures in Table 6.

[Figure 5 about here.]

We verify the cumulative argument by considering the total number of adopters in relation-

ship to WPCt. In Figure 5, we graph the cumulative adoption () of the CT-scanner reported by 15 Trajtenberg (1990a). We also graph the number of hospitals () in the U.S. in 2000 (Google

Answers 2006) and 2009 (AHA 2009). We compare this cumulative adoption pattern to the WPCt for all patents under consideration (we compute every patent’s WPCt and sum the results). Indeed,

WPCt represents a cumulative measure.

14 (Trajtenberg 1990a, Table 2, footnote a). We also need to consider an appropriate lag to account for both WPCt and PRt since they represent ex post indicators of the value of innovation at time t. We choose three years, based on average time for a ﬁled patent application to be successfully prosecuted (Hall, Jaffe, and Trajtenberg 2001) which Trajtenberg (1990a) argues is the nature of the lag. Lag times are upward skewed, meaning a few patents took an extraordinary amount of time to be granted. We report a M=2.035 and a SD=0.72; about 93% of the patents are accounted for in a 3-year lag. From Trajtenberg (1990a), this reverse lag means that WPC1976 and PR1976 is an ex post indicator of the economic value of all innovation through 1973; WPC1977 and PR1977 is an ex post indicator of the economic value of all innovation through 1974; and so on. 15This assumes every modern hospital has a CT-scanner.

35 To appropriately compare Patent Rank to WPC, we sum relevant patents’ PRt and WPCt, thereby answering the questions: (1) how much cumulative information do we have from the adop-

tion of all patents under consideration in year t? (2) how does this cumulative information corre-

late with cumulative measures of innovation (and total market-related adoption)? For WPCt+3, we report correlations 0.7863 and 0.8104 with economic measures ∆W and TW , respectively. For

PRt+3, we report correlations 0.8537 and 0.8747 with ∆W and TW , respectively. The explanatory power of both longitudinal WPC scores and Patent Rank scores are signiﬁcant. Since Patent Rank scores include more information, they demonstrate higher explanatory power than a comparable longitudinal WPC score. As summarized in Table 7, we have developed a more precise metric.

[Table 7 about here.]

2.6 Discussion

To summarize, we have presented a logical argument why Patent Rank scores should be a more precise measure of a patent’s intrinsic value that the traditional Trajtenberg (1990a) metric. We then empirically validated this claim. Finally, we conclude by discussing the limitations and potential avenues of future research.

2.6.1 Limitations

The first limitation of this current application for Patent Rank is its geographic scope—it only considers U.S. Patents. Though the United States is the largest economy, and global innovations are generally secured within the U.S. patent system, we note that some global innovations may not exist within the U.S. network. The second limitation of this measure is its temporal scope—it defines the network based on backward citations of utility patents granted after 1976. The third limitation of this measure is its classification scope—it ranks innovations based on patents and patent-citation structures. Certainly, other information exists that may further enhance this network treatment. Within these limitation, the measure has merit: (1) traditional measures already use

36 patent data; (2) patent data is publicly available for both privately-held and publicly-traded ﬁrms; (3) patents represent a legal protection to extract monopolistic rents for a speciﬁed period of time in exchange for disclosure; (4) a governing body regulates patent approval based on unique claims; and (5) patent-citation structure is monitored through the legal review of the application.

2.6.2 Future Research

“Indeed, as Mairesse noted in a recent roundtable of some of the leading thinkers on the topic, ‘We have exhausted all we can get from our old data sets on R&D, patents, citation counts.’” (Tellis, Prabhu, and Chandy 2009)

We present Patent Rank as a continuous, objective measure of patent innovation. We believe that this new measure, and its related data, will contribute to the marketing community, and we hope it will accelerate important innovation topics related to marketing (Hauser, Tellis, and Griffin 2006). First, we foresee extensions to the “total effects centrality” model presented. We emphasize that this cumulative network will necessarily assign higher Patent Rank scores to originating innovations (Golder, Shacham, and Mitra 2009). Future research can ascertain different model specifications and assumptions to address different research questions in innovation. Model extensions based on the formation of the network and the formulation of its structure need to be further explored. In our domain of research, we many times want to consider recency conditions based on some temporally-constrained measure; that is, a noncumulative model based on“local” effects centrality. Adjacent temporal considerations in respect to lag and truncation also need to be further explored. In addition, introducing additional information into matrix M could further improve Patent Rank. Utilizing Patent Rank and its derivatives, we foresee various avenues of future research in innovation: (1) at the patent level, adoption characteristics of the patent innovation to predict the patent’s expected lifetime value; (2) at the product level, life cycles of high-tech products with multiple, patent-innovation ingredients can be evaluated; (3) at the firm level, Patent Rank as a

37 measure of ﬁrm performance; (4) at the manager level, Patent Rank as informative content related to insider-trading; and (5) at a macro level, Patent Rank as an explanation of economic processes.

2.7 Conclusion

38 CHAPTER 3

ENTREPRENEURIAL INNOVATION: IDENTIFYING SCHUMPETERIAN SHOCKS AND KIRZNERIAN COMPETITION USING PATENT RANK Utilizing the patent network, we identify the Austrian phenomena of entrepreneurial innovation—a perspective that respects the differences between the Kirznerian entrepreneur and the Schumpeterian entrepreneur. The Schumpeterian entrepreneur creates the disequilibrium in the market. The Kirznerian entrepreneur is alert to the disequilibrium and exploits it, which ultimately returns the market back to a new dynamic equilibrium. Using the entire network of utility patents from 1976–2009, we empirically test entrepreneurial innovation using a a marginal form of a network- valuation metric, Patent Rank. We outline the foundations of Patent Rank, define its generalized model specification and computation. Using over 60 million marginal (ms) Patent Rank scores, we demonstrate (1) the marginal nature of radical innovations as “creative destructions” with discernible Schumpeterian shocks (intensity and duration), (2) the distributional properties of this marginal network formation is unique to network studies, and (3) a double-logarithmic transformation of these Patent Rank scores verifies the symbiotic relationship between Schumpeterian and Kirznerian entrepreneurial activities. We conclude by revisiting the CT-scanner case study (Tra- jtenberg 1990a) using the integrated economic theory of entrepreneurial innovation.

“The violin string is plucked by innovation, without innovation it dies down to stationariness, but then along comes a new innovation to pluck it back into dynamic motion again.” (Kirzner 1989)

3.1 Introduction

Can we identify the Austrian phenomena associated with entrepreneurial innovation? Can we objectively distinguish an innovation as a Schumpeterian shock or Kirznerian competition? To address these research questions, we integrate the two competing Austrian views of entrepreneurship into a complementary theory of entrepreneurial innovation and empirically test the theory using Patent Rank.

39 In efforts to develop theory in entrepreneurship, researchers have interpreted the two competing Austrian views of entrepreneurship to strengthen their suppositions (Shane and Venkataraman 2000; McMullen and Shepherd 2006). We posit that such interpretations have convoluted our understanding of entrepreneurial innovation. The purpose of this chapter is to put the record straight. To do so, we review the Austrian view generally and the Schumpeterian/Kirznerian views speciﬁ- cally. We next identify the patent network as an objective, observable market system. Within this system, we develop empirical models, Patent Rank, based on network theory to observe the market process. Patent Rank identiﬁes a patent’s intrinsic value in the system at any point in time. Speci- fying a marginal Patent Rank model (based on an Austrian world-view), we observe the residuals of dynamic entrepreneurial activity within the market process to verify entrepreneurial innovation.

[Figure 6 about here.]

3.2 Austrian Economics and Theory of Entrepreneurial Innovation

In 1973, Israel Kirzner published Competition and Entrepreneurship. He challenged Joseph Schum- peter’s perspective of the entrepreneur as the ‘captain of industry’ who is the hero in the development of economies (Kirzner 1973; Schumpeter 1911). Kirzner “drew attention to a different way of seeing the impact of the individual entrepreneurial decision upon the market phenomena of the real world. In this perspective, the entrepreneur is not seen as disturbing any existing or prospective states of equilibrium. Rather he is seen as driving the process of equilibrium” (Kirzner 2009, p. 3). In the literature, this has been interpreted to mean the two views are mutually exclusive and cannot be fused (Holcombe 1998, 2003; Glancey and McQuaid 2000; Fu-Lai Yu 2001). With more real-world explanatory power, the Kirznerian entrepreneur has become the basis for explaining entrepreneurship—exploiters of discovered opportunities (Shane and Venkataraman 2000). This line of thinking disturbs Kirzner (2009, p. 7) as it ignores the source of the entrepreneurial opportunities: “there must be scope for both a creative (‘Schumpeterian’) entrepreneur (one who generates pure proﬁt) and a ‘passive’, alert (‘Kirznerian’) entrepreneur (one who snuffs out given

40 proﬁt opportunities by promptly exploiting them).” We present such an integrated approach within the Austrian view.

3.2.1 Austrian economics

Austrian economics is the basis for entrepreneurial innovation (Kirzner 1979, p. 3): “A characteristic feature of the Austrian approach to economic theory is its emphasis on the market as a process, rather than a configuration of prices, qualities, and quantities.” The Austrian entrepreneur is proactive and purposeful whereas the neoclassical entrepreneur is passive (Glancey and McQuaid 2000). Jacobson (1992) describes the many nuances of Austrian thinking as it compares to traditional structural views of industrial organizations. Although important to understand how Austrian economics differs from neoclassical1 perspectives, we explicitly focus on delineating differences within the Austrian tradition based on two contrasting entrepreneurial philosophies regarding market processes (Schumpeterian and Kirznerian). In Table 6 we summarize this comparison. We begin a review of these differences by delineating the history of the Austrian school with respect to Kirzner and Schumpeter. In 1906, both Ludwig von Mises and Joseph Schumpeter were awarded Ph.D.s from the University of Vienna. Both were trained by Friedrich von Wieser, who in turn was a disciple of the Austrian School’s founder Carl Menger. Friedrich von Wieser delineated, from an Austrian perspective, the importance of opportunity costs, marginalism (Gren- znutzen), and the entrepreneur as “the heroic intervention of individual men who appear as leaders toward new economic shores.” Schumpeter’s work to describe the entrepreneur as the captain of industry was strongly influenced by this perspective. Mises, on the other hand, saw a more practical entrepreneur, which later would influence the views of his student, Israel Kirzner.

1Austrian economists see the individual entrepreneur as an actor who makes decisions based on opportunity costs, marginal utility, imperfect rationalism, and unknowable subjectivism. Necessarily, the Austrian market is constantly changing based on asymmetric information, which we deﬁne as a dynamic equilibrium.

41 Schumpeterian Entrepreneur: ‘Creative Destructions’

From Schumpeter’s perspective, the market has two mutually-exclusive, yet symbiotic actors: entrepreneurs and capitalists. Schumpeter delineates the entrepreneur from his foundation of business-cycle equilibrium. This equilibrium suggests a circular flow where production, labor, cost of capital, etc. lumped together as capital define a dynamic state of equilibrium. His entrepreneur, the Unternehmergeist, is a fiery spirit that destroys this equilibrium with a creation. Schumpeter defines these creative destructions as innovation; specifically, he sees innovation as combinations of five different entrepreneurial activities (Schumpeter 1911, p. 66):

[products] “The introduction of a new good – that is one with which consumers are not yet familiar – or of a new quality of a good

[production] The introduction of a new method of production, that is one not yet tested by experience in the branch of manufacture concerned, which need by no means be founded upon a discovery scientiﬁcally new, and can also exist in a new way of handling a commodity commercially

[markets] The opening of a new market, that is a market into which the particular branch of manufacture of the country in question has not previously entered, whether or not this market existed before

[resources] The conquest of a new source of supply of raw materials or half-manufactured goods, again irrespective of whether this source already exists or whether it has ﬁrst to be created

[monopoly] The carrying out of the new organization of any industry, like the creation of a monopoly position (for example through trustiﬁcation) or the breaking up of a monopoly position.”

The Schumpeterian entrepreneur represents a leader, innovator, or pioneer. The innovations disrupt the market place yet there is no guarantee the originator of the disruption is capable of extracting entrepreneurial profits. Regardless, Schumpeter argues that this entrepreneur creates value and re- configures the marketplace and these disruptions have long-term benefit to economic development.

42 Kirznerian Entrepreneur: Alert Arbitrageur

From Kirzner’s perspective, the market also only has two actors, entrepreneurs and capitalists, but they are not mutually exclusive. Kirzner, as a student of Mises, sees the entrepreneur and the market process from a competitive perspective. Borrowing from Mises’ homo agens, Kirzner describes his practical entrepreneur as having the propensity for alertness to disequilibrium in the marketplace and the discovery of unknown resources. As such, every actor in the market is always an entrepreneur: producers, consumers, competitors, etc. Consequently, the Kirznerian entrepreneur represents a leader of a different form. This leader has superior command over information, and simply knows more; utilizing this alertness, this entrepreneur innovates by exploiting knowledge specifically to extract entrepreneurial profit. As a result, this actor is an imitator/arbitrageur; the competitive innovations are equilibriating influences on the market (Kirzner 1973).

[Figure 7 about here.]

3.2.2 Entrepreneurial Innovation

Both types of entrepreneurs need to be alert, one needs to be creative. So, to merge these distinct processes, we present a continuum in Figure 7 to define entrepreneurial innovation. Radical (incremental) innovations are more (less) disruptive and need (don’t need) the Schumpeterian entrepreneur’s creative destructions. By definition, the Kirznerian activities should occur more often than the radical Schumpeterian shocks. Shane (2001) illustrates this point in his discussion of a process innovation (3D-printing process), Patent # 5,204,055; he argues that many different entrepreneurs were alert to their respective markets (e.g., prior knowledge) and this patent innovation was a discovered opportunity. Specifically, he describes how eight different entrepreneurial startups licensed this innovation in context of CAD engineering and architectural design, orthopedic modeling for bones, surgical modeling, etc. The patent innovation was radical and disrupted the market place—the originating patent innovation represents an activity from the Schumpeterian

43 entrepreneur—and created new value. Various Kirznerian entrepreneurs were alert to this disequilibrium in the market place and based on their knowledge and experience, exploited it; however, the patent innovation was a necessary creation for discovery and exploitation to follow. Kirzner (1979, p. 115) summarizes: “Development is initiated by innovators who are generating new opportunities. The Schumpeterian innovators stir the economy from its sluggish stationariness. The imitators compete away the innovational proﬁts, restoring the stationary lethargy of a new circular ﬂow, until a new spurt of innovational activity emerges to spark development once again.” This Texas-two step is the essence of entrepreneurial innovation. Fused together into an integrated framework creates opportunity to expand entrepreneurship theory (Shane and Venkataraman 2000; Gartner 2001; Ucbasaran, Westhead, and Wright 2001).

3.3 Patent Innovations as an Observable Market Process

We choose the patent system to observe innovations as an Austrian-based market process. Patents have been used extensively to study and understand market conditions in relation to innovation (Arrow 1962; Schmookler 1966; Trajtenberg 1990a; Hall, Jaffe, and Trajtenberg 2005).

3.3.1 Patent Innovations and the Entrepreneurial Phenomenon

The Schumpeterian entrepreneur will seek to protect a “creative destruction” or radical innovation using patent protection. During the prosecution of a patent, prior art must be acknowledged and unique claims must be demonstrated. Once approved, a patent innovation represents exclusionary “intellectual property rights” granted by the government to the ﬁrm for a speciﬁc period of time in exchange for public disclosure. The originating innovation is many times followed by scope-related patent innovations. The Schumpeterian entrepreneur is alert to competition, and will attempt to secure the boundaries of the intellectual property to protect monopolistic possibilities, thereby also engaging in Kirznerian entrepreneurial activities. Alert competitors, the Kirznerian entrepreneurs, will respond to the Schumpeterian shock by securing their own adjacent intellectual property with an intent to exploit. These imitators will attempt to minimize the monopolistic power

44 of the Schumpeterian entrepreneur.

3.3.2 Proﬁtability from Monopoly Positions

A monopoly position defines the ‘degree of control’ a firm has over a market process. Due to the inherent self-destruction of equilibrating/disequilibrating forces, patent protection motivates Schumpeterian entrepreneurial activity. If the actor of the Schumpeterian shock is alert to the potential of the market disruption, this actor will attempt to allow the plucked innovation to res- onate as long as possible by also engaging in protective Kirznerian entrepreneurial activities. In juxtaposition, competitors have a different motivation. These actors will attempt to minimize the resonance, redirect it, or even attempt to dampen it. That is, these Kirznerian entrepreneurs want to limit the originator’s possible profits while maximizing profits based on exploiting their own alertness. Therefore, within the Austrian tradition of a dynamic market, a pure monopoly is unrealistic. The overall profitability of such entrepreneurial activities will depend on the ability to control the supply related to the monopolistic resource. In addition, profitability of the Schumpeterian entrepreneur will rely heavily on the Kirznerian entrepreneur’s inability to imitate (Glancey and McQuaid 2000). This further solidifies the relationship of these two distinct entrepreneurial activities: the Schumpeterian entrepreneur has possibilities for entrepreneurial profits based on an ex ante, long-term vision of the potential of the creative destruction whereas the Kirznerian entrepreneur is responding to the disequilibrium ex post with a competitive, short-term outlook on how to extract (or minimize competitor’s) profits (Kirzner 1973, 1979, 1989, 1992).

3.3.3 Overview of Patent Rank

In this section, we review a framework to test our integrated model of entrepreneurial innovation. We need a measure that can objectively observe changes or shocks within the patent system based on Austrian principles. We present Patent Rank as such a measure. With Patent Rank, we deﬁne a patent’s intrinsic value as a function of its prior art (backward citations diminish the patent’s radicalness), its subsequent adoption (forward citations demonstrate the patent’s radicalness), and

45 network dynamics (forward citations and the patent network are changing over time).

[Figure 8 about here.]

Patent Rank uses network centrality to intrinsically ascertain a patent’s value within a network at any point in time. This valuation metric is comprehensive, simultaneous, and endogenous. We address the formation and structure of the patent network based on a fundamental principle of Austrian economics: marginal utility. Specifically, to capture Schumpeterian shocks, we need to consider a marginal form of Patent Rank scores. ‘On the margin’ represents recent fluctuations in the market; that is, how much influence did a patent innovation have on the market process recently? Recency is subjective to time frames, and in context of patent innovations, we choose an event window of 5 years (Hall, Jaffe, and Trajtenberg 2001, 2005). Therefore, to valuate a patent’s marginal intrinsic value in 1980, we define the patent network using the most recent 5 years (1976- 1980), and compute Patent Rank scores. We define these Patent Rank scores to be marginal scores, representing deviations from the cyclical flow of business (Schumpeter 1911).

3.3.4 Hypotheses on the Margin

[Figure 9 about here.]

In Austrian economics, perfect equilibrium does not exist; however, Schumpeter (1911) posits that business cycles between entrepreneurs and capitalists represents a stable, dynamic equilibrium (e.g., constantly changing). We posit that within the patent system, vibrations from equilibrium, Schumpeterian shocks, can be observed and measured using marginal Patent Rank:

Hypothesis 1. Radical innovations have measurable Schumpeterian shocks.

As outlined2 in Figure 9, a Schumpeterian shock will have key characteristics: intensity, duration, and overall volume where intensity is the maximum Patent Rank score the innovation receives over 2The x-axis represents the marginal Patent Rank score for a patent at a particular point in time (the y-axis is years) and the shaded area represents total deviations from equilibrium which we deﬁne as a patent’s Schumpeterian shock.

46 time; duration is the time in years the patent innovation has Patent Rank scores greater than the trivial score of 1; volume is the shaded area representing the total impact of the patent innovation. In a dynamic market process every Schumpeterian shock will be unique in context of the current market conditions (industry, competition, consumer adoption, societal beneﬁt, and so on). The residuals of contrasting entrepreneurial activity within the market process should be ob- servably different. Schumpeterian (Kirznerian) innovations should occur less (more) frequently, have higher (lower) Patent Rank scores to represent radicalness (incrementalness), and should represent a unique conﬁguration (common imitation structure) within the marginal network:

Hypothesis 2. Patent innovations consist of both Kirznerian and Schumpeterian entrepreneurial activities.

As proposed in Figure 7, both exploitive and creative activities should be discernible from the residuals of the dynamic market process.

3.4 Generalized Model Speciﬁcation of Patent Rank

As discussed previously, we need to compute Patent Rank scores on the margin. As a variation from cumulative scores, we formally deﬁne a generalized model for these Patent Rank scores and detail how they are computed. Evaluating every patent based on its relationships within the patent network is the foundation of Patent Rank scores. As there are millions of patents, it is helpful to understand the principles using a toy example, see Figure 8.

47 The associations of the network can be deﬁned3 using matrix notation, and using principles of eigenvector centrality, unique Patent Rank scores (eigenvector π) can be computed:

π = P T π where P = diag(d)−1M and d = Me. (3.1)

By sorting the matrix based on common patent structures, we can solve a system of equations using linear algebra to efficiently define Patent Rank scores. The idea is to partition the adjacency matrix into types, augment it to include the U.S. Patent office, row-normalize it, and then define and solve a partitioned linear system of equations (Del Corso, Gulli, and Romani 2005; Bini, Del Corso, and Romani 2008).

3.4.1 Toy Example computation of Patent Rank

We brieﬂy delineate the computation of Patent Rank using the toy example. We represent the graph (Figure 8) as a table which we then update to deﬁne the adjacency matrix M in Equation (3.2). In either the table or matrix form, a row indicates backward citations for its respective patent and a column indicates forward citations for its respective patent. In this basic example, the citation indicator is binary: zero if no citation exists and one if a citation exists.

Parent Patent P P P P P P P P P P 0 0 0 0 0 0 0 0 0 0 → 1 2 3 4 5 6 7 8 9 10 P1 0000000000 0 0 0 0 0 0 0 0 0 0   P2 0000000000 0 0 0 0 0 0 0 0 0 0 P3 0 000000000  0 1 1 0 0 0 0 0 0 0    P4 0 1 10000000  1 1 0 0 0 0 0 0 0 0  becomes M =   (3.2) P 1 1 0 0000000  0 0 0 0 0 0 0 0 0 0  5   0 0 0 0 000000 Child Patent P  1 1 0 0 1 1 0 0 0 0  6   P 1 1 0 0 1 10000  1 0 0 1 0 1 0 0 0 0  7   P 1 0 0 1 0 1 0000  0 0 1 0 0 0 0 0 0 0  8   P 0 0 1 0 0 0 0 000  0 0 0 0 0 1 0 1 0 0  9   P10 0 0 0 0 0 1 0 1 00  

3We denote the vector e as a unitary vector of length n with all elements equal to one. A matrix that is row- stochastic and row-normalized is scaled so the sum of every row is equal to one (that is, j pi,j = 1). The scaling factors di = hi,j deﬁne the vector d = (di), which satisﬁes the equation d = Me. We denote the diagonal j P matrix D as diag(d) with diagonal entries composed of vector d = (d ) for i = 1, 2, . . . , n. P T is the transpose n×n P i of matrix P .

48 We next classify the patents as follows:

[Type C1] Patents with forward citations but without backward citations (dangling nodes); let c1 = size(C1).

[Type C2] Patents with both forward and backward citations (core patents); let c2 = size(C2).

[Type C3] Everything else (dud patents with no forward citations); let c3 = size(C3).

In our toy example, this classiﬁcation of patents produces these sets C = P ,P ,P ,P , C = 1 { 1 2 3 6} 2 P ,P ,P , and C = P ,P ,P . Without loss of generality, we can reorganize the elements { 4 5 8} 3 { 7 9 10}

of the network by type. Speciﬁcally, we order by time and type (σtime, σtype)

P ,P ,P ,P ,P ,P ,P ,P ,P ,P = sort(C ) sort(C ) sort(C ) (3.3) { 1 2 3 6 4 5 8 7 9 10} 1 ∪ 2 ∪ 3

and update the adjacency matrix to reﬂect this reordering

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  0 0 0 0 0 0 0 0 0 0   0 0 0 0 0 0 0 0 0 0    ˆ  0 1 1 0 0 0 0 0 0 0  M = M(σtime, σtype) =   . (3.4)  1 1 0 0 0 0 0 0 0 0     1 0 0 1 1 0 0 0 0 0     1 1 0 1 0 1 0 0 0 0     0 0 1 0 0 0 0 0 0 0     0 0 0 1 0 0 1 0 0 0     

In Equation (3.2), we now introduce the U.S. Patent Ofﬁce (P0) by augmenting this partitioned adjacency matrix. The ﬁrst row and column are both augmented4 with binary values to indicate a

link to and from the U.S. Patent Office. Referring to Equation (3.5), the first association to P0 (the U.S. Patent Office is cited by all patents) represents the first column of matrix M and the second association to P0 (the U.S. Patent Office cites all patents) represents the first row of matrix M.

4 Recall, that by deﬁnition, a node does not cite itself, so the element (P0,P0) is equal to zero. In fact, based on this deﬁnition, every diagonal entry of this matrix in Equation (3.2) is equal to zero.

49 The row-normalization to deﬁne matrix P is then updated: (1) the sum of each row is calculated

(di) and (2) the value of each element in the row is divided by its scaling factor di, which now is

5 such that di 1. Consider patent P in our toy example which we highlight in Equation (3.5). ≥ 7

The row P7 has 4 backward citations plus the P0 backward citation, so its scaling factor is now d7 = 5. The corresponding row for matrix P is updated by dividing the row in matrix M by the scaling factor d7:

01111111111 0 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 10000000000 10000000000  10000000000   10000000000   10000000000   10000000000       10000000000   10000000000    −1   M =  10110000000  , P = diag(d) M =  1/3 0 1/3 1/3 0 0 0 0 0 0 0  , (3.5)      11100000000   1/3 1/3 1/3 0 0 0 0 0 0 0 0       11001100000   1/4 1/4 0 0 1/4 1/4 0 0 0 0 0       11100110000   1/5 1/5 1/5 00 1/5 1/5 0000       10010000000   1/2 0 0 1/2 0 0 0 0 0 0 0       10000010100   1/3 0 0 0 1/3 0 0 1/3 0 0 0         

where d = (10, 1, 1, 1, 1, 3, 3, 4, 5, 2, 3) represents each row sum of the augmented matrix M. This speciﬁc normalization of one row is addressed within the entire matrix, as deﬁned by Equa- tion (3.1).

3.4.2 Linear Solution

In Appendix D we explicitly solve the toy example from Equation (3.5) using both the traditional power method and a most efﬁcient linear-algebra method, demonstrating their equivalence. Below, we generalize the form of the linear solution, beginning with matrices M and P in partitioned form:

T T T 1 T 1 T 1 T 0 e1 e2 e3 0 /n e1 /n e2 /n e3     e1 0 0 0 v1 0 0 0 M =   , P =   (3.6)  T T   ¯T ¯T   e2 Q R 0   v2 Q R 0       T T   ¯T ¯T   e3 S T 0   v3 S T 0          5After reordering, this patent is in the ninth position (the U.S. Patent Office is in the first position). We highlight this row in Equation (3.5) to clarify this process; the matrix notation to row-normalize we defined in Equation (3.1).

50 where e1, e2, e3 are unitary vectors of size c1, c2, c3 respectively; O is an appropriately dimen-

sioned null matrix; Qc1×c2 ,Rc2×c2 ,Sc1×c3 ,Tc2×c3 are submatrices; vi is a normalization of ei; and Q,¯ R,¯ S¯ and T¯ represent the normalization of each respective submatrix (Q, R, S and T ); therefore, P is row-stochastic. We solve the following for π

P T π = π, (3.7) which, in our partitioned form, is equivalent to

T T T 0 v1 v2 v3 π0 π0

 1      /n e1 0 Q¯ S¯ π1 π1     =   . (3.8)  1 ¯ ¯       /n e2 0 R T   π2   π2         1       /n e3 0 0 0   π3   π3              Writing the eigenvalue relation as a linear system we get

T T T v1 π1 + v2 π2 + v3 π3 = π0     π  0 e + Q¯ π + S¯ π = π  1 2 3 1  n   (3.9)   π0 e + R¯ π + T¯ π = π n 2 2 3 2      π0  e3 = π3  n   

51 Among the inﬁnite vectors which are solutions to the linear system in Equation (3.9), we choose

the vector which assigns a score equal to n to the U.S. Patent Ofﬁce P0; that is, π0 = n. We then obtain by substitution

π3 = e3  ¯ −1 ¯  π2 = (I R) (e2 + T e3) (3.10)  −  π1 = e1 + Q¯ π2 + S¯ e3   where the subscript deﬁnes the Patent Rank scores for the speciﬁc type of patents. For example,

π3 = e3 represent the Patent Rank scores for dud patents (of Type C3); they are assigned trivial

scores of 1s. From the system of solutions identiﬁed in Equation (3.10), we note π1 can be solved

via substitution once π2 is calculated. In essence, the partitioning technique has reduced the (n + 1) (n + 1) problem to a c c system. We simply need to solve × 2 × 2

(I R¯)π = (e + T¯ e ). (3.11) − 2 2 3

This technique normalizes the vector of Patent Rank scores π such that the minimum score a

patent receives is one (π3 = e3). This conveniently anchors Patent Rank to the two traditional patent-valuation measures: simple patent count and weighted patent count (Trajtenberg 1990a). By deﬁnition, a simple patent count assigns each patent a score of one; a weighted patent count assigns each patent a score of 1 + F where F is the number of forward citations (minimum score is also one). This minimal value means the patent exists in the network, yet has no intrinsic value at the observed point in time.

3.4.3 Generalized Model

In Figure 10, we compare our Patent Rank algorithm to Google’s PageRankTMdemonstrating that although the model specifications are different, the final scores are very similar (in our toy example the correlations among final scores is 0.9891). We posit that our algorithm is better suited to represent the patent system, and from this base model, we can now specify a generalized model.

52 [Figure 10 about here.]

From construction of our model, there are four key attributes to deﬁne and compute Patent

Rank scores at a particular point in time t. The first, f as the formation of the network, describes how the network is defined. In the original manuscript (under review), we define a cumulative

model of all patents (f = c). For this manuscript, we define a marginal model of patents and associations in a 5-year moving window (f = m). Other models could be specified to determine which patents to include in the network analysis. The remaining three attributes are related to definition of the adjacency matrix and its augmen- tation. The definition of association of matrix M can also be generalized (m). Recall that the current adjacency matrix M contains binary data (1s and 0s) to indicate the presence or absence of a link between two nodes. We define this dichotomous schema as a Structure or Structure- Only model. We could define other schema; for example, we could include additional information about the value of each association. That is, we could use a metric to describe the strength of association, not merely its presence. We could include a measure of similarity to these patent associations that was determined by the patent owner. For example, technology classifications, field of search, or international classifications could be compared to define a soft-match. This soft-match, or ClassMatch could be considered in a Patent Rank model. Stated mathematically, (mij) would represent an association between patent Pi and patent Pj. Analogous to ClassMatch , we can also define associations between patents and the U.S. Patent

Ofﬁce (P0). This second generalization updates the augmented adjacency matrix M by replacing this augmented row and column of 1s with unique values. We replace these binary 1s with appropriate relational weighting factors. Most generally, the ﬁrst column can be represented as a vector

α where each patent Pi could be uniquely weighted by a factor αi to represent its first association with the Patent Office. Similarly, the first row can be represented as a vector β where each patent Pi

could be uniquely weighted by a factor βi to represent its second association with the Patent Ofﬁce.

53 In generalized form, this model speciﬁcation allows for asymmetric associations with the Patent

Office P0. Some possible examples of association with the Patent Office include: (1) weighting each patent’s association based on the time it took the patent to be granted (Chandy, Hopstaken, Narasimhan, and Prabhu 2006); (2) weighting each patent’s association based on industry controls (e.g., pharmaceutical patents are more likely, so all of these patents are dampened by a factor); (3) weighting each patent’s association based on years remaining (e.g., utility patent protection generally endures for 20 years from the time the application was filed); (4) weighting each patent’s association based on some external factor such as the payment of renewal fees (Moore 2005) or a patent’s litigation value (Allison, Lemley, Moore, and Trunkey 2004); and so on.

3.4.4 Computation of Generalized Model

Utilizing this generalized model speciﬁcation, the base model from Equation (3.6) can be updated in a general form π(t)fabm:

T T T T T T 0 β1 β2 β3 0 u1 u2 , u3     α 0 0 0 v 0 0 0 T 1 1 π = P π where M =   and P =   . (3.12)  T T   ¯T ¯T   α2 Q R 0   v2 Q R 0       T T   ¯T ¯T   α3 S T 0   v3 S T 0          where t represents when the network was formed, f represents how the network is formed (e.g., cumulative as π(7609)c or marginal as π(8690)m), a represents the prior associations with P0 (e.g., structural as a = 1 or other as a = α(renewal fees)), b represents the posterior associations with P0

(e.g., structural as b = 1 or other as b = β(litigation)), and m represents the associations among nodes (e.g., structural as s, ClassMatch as c). The partitioning of the matrices is based on the classiﬁcation of patents. The only constraint on these associations, is that every element deﬁned is strictly positive

(αi > 0 and βi > 0 and (mij) > 0). This ensures that the Patent Rank scores π can be computed.

54 Introducing such additional weighting factors changes the nature of the network, and therefore,

changes the ﬁnal Patent Rank scores. Mathematically, the ﬁrst column of the adjacency matrix M,

T partitioned accordingly with the three blocks, becomes α = (α1, α2, α3) , while the ﬁrst row is

T β = (β1, β2, β3) . Without loss of generality, the linear system can be solved to identify Patent Rank scores; we update Equation (3.6) as follows:

T T T T T T 0 β1 β2 β3 0 u1 u2 , u3     α1 0 0 0 v1 0 0 0 M =   , P =   (3.13)  T T   ¯T ¯T   α2 Q R 0   v2 Q R 0       T T   ¯T ¯T   α3 S T 0   v3 S T 0         

where the row-normalization of vi and ui and the partitioned matrices (e.g., Q¯) are altered to

account for these new asymmetric values of αi and βi. Note that if all the βi’s are the same, the

1 normalization of the ﬁrst row, will produce vectors ui = /n ei equivalent to the case where all the

βi’s are equal to one. Repeating the same calculation performed in Equations from (3.8) to (3.10), and setting π0 = n, we get the following system which replaces the system deﬁned in Equation (3.10).

π3 = n u3  ¯ −1 ¯  π2 = (I R) (n u2 + T π3) (3.14)  −  π1 = n u1 + Q¯ π2 + S¯ π3   

which still requires only the solution of a c c linear system. Note now that, since in general 2 × 2 u = 1/n e , the minimum Patent Rank score can be less than 1, yet still positive. 3 6 3

55 3.5 Application of (ms) Patent Rank model

To test our hypotheses of entrepreneurial innovation, we consider a marginal, structure (ms) Patent

Rank model: π(t)ms. This means that by default we choose structural associations with the U.S.

Patent Ofﬁce P0 (a = 1 and b = 1) and we consider marginal windows t from 1976–1980, 1977– 1981, 1978–1982, . . . , 2005–2009. Therefore, our speciﬁed model (ms) is a “total-effects” model temporally constrained to provide marginal results, thereby capturing local maxima. That is, the

(ms) Patent Rank scores for t = 1980 represents the marginal utility of each patent present in the network based on all citations observable in the years 1976–1980, t = 1981 represents the marginal utility of each patent present in the network based on all citations observable in the years 1977– 1981, and so on. Any (ms) Patent Rank value above the trivial score of 1 represents a snapshot of a patent’s Schumpeterian shock for time t.

3.5.1 Data preparation

Beginning in December 2008, all of the utility patents were programmatically6 harvested from the USPTO website. Once all the patents were collected, we parsed all utility patents granted in a year to extract their backward citations, as one patent’s backward citation represents another patent’s forward citation. From this information, we constructed a directed graph in matrix form (M). We partition this matrix based on an ordering schema which classiﬁes each patent into one of three types: dangling nodes, core patents, and duds. We augment the matrix to include the the

U.S. Patent Office (P0), and finally, we row-normalize as defined in Equation (3.1). After this preparation, we solve the partitioned linear system to identify the unique Patent Rank scores (Del Corso, Gulli, and Romani 2005; Bini, Del Corso, and Romani 2008). We temporally constrain the patent network based on the year a patent was granted. In Fig- ure 11(a) we summarize some general trends regarding the size of the network formation at a specific marginal time. In Figure 11(b) we visualize the structure of the adjacency matrix M for

6A robot script was written that saved each patent webpage to a local machine. A parsing algorithm was created to extract the appropriate data from each patent. This process took approximately 18 months.

56 π(7680)ms which contains 312,486 patents as inputs which deﬁne 1,619,936 associations (links) based on 1,083,196 unique patents (nodes). Compare this to the the adjacency matrix M for

π(0509)ms which contains 803,699 patents as inputs which deﬁne 13,201,985 associations (links) based on 3,170,177 unique patents. If a patent was granted in the particular marginal window (e.g., 1976–1980), it will be included in the analysis merely because it was granted. From this anchor of granted patents in a marginal window, we parse each granted patents’ backward citations. These granted patents represent forward citations for each respective backward citation (one patent’s backward citation is the other patent’s forward citation). This allows us to proceed and compute scores. For example, a patent granted in 1980 will necessarily appear in the patent network for 1976–1980, 1977–1981, 1978–1982, 1979–1983, 1980–1984 merely because it was granted in 1980. If it has no inﬂuence on the patent network based on this marginal formation, during this mandatory inclusion period, this patent would receive the minimal, trivial score of 1. If, however, it appears in the network formation after the moving window has left 1980, it is because the patent has some measurable deviation7 from equilibrium.

[Figure 11 about here.]

3.5.2 Summary of Results

As shown in Table 8, marginal Patent Rank scores changes dramatically over time. In this table we highlight three patents (4,237,224, 4,683,195, and 4,720,480), to demonstrate the variability of ranks over time. The ﬁrst patent, 4,237,224, is a DNA-chimera patent granted in December 1980; it has one backward citation and over 250 forward citations. In the (ms) model, it peaked to the top rank for years (1986–1987). The second patent, 4,683,195, is a DNA-ampliﬁcation patent granted in July 1987; it has one backward citation and has over 1950 forward citations. In the (ms) models, this patent peaked to number one for many years (1996–2002). The third patent, 4,720,480, is

7This implies that during the moving window of interest, the patent received a least one forward citation.

57 heat-transference-sheet patent was granted in January 1988; it has one backward citation and has over 100 forward citations. In the (ms) models, this patent peaked to number four in 1994.

3.5.3 Hypothesis 1: Schumpeterian shocks

The dynamics of these highlighted results confirm that the patent-network market process defined by marginal (ms) Patent Rank scores consists of observable marginal shocks. To fully appreciate the existence and uniqueness of Schumpeterian shocks, we reconsider the top patent innovations reported in Table 8. For each (ms) computation, we identify the top patent innovation, and measure its longitudinal shock8 across the entire time frame (1976–1980 to 2005–2009). We visually display these Schumpeterian shocks in Table 12. For each patent we include its number, its associated firm, the year it was granted, and some general information about its content. Based on construction, we shade the area above the trivial score of 1; we represent the intensity of a patent- innovation’s shock with a horizontal line; vertically, we represent the time it was included in the full (1980–2009) longitudinal window. We maintain scale across a decade and report that as the MAX.

[Table 8 about here.]

[Figure 12 about here.]

We ﬁrst note that there are different diffusion patterns of these top patent innovations using (ms) Patent Rank scores. The Stanford patent # 4,237,224 appears to have a ragged double-shock pattern. The ﬁrst peak of intensity occurs about six years after granted and the second appears in 2001, after its exclusionary patent-protection expired. Over time, we note that these diffusion patterns are becoming smoother. The University of Washington patent # 4,358,535 appears to have a bimodal diffusion, with a more sustained trough. Many are unimodal, partly because the

8If a patent’s shock pattern was presented in the table in a previous year, we report the Schumpeterian shock for the second ranked patent innovation for that year; if this patent’s shock pattern was previously presented, we report the Schumpeterian shock for the third ranked patent innovation for that year; and so on.

58 patent has yet to expire, or partly because diffusion did not occur until the patent expired. Of special note are ﬁve top Canon patents that have very similar diffusion patterns (is this a controlled diffusion pattern based on a closed-licensing patent strategy?). This suggests that Canon’s radical innovations have a slow diffusion that peaks after the patent-protection expires. We also note a few ‘old bird’ innovations—patents that may be experiencing a second- or third- generation shock (e.g., plastics by Shell and DuPont).

3.5.4 Shock Patterns: Intensity, Duration, and Volume

We have demonstrated using our (ms) model that Schumpeterian shocks exist, and that patents have unique diffusion patterns. Subsequently, we consider basic questions about diffusion patterns: (1) when does a patent peak; that is, when does the Schumpeterian shock have its highest intensity? (2) what is the shock’s peak? (3) what is the total impact of the shock (volume)? (4) how long does the shock endure? We are cautious to make general statements from the examples we previously presented to answer these questions. We are also cautious to directly address how long a shock endures in light of the few older innovations that appeared in the top results. In order to explore the ﬁrst three questions regarding shock patterns, we consider three samples: a random sample, a sample of dangling nodes (e.g., no backward citations as prior art), and a sample of high technology. We use each sample to descriptively address the questions about shock patterns. For each patent, we identify the year it was granted (t0), then we record (ms) Patent Rank scores for that year, and all subsequent years (up to t30) as data is available. Though this may not properly control for year-effects, it does allow us to get a rough sense of these patterns of (ms) Patent Rank scores. From this framework, we can measure intensity (height and when), duration, and volume.

Sample selections

First, we randomly select patents from 1976–2008, proportionally sampling by year such that the random sub-sample for 2006 was 1000 patents for a total of 20,054 patents. Second, we consider

59 a special subset of patents in the network, dangling nodes that were granted 1976–2009. These patents (n=58,062) represent about 1% of the entire network and have no legally-defined prior art (no backward citations). Finally, we consider an industry-specific, time-constrained (patents granted from 1996 through 2005) subset of patents in the network. From the S&P 500, we selected 81 high-technology firms. For each firm in the list, we searched for all relevant patents (n=108,002) in the timeframe to include in our exploratory analysis. Conveniently, each of these samples has a patent previously presented in Table 12 or Table 8. The random sample has a patent assigned to UC-Berkeley that is a FinFET transistor (# 6,413,802); the dangling-node sample has the uber- radical DNA-amplifying patent (# 4,683,202); and the high-tech sample has a patent assigned to IBM that is non-volatile, magnetic RAM (# 5,640,343). Dangling nodes have the highest conversion rate (a patent has some diffusion within the network): 99.6% for dangling nodes, 89.5% for hi-tech, and 82.1% for the random sample. Hi-tech patents have the fastest conversion rate (based on the percentage of patents had a first citation in the same year as the patent was granted): 5.09% for hi-tech, 2.22% for the random sample, and 1.75% for dangling nodes. To address differences in diffusion patterns across samples, we overlay the entire samples diffusion patterns, anchored to the year a patent was granted (t0). We report details about each sample with results in Figure 13. We also report a histogram for the total volume of each patent with nontrivial Patent Rank scores (some minimal amount of diffusion). In addition, we report a histogram for maximum (as intensity) and a summary of when that maximum was reached. We emphasize that unique patent shocks typify this skewed data. As a result, reporting averages would be misleading, so we will describe the data reporting medians and interquartile ranges [IQR]. We begin by considering the time for a shock to peak (that is, the time of maximum intensity): the median for the random sample is 6 years [3-9]; the median for the dangling sample is 7 years [5-10]; and the median for the high-tech sample is 5 years [4-6]. The maximum intensity we report as follows: the median for the random sample is 1.29 [1.07-1.64]; the median for the

60 dangling sample is 1.39 [1.18-1.78]; and the median for the high-tech sample is 1.45 [1.16-1.95]. The total volume of a shock we report as follows: the median for the random sample is 1.33 [0.135- 3.825]; the median for the dangling sample is 1.695 [0.47-4.22]; and the median for the high-tech sample is 1.38 [0.36-3.63]. Since the (ms) Patent Rank scores are used to measure both the maximum intensity and total volume, we also report the following correlations: 0.9047565, 0.8863892, and 0.937324 for the random sample, the dangling sample, and the high-tech sample, respectively. From these descriptive results, we note: (1) a patent’s Schumpeterian shock is unique, (2) within-sample variability is nonnormal, (3) between-sample variability is heterogeneous. From this, we conclude that our model speciﬁcation represents an objective method to address this extreme data which has been elusive in the study of innovation using patent data (Trajtenberg 1990a).

[Figure 13 about here.]

3.5.5 Hypothesis 2: Austrian economics market process

To this point, we have demonstrated that Schumpeterian shocks exist among Austrian-based, marginal (ms) Patent Rank scores. Like most citation-based results, the distributions (intensity, volume) derived from the (ms) Patent Rank scores are heavily skewed and appears to follow a power-law distribution (Simon 1955). Such distributional results are common in the study of extremely rare events and natural phenomenon, so we would expect this in the study of radical innovation. To further explore this phenomenon, we consider the most recent set (2005–2009 as t = 0509) of (ms) Patent Rank scores; in Figure 14(a), we report the distribution of all nontrivial scores—scores that are not definitionally assigned to be the minimum score of 1 (we exclude the dud patents as they have no shock value.) Even a natural logarithmic transformation, see Figure 14(b), does not improve the skewness. However, we see in Figure 14(c) that a double logarithmic transformation normalizes the data into what appears to be a Gaussian mixture. This result is uncommon for power-law distributions; in fact, we may have identified the first9 citation network

9We conjecture this result is due to the constrained nature of the citation network; that is, backward citations represent prior art determined through the legal prosecution of a patent application.

61 that has such beneﬁcial distributional properties. The monotonic10 transformation is mathematically deﬁned as

x = ln(ln(π)) for all elements where πi > 1, (3.15)

which implies π = eex . Although imperfect, we do have some evidence to suggest that there is a mixture of two types of structures in the patent market process. The right-most normal curve is smaller, and has the highest overall double-log transformed (ms) Patent Rank scores (e.g., radical). The left-most normal curve seems disjoint and truncated, but is larger, and has the lowest overall double-log transformed (ms) Patent Rank scores (e.g., incremental). More patents will have the exact same score if they imitate a common patent-citation structure.

[Figure 14 about here.]

3.5.6 Improving Normality with ClassMatch

We now proceed to consider how to improve normality of the disjoint double-log-normal distribution seen in Figure 14(c) by determining how to define the adjacency matrix M (based on network information) so the model produces results with beneficial distributional properties. Simply stated, we update the adjacency matrix M to include additional information about the strength of any link between two patents and call this ClassMatch . Recall that the current adjacency matrix M contains binary data (1’s and 0’s) to indicate the presence or absence of a link between two nodes. We define this dichotomous schema as a Structure or Structure-Only model. We define a new schema, ClassMatch that includes additional information about the value of each association which will. ClassMatch is a way to compare two patents based on their shared technology classifications:

10A monotonic transformation preserves order. This means that the ranking of patents based on these new scores is equivalent to the ranking based on the raw scores.

62 ClassMatch (X,Y ) = P rob(CX ) P rob(CY ). (3.16) i ∩ j X

which is essentially a soft-match or overlap of intersecting technologies which demonstrates patent relatedness. In Appendix E we delineate how ClassMatch works by providing a sample computation. This computation is performed on each parent-child (as forward-backward) citation pair. With this ClassMatch deﬁnition we have two options: (1) just use the ClassMatch matrix or (2) combine the ClassMatch matrix with the Structure matrix. The matrices are conformable so addition is possible (see, for example, Figure 10 where the Google algorithm adds two matrices to the original matrix). In Appendix F we compare the original Structure model to a ClassMatch model and a combined model. Based on the ﬁnal distribution and related correlations, we conclude11 that a combined approach gives very similar scores to the Structure and ClassMatch models with im-

12 provement in the double-log-normal distribution. Updating the cumulative π(t)cs and marginal

π(t)ms structural models, we formally specify respective combined models: π(t)cc and π(t)mc. Based on structural and temporal considerations, we summarize four basic Patent Rank models in Table 9.

[Table 9 about here.]

11We conclude that the three models (Structure, ClassMatch , and Combined) are all very similar but posit that the combined model, when trying to improve the double-log-normal distribution, is the best choice for the patent system. If patent X has a backward citation (patent W ) the legal review process deﬁned patent W as prior art, which is an essential aspect of validating a patent (Carr 1995). If the two patents have no technological classiﬁcations in common, the link that was present in the Structure model will be nonexistent in the ClassMatch model. With that in mind, we overlay the two matrices to build a network model that basically accounts for both the legal structure and the technological-relatedness: “the association is present and is this strong.” 12Although we only demonstrate these results using one network formation, various formations and timeframes were tested for all models, with similar results.

63 3.6 Case Study and Discussion: First generation CT-scanners

To further discuss entrepreneurial innovation, we choose13 the first-generation CT-scanners. Tra- jtenberg (1990a) considers all first-generation patent innovations (400+) related to the X-ray computed tomography (CT) scanners—scanners that use multiple X-ray measurements to create three- dimensional images. During the early 1970s, this product segment was absolutely new and several innovations developed rapidly. Most important was the originating innovation, which we posit was a Schumpeterian shock (see 1980 in Table 12). The inventor of this patent was a co-awardee of the Nobel Prize in medicine (Nobel Prize 1979). Following the introduction of this radical innovation, we posit that many different stakeholders were alert to the potential impact of the innovation, and acted on this knowledge by engaging in various types of entrepreneurial activities of the Kirzne- rian type. The firm with the originating innovation (EMI as Electrical and Musical Industries) in efforts to protect the intellectual-property claim, secured 119 additional patents, 50 of which cited the originating innovation (see Figure 15). Three incumbents in the healthcare industry, General Electric, Philips, and Siemens each secured 51, 50, and 50 patents respectively. It is remarkable that the pioneer (EMI) secured 50 patents from its radical innovation and each of the incumbents, almost to the number, also secured exactly 50 patents. In Figure 15 we delineate the competitive landscape based on the total count of patents in each firm’s portfolio and the subsequent value (as measured by the total volume of all patents in the portfolio) of a firm’s portfolio over time. Clearly, EMI had a pioneering advantage. By 1995, however, we see that the disruption has settled into a new competitive landscape, and note that from the OTHER patents, a second-generation phenomena is unfolding. Today, the three major incumbents (General Electric, Philips, and Siemens) are still around selling latest-generation CT-scanner in the healthcare industry. EMI, the creator of this radical innovation, is not longer in this business, divesting its interests in CT-scanners to Thorn EMI. Although out of the economic market, the longitudinal pattern of the patent innovations are

13The ﬁrst author contacted Manual Trajtenberg who was kind enough to provide the list of patents from his research. Without this list, it would have been impossible to perform the subsequent analyses. Truly, we are standing on the shoulders of a giant (John of Salisbury 1159).

64 still very much part of the noneconomic market. We conclude that the value of this originating shock is measurable and benefits society generally although the originating firm no longer benefits (Golder, Shacham, and Mitra 2009). With this context, we see that entrepreneurial purpose drives the radical innovator to secure a patent; subsequently, competition to economize the patent innovations take over. This aligns with previous research that demonstrates the enormous market value of top performers (Hall, Jaffe, and Trajtenberg 2001; Allison, Lemley, Moore, and Trunkey 2004) and even may explain why so- called ‘worthless patents’ exist (Moore 2005). The imitating patents have a competitive purpose based on Kirznerian competition. We posit five reasons why a firm would spend the resources to secure a ‘worthless patent’: (1) the firm was ignorant to the true market process (e.g., the firm acted on a perception of alertness that merely was incorrect); (2) the firm was developing human capital (e.g., giving employees experience in R&D and the patent process); (3) the firm was inten- tionally competitive (e.g., a short-term responsive strategy to stifle adjacent innovations described by Hegde, Mowery, and Graham (2009) as thicket building); (4) the market is not ready for this innovation and it is unlikely the market will be ready by the time the exclusionary period expires; (5) the market shifted direction, so the patent innovation is irrelevant.

[Figure 15 about here.]

65 3.7 Conclusion

Using Patent Rank, we have verified that the Austrian perspectives of entrepreneurship are identifi- able in the market process. Empirical evidence identifies that unique Schumpeterian shocks occur. In addition, we identify the structural residuals of Kirznerian competition, responsive activities to the Schumpeterian shock. This verifies our supposition that entrepreneurial innovation exists in the market. Creators and exploiters are both necessary for economic development. Finally, we have generalized a model specification for Patent Rank. Using the (ms) model we demonstrated the structural residuals of the market process was a mixture of two distributions which we claim represents the Schumpeterian activity (radical innovation) and the Kirznerian activity (competitive imitation). Within the generalized form of Patent Rank, we also presented a combined model (mc) which we posit will be beneficial when the goal is not to identify differences in types of entrepreneurial activity, but rather to identify the degree of radicalness each patent possesses in the market system.

66 CHAPTER 4

ASSESSING DIFFUSION OF RADICAL INNOVATION: LONGITUDINAL IDENTIFICATION OF A PATENT’S LIFETIME VALUE (PLV) AND A FIRM’S PATENT PORTFOLIO USING PATENT RANK Assessing a patent’s value objectively is a vital element of venturing. Identifying and managing radical innovation separates successful business ventures from failures. Using Patent Rank, we present a statistical assessment technique that uniquely deﬁnes each patent in terms of its growth rate, the speed of its growth, and its overall value. This technique objectively discerns race horses (patents fast out of the gate that win races) from show ponies (patents that look good on paper).

4.1 Executive Summary

This research delineates a methodology to assess patent innovations and identify radical innovation so it can be properly managed as such. Annually considering the universe of utility patents (5.6 million patents from 1976–2009), we objectively delineate how to estimate if a patent will diffuse (receive its ﬁrst forward citation). Using a random sample, we delineate some fundamental properties of patents and provide a conditional probability for a patent: if a patent was granted in year Yg, what is the probability it receives its ﬁrst forward patent in year Yx if it has yet to receive a forward citation previously? Once a patent begins diffusion, we utilize marginal (mc) Patent Rank scores to measure the impact of the patent innovation on the patent network over time. Each patent has a unique Schum- peterian shock that can be modeled in terms of intensity, duration and total volume. We convert these variables into three variables with conceptual meaning for investors: velocity, growth, and expected patent lifetime value (PLV). Velocity captures the adoption speed of a patent within the patent system; growth captures the maximum growth rate; PLV predicts the expected total lifetime value of the patent innovation.

67 We then compare IBM’s patent inventory head-to-head with the patent inventory of other pro- liﬁc patenteers. Finally, we evaluate IBM’s patent portfolio identifying its race horses (fast and valuable), its mules (slow but they get work done), and its ‘show ponies’ (they look good) for patents granted in 1986, 1996, and 2006. We conclude by discussing how this assessment strategy can beneﬁt investors not only in measuring individual patent innovations but also in appraising and managing patent inventories.

4.2 Introduction

Innovation drives economic growth, and money fuels innovation. Entrepreneurial innovation, from an Austrian perspective, requires two things: the entrepreneur and the capitalist (Schumpeter 1911; Kirzner 1973). The study of business venturing essentially is the study of these two important actors in the market process based on available information: “Entrepreneurship is a key driver of economic development and wealth creation, but the failure rate among new ventures is high. One possible explanation for these failure rates arises from differences across ventures in their col- lection and use of market information” (Song, Wang, and Parry 2010). Identification of viable entrepreneurial opportunities requires capable venture capitalists to appropriately discern the market information: “how venture capitalists select start-ups for financing has been an interesting topic for many researchers and practitioners. The underlying assumption is that people who make money investing in new businesses by assessing the proposals should be experienced enough to distinguish losers from winners” (Riquelme and Rickards 1992). “Venture capital investors sensibly incorpo- rate a great deal of relevant information [...] when they invest new capital” into entrepreneurial activity and the resulting “realized returns are affected by a wide variety of sector-wide, firm- specific and investor-specific news” (Hand 2007). Venture capitalist do not carry a crystal ball, and “the present lack of instruments for measuring entrepreneurial opportunity is hampering progress in entrepreneurship research” (Dahlqvist and Wiklund 2011). Although “public market information can be useful in assessing venture prospects” (Li and Mahoney 2011), there is a need to expand

68 due diligence through audits of entrepreneurial opportunities (Harvey and Lusch 1995). Assessing technology appropriately impacts all types of venture capitalists in their efforts to reduce subjectivity errors (e.g., jumping on the ‘bandwagon’ (Low and Abrahamson 1997)). We discuss venturing from both an internal and external perspective.

4.2.1 Internal venturing

Within corporations, venture development to support innovation requires ‘patient money’ (Kan- ter 1985), a shift in thinking for the firm (Nonaka and Yamanouchi 1989), and a ’individualistic and non-hierarchical’ culture (Shane 1992); “one of the most serious challenges facing an entrepreneurial company, particularly a high-technology firm, is knowing how to manage innovation as the organization evolves” (Koberg, Uhlenbruck, and Sarason 1996). Firms with financial slack (Patzelt, Shepherd, Deeds, and Bradley 2008) also search for viable entrepreneurial opportunities through corporate venturing (CVCs). Basu, Phelps, and Kotha (2011) describe the complexity and paradoxes of identifying CVC investments based on internal motivations and concluded that there is need for better information to make decisions. Regardless, Dushnitsky and Lenox (2006) “propose that corporate venture capital investment will create greater firm value when firms explicitly [...] harness novel technology.” Technology, especially patented technology, is a key characteristic in the consideration of venture investment because it offers the potential of first-mover advantages: “this is certainly true in industries such as pharmaceuticals, where the relative effectiveness of patent protection leads to patent races in which a ‘winner takes all’ scenario exists” (Deeds and Hill 1996). Therefore, “executives should develop and use a comprehensive technology strategy” (Zahra 1996) in the careful analysis of technology acquisition (Jones, Lanctot, and Teegen 2001) or alliance management (Rothaermel and Deeds 2006).

69 4.2.2 External venturing

Externally, the VC role is also important (Timmons and Bygrave 1986); “financial intermedi- aries such as venture capital firms (VCs) are perhaps the dominant source of selection shaping the environment within which new ventures evolve” (Baum and Silverman 2004). The infusion of capital at opportune times determines the success or failure of important innovation developments. For example, positive signals in biotech (product and process development) will influence favorable IPOs (Deeds, Decarolis, and Coombs 1997) and can make or break nascent firms (Junkunc 2007). Clearly, the ability to “evaluate the innovative potential of small firms is likely to attenuate the risk” and “enhance the efficient allocation of resources” (Khan and Manopichet- wattana 1989). Even angels, representing the ‘informal venture capital market’ (Wetzel 1987), utilize “elimination-by-aspects” techniques to assess innovative opportunities (Maxwell, Jeffrey, and Lvesque 2011). Many times, the choice of a formal VC or a private angel depends on the entrepreneur’s technological expertise (Fairchild 2011) as “venture capitalists can add value beyond the money supplied” (Florida and Kenney 1988; Sapienza 1992).

4.2.3 Managing Radical Innovation

The experience of VCs and angels is very important in managing radical innovation within a nascent ﬁrm. Technology shocks need to be carefully managed (Yim 2008) as the represent a higher risk and reward in the complex market system (Baden-Fuller, Dean, McNamara, and Hilliard 2006): “integrating different streams of technological knowledge is becoming crucial to innovativeness in an era of rapidly changing technology” (Tsai and Wang 2008). Therefore, “as science and technology become more highly specialized, and breakthrough discoveries are at risk of taking longer, costing more, and becoming more obscure to the traditional business manager, it is critical that we explore the mechanisms that both promote and inhibit entrepreneurial activity in the context of scientiﬁc advancement” (Junkunc 2007). As a result, a specialized group of VCs have emerged, known as IPVCs, intellectual property venture capitalists. Firms such as ipIQ and

70 Intellectual Ventures directly act as VCs and provide research services to other VCs/CVCs in the assessment of patents, patent portfolios, and the viability of venturing opportunities: direct sales of patents (Serrano 2010), joint ventures, mergers and acquisitions, incubators (Mian 1997; Phan, Siegel, and Wright 2005), new ventures, and so on. In this information age, a world of ‘too much information,’ we offer a methodology that can benefit any and all venture investors in the objective assessment of patented technology. First, we define Patent Rank as a valuation-metric to assess a patent’s intrinsic value at any point of time by considering its quality in relationship to the entire patent network. We then describe each patent’s unique diffusion pattern as a Schumpeterian shock. Next we delineate how the trajectory of this unique shock can be modeled using the classic S-curve to predict the patent’s expected lifetime value. Whether an enterprise is in an early stage with underdeveloped intellectual property that needs to be adequately valuated or in later funding stages where the developed intellectual property needs to be properly managed, our technique will objectively assess each patent innovation to the benefit of venturing stakeholders. We present a measurement methodology to audit the value of patented technology in the assessment of entrepreneurial opportunities. Specifically, using Patent Rank, we describe how a patent’s intrinsic value can be calculated and a patent’s expected intrinsic value can be estimated.

4.3 Patent Rank

Utilizing the citations among patents, the patent system can be defined as a network with nodes and links. In Figure 16, we utilize a simple example to demonstrate the relationships among patents within the network. A citation is directed, meaning it is a backward citation (it borrows from past innovation) or it is a forward citation (it lends to future innovation). Traditionally, one would valuate a patent by counting its forward citations (Trajtenberg 1990a). Trajtenberg (1990b) describes how forward-citation counts correlate with an economic variable of social value, from which, firms can extract entrepreneurial profits. We present Patent Rank as a more precise measure

71 of a patent’s intrinsic value. Patent Rank considers forward citations, backward citations, and their relative importance within the entire network to assess each individual patent. This is similar to “power rankings” commonly used to rank collegiate sports teams based on strengths of schedules, who beat whom, and by how much.

[Figure 16 about here.]

4.3.1 The Problem

The principles of extracting value from citations can be understood by considering an analogy of patents in the market: pebbles in a pond. If I drop a single pebble into the pond, it will create a wake; ripples will diffuse across the pond. Based on Archimedes’ principle, the influence on the pond will depend on the pebble’s momentum when it hit the surface of the pond. Mathematically, momentum is a function of the pebble’s mass and velocity; mass is a function of the pebble’s volume and density. The disruption in the pond can be estimated based on the intrinsic nature of the pebble: its velocity, its volume and its density. The Trajtenberg (1990a) measure, determines the patent’s impact on the market by defining its volume using forward-citation counts. It assumes density is constant; that is, each forward citation is weighted equally. However, we know the variability of patent value is extreme. Some patents are deemed worthless (Allison, Lemley, Moore, and Trunkey 2004); others are valuable (Moore 2005). By treating them as equal, there is a bias that is introduced. In addition, the forward- citation measure valuates a patent independent of all other patents. If two pebbles were dropped in a pond at the same time, the disruption of the second would influence the disruption of the first synergistically or deleteriously. Now, compound the problem by throwing a million pebbles into the pond at the same time. The independence assumption of forward-citation counts creates more bias. Further compound the problem by throwing a million pebbles into the pond at different times and speeds. The forward-citation measure assumes velocity is constant. We would expect that the speed of a patent should be influenced by various factors: market conditions, competitive

72 landscape, capital investment, consumer adoption, and so on. Finally, compound the problem by recognizing that every pebble’s composition (its density) changes over time.

4.3.2 Patent Rank as the Solution

How can we identify a pebble’s unique density (e.g., a patent’s intrinsic value) which will allow us to then understand its impact on the pond (e.g., a patent’s market value)? We need a method to objectively measure the pebble’s density by accounting for its geological ancestry and heritage. Patent Rank is such an objective measure. It considers the associations among all patents in a comprehensive, endogenous, and simultaneous manner. At any point in time, a patent’s intrinsic value can be computed using Patent Rank. The cumulation of Patent Rank scores can uniquely deﬁne a patent’s diffusion. Once enough data is available, the trajectory of the patent’s diffusion pattern can be modeled to identify the patent’s expected lifetime value.

4.4 Patent Rank as an indicator of Schumpeterian shocks

To demonstrate this process, we utilize annual scores of the (mc) Patent Rank model. The (mc) model is marginal and combined. Marginal means it considers the patent’s intrinsic value in a temporally-constrained network. For example, to compute the patent’s intrinsic value in 2005, the network is formed to include only recent patent associations from 2001 to 2005. To compute the patent’s intrinsic value in 2006, the network is formed to include patent associations from 2002 to 2006, and so on. This moving-window technique applies the Austrian principle of marginal utility to evaluate a patent’s influence over time. Combined means the associations are defined within the network as ‘present and being this strong’ based on the technology overlap of a patent and its citation. A formal model specification and mathematical derivation is available. To assess just one patent, the entire network needs to be formed, Patent Rank scores need to be computed for every patent in the network based on the model specifications, and then the single patent’s score can be reported. These scores are computed longitudinally to ascertain the changes in a patent’s intrinsic value over time. These longitudinal computations of Patent Rank scores for a

73 single patent uniquely deﬁnes its Schumpeterian shock (see Figure 17) based on intensity, duration, and total volume (shaded region). This shock pattern represents how the given patent inﬂuences the patent network and ultimately the market place. We descriptively report the shock patterns of top patent innovations in Table 18.

[Figure 17 about here.]

[Figure 18 about here.]

4.5 Diffusion Patterns and Trajectory Models

In Figure 19, we describe a process that will convert a unique Schumpeterian shock to a trajectory model using the generalized logistic function, commonly referred to as the Richards’ curve (Richards 1959):

βit Yit = f(Xit;Θit) = f(Xit; βit, δit, τit) = (4.1) (1 + e−δit(Xit−τit)) where Y represents the total volume of the Schumpeterian shock for patent i measured in year X it e it utilizing information up-to, and including time t.

[Figure 19 about here.]

Although more parameters could be used in the generalized logistic function, we choose a

three-parameter model which captures the maximum growth rate δ (GROWTH), the time of maximum growth τ (VELOCITY), and the ceiling value β (VOLUME) which represents the expected total volume of the Schumpeterian shock. Since we compute Patent Rank scores annually, we can update the actual shock pattern and resulting modeled trajectory every1 year. In Figure 20, we explicitly provide an example of how this modeling procedure updates2 over time. 1We note that we could compute scores more often to monitor the trajectory of a patent more carefully. Future research should address the optimal aggregation level for modeling trajectories effectively. 2If not enough data is available to ﬁt the model, we are left with using the actual value of the shock rather than the modeled expected value. This means we do not have data for the growth δ nor the velocity τ, but we do have data for the volume β.

74 Using these variables, we can predict winners and losers. Winners will have high expected

values β (VOLUME), losers will have low expected values. We deﬁne losers as “show ponies” as they appear to have value (the ﬁrm secured a patent), but the have not meaningful value. Among

winners, we distinguish “race horse” from mules based on the the growth rate δ (GROWTH). Faster growth rates signiﬁes more potential for overall value (race horses). Slower growth rates over a longer time period can still have value (mules).

[Figure 20 about here.]

To assess a patent at a speciﬁc time, we have several options: (1) we use the actual value, (2)

we use changes in the actual value, (3) we use the expected value β, (4) we use changes in the expected value. To build a patent portfolio we can sum any of these four options. From which, we can develop additional valuation-options: (a) we can normalize a ﬁrm’s portfolio by dividing the total score by the number of patents present in the network, an averaging technique; (b) we can create standardized scores within a ﬁrm over time.

4.6 Application of Methodology: IBM, the most proliﬁc patent producer

To demonstrate this methodology’s utility, we valuate IBM’s patent inventory. Every year for the past 18 years, IBM has been identified as the most prolific producer of approved patents (Schecter 2011); the firm has incentives for prolific inventors within the firm (Ladendorf 2011), and has been been creating news with its apparent ‘open patent policy’ (McMillan 2005; O’Gara 2010). Some have accused IBM of patent trolling (Asay 2007), others have argued that quantity does not imply quality (Asay 2009). IBM, therefore, represents a useful case study as it gets lots of press because it emphasizes patenting technology as a key element of its overall market strategy in its competitive environment.

75 4.6.1 Sample selection

We consider various lists of innovation to select firms that also patent prolifically. Although the sample emphasizes industry competitors to IBM, it also includes prolific patenteers from other industries. We report our sample of firms in Appendix I. For each selected firm, we do search and matching technique to associate patents with respective firms. We delineate this procedure in Appendix H. We also include a random sample in our analysis.

4.6.2 Introduction of a Random Sample

A fundamental element of statistical theory is the random sample. A random sample allows us to draw inferences from a population; in essence, it allows us to compare a single patent to the universe of patents. Since patent-citation structure was not available until 1976, we construct our random sample beginning in 1976. We representatively sample over time, from 1976–2008, based on when a patent was granted. Since 2006 was the most prolific patent year within this timeframe, we choose to randomly select 1000 patents in 2006 and representatively sample patents in all remaining years proportionally; for example, 904 patents were randomly selected from 2008, 847 patents were randomly selected from 1998, 443 patents were randomly selected from 1988, and 377 patents were randomly selected from 1978. For the random sample, we report a few summary statistics in Table 10. Since patent data is inherently skewed, we report the median with the first (Q1) and third (Q3) quartiles. Over time, we observe more inventors, more claims, more tables/figures/examples, and longer review times to get a filed patent approved. All of these factors suggest that patented technology is more complex in the modern information age. For older patents, we observe that diffusion occurs for about 90% of patents. Since newer patents have had less time to diffuse, they should have a much lower diffusion percentage. Although the literature has focused efforts on addressing this as truncation, we emphasize that this information provides insights that can be used to evaluate patents; that is, the few patents that diffuse early potentially signal a winner: they are fast-out-of-the-gate and show value early.

76 [Table 10 about here.]

4.6.3 Probability of Diffusion

The ﬁrst question of interest is: will a patent diffuse? By construction, a patent needs a minimum of one forward citation to start a Schumpeterian shock. First, we report the absolute cumulative probability in Table 11: if a patent was granted in year Yg, what is the probability it has received its ﬁrst forward patent by year Yx?

[Table 11 about here.]

Next, we report a conditional probability in Table 12: if a patent was granted in year Yg, what is the probability it receives its ﬁrst forward patent in year Yx if it has yet to receive a forward citation previously?

[Table 12 about here.]

Before proceeding, we emphasize that this random sample is invaluable as it offers an objective benchmark of key variables that can be utilized in the identiﬁcation of patents as radical innovations.

4.6.4 Patent Quantity

In Figure 21, we report the quantity of patents granted by the top-15 ﬁrms from our sample. As expected, the high-technology industry leads in the patent-race at the ﬁrm level. In the timeframe, IBM holds the lead, but Samsung (with its diverse subsidiary corporations) appears to be gaining fast.

[Figure 21 about here.]

77 4.6.5 Patent Quality

Using Patent Rank, we can identify the quality of patents. As outlined above, we utilize the compare actual values to expected modeled values for TOTAL, DELTA, and NORM: TOTAL represents the total value of a firm’s patent inventory in a given year; DELTA represents changes in the value of a firm’s patent inventory from the previous year, and NORM represents an average value per patent (divide DELTA by the number of patents the firm has in the patent network for the given year). In Figure 22, we report the TOTAL quality of patents granted by the top-15 firms from our sample. Of note, Samsung falls from #2 on quantity to #13 on quality and Microsoft falls from #3 on quantity to #20 on quality. In contrast, we note that Motorola jumps from #21 on quantity to #3 on quality and Xerox jumpers from #13 on quantity to #5 on quality. We report similar results in Figure 23, for the DELTA quality of patents granted by the top-15 firms from our sample. In Figure 24, we report the NORM quality of patents granted by the top-15 firms from our sample. This represents a per-capita valuation of quality; that is, what is the average value of a patent a firm possesses that has some degree of quality within the patent network. We first note the Intel trend, in the glory days of its ‘Intel inside’ campaign (Mizik and Jacobson 2003). We also note that two acquisitions of HP, DEC and Compaq, demonstrate the best quality per capita. Indeed, HP rightfully asserts that they are competing with IBM on quality, not quality (Asay 2009).

[Figure 22 about here.]

[Figure 23 about here.]

[Figure 24 about here.]

78 4.6.6 Modeled values to identify winners and losers

We generate decision rules to identify winners and losers among a portfolio of patents. For a

given grant year, we identify the most recent modeled values for growth δ, speed τ and volume β. Obviously, there is more data for older patents (granted in 1981 for example), so the identified speed will be different that patents granted in a different year (1991 for example). If a patent’s growth δ is slower than half of the sample for the year, the patent is flagged as potentially being a mule; it also must demonstrate value (we define this to mean that it falls in the upper quartile based on volume β). If both these conditions are met, we identify the patent as a mule. Race horses, on the other hand need to be faster (δ) than 3/4 of the sample and must belong to the top 10% of all patents based on volume β. Finally, regardless of growth, a patent is classified as a show pony if it belongs to the lowest quartile based on volume β. To illustrate, we report percentages of race horses, mules, and show ponies for the random sample: 14.04%, 5.70%, and 32.30%, respectively. Using the same within-portfolio comparison for IBM, we report: 14.36%, 4.01%, and 32.87%. Additional comparisons could be performed to the random sample as a benchmark, see Tables 13 14 15.

[Table 13 about here.]

[Table 14 about here.]

[Table 15 about here.]

4.6.7 IBM’s race horses, mules and show ponies

With an intent to identify IBM’s up-and-coming patent innovations, we utilize these decision rules only among IBM’s patents. From Table 18 we note that IBM’s patent # 5,640,343 (nonvolatile mRAM) represents one of the most radical patent innovations of the modern era. However, IBM holds thousands of other patents that can be assessed and appraised. We consider years 1986, 1996, and 2006 as examples to identify IBM’s winners and losers within those years. As discussed

79 earlier, we emphasize that we are defining value among patents that have at least one forward citation. The worst show ponies, therefore would have the lowest expected value among all patents granted in that year. The race horses and mules are defined based on two parameters, so other decision rules are defined. The best mules get the most work done, and speed is irrelevant (accept in the defining of a patent as a mule). The best racehorses win races, so speed is important, but overall value is most relevant. In Tables 16, 17, 18 we report the best race horses, mules, and show ponies for years 1986, 1996 and 2006, respectively. We emphasize that these results are based on calculations executed in 2006; like all assessment strategies, results should update and change as more market information is available.

[Table 16 about here.]

[Table 17 about here.]

[Table 18 about here.]

4.7 Concluding Discussion

Objectively assessing innovation as radical and thereafter appropriately managing it as such is the essence of successful business venturing. Whether the venturing is internal or external, insights from this research can help decision makers appraise patent innovations objectively. Patent Rank provides a method to use market information about the patent network to assess individual patents and patent portfolios. As such, it should have practical value to venture capitalists. Above all, it can helps VCs discern the winners from the losers and, among the winners, find those that may need ‘patient money’ (Kanter 1985). Ultimately, this allows venturing stakeholders to choose wisely in their assessment and management of patent innovations. For example, two of the most prolific patents of the modern era belong to an entrepreneurial firm that did not survive due to delays in the FDA approval of a drug and funding issues (Wikipedia

80 2010). These two patents involve process innovations that defined the polymerase chain reaction (PCR) which allows DNA to be cloned. Kary Mullis, the inventor on these two patents (# 4,683,202 and # 4,683,195), received the 1993 Nobel Prize in Chemistry for this scientific breakthrough (Nobel Prize 1993; Wikipedia 2011). In 1991, because of a major funding crisis, the firm was sold to Chiron (who was later acquired by Novartis). These patent innovations revolutionized the genetic biotechnology industry, yet the firm failed. In our estimation, the VCs failed, as the innovation has proved its merit to society and is the basis of the current multi-billion dollar industry. Why did Cetus fail? Where was the objective assessment of the value of these patent innovations? We believe that the methodology delineated here could have helped those involved with Cetus to better assess and ultimately manage this radical innovation. Utilizing our outlined technique, the patents could have been assessed based on their trajectories (see 1995 and 1996 of Figure 18) which by our estimation, were unprecedented. What will be the next radical innovation to rival Mullis’ revolutionary invention? We believe that our methodology can help determine that as well.

81 CHAPTER 5

PATENT RANK: AN OBJECTIVE MEASURE OF RADICAL INNOVATION Utilizing Patent Rank, we link patent data to radical innovation.

5.1 Introduction

Fundamentally, innovation is defined as action that leads to change. Joseph Schumpeter, considered the “Prophet of Innovation” (McCraw 2007), specifically defines innovation based on combinations of five different entrepreneurial activities (Schumpeter 1911, p. 66):

[products] “The introduction of a new good – that is one with which consumers are not yet familiar – or of a new quality of a good

[resources] The conquest of a new source of supply of raw materials or half-manufactured goods, again irrespective of whether this source already exists or whether it has ﬁrst to be created

[monopoly] The carrying out of the new organization of any industry, like the creation of a monopoly position (for example through trustiﬁcation) or the breaking up of a monopoly position.”

From a firm’s perspective, these activities represent elemental components of marketing strategy. The firm attempts to do things better, faster, and cheaper to improve profitability. The degree of innovation has generally been described in terms of its radicalness: how new is the product, production method, market, or raw material supply? How strong is (was) the monopoly position?

82 Radical innovations represent actions that lead to major change; e.g., the Schumpeter entrepreneur shocks the market with a “creative destruction” (Schumpeter 1911). Incremental innovation, on the other hand, represents action that leads to minor change; e.g., the Kirzner entrepreneur exploits alertness to market disequilibrium (Kirzner 1973). In Figure 25, we present a continuum of entrepreneurial innovation based on these two fundamentally different types of entrepreneurial activities.

[Figure 25 about here.]

Consider the iPod introduced by Apple. Is it a radical innovation? How can we assess its degree of innovativeness? Can we find an objective measure to determine its radicalness? These questions are all difficult to answer. Is the innovation radical because consumers adopt it? Is the innovation radical because the marketing manager believes it is? Is the innovation radical because the entrepreneurial firm possesses intangible marketing assets such as brand? Such additional questions are fundamentally important to the marketing community. With an objective measure, it would then be possible to separate such important intangibles from a baseline metric. We present Patent Rank as such an objective measure–an objective measure of radical innovation using patent data.

5.2 Radical Innovation as better, faster, cheaper

In marketing science literature, radical innovation has a product focus. We study radical product innovation and utilize two dimensions (newness of technology and customer need fulfillment) to determine if a product is a radical innovation (Chandy and Tellis 1998). There are, of course, other ways to characterize innovation (Ansoff 1957; Schmookler 1966; Henderson and Clark 1990; Ku- mar, Scheer, and Kotler 2000; Chesbrough 2003). Schmookler (1966) defines the importance of technology and social value to describe patented railroad innovation from an economic perspective. Henderson and Clark (1990) define architectural/modular dimensions to describe process

83 innovation—to describe how the product is conceived, designed, developed, and manufactured. Others have developed boolean logic (Garcia and Calantone 2002, Appendix B) and nested hierar- chies to define radical innovation (Dahlin and Behrens 2005). In order to synthesize the competing frameworks, we define radical innovation in a general form (Leifer, McDermott, O’Connor, Peters, Rice, and Veryzer 2000, p. 5): “A radical innovation is a product, process, or service with either unprecedented performance features or familiar features that offer potential for significant improvements in performance or cost. [...] Radical innovations create such a dramatic change in products, processes, or services that they transform existing markets or industries, or create new ones.” From this definition we emphasize that a dramatic change in a market creates potential for financial returns, but does not guarantee them. In fact, Golder, Shacham, and Mitra (2009) demonstrate that many radical innovations are commercialized by firms that were not the firm that created the radical innovation. In Figure 26, we demonstrate how radical innovation has been studied in marketing. Adding the marketing concept to the original Ansoff matrix (Ansoff 1957), radical product innovation is defined as new technology that has radical improvements in fulfilling customer needs (Chandy and Tellis 1998). Similarly, Kumar, Scheer, and Kotler (2000) introduce the marketing concept to process innovation defined by Henderson and Clark (1990). We note that regardless of the innovation type (product or process) or the dimensions used to describe innovation, the most radical innovations fall in the high/high category (top-right quadrant) and the incremental innovations fall in the low/low category (bottom-left quadrant). Therefore, we reiterate that (Henderson and Clark 1990, p. 13) “the distinctions between radical [and] incremental innovations are matters of degree.” Rather than focusing on characterizing innovation by type, we posit to objectively consider any innovation’s merit based on its degree of radicalness (incrementalness); was it a major (minor) change?

[Figure 26 about here.]

84 Whether an innovation is classiﬁed as a product or a process innovation, we utilize the Austrian perspective of entrepreneurial innovation to deﬁne radical innovation as an observable Schumpete- rian shock (see Figure 27). The degree of the radical innovation can then be measured as the total volume of the Schumpeterian shock.

[Figure 27 about here.]

5.3 Patents

Patents are often used to measure the innovativeness of entrepreneurial activity (Arrow 1962; Mansfield 1986; Cohen, Nelson, and Walsh 2000; Cohen, Goto, Nagata, Nelson, and Walsh 2002). Patent data is available for both private and publicly-traded companies. Patents represent exclusive “intellectual property rights” granted by the government to the firm for a specific period of time in exchange for public disclosure. Although Sood and Tellis (2009) identify that patents represent an event in the ultimate commercialization and success of product innovations, we agree with the general consensus that current patent data (simple patent counts, forward-citations counts) are old and tired in trying to predict radical product innovation (Tellis, Prabhu, and Chandy 2009). Therefore, in this paper, we re-evaluate what we know about patents in relationship to our study on innovation. In turn, we also reflect on how radical innovation is studied generally, how radical product innovation is studied specifically within marketing, and how patent innovations can help disentangle objectivity from subjectivity in the measure of radical innovation.

5.3.1 Patent Data is Practically Messy

Although patents are universal and comprehensive, the ability for patent data, in its historic form, to predict a ﬁrm’s ﬁnancial success has been disappointing. The nature of patent data is inherently messy. First, radicalness as a rare event implies skewed data with extreme variability (Schmookler 1966; Trajtenberg 1990b). Many patents may be worthless (Moore 2005). Others are very valuable (Allison, Lemley, Moore, and Trunkey 2004); a single patent could be worth billions of dollars.

85 Second, patent motivation and utility may be unknowable. How does the firm use the patent? Does it shelf the patent (e.g., put aside until later)? Does the firm have an open or closed licensing strategy for the particular patent? Is the patent a process innovation, a product innovation, or both? What motivates the patent innovation and how the firm chooses to utilize it may be unknowable. Purohit (1994) delineates how the competitive environment and the nature of the innovation are factors in determining how an innovation is manifested in the market. Is the firm an incumbent? Is the innovation drastic? How competitive is the environment? Does the firm have a monopolistic position? Does the firm perceive that it has a monopolistic position? All of these questions will influence how the firm proceeds with the innovation. The firm may (1) implement the patent (e.g., an incumbent may internally leap-frog and cannibalize, joint-ventures may initiate, the firm may sell the patent innovation, develop an open or closed licensing strategy, etc.), (2) intend to shelf the innovation to protect an incumbent product/process (Gilbert and Newbery 1982; Hegde, Mowery, and Graham 2009), (3) shelf the innovation unintentionally for a myriad of reasons (e.g., the firm does not (a) know how to implement it, (b) have the resources/capabilities to implement it, (c) have the relationships to implement it). Nonetheless, as intellectual property, the patent innovation represents another resource that is at the disposal of the firm in determining its product offerings (Penrose 1959). Third, a patent’s exclusionary protection does not guarantee financial gains. We posit that a granted utility patent represents an appropriable innovation with legal protection which enables the firm to extract monopolistic rents. Financially, a firm has fiduciary responsibility to initiate and execute projects (such as securing a patent) that have an expected non-negative net present value. However, in the dynamic competitive landscape, it may be difficult for the firm to actually convert the patent into profit; that is, being appropriable is quite different from a firm being capable of appropriating (Mizik and Jacobson 2003). The patent only enables the firm to extract monopolistic rents by excluding others from making, using, or selling the innovation in question. It does not

86 guarantee in any way that the patent holder can actually extract those rents from the market. Appro- priability in no way implies conversionability (Allison, Lemley, Moore, and Trunkey 2004; Moore 2005; Chandy, Hopstaken, Narasimhan, and Prabhu 2006; Tellis, Prabhu, and Chandy 2009). Fourth, patents may be impossible to disentangle as discrete product offerings. That is to say, patents are elemental innovations from which: one patent may be shelved for competitive reasons; one patent may lead to multiple entrepreneurial opportunities (Shane 2001); one patent may be an ingredient in one or more manifestations of sophisticated product/process innovations (Henderson and Clark 1990); or some may say, one patent may simply represent a potential for radical innovation.

5.3.2 Patent Data is Conceptually Useful

Rather than trying to disentangle which patent innovations go with which radical product innovations, we can consider patents as discrete resources the firm has at its disposal to determine its product offerings (Penrose 1959) which ultimately should influence firm performance. Since disclosure is necessary for patent innovations for all firms, both public and private, patent data serves as a universal metric: firms either have patents or they do not have patents. With this observable metric, we can link patent data to financial performance. For over 50 years, the link between patent data and financial performance has been considered (Schmookler 1966; Trajtenberg 1990b; Hall, Jaffe, and Trajtenberg 2001, 2005). Forward-citation counts is the most common metric to valuate a patent and ultimately link patents to firm performance. Because patent innovations have extreme, nonnormal variability, this link has not been fully developed. In this manuscript, we aim to create a general link between patent innovations and firm performance; to do so, we present Patent Rank.

5.3.3 Patent Rank as Solution

To address the extreme variability inherent in the study of patent innovations, we need to reduce as much systematic error and bias as possible. We present Patent Rank as a precise, continuous

87 measure of patent valuation having previously demonstrated its merit. Patent Rank is a continuous measure that deﬁnes a patent’s intrinsic value relative to the entire patent network using the mathematics of eigenvector centrality (see Figure 28).

[Figure 28 about here.]

5.4 Propositions: Patent Rank and Radical Innovation

To create the link between patents and radical innovation using Patent Rank, we need to reflect on what we do know about patents. In a previous manuscript, we identified first principles based on the legal process of securing a patent: (1) all approved patents are innovation in a basic form; (2) a patent’s ancestry (backward citations) must be considered when assessing a patent’s intrinsic value; (3) a patent’s heritage (forward citations) must be considered when assessing a patent’s intrinsic value; (4) a patent’s intrinsic value changes over time. From these first principles, we assert the following two propositions:

Proposition 1. Top patent innovations deﬁned by Patent Rank should correspond to our perceptions of radical innovation [Face validity].

Proposition 2. Top patent innovations deﬁned by Patent Rank should correspond to expert perceptions of radical innovation [Concurrent validity].

To these first principles, we add the following four firm-level insights: (1) a patent represents a discrete, atomic portion of intellectual property the firm possesses as a resource; (2) a patent’s actual (expected) lifetime value can be observed (predicted); (3) a firm’s patent portfolio can be defined empirically (which we define as a firm’s patent stock) by summing each patent’s intrinsic value; (4) changes in this patent stock should be observable in the financial market. From these firm-level insights, we assert the following proposition:

88 Proposition 3. Changes in a firm’s patent stock should be observable in the financial market. Specifically, abnormal market returns (above and beyond market returns for firms that do not have patents) should exist for firms with different degrees of changes in patent stock [Predictive validity].

We do not claim that all patents are radical innovations; however, we do posit that all patents are innovation, to some degree. Patent Rank will precisely assess a patent innovation on a continuum by considering it in context of the entire utility-patent network. From this objective benchmark, we can assess both a firm’s actual patent stock and expected patent stock, and monitor changes over time. These changes in a firm’s patent stock should be observable in the financial market. We posit that firm’s with patent stock should have positive abnormal returns above and beyond firm’s without patent stock. More specifically, we also posit that changes in patent stock can be measured by degree these differences should also be observable in the financial market.

5.4.1 Getting to Financial Performance Using Patent Rank

We emphasize that this unit of analysis is different from how we commonly consider innovation in marketing science (e.g., product innovation). This atomic approach will give us an objective foundation to better appreciate the influence of discrete patent innovations within complex product manifestations. Although this approach is similar to the Hall, Jaffe, and Trajtenberg (2005) approach, we emphasize the differences. First, we use Patent Rank instead of forward-citation counts, which we have demonstrated is a more precise, less biased, measure of the patent’s intrinsic value. Second, we consider 1980–2009 instead of 1996-1996. Third, we use a predictive model of diffusion for each unique patent innovation to identify its lifetime value rather than imposing truncations techniques based on averages and assumptions. That is, based on the concept of information in an efficient market, we utilize expectations to determine a financial outcome. Fourth, we use abnormal financial returns based on unexpected changes in patent stock rather than Tobin’s Q.

[Figure 29 about here.]

89 In Figure 29, we summarize how we valuate a single patent’s expected lifetime value for a given year. First, the network is formed using the (mc) Patent Rank specification. Any deviations above the nontrivial score of one defines the patent’s Schumpeterian shock. That is, a firm has zero value as radical innovation unless it diffuses within the network. Each year, we compute the (mc) Patent Rank score for the patent, and longitudinally we observe the diffusion pattern of the patent’s unique Schumpeterian shock. When enough data are available, the total volume of the Schumpeterian shock is modeled using the generalized logistic function (a nonlinear S-curve). As noted in the previous chapter (Chapter 4), to model a patent’s expected lifetime value, we use a three-parameter form of the Richards’ curve (Richards 1959):

βit Yit = f(Xit;Θit) = f(Xit; βit, δit, τit) = (1 + e−δit(Xit−τit)) where Y represents the total volume of the Schumpeterian shock for patent i measured in year it e

Xit utilizing information up-to, and including time t. The selected three-parameter model helps identify key aspects of the growth of a patent innovation: the maximum growth rate δ, the time of maximum growth τ, and the ceiling value β which represents the expected total volume of the Schumpeterian shock. We demonstrate the updating/modeling process in Figure 30. The parameter estimates provide information about the growth rate δˆt, the time of maximum growth τˆt, and the expected ceiling βˆt.

We define βˆt to represent the expected lifetime value for a patent at time t. Another year passes and similar calculations are performed (t + 1). We define ∆βˆt+1 to be the difference between βˆt+1 and βˆt. Since each patent innovation is atomic, discrete, and unique, we sum the expected patent lifetime values βˆt and changes ∆βˆt+1 to similarly define a firm’s patent stock and changes in patent stock.

[Figure 30 about here.]

90 5.4.2 Information and Financial Markets

Our approach is anchored to financial principles of informative content, net-present value, and changes in expectations. A firm and its decision makers have a fiduciary responsibility to its stakeholders. Within finance, a project (or a set of projects) will not begin unless there exists an expectation of nonnegative financial returns. Expectations change over time, and will influence a firm’s abnormal returns. We illustrate this point with the domain of patents by considering the 1986 patent infringement case between Polaroid and Eastman Kodak. Kodak had expectations of positive financial returns from projects relating to instant cameras. However, when the federal court ruled that these projects infringed upon Polaroid’s intellectual property, Kodak’s expectations from these projects changed. Specifically, Kodak had to: recall 16 million cameras, pay retailers full price for the recalled cameras, shut down a $200 million dollar plant, lay of 4500 employees, and pay legal fees and punitive damages to Polaroid (Foltz and Penn 1989, p. 28). We posit that such changes in expectations will influence abnormal financial returns. We emphasize the importance of variability of patents. First, every patent has a different intrinsic value. Second, every patent’s value changes over time. Third, every patent’s diffusion is unique and should be modeled individually. Fourth, due to the inherent skewness of the data, or- dered statistics (medians, quartiles, quantiles, and so on) should be used whenever possible. Fifth, addressing truncations should be approached cautiously, if at all. Traditionally, truncation is addressed by offsetting “newer” patents with some average expected value. Based on the inherent variability of patents, this may actually introduce more systematic error into patent analysis. From a finance perspective, subsequent forward citations represents new information to the market regarding specific patent innovations. This natural truncation actually helps discern winner patents from loser patents. Rather than imposing an average assumption on what a given patent will do, a diffusion model will more precisely valuate a patent based on its past trajectory; that is, what it has done. If it has not done anything, it only has a probability of diffusion, and therefore

91 should not be assigned an arbitrary average value. Market information is about expectation, but blanket assumptions about patent innovations with such variability is not the answer.

5.5 Data Preparation and Computation of Patent Rank scores

In December 2008, we began collecting patent data, programmatically1 harvesting all utility patents from the USPTO website. Once all the patents were collected, we parsed all utility patents granted in a year to extract their backward citations, as one patent’s backward citation represents another patent’s forward citation. From this information, we construct a directed graph in matrix form (M). We partition this matrix based on an ordering schema which classifies each patent into one of three types: dangling nodes, core patents, and duds. We augment the matrix to include the super-node, and finally, we row-normalize as defined in Equation (3.1). After this preparation, we solve the partitioned linear system to identify the unique Patent Rank scores (Del Corso, Gulli, and Romani 2005; Bini, Del Corso, and Romani 2008). With an intent to understand the quality of any patent over time, we annually2 calculate scores for four different Patent Rank models:

(cs) This is the most basic model, a cumulative-structure model, and is useful in identifying the originating innovation (Golder, Shacham, and Mitra 2009).

(cc) This model, cumulative-combined, is also useful in identifying the originating innovation while accounting for the technological overlap of a patent and its citation.

(ms) This model, marginal-structure, is useful in identifying a patent’s marginal utility, a fundamental principle of Austrian economics.

(mc) This model, marginal-combined, is also useful in identifying a patent’s marginal utility while accounting for the technological overlap of a patent and its citation.

Based on these four model speciﬁcations, more than 300 million unique Patent Rank scores are computed. For example, the ﬁnal cumulative model (1976–2009) has 5,608,070 patents and

1A robot script was written that saved each patent webpage to a local machine. A parsing algorithm was created to extract the appropriate data from each patent. This process took approximately 18 months. 2Although newly granted patents are publicly available every-other Tuesday, we choose to update the patent network every year.

92 39,811,892 associations. Of these patents, 35.8% are dangling3 nodes (2,007,245); 50.5% are core patents (2,833,808); and 13.7% are dud patents (767,017). Starting with 70,000 patents and 345,000 citations in 1976 and ﬁnishing with 5.6 million patents and 40 million citations in 2009, we compute the annual Patent Rank scores; e.g., 1976–1976, 1976–1977, 1976–1978, . . . , 1976– 2007, 1976–2008, 1976–2009.

5.6 Proposition Validation and Discussion

Validity is deﬁned as the “degree to which the measures or observations are appropriate or meaningful in the way they claim to be” (Rosenthal and Rosnow 2008, p. 763). We claim that these Patent Rank scores represent a measure of radical innovation; therefore, we proceed to verify this claim by considering face validity, concurrent validity, and predictive validity (Smith and Albaum 2005). In this section, we begin by considering the top results at face value—would a reasonable person deem our top results to represent radical innovations. We next cross-validate our top results with a panel of experts from Wharton. Finally, we verify the predictive-validity claim by demonstrating that patent stocks equates to abnormal market returns, for some ﬁrms.

5.6.1 Face Validity: Top 10 Innovations by Model and Year

To address face validity, we ask ourselves: are these top patent innovations radical? In Tables 19, 20, 21, and 22, we report the patent numbers (and Patent Rank) for the top-10 patent innovations for each respective model4: (cs), (cc), (ms), and (mc) where year ending represents the last year of the appropriate model. Details about each patent can be found using the USPTO5 website. Though space limitations prevent us from discussing all the longitudinal results for close to six million patents, we representatively discuss the merit of the results by providing speciﬁc illustrative examples.

3We note that about 1% of the total patents (58,521) are dangling nodes (post-1976) without any speciﬁed “prior art”. 4 Temporal models for 1976–1980 are deﬁnitionally equivalent; that is, πms(7680) = πcs(7680). 5http://patft.uspto.gov/netahtml/PTO/srchnum.htm

93 [Table 19 about here.]

[Table 20 about here.]

[Table 21 about here.]

[Table 22 about here.]

We review our findings. Within the field of medicine, we see medical devices such as X- ray machines (3,778,614; 4,258,264), MRIs (4,318,043), and as simple as syringes (4,425,120); medicines such as antibiotics (3,950,357), antibacterials (4,166,115), and pain killers (3,622,615); medical treatments including cardiovascular drugs (3,622,615; 4,105,776; 4,532,248; 4,880,804), bronchial hormones (4,054,595), and gastric treatments (4,128,658); and medical processes such as producing chimeras (4,237,224), tumor antibodies (4,172,124), and mapping/synthesizing/cloning DNA (4,683,202; 4,683,195; 5,536,637). Within the field of agriculture, we see insecticides (3,835,176; 4,024,163), hybrid corn (4,812,599; 5,367,109; 5,304,719; 5,850,009; 5,276,263), soybean regeneration (5,968,830), and mutated dwarf petunias (5,523,520). Within the field of physics, we see processes to separate uranium isotopes (3,772,519) and to prepare supercon- ducting materials (4,826,808). Within the automotive industry, we see the catalytic converter (3,827,237; 3,702,886) and the distributor (4,002,155). Within general manufacturing, we wee high-temperature plastics (3,694,412) and stereolithography (4,575,330) using such plastics. Within computer technology, we see metal treatments (3,856,513) that allow silicon to be bonded to steel (2,813,048); subroutine programming and storage (3760171); transistors (6,413,802), semi- conductors (4,064,521), silicon integrated circuits (4,353,086), miniaturization of such circuits (5,045,417), RAM (4,742,018; 5,640,343), and LCDs (4,367,924). Within the printing domain we see ink-jet (4,723,129; 4,463,359; 4,345,262; 4,558,333; 4,313,124; 4,459,600; 3,747,120; 4,740,796) and heat-based innovations (4720480; 4399209). We even see an internet-related innovation—the first web browser with caching/printing capabilities(5,572,643). Of these, we note

94 that government regulations and policies may have directly motivated some innovations; specifically, emission standards in the automotive industry with improved exhaust systems (catalytic converter) and more efficient engines (distributor) or the shielded hypodermic syringe to prevent the spread of disease once a syringe is discarded. We emphasize that these innovations represent various combinations of entrepreneurial activities. For example, the ink-jet technology can be used in many manufacturing processes unrelated to the consumer desktop printers. Stated generally, one innovation may lead to multiple entrepreneurial opportunities (Shane 2001). Other innovations are modules within larger innovations as demonstrated by the many component-innovations described within computer technology (Henderson and Clark 1990). Again stated generally, a unique configuration of multiple innovations may lead to a single entrepreneurial opportunity. Finally some innovations (specifically consider two of the most radical DNA innovations in biotechnology) are never intended to be products; rather, they are process innovations from which commercializable products may develop. We also observe that these radical innovations benefit society, even though it may not directly benefit the firm’s bottom line. For example, the top two radical innovations of the modern era (DNA process innovations 4,683,195 and 4,237,224 from Table 19) were developed by a firm6 that did not survive. However, these radical innovations jump-started the multi-billion dollar genetic engineering industry within the bio-tech sector which ultimately has benefited society. This rein- forces the idea that appropriability defined by the Patent Office for patent approval is very different from the ability of the firm to actually monetize the innovation (Mizik and Jacobson 2003; Golder, Shacham, and Mitra 2009; Tellis, Prabhu, and Chandy 2009).

6Cetus, innovator of the DNA ampliﬁcation technique, was sold to Chiron because of a funding crisis, which was then acquired by Novartis (Wikipedia 2010). As described in the previous chapter (Chapter 4), these process innovations made it possible to replicate DNA. Chiron purchased Cetus contingent on the sale of these two patents for $300 million to Roche which we estimate was a ‘payday’ for Cetus’ investors.

95 5.6.2 Concurrent Validity: Expert Comparison

To continue the validation of our Patent Rank scores, we searched for concurrent rankings of innovations from an external source. We found a study performed by PBS in partnership with Knowledge@Wharton (PBS 2009). To celebrate 30 years of the Nightly Business Report (NBR) on PBS, a viewership survey was performed where the audience was asked “What innovations have changed the way life is lived and the way business is done since NBR premiered in 1979?” Over 1200 viewer responses were summarized. From this list, a panel of eight distinguished scholars from Wharton School categorized and ranked the top innovations (see Table 23). We emphasize that this comparison is imperfect, but it further supports our claim. Although an imperfect proxy to “What are the most radical innovations of the modern era?” the results were presented with a title “Top 30 Innovations of the Last 30 Years.”

[Table 23 about here.]

With these imperfections, we develop a set of decision rules to generally compare our top Patent Rank scores to Wharton’s top innovations. Utilizing the most recent cumulative model (1976–2009) consisting of 5.6 million patents, we searched among our top-300 Patent Rank results to ﬁnd patents innovations relevant to the Wharton innovations. Since our Patent Rank scores are computed comprehensively considering all patent innovations, a top-ranked patent innovation should correspond generally to a top-ranked Wharton innovation. We also reverse the comparison to identify patent innovations that were not found in Whar- ton’s list but were among our top Patent Rank results. Ink-jet technology was one such category of patent innovation. With the development of high-temperature plastics, ink-jet technology can be used in many manufacturing processes unrelated to consumer desktop printers. Although not glamorous, this domain of process innovations are deemed by our measure to be radical among patent innovations.

96 5.6.3 Predictive Validity: Empirical Test of Abnormal Market Returns

To conclude our validation, we empirically test predictive validity at the firm level: does changes in information regarding the patent stock of firms influence the firm’s market returns? For this validation, we utilize7 marginal (mc) Patent Rank scores. To assess a patent at a specific time, we have several options: (1) we use the actual value, (2) we use changes in the actual value, (3) we use

the expected value β, (4) we use changes in the expected value. To build a patent portfolio we can sum any of these four options. From which, we can develop additional valuation-options: (a) we can normalize a ﬁrm’s portfolio by dividing the total score by the number of patents present in the network, an averaging technique; (b) we can create standardized scores within a ﬁrm over time. Within the manuscript, we report results from option (2) above; additional results are available in Appendix J.

Four-Factor Fama-French/Carhart Model

The Fama-French/Carhart four-factor model for computing portfolio returns is deﬁned as (Fama and French 1993; Carhart 1997; Core and Guay 1999; Barber and Odean 2001; Fama and French 2006):

Rjt Rft = αj + βj(Rmt Rft) + sj(SMBt) + hj(HMLt) + uj(UMDt) + jt − −

where j represent a portfolio, t is a month in years 1980–2009, Rjt is the median return for portfolio

j at time t, Rft is the risk-free rate for time t, Rmt is the market return for t, βj is the classical

CAPM β for portfolio j, sj is the coefﬁcient associated with size of market capitalization (SMB as

small minus big) for portfolio j, sj is the coefﬁcient associated with value/growth (HML as high

minus low book-to-market ratio) for portfolio j, uj is the coefﬁcient associated with momentum

(UMD as up minus down) for portfolio j, jt is the disturbance (residuals from unobservables) for

7This model was selected for two reasons: (1) the marginal form captures recent utility of a patent and its stock value to the firm, (2) the combined form has beneficial distribution properties. See Chapter 3 for more details regarding the model specifications.

97 portfolio j at time t, and αj +jt is deﬁned as the abnormal return for portfolio j. Abnormal returns represent excess returns; that is, returns above and beyond the market’s risk-free rate. This model controls for risk where risk is decomposed into the four factors: market risk, ﬁrm- size risk, value/growth risk, and momentum risk. Although industry is another control that may be considered, the proposition (patents and abnormal returns) is tested in this most basic form.

Firm Selection

Since the financial market is considered to be a zero-sum game (that is, the expected abnormal returns are zero), we initially consider the S&P 500 for years 1976–2009; this list consists of 1248 firms as the S&P 500 is constantly changing. In addition, matching patents to firms is a nontrivial task, so we attempt to efficiently select ‘survival’ firms that have a reasonable tenure as a member of the S&P 500. We report our sample of 381 firms in Appendix I. For each selected firm, we do a search-and-matching technique to associate patents with respective firms. We delineate this procedure in Appendix H. Since we have annual changes in patent stock, we choose to aggregate financial returns more granularly. From the CRSP/Compustat database, we harvest monthly returns for each firm in the sample. By construction of the annual patent stocks, our timeframe is further reduced to 1981– 2009. In total, this patent-stock/firm-stock matching for these 381 firms offers 83,457 month-firm observations to do our analysis. For example, we compute the change in patent stock for a firm for the year 1995. This change includes information about the total patent stock at the end of 1995 and subtracts it from the total patent stock at the end of 1994. In an efficient market, this information should diffuse throughout the year, so the change is linked to monthly returns during the year 1995. A portfolio is created based on some decision criteria (e.g., a firm has patents or doesn’t) and all month-firm observations that fit the criteria are thrown into a portfolio. For a given month, we utilize the median return from the portfolio in the Fama-French/Carhart model.

98 Financial Equity Strategy and the Portfolio-Difference Approach

A difference portfolio represents a financial equity long-short strategy. Simply stated, a wise investor would take a long position (hold onto or buy financial shares) with firms that are demonstrating high financial returns; conversely, the same wise investor would take a short position (let go or sell financial shares) with firms that are demonstrating low or negative returns. In context of our research, the most basic difference portfolio to consider would be going long with firms that have SOME patent innovations and going short with firms that have NO patent innovations. This difference portfolio (SOME patents less NO patents) is created and modeled using the Fama- French/Carhart model.

Basic Results for SOME less NONE

In Table 24, we report four basic portfolios: (1) the portfolio of returns for ALL firms, (2) the portfolio of returns for firms with SOME patents, (3) the portfolio of returns for firms with NO patents, and (4) the difference (SOME less NONE) portfolio. For each portfolio, we report the coefficients for each of the four Fama-French/Carhart factors, and we report the intercept, which is conventionally described to represent the abnormal return. Since these returns are monthly returns, we also report an equivalent annual return. We emphasize that an abnormal return represents a financial return above and beyond the expected market return.

[Table 24 about here.]

The ﬁrst model suggests that our selection of 381 ﬁrm is slightly skewed towards positive

returns (2.675% abnormal annual return); however, this result is not statistically significant (t = 0.941). Similar conclusions can also be drawn from the second and third model. Likewise, the fourth model suggests that investing in firms with SOME patents provides slightly higher abnormal returns (0.733%); again, this result is not statistically significant.

99 Decile-Difference Portfolios Based On Patent Stock

From above, we have some evidence that supports our proposition regarding abnormal financial returns, but we have yet to utilize information regarding the nature of the patent stock (that is, we only considered: does a firm have patent stock or not). To proceed, we divide firm-patent-stock returns into ten groups, or deciles. For a given year, we consider all firms with patent stock8, and order the firms by their stock. Firms with the least amount of patent stock, the lowest 10%, or

decile, go in the ﬁrst group (Decile1), the next 10% go into the second group (Decile2), the third

10% go into the third group (Decile3), and so on, up to ﬁrms with the most amount of patent stock,

the top 10%, which go into the tenth group (Decile10). Using these decile-groups, we then create difference portfolios as before; we ‘go long’ with the decile portfolio and ‘go short’ with the no patents portfolio. In Table 25, we report the ten difference portfolios. Deciles 1,2,3, and 5 show statistically significant positive abnormal returns. We also note that higher Deciles (7, 8, 10) suggest negative abnormal returns. Such results align with the Blue Ocean strategy—if you have secured intellectual property around a patent innovation that has value, but not severe competition, you demonstrate significant financial returns.

[Table 25 about here.]

8In this case, we deﬁne the patent stock as the actual change in stock. We develop two measures of patent stock (actual vs. modeled) and with a measure, we develop four manifestations (total stock as TOTAL, changes in total stock as DELTA, average change within a ﬁrm as NORM, and average changes standardized as Z). Appendix J considers all models and their correlations.

100 5.7 Conclusion

We have presented Patent Rank as a precise, robust measure of a patent’s intrinsic value. Uti- lizing the basic results from the (cs) model, we have conceptually linked patent innovations to radical innovation using face validity and concurrent validity. Finally, utilizing longitudinal (mc) Patent Rank scores, and a model for unique diffusion patterns, we can develop a financial portfolio strategy from a firm’s patent stock. Empirically, we demonstrate that patent innovations lead to abnormal financial returns in some cases. Future research should address moderating effects of these findings and account for additional factors as controls (industry, research and development expenditures, advertising expenditures, and so on). Such an approach may require a Heckman two-step model formulation: a first model that accounts for additional factors and a portfolio model that utilizes the residuals to identify abnormal financial returns (Mizik and Jacobson 2003, strategic emphasis).

101 CHAPTER 6

CONCLUSION

“A model is not itself a theory; it is only an available or possible or potential theory until a segment of the real world has been mapped into it. Then the model becomes a theory about the real world. As a theory, it can be accepted or rejected on the basis of how well it works. As a model it can only be right or wrong on logical grounds. A model must satisfy only internal criteria; a theory must satisfy external criteria as well.” (Eyring 1963, p. 5)

Patent Rank is a mathematical model to describe the patent network. In this dissertation, I have attempted to test Patent Rank in the study of entrepreneurial innovation. The specified model accounts for the inherent variability of a patent’s quality. Trajtenberg (1990b) identified the importance of cautiously considering the extreme variability among patents, in terms of their intrinsic valuation; in this dissertation, I do just that. First, I more precisely valuate patents using not only the relationship of subsequent citations (forward citations), but also include prior-art citations (backward citations) and the endogenous value of each patent and citation in the entire patent network. Patent Rank reduces bias in measuring a patent’s intrinsic value. Second, I address variability by modeling the nonlinear S-curve at the patent level, thereby capturing its unique diffusion pattern. Third, I carefully consider the matching of patents to firms (see Appendix H for details). In the process of developing these precise methodologies using the comprehensive patent dataset, key findings are developed.

102 6.1 Summary of Contribution

To summarize:

Variability Using patent data requires careful methodologies that consistently considers the extreme nature of rare events.

First Principles By law, an approved patent represents an innovation, to a basic degree.

Patent Rank Assessing a patent innovation objectively can beneﬁt marketing’s pursuit of subjective measures.

Appropriability The ability to convert a patent innovation into ﬁnancial proﬁts is different than asking the question: ‘is a patent innovation radical?

Market Returns Abnormal financial returns can be demonstrated among firms with patent stock. More research needs to address the moderating influences on these abnormal returns.

I conclude by discussing this dissertation in context of patent-related topics. First, I generally consider the macro question ‘Does Patent Protection Beneﬁt Society?’ Next, I consider a few important legal implications of patent innovations. Then, I discuss the most radical patent innovations of the modern era, identify the complexity of the legal issues and venturing concern in developing a marketing strategy, and delineate how Patent Rank can help those who manage radical innovation. Finally, I summarize the importance of Patent Rank as a mathematical model in the future development of scientiﬁc theory for entrepreneurship and innovation.

6.2 Does Patent Protection Beneﬁt Society?

Would society benefit more with or without patent protection? Certainly there are financial costs associated with developing a patent innovation, and without the monopolistic protection, some argue that it would not be in the best interest of the firm to pursue the innovation. Others argue that patent protection stifles innovation. Further, arguments also exist about the optimal length of patent protection (Nordhaus 1972; Scherer 1972; Kamien and Schwartz 1974; Gilbert and Shapiro 1990). Should a patent have unlimited (infinite) protection? Or should the protection not even exist?

103 The word patent is derived from Latin patere meaning to lay open or disclose, a shortened form of the original meaning, letters patent. The monarch of England would issue an open let- ter to the innovator, guaranteeing monopolistic protection for a specified, and varying, length of time. The Licensing Act (1662), and the Statute of Anne (1709) were the basis of common law in the American Colonies. Two of the founding fathers of this nation, Benjamin Franklin and Thomas Jefferson, thought this issue of intellectual property, would be the key element of debate at the Constitutional Convention. These innovators in both politic and science had intrinsic beliefs that a society based on an open-innovation (Chesbrough 2003) paradigm would benefit. In personal correspondence, they spoke of the problems associated with the power of the monarchy and how the abuse of this power created large corporations that economically enslaved the poor, working classes. They believed the focus of the Constitutional Convention would be to determine if the United States would inherit intellectual property law from the English tradition and if large corporations would be allowed to exist. They felt these two principles contradicted their forward- thinking views of democracy, and both the monarchy (government) and large corporations could unduly exert monopolistic power in the market which generally is deleterious to society. The business men at the convention, specifically James Madison and John Adams, recognized the concerns of Franklin and Jefferson. As a result, both Franklin and Jefferson were involved with the drafting of the First Copyright Law (1790) in the United States. The law was signed by President George Washington three months after the death of Franklin. Thomas Jefferson, the Secretary of State, become the guardian of enforcing this law, and granting patents. Ultimately, both of these issues were given a backseat to the issue of slavery and states-rights at the Constitutional Convention. Where would our nation be today if this issue were given more attention during the founding of this country? Would we be a more advanced society? Or less advanced?

104 6.3 Patent Rank and Marketing Science

Since Schmookler (1966) first studied patent innovations in the railroad industry, those who study markets have been interested in using the universal data of patents to study innovations. In this dissertation, I present Patent Rank as an objective measure of a patent’s intrinsic value. Utilizing Patent Rank, I demonstrate that Schumpeterian shocks exists, can be measured and modeled. Such shock assessments are invaluable in the audit of entrepreneurial opportunities and ultimately influence a firm’s financial performance. Indeed Patent Rank serves as an objective measure of radical innovation. Future research can build from this basic model within the network context. I propose including other subnetworks in the analysis. The central network, of patents and citations, can be enveloped by the following nested networks: the network of inventors, the network of technology classifications, the network of firms, the network of countries, and so on. These additional networks will further enable us to understand the patent network utilizing more information. Most importantly, with this extended network, it will help us identify all patent innovations within an innovation class (Trajtenberg 1990b). This identification can be further enhanced by utilizing another principle of networks, connectedness, in addition to eigenvector centrality. This principle will define similarity and relatedness of patent innovation within the network, which has been described in other network research as ‘closely knit communities’ (Lempel and Moran 2000, 2001; Najork, Gollapudi, and Panigrahy 2009).

Legal Implications

Understanding how to manage innovations legally is very important. Patent disputes are strategic signals to the market that can have significant payouts; ignorance is not defensible in a court of law. Consider the Blackberry products by Research in Motion (RIM). The company failed to secure its intellectual property in both the US and Europe. In Europe, the legal differences between ‘first to invent’ and ‘first to file’ cost Blackberry $300 million.

105 Most disputes are delayed and appealed for years. The rationale is that delays will ultimately define punitive damages based on disclosed profits rather than expectations of profits. This is exemplified by a $1 billion lawsuit filed by Brigham Young University against Pfizer, on behalf of Dr. David Simmons, who was not appropriately given credit for the first COX-2 inhibitor patent innovation (Court 2009, Patent # 5,760,068). Recently, Pfizer has been fined about a $1 million for delaying the process, but ultimately, it may be in Pfizer’s best interest to delay as long as possible. This category of drugs (Celebrex and Bextra by Pfizer and Vioxx by Merck) has come under attack in civil action lawsuits. The longer Pfizer delays, the more information they have to demonstrate lower than expected returns on this patent innovation. In an industry that is facing a ‘patent cliff,’ these delays are necessary for survival (MCardle 2010; Aboulnasr, Narasimhan, Blair, and Chandy 2008). Strategic implications of patent-related lawsuits is not new. Recently, with the popularity of smartphones, Apple has filed a lawsuit with HTC (Court 2010) over ten patents that have been infringed1. In turn, HTC countered with its own lawsuit against Apple (VanHemert 2010) over five2 of its patents. Other lawsuits are deemed by infringers to stifle innovation. They accuse the prosecuting parties to be patent trolls who never intend to commercialize the innovation. Hiding behind the law, these patent trolls are protecting their exclusionary rights by suing others who attempt to bring the innovation to the market. Legislation is currently before Congress to change ‘first to file’ and ‘first to invent’ here in the United States, to align patent-protection with international standards (O’Banion 2011). Will this new law curtail patent-trolling or encourage it?

Patent Rank as a Valuation Vehicle

Valuation is a key variable in understanding legal challenges of a patent’s validity and competitive infringement. Utilizing the procedures outlined in this dissertation, any patent can be assessed

1Patent #s 7,362,331;7,479,949;7,657,849;7,469,381;5,920,726;7,633,076;5,848,105;7,383,453;5,455,599;6,424,354. 2Patent #s 6,999,800; 5,541,988; 6,058,183; 6,320,957; 7,716,505.

106 over time. To illustrate, I discuss the merit of the two most valuable patents in the patent network, according to Patent Rank. These two patents are DNA amplification/cloning patents (# 4,683,202 and # 4,683,195). Based on expected trajectories, these two patents were classified in the 99th percentile on both value and growth in 1990. However, the assignee, Cetus, was not fully aware of the intrinsic value of these two patent innovations. With a myopic strategy to compete in pharmaceuticals, Cetus was facing a major financial crisis. Ultimately, Cetus was acquired by Chiron, predicated upon the purchases of these two patents by Roche for $300 million, which was successfully executed after DuPont’s legal attack on the validity of these patents was thwarted. Certainly the VCs involved with the transaction finally had a payday, but I would argue that their inability to objectively valuate these two patents left a lot of money on the table. In my estimation they sold the two patents for a fraction of their total value.

6.4 Final remarks

We live in a beautiful world. We live in a world of opportunity and change, a world that recreates itself from its past in efforts to build a better future. In that spirit, I introduce Patent Rank as an objective measure of radical innovation to the marketing community. Recognizing that objectivity is essential in science’s pursuit for truth, I hope that Patent Rank can contribute to the advancement of marketing science in its study of innovation.

107 APPENDIX APPENDIX A

F RATIO DERIVATION B OF PATENT RANK

Prior to approval, during the patent application review process, the applicant must provide relevant information related to the ﬁeld/background of the invention and delineate unique claims (e.g., describe its radicalness). Through legal review, “prior art” must be acknowledged and cited within the patent; U.S. patent documents, scholarly articles, and foreign patents are the most common citations. Of these, the most relevant to our research are the citations to historic patents referred to as backward citations. By law, “prior art” is carefully considered as it determines the monopolistic scope of the innovation. Throughout the patent review process, parties contest the inclusion and/or omission of citations. Even after a patent is approved, a lawsuit may arise because appropriate “prior art” was not fully acknowledged. Therefore, backward citations, representing a borrowing of past innovation in the current innovation (Golder et al. 2009), discount the current patent’s radicalness (e.g., the patent is incremental). Conversely, if in the future a new patent cites the current patent, that future patent is borrowing from the current patent which increases the current patent’s radicalness; such citations are referred to as forward citations which Chandy et al. (2006, p. 7) succinctly characterize: “the greater the number of forward citations, the higher is the importance.”

Taken together, any patent X can be appraised at any point in time based on both its backward and forward citations:

Wb X Yf ← → where Wb represents a backward citation of X and b represents the index of the backward citation, for b = 1,...,B; thus, B represents the total number of backward citations. Similarly, Yf represents a forward citation of X and f represents the index of the forward citation, for f = 1,...,F ; F represents the total number of forward citations. Backward citations represent a borrowing of

109 radicalness to X. Forward citations represent a lending of radicalness from X. The direction of the arrows represent the temporal dependency of the citations; that is, are the citations forward-looking or backward-looking from X (Trajtenberg et al. 1997)? Therefore, we can assess X based on its entire genealogy—its upstream antecedents and its downstream descendants.

Ratio Concept

How can the citation structure be used to evaluate the innovativeness of a patent? Conceptually, since forward citations augment and backward citations discount, we deﬁne the following ratio:

F Conceptual (X) = , B

where F represents the count of forward citations and B represents the count of the backward citations for patent X. Mathematically, this ratio represents how many times the forward citations contain the backward citations. A ratio less than one would represent an innovation that is relatively incremental (borrowing from past innovations) whereas a number greater than one would represent an innovation that is relatively radical (lending to future innovations). For mathematical stability and invertibility1, we recast this conceptual ratio into a basic ratio:

1 + F Basic (X) = . 1 + B

We propose that this basic ratio provides a rough value of the importance of a patent (e.g., its radicalness) based on both its forward and backward citations.

1Mathematical stability means we prevent division by zero by adding to the denominator; invertibility means we prevent division by zero of the ratio’s inverse by adding to the numerator. Although this is analagous to ridge regression (introduction of bias to provide a better variance proﬁle and ensure computation), this is generally referred to as the bias of a ratio estimator since ratios are commonly altered to prevent division by zero (Cochran 1977; Placket 1981). Adding 1 or 1/2 is a common practice used in deﬁning ratios in proportional statistics, including examples such as the Lincoln-Petersen or Chapman estimators (Evans and Bonett 2004). In addition, we tested the robustness of the basic (recursive) ratios by randomly simulating 14 links in a 10 network model, and convergence of scores always occurs.

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= 0.667

Inverse = 1.500

In this case, we conclude that patent P5 is incremental. Using the inverse of the ratio, we state that it has 1.5 times more backward-borrowing than future-impact. Rather than treating each patent equally within the basic ratio, each citation for a patent can also be weighted according to its basic ratio; that is, a more accurate ratio would also evaluate each citation’s degree of innovativeness (both forward F and backward B). In other words, if many important patents cite a particular patent X, then the importance (radicalness) of X goes up. Hence, the weight is proportional to how important the future patents are that cite patent X. Similarly, the weight for historic patents that cite patent X go up if they are themselves important which ultimately discounts the overall value of X. We deﬁne a new ratio, a one-level recursive ratio, to include the importance of the citations of the citations of X.

F 1 + f=1 Basic (Yf ) Recursion1(X) = B 1 + Pb=1 Basic (Wb) P We now calculate this recursive ratio for P5. This new calculation includes more information relevant to patent P5. Speciﬁcally, it weights each of the two backward citations (P1 and P2) and the one forward citation (P7) based on their respective basic ratios.

112 1 + Basic (P7) Recursion1(P5) = 1 + Basic (P1) + Basic (P2)

1+0 1 + ( 1+4 ) = 1+3 1+3 1 + ( 1+0 ) + ( 1+0 )

= 0.133

Inverse = 7.500

Comparing the basic ratio of P5 to its one-level recursion ratios, we draw the same conclusion regarding its innovativeness. The additional information further reﬁned the value of the ratio.

Using the inverse of the ratio, we state that patent P5 has 7.5 times more backward-borrowing than future-impact. We next deﬁne a two-level recursive ratio, wich accounts for the importance of the citations of the citations of the citations of X.

F 1 + f=1 Recursion1(Yf ) Recursion2(X) = B 1 + Pb=1 Recursion1(Wb)

We now calculate this new recursive ratio for PP5. This calculation includes more information relevant to patent P5. Speciﬁcally, it weights each of the two backward citations (P1 and P2) and the one forward citation (P7) based on their respective one-level recursive ratios.

113 1 + Recursion1(P7) Recursion2(P5) = 1 + Recursion1(P1) + Recursion1(P2)

1 1 + P P P P = 1+Basic( 1)+Basic( 2)+Basic( 5)+Basic( 6) 1+Basic(P5)+Basic(P7)+Basic(P8) 1+Basic(P4)+Basic(P5)+Basic(P7) 1 + 1 + 1

1 1 + 4/1 4/1 2/3 4/1 = 1+( )+( )+( )+( ) 1+(2/3)+(1/5)+(2/4) 1+(2/3)+(2/3)+(1/5) 1 + 1 + 1

= 0.182

Inverse = 5.498

We see that this second-level recursion further reﬁnes the value of incrementalness. Using the

inverse of the ratio, we state that patent P5 has 5.5 times more backward-borrowing than future- impact.

Optimally, this recursion process should converge as the number of iterations increases (i → ); this value is called a unique score. That is, an optimal solution considers all recursive levels ∞ of citations

F 1 + f=1 Recursioni−1(Yf ) lim Recursioni(X) = , i→∞ B 1 + Pb=1 Recursioni−1(Wb) P where i represents the level of recursion and ‘Recursioni−1(Yf )’ and ‘Recursioni−1(Wb)’ would represent the ratios calculated at the previous level of recursion. This full-recursive ratio fully

114 addresses the question “what about the importance of every citations’ citations?” Conceptually, this recursive fraction represents an infinite continued fraction where each iteration of the recursion creates an additional nesting of fractions. A few well-known examples of infinite continued fractions is presented below; specifically, these fractions define irrational num-

bers using recursive patterns of rational numbers. The Golden ratio φ has a nested pattern that is elegantly simple [1; 1, 1,..., 1] and has a fractal nature around the number one—any scale of the fraction looks like any other scale. Only the value of the recursive denominator changes for the square root of two [1; 2, 2,..., 2]. The last example, Euler’s number e demonstrates a more sophisticated recursive pattern [1; 0, 1, 1, 2, 1, 1, 4, 1, 1, 6, 1, 1, 8, 1, 1, 10,...].

Golden ratio Pythagoras’ bane Euler’s number

converging value φ 1.61803 ... √2 1.41421 . . . e 2.71828 ... ≈ ≈ ≈

1 1 1 fractional form 1 + 1 1 + 1 1 + 1 1+ 1 2+ 1 0+ 1 1+ 1 2+ 1 1+ 1 1+ 1 2+ 1 1+ 1 1+ 1 2+ 1 2+ 1 1+ 1 2+ 1 1+ 1 1+ 1 2+ 1 1+ 1 1+ 1+etc. 2+ 2+etc. 4+ 1+etc.

We note these examples because they demonstrate how infinite continued fractions may converge to a definite, albeit irrational, value. For our recursive ratios, we target a definitive value that can be assigned to each fraction. It is possible that a fraction will not converge; the recursive scores could oscillate or get larger and larger. We attempt to assure convergence by stabilizing the basic ratio—by adding 1 to the numerator and denominator (as is commonly done by researchers who develop ratio estimators), the toy example converges. The recursive model includes all relevant information to calculate a specific and unique score for each patent. We define our optimal recursive ratio as a Markov chain; that is, a stochastic process in which any specific state in a series is dependent only on the previous state of the series. This memoryless

115 property is an important characteristic in the mathematics of Markov chain theory. If additional properties of the Markov chain exist, convergence to a limiting distribution becomes certain. To demonstrate how our recursive ratio is a Markov process using the toy example, below we com-

puted ratios for each patent at each level of recursion. Reconsider P8. We identify (from row P8)

its three backward citations P1, P4, and P6; and we identify (from column P8) its one forward citation. As described previously, the link structure of each patent deﬁnes and updates the next

iterative calculation, meaning the i 1 calculation necessarily needs to be evaluated before the i − calculation can be defined. Each level of iteration further refines a specific score and segregates its

value from other scores. This means that only the previous (i 1) recursive scores from relevant −

patents P1, P4, P6, and P10 are needed to calculate the current (i) recursive score for patent P8. Therefore, this is a Markov process.

116 Patent Number P1 P2 P3 P4 P5 P6 P7 P8 P9 P10

iteration

4/1 4/1 3/1 2/3 2/3 4/1 1/5 2/4 1/2 1/3 Basic 0 ⇓ ⇓ ⇓ ⇓ ⇓ ⇓ ⇓ ⇓ ⇓ ⇓ 4.000 4.000 3.000 0.667 0.667 4.000 0.200 0.500 0.500 0.333

Recursion 1 2.367 2.533 2.167 0.188 0.133 2.033 0.073 0.138 0.250 0.182 2 1.344 1.394 1.438 0.200 0.182 1.393 0.124 0.212 0.316 0.315

Ratio Calculations 3 1.517 1.506 1.515 0.316 0.301 1.651 0.188 0.334 0.410 0.384 4 1.823 1.805 1.727 0.332 0.295 1.906 0.167 0.309 0.398 0.335 5 1.771 1.795 1.729 0.289 0.252 1.811 0.146 0.264 0.367 0.311 ...... 1.697 1.710 1.667 0.292 0.262 1.764 0.156 0.280 0.375 0.329 ∞

Score 1.6972 1.7099 1.6672 0.2923 0.2622 1.7636 0.1555 0.2795 0.3749 0.3286

Percentage 19.89% 20.04% 19.54% 3.43% 3.07% 20.67% 1.82% 3.28% 4.39% 3.85%

Rank 3 2 4 7 9 1 10 8 5 6

Utilizing this toy example, we see that our recursive ratios converge. Below, we report the convergent values for each patent. These convergent values represent the iterative importance of each patent based on the importance of all relevant information from the network. We deﬁne radicalness using these convergent scores; that is, radicalness is a function of understanding the importance of a patent by considering all citations (both forward and backward) within the patent network. With the radical scores, we can then mathematically describe the relative importance of each patent as a percentage by normalizing the convergent scores, or merely by ranking the convergent scores.

117 Example: Patent X = 6,040,309

To review the ratio concept, we consider an arbitrary pharmaceutical patent #6,040,309. Pfizer filed this patent on December 3, 1998 and the U.S. Patent Office approved the application on March 21, 2000. The innovation represents a chemical compound (a molecule) that can treat restenosis – that is, it prevents blood vessels from narrowing. The patent has origins in both Great Britain and the United States, though a commercialized product for this patent has yet to appear in the FDA database in the United States. As part of the patent application review process, this patent cited

B = 3 backward citations. As of December 2008, the patent has received F = 5 forward citations (that is, 5 patents since March 21, 2000 have cited this patent).

Backward Citations: Patents Loaning to X Forward Citations: Patents Borrowing from X

W3 W2 W1 XY1 Y2 Y3 Y4 Y5

patent 5,440,046 5,482,960 5,703,116 6,040,309 6,162,930 6,350,777 6,555,555 6,593,374 7,335,680

granted Aug 8, 1995 Jan 9, 1996 Dec 30, 1997 Mar 21, 2000 Dec 19, 2000 Feb 26, 2002 Apr 29, 2003 Jul 15, 2003 Feb 26, 2008

forward 1 53 16 5 17 7 1 3 0

backward 35 1 3 3 5 10 16 19 29

118 Therefore, we can compute its basic ratio.

1 + 5 Basic (6, 040, 309) = 1 + 3

6 = 4

= 1.5

Utilizing the basic ratio as a rough estimate of the patent’s radicalness, we conclude that this patent is relatively radical as it has 1.5 times more future-impact than backward-borrowing. However, this basic ratio still has similar limitations to traditional measures, as it treats each citation equally. Rather that treating each patent equally (traditional patent counts), each patent that cites the Pﬁzer patent or is cited by the Pﬁzer patent can be weighted according to its own radicalness measure.

More detail about the citations related to X and includes information about their respective forward and backward citations is provided below. Backward citations W1 and W2 severely discount the radicalness of X whereas forward citation Y1 augments its radicalness.

119 The one-level recursive ratio includes more information, and therefore, should provide a more accurate measure of the patent’s radicalness.

F 1 + f=1 Basic (Yf ) Recursion1(6, 040, 309) = B 1 + Pb=1 Basic (Wb) P

1+17 1+7 1+1 1+3 1+0 1 + ( 1+5 ) + ( 1+10 ) + ( 1+16 ) + ( 1+19 ) + ( 1+29 ) = 1+16 1+53 1+1 1 + ( 1+3 ) + ( 1+1 ) + ( 1+35 )

5.078 = 32.306

= 0.1572

Inverse = 6.362

We now conclude that this patent is relatively incremental. Using the inverse of the calculated ratio,

we state that patent X has 6.36 times more backward-borrowing than future-impact. This addition of information reversed our conclusion regarding the radicalness of patent X. If we included more information, would the conclusion reverse again or stabilize? It depends most on the importance of the 53 forward citations and 1 backward citation for W2; the 1 forward citation and 35 backward citations for W3; the 17 forward citation and 5 backward citations for Y1; and the 3 forward citation and 19 backward citations for Y4.

120 The computation of the two-level recursive ratio again provides more information, but at a computational cost. To ﬁnd this next ratio, 216 more patents need to be considered, of which 194 are unique.

F 1 + f=1 Recursion1(Yf ) Recursion2(6, 040, 309) = B 1 + Pb=1 Recursion1(Wb) P 1 + (0.2) + (0.03) + (0.034) + (0.01) + (0.007) = 1 + (0.266) + (3.57) + (0.018)

1.2726 = 4.8504

= 0.2624

Inverse = 3.811

After this iteration, we again conclude the patent is relatively incremental. It appears that our score is stabilizing; from 1.5 to 0.1572, and now to 0.2624. The recursive value may be converging to a fraction less than one, so we could conclude that this patent is incremental in nature. As demonstrated, the computation at each recursive level becomes more and more costly as more information from the network has to be included: specifically in this case, the unique patents needed for the first few ratio calculations3 are as follows: 1, 8, 194, 1896, and 23,132. This inefficient method represents a quadratic N 2 computation model—for N patents, approximately N 2 computations must be performed. Trillions of computations would need to be performed to

3The number of calculations are growing exponentially at a rate of about 13, which roughly corresponds to the average backward citations per patent (for patents granted in 2000, the average was 11.4 backward citations per patent).

121 analyze the millions of utility patents. This lack of computational efﬁciency is compounded by the natural growth of the patent network. About every two weeks, new patents are granted, and the network updates which necessarily means more computations.

Forward and Backward Correlations

Below, we present correlations of the Patent Rank scores with the count of backward and forward citations of the patents within the network. As expected, forward citations positively correlate with Patent Rank scores, and backward citations negatively correlate. Both the strength and direction of these correlations match our conceptualization of Patent Rank. That is, forward citations augment and backward citations discount. The correlation with forward citations, in absolute value, is an order of magnitude larger than the correlation with backward citations which, we posit, is partly due to lack of information; about 1/3 of all patents (e.g., dangling nodes) have no backward-citation information available. Additional comparisons support our position; the cumulative models (cs) and (cc) correlate stronger with forward citations than moving-window models (ms) and (mc); whereas, the moving-window models (ms) and (mc), in absolute value, correlate stronger with backward citations.

122 Model πcs(7609) πcc(7609) πms(0509) πmc(0509)

Forward Citations Correlation 0.737 0.716 0.693 0.674

Backward Citations Correlation -0.031 -0.032 -0.074 -0.072

Distribution of Double Log Transformation Distribution of Double Log Transformation Distribution of Double Log Transformation Distribution of Double Log Transformation 0 0 0 0 0 0 0 0 2 4 0 0 0 0 0 0 0 3 0 1 0 0 0 0 0 0 0 0 0 5 5 0 1 0 2 3 0 0 8 0 0 0 0 y y y y 2 c c c c 0 0 0 n n n n 0 0 0 e e e e 0 0 0 u u u u 0 0 6 0 q q q q 0 1 2 e e e e 0 r r r r 5 F F F F 1 0 0 0 0 0 4 0 0 0 0 0 0 1 0 0 0 5 1 0 0 0 0 0 2 0 5 0 0 0 0

-6 -4 -2 0 2 -6 -4 -2 0 2 -6 -4 -2 0 -6 -4 -2 0 Distributional Properties Nontrivial (cs) Patent Scores Nontrivial (cc) Patent Scores Nontrivial (ms) Patent Scores Nontrivial (mc) Patent Scores Summary M=-1.036, SD=1.07 M=-1.005, SD=1.10 M=-2.095, SD=1.09 M=-2.088 SD=1.12

123 APPENDIX B

GLOSSARY OF TERMS

Backward Citation reference within a current patent to a historic patent, graphically described as an outbound link, and conceptually discounts the radicalness of the innovation

Core Patents patents having both forward and backward citations

Dangling Nodes patents having forward citations but not having any backward citations

Dud Patents patents having no forward citations [includes solitary nodes (having no forward and no backward citations)]

Entrepreneurial Innovation placing innovation along a continuum (from incremental to radical) and appropriately clas- sifying it (Kirznerian or Schumpeterian) based on its unique conﬁguration of entrepreneurial activities

Forward Citation reference within a future patent to a current patent, graphically described as an inbound link, and conceptually augments the radicalness of the innovation

Full Association a bi-directional, reciprocal link between objects (A B and B A) → → Incremental Innovation entrepreneurial activities that lead to minor change

Kirznerian Innovation entrepreneurial innovation that has a competitive focus

Markov Chain a stochastic process in which any speciﬁc state in a series is dependent only on the previous state of the series

Patent Intrinsic Value the valuation of a patent by considering its forward-citation count (Trajtenberg 1990a) or by considering its Patent Rank

Patent Inventory (or Portfolio) the patents a ﬁrm possesses or has ats its disposal as a resource to determine its product offerings

Patent Lifetime Value the valuation of a patent’s Schumpeterian shock which ultimately deﬁnes a patent as a winner (e.g., a racehorse [fast and valuable] or mule [slow but they get work done]) or a loser (e.g., a show pony [looks good on paper])

Patent Rank application of network citation analysis to the patent system with the inclusion of the Patent Ofﬁce as a super-node

Patent Stock a measure of value for patents a ﬁrm has at a particular point in time (traditional measures are simple patent counts and weighted patent counts; Patent Rank is a new measure)

Patent Value a measure of value for different stakeholders: IPV represents the value of the patent as a resource that can be transferred in the market, FV represents the value of the patent to the ﬁrm based on its current strategies (in conﬁguration with its other resources), and SV represents the value of the patent to society (Trajtenberg 1990b)

Radical Innovation entrepreneurial activities that lead to major change

Scaling Factor a multiplier used in the row-normalization of a matrix (see Appendix C for additional deﬁnitions related to the mathematics and computation of Patent Rank)

Schumpeterian Innovation entrepreneurial innovation that has a creative focus

Schumpeterian Shock a disruption from market equilibrium that can be observed and measured using Patent Rank

Sparse Table (Matrix) a table (matrix) with mostly null values

124 APPENDIX C

MATHEMATICS

Vector Normalization

In the paper we will use different types of normalization1. Generally a normalization of a vector is performed to guarantee that the transformed (normalized) vector has unitary norm, but we use this term in a broader sense. Formally the norm of a vector is deﬁned as follows.

Deﬁnition of Norm: A function . : Cn R is a norm if | | →

a) x 0 and x = 0 if and only if x = 0, | | ≥ | |

b) α x = α x for all α C, | | | | | | ∈

n 2 1. The Euclidean norm: x = xi , | |2 i=1 | |

n pP 2. the 1-norm : x = xi , | |1 i=1 | | P 3. the inﬁnity-norm : x ∞ = maxi xi . | | | | in the following we review some rescaling of a vector that are typically used together with some normalization that are particularly used in this paper. 1. Normalization in the Euclidean norm. x x v = = n 2 . x xi | |2 i=1 | | n 2 We have the the vector v has unit Euclidean-norm,P i.e. vi = 1 and i=1 | | all the entries are such that 1 vi 1. − ≤ ≤ P 1A normalization of a vector is any multiplication (or division) by a nonzero constant. As we explained this operation changes the norm of the vector but not the vector space to which the vector belongs. The basis vector is the same.

125 2. Normalization in the 1-norm. x x v = = n . x xi | |1 i=1 | | n We have the the vector v has unit 1-norm,P i.e. vi = 1 and all the i=1 | | entries are such that 1 vi 1. − ≤ ≤ P 3. Normalization in the inﬁnity-norm. x x v = = n . x ∞ max xi | | i=1 | |

In this case maxi vi = 1, and again 1 vi 1. | | − ≤ ≤ n 4. Normalization to the minimum. It is possible only if min xi = 0. i=1 | | 6 x v = n . min xi i=1 | |

In this case mini vi = 1. | |

5. Normalization to the i-th component. It is possible only if xi = 0. 6 x v = . xi | |

In this case vi = 1, and the sets of indices L(x) = j xj xi and | | { | | | ≤ | |} U(x) = j xj > xi , are such that L(x) = L(v) and U(x) = U(v). { | | | | |} 6. Normalization so that the modulus of the i-th component is α, α = 0. It is 6 possible only if xi = 0. 6 x y = , v = α y. xi | |

In this case vi = α, and the sets of indices L(x) = j xj xi and | | { | | | ≤ | |} U(x) = j xj > xi , are such that L(x) = L(v) and U(x) = U(v). { | | | | |}

126 Eigenvalues

Let A be a n n real matrix. A number λ and an n-vector x, x = 0 are respectively an eigenvalue × 6 and the corresponding eigenvector of A if

A x = λ x.

The linear system above can be rewritten as

(A λ I) x = 0, −

and admits a non null solution only if the determinant, det(A λ I) = 0. The polynomial P (λ) = − det(A λ I), is called a characteristic polynomial, has degree n, and the eigenvalues2 of A are the − n roots of P (λ) = 0.

Denoting by λ1, λ2, . . . , λn the n (eventually complex) eigenvalues of A, the spectral radius of a

matrix is the nonnegative real value ρ(A) = maxi λ . The algebraic multiplicity of an eigen- {| |} value is deﬁned as the multiplicity of the corresponding root of the characteristic polynomial, while the geometric multiplicity of an eigenvalue is deﬁned as the dimension of the associated eigenspace, i.e. number of linearly independent eigenvectors with that eigenvalue.

Note that if x is an eigenvector corresponding to the eigenvalue λ, then for each nonzero constant α we have that α x is an eigenvector corresponding to λ. This is an important fact since it state that an eigenvector remains an eigenvector also after its normalization.

2The fundamental theorem of algebra guarantees that a every non-zero single-variable polynomial with complex coefﬁcients has exactly as many complex roots as its degree, if each root is counted up to its multiplicity.

127 Matrices

T Definition of Transpose: The transpose matrix B = A is such that bij = aji, and has the same eigenvalues of A, while the eigenvectors are not the same. Definition of Nonnegative matrix: A nonnegative matrix A is such that each element in the matrix is not negative; that is, aij 0. ≥ Definition of Irreducible matrix: An n n matrix A, n 2 is reducible if there exists a permu- × ≥ tation matrix Π, and an integer k, 0 < k < n such that

A A k rows T 11 12 } B = Π AΠ =   OA22 n k rows,   } −   where A is a k k matrix, A is (n k) (n k). If A is not reducible, then it is caller 11 × 22 − × − irreducible. There is an easy way to check if a matrix is reducible; it is sufﬁcient to use the underlying graph

is strongly connected. To each square matrix A we can associate a direct graph, that has as many

nodes pi as the dimension of A, and has a direct arc from pi to pj if aij = 0. 6 Deﬁnition of Directed graph: A directed graph is strongly connected if for each pair (i, j), 1 ≤ i, j n, and i = j, there exists a direct path that starting from pi allows to arrive at node pj by ≤ 6 traversing edges in the direction(s) in which they point.

Theorem of Irreducibility: A matrix A is irreducible if and only if the associated direct graph is strongly connected.

128 Perron-Frobenius Theorem

The following Theorem is fundamental in the theory of nonnegative matrices:

Let A = (aij) be an n n nonnegative matrix, that is aij 0 for 1 i, j n. Then, if A is × ≥ ≤ ≤ irreducible, the following statements hold.

1. There is a unique positive real number r, called the Perron root or the Perron-Frobenius eigenvalue, such that r is an eigenvalue of A and any other eigenvalue λ (possibly, complex) is such that λ < r. Thus, the | | spectral radius ρ(A) is equal to r.

2. There exists an eigenvector w = (w1, . . . , wn) of A corresponding to the

eigenvalue r such that all components of w are positive, i.e. wi > 0 for 1 i, j n. ≤ ≤

Note that, r in the previous theorem need not to be the only eigenvalue on the spectral circle of A. To guarantee that there are no other eigenvalues with modulus r we need A to be irreducible and primitive.

Theorem of Primitive Matrix: A square nonnegative matrix A is primitive if and only if Am > 0 for some m > 0.

Markov chains

In this section we review some basic concepts about the theory of Markov Chains.

∞ Deﬁnition of Stochastic Process: A stochastic process is a set of random variables Xt having { }t=0 a common state space S ,S 2,...,Sn . Parameter t is generally thought as time, and Xt { 1 − } represents the state of the process at time t. In the framework of our patent network, the state space is the set of patents, and the stochastic process is represented by the act of passing from a patent to another by following the backward

citations. Xt tells us which patent we are examining at time t.

129 Deﬁnition of Markov Chain: A Markov chain is a stochastic process such that

P(Xt = Sj Xt = Si ,Xt = Si ,...,X = Si ) = P (Xt = Sj Xt = Si ), for each t = 0, 1, 2,.... +1 | t 1 t−1 0 0 +1 | t

This deﬁnition assets that the process is memoryless in the sense that the state the process is at time t + 1 depends only on the state it is in the present time t.

Deﬁnition of Transition Probability: The transition probability pij(t) = P (Xt = Sj Xt = +1 | Si) is the probability of being in state Sj at time t given that the chain was in state Si at time t 1. − So, it represents the probability of moving from Si to Sj at time t. Deﬁnition of Stationarity: A Stationary Markov chain is a Markov chain in which the transition probabilities do not vary with time , that is pij(t) = pij, for all t. Stationary chains are also called homogeneous chains.

We can associate to stationary Markov chains a transition matrix P such that Pij = pij. We have that every Markov chain defines a row-stochastic matrix and vice-versa, every stochastic n n × matrix P defines an n-state Markov chain. Definition of Irreducibility: An irreducible Markov chain is Markov chain whose transition matrix P is irreducible. Definition of Periodicity: A periodic Markov chain is an irreducible chain whose transition matrix

P is imprimitive, that is has more than an eigenvalue of norm 1. We say that a chain is aperiodic if the associated transition matrix is irreducible and primitive. A stationary probability distribution vector for a Markov chain whose transition probability matrix

T T n is P is a vector π such that P π = π, where πi 0, for i = 1, 2, . . . , n, πi = 1. ≥ i=1 So the stationary distribution of an homogenous, aperiodic Markov chain turnsP out to be the Perron eigenvector of P T .

130 APPENDIX D

COMPUTATIONS OF PATENT RANK

Power Method

Below is the computation of the matrix P 100.

0.4056 0.0784 0.0829 0.0789 0.0541 0.0487 0.0757 0.0406 0.0541 0.0406 0.0406   0.4056 0.0784 0.0829 0.0789 0.0541 0.0487 0.0757 0.0406 0.0541 0.0406 0.0406      0.4056 0.0784 0.0829 0.0789 0.0541 0.0487 0.0757 0.0406 0.0541 0.0406 0.0406       0.4056 0.0784 0.0829 0.0789 0.0541 0.0487 0.0757 0.0406 0.0541 0.0406 0.0406         0.4056 0.0784 0.0829 0.0789 0.0541 0.0487 0.0757 0.0406 0.0541 0.0406 0.0406    100   P =  0.4056 0.0784 0.0829 0.0789 0.0541 0.0487 0.0757 0.0406 0.0541 0.0406 0.0406  .      0.4056 0.0784 0.0829 0.0789 0.0541 0.0487 0.0757 0.0406 0.0541 0.0406 0.0406         0.4056 0.0784 0.0829 0.0789 0.0541 0.0487 0.0757 0.0406 0.0541 0.0406 0.0406       0.4056 0.0784 0.0829 0.0789 0.0541 0.0487 0.0757 0.0406 0.0541 0.0406 0.0406       0.4056 0.0784 0.0829 0.0789 0.0541 0.0487 0.0757 0.0406 0.0541 0.0406 0.0406         0.4056 0.0784 0.0829 0.0789 0.0541 0.0487 0.0757 0.0406 0.0541 0.0406 0.0406      The converging vector π is simply a row of this result:

πT = (0.4056, 0.0784, 0.0829, 0.0789, 0.0541, 0.0487, 0.0757, 0.0406, 0.0541, 0.0406, 0.0406).

We remove the super-node,

πT = (0.0784, 0.0829, 0.0789, 0.0541, 0.0487, 0.0757, 0.0406, 0.0541, 0.0406, 0.0406),

and normalize the remaining results such that the sum of all scores equals 1 (divide vector by

131 the sum of the vector). These scores represent a relative probability,

πT = (0.13190.13940.13270.09100.08190.12730.06830.09100.06830.0683).

Partitioned Linear Algebra

We reorder the matrix M based on time, and patent types: Mˆ = M(σtime, σtype). Without loss of generality, we can redeﬁne M to represent the partitioned adjacency matrix, and P as a row- normalization of M

01111111111 0 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10     10000000000 10000000000          10000000000   10000000000           10000000000   10000000000               10000000000   10000000000          M =  10110000000  , P =  1/3 0 1/3 1/3 0 0 0 0 0 0 0  .          11100000000   1/3 1/3 1/3 0 0 0 0 0 0 0 0               11001100000   1/4 1/4 0 0 1/4 1/4 0 0 0 0 0           11101010000   1/5 1/5 1/5 0 1/5 0 1/5 0 0 0 0           10010000000   1/2 0 0 1/2 0 0 0 0 0 0 0               10001001000   1/3 0 0 0 1/3 0 0 1/3 0 0 0          Both matrix M and matrix P are just a simple reordering of the original network structure.

T In this form, we can solve the linear system. Speciﬁcally, we denote π1 = (π1, π2, π3, π6) ,

T T π2 = (π4, π5, π8) and π3 = (π7, π9, π10) . For Dud patents (Type C = P ,P ,P ), we assign to the minimum Patent Rank scores: 3 { 7 9 10} T π3 = (1, 1, 1) . We deﬁne these scores as trivial scores. We calculate Patent Rank scores for the core patents (Type C = P ,P ,P ) by solving the 2 { 4 5 8} 3 3 linear system (I R¯) π = e + T¯ e , × − 2 2 3

132 1 1 0 0 0 0 /4 π4 1 0 0 0 1             0 1 0 0 0 0 π = 1 + 1/5 0 0 1 , − 5                      1     0 0 1   0 0 0   π8   1   0 0 /3   1                         

1 0 1/4 π 1 − 4       6 0 1 0 π5 = /5 .            4   0 0 1   π8   /3              4 6 4 T which produces the solution π2 = ( /3, /5, /3) . We calculate Patent Rank scores for the dangling nodes (Type C = P ,P ,P ,P ) by sub- 1 { 1 2 3 6} stitution,

1 1 1 29 π1 1 0 /3 /4 /5 0 0 /15 4/3 1      1 1   1   92  π2 1 /3 /3 0   /5 0 0   /45 π1 =   =   +   6/5 +   1 =   . 1 1 35  π3   1   /3 0 0     0 /2 0     /18         4               /3     1         1   1 1   28   π6   1   0 0 /4     /5 0 /3     /15                          We have now determined the dominant eigenvalue corresponding to the eigenvalue 1 of P T solving

a 3 3 linear system and the solution. Reordering the results back to P ,P ,P ,P ,P ,P ,P ,P ,P ,P , × { 1 2 3 4 5 6 7 8 9 10} we can verify that these results are equivalent to those obtained by the power method

T π(σ) = (29/15, 92/45, 35/18, 4/3, 6/5, 28/15, 1, 4/3, 1, 1)

. We normalize these remaining results such that the sum of all scores equals 1:

πT = (0.13190.13950.13270.09100.08190.12740.06820.09100.06820.0682) and note their equivalence.

133 APPENDIX E

COMPUTATION OF ClassMatch

Simply stated, we update the adjacency matrix M to include additional information about the strength of any link between two patents. Recall that the current adjacency matrix M contains binary data (1’s and 0’s) to indicate the presence or absence of a link between two nodes. We define this dichotomous schema as a Structure Only model. We define a new schema, ClassMatch that includes additional information about the value of each association. For non-zero values in the adjacency matrix, we add a ClassMatch score between [0, 1] to the binary value 1, to represent “the association is present and is this strong.” We define a matching technique that is consistent with mathematical principles. Due to the inherent linking of patents through citations, an algebra of similarity, as defined below, can be used to describe the relationship between two patents (consider patents X, Y , and Z). For any such patent comparison, we define ClassMatch as an overlap of any two patents’ total classification structure.

Axiom 1 X X = 1 [unity for perfect similarity] →

Axiom 2 X Z = 0 [null for no similarity; that is, nothing in common] →

Axiom 3 X Y = Y X [symmetric property of comparison] → →

134 For example, an arbitrary Pfizer patent X (# 6,040,309) has a primary classification of 514/252.01 meaning that it falls into the “514” class (Drug, Bio-affecting And Body Treating Compositions) and “252.01” subclass. Although the primary classification is important, a patent generally has several additional classifications which describes its technological breadth. For example, patent X is classified as

514/252.01; 514/252.02; 514/252.04; 514/255.05; 514/269; 514/337; 514/397; 514/443;

544/238; 544/333; 544/376; 546/118; 546/281.1; 548/311.4; 549/52; 549/54

As we see, the primary class 514 is mentioned 8 times within the patent (with unique1 sub- classes 252.01, 252.02, 252.04, 252.05, 269, 337, 397, 443). In addition, the 544 class is mentioned 3 times; the 546 class is mentioned twice; the 548 class is mentioned once; and the 549 class is mentioned twice. In total, classifications are mentioned 16 times. Based on this information, we define patent X’s total classification structure in terms of percentages of unique classes (respectively, 50%, 18.75%, 12.5%, 6.25%, and 12.5%).

We can similarly classify an arbitrary forward citation of X, patent Y (# 6,350,777), based on its total classification structure (10 total classifications, with 4 unique classes, 3 of which overlap with X). We can now compare them; that is, based on the U.S. Patent Office’s classification system, any two patents X and Y can be defined as

ClassMatch (X,Y ) = P rob(CX ) P rob(CY ). i ∩ j X

1Classes, as the primary component of the U.S. Patent Office classification system, are more stable over time than class-subclass combinations. The data was collected, beginning in December 2008, after the last USPTO reclassi- fication. The USPTO website has a class-subclass search utility: http://www.uspto.gov/web/patents/ classification/

135 Below, we summarize2 the computation of a speciﬁc ClassMatch score to compare Pﬁzer patent X = 6, 040, 309 and one of its forward citations Y = 6, 350, 777. In total, the patents have a 58.75% overlap.

Patent X Patent Y ClassMatch

6,040,309 6,350,777

514: 8 [8/16 = 0.5000] 514: 4 [4/10 = 0.4000] 0.400

544: 3 [3/16 = 0.1875] -

546: 2 [2/16 = 0.1250] -

548: 1 [1/16 = 0.0625] 548: 3 [3/10 = 0.3000] 0.0625

549: 2 [2/16 = 0.1250] 549: 2 [2/10 = 0.2000] 0.1250

568: 1 [1/10 = 0.1000] -

TOTAL = 16 TOTAL = 10

Total ClassMatch score 0.5875

2For each patent, we (1) identify the total number of classifications, (2) ascertain how many unique classes are referenced, (3) count how many classifications fit into each unique class, (4) compute a percentage. To compare patents, we sum the percent overlap of each common class (which, by definition, is the minimum of the two percentages for the specific class).

136 APPENDIX F

SELECTION OF COMBINED MODEL

Below, we report the distribution patterns for the three models of interest, based on a network formed from 1976–1990. The ClassMatch model dampens the structural disjointness, and merging with the Structure model, we demonstrate that the Combined model improves the normality further.

Distribution of Nontrivial Patent Rank scores Distribution of Nontrivial Patent Rank scores Distribution of Nontrivial Patent Rank scores 1976-1990 Structure Only (cs) 1976-1990 ClassMatch Only 1976-1990 Combined (cc)

0 (Structure and ClassMatch) 0 0 0 0 2 0 0 0 0 0 0 4 0 2 0 0 0 0 5 0 0 1 0 0 0 0 5 3 1 y y y c c c 0 n n n 0 e e 0 e 0 0 u u 0 u 0 0 q q 0 q 1 0 e e 0 r r e 0 r 2 F F 1 F 0 0 0 0 0 0 0 0 5 0 0 5 1 0 0 0

-5 -4 -3 -2 -1 0 1 -6 -4 -2 0 -6 -4 -2 0

Double-Log Transformation Double-Log Transformation Double-Log Transformation

Structure (S) ClassMatch (CM) Combined (Co)

Below, we demonstrate that the correlations of these three models are very similar, with the Combined model (Co) having a slightly stronger correlation to the original Structure model (S) than the ClassMatch model (CM). Mathematically, this Combined model updates the adjacency

matrix M by overlaying the two matrices or adding the ClassMatch score to one. In the previous

example, this would result in (mij) = 1 + 0.5875 = 1.5875.

cor(S,CM) = 0.99914 cor(CM,Co) = 0.99853 cor(S,Co) = 0.99981

137 APPENDIX G

GOLDEN RATIO φ AND PATENT RANK

Below, we present the distributional properties for πcc(7680) (cumulative-combined Patent Rank model), consisting of the raw (Raw scores), nontrivial scores—scores that are not definitionally assigned to be the minimum score of 1 (we exclude the dud patents). Like most citation-based results, the distribution is heavily skewed and appears to follow a power-law distribution (Simon 1955). Such distributional results are common in the study of extremely rare events and natural phenomenon. Inherent to our study of entrepreneurial innovation, radical innovations should be rare. Even a natural logarithmic tranformation ln(Raw Scores) does not improve the skewness. However, we see that a double logarithmic transformation ln(ln(Raw Scores)) normalizes the data. This result is uncommon for power-law distributions; in fact, we may have identified the first link network that has such beneficial distributional properties.

Distribution of Patent Rank scores Distribution of Natural Log Transformation Distribution of Double Natural Log Transformation 0 0 0 5 2 0 0 5 0 0 0 0 + 5 0 e 1 0 6 0 2 0 0 0 0 0 5 0 5 y y y 0 0 c c c 1 + 0 n n n e 1 e e e 4 u u u q q q e e e r r r 0 F F F 0 0 0 1 0 5 0 0 0 + 0 e 5 2 0 0 0 5 0 0 + 0 0 e 0

0 5 10 15 20 25 30 35 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 -5 -4 -3 -2 -1 0 1

Nontrivial (cc) or (mc) Patent Rank scores x*=ln(Patent Rank score) x=ln(x*)=ln(ln(Patent Rank scores))

a) (Raw Scores) b) ln(Raw Scores) c) ln(ln(Raw Scores))

The monotonic1 tranformation is mathematically deﬁned as

x = ln(ln(π)) for all elements where πi > 1, which implies π = eex .

1A monotonic transformation preserves order. This means that the ranking of patents based on these new scores is equivalent to the ranking based on the raw scores.

138 We verify that the double-natural log transformation has similar results across the four basic Patent Rank models: (cs), (cc), (ms), and (mc). Using the transformed (mc) Patent Rank model, we notice that the distributional distributional phenomenon2 is drifting over time. As we see below, the average score is getting smaller and variability of scores is increasing.

Drift of (mc) Normal Scores

These values appear to follow a normal distribution x N(M = 1.602,SD = 0.704). ∼ √ − The parameter values (M = φ 1.618 Golden3 ratio, SD = 2 0.707) of this double-log ≈ 2 ≈ normal distribution are amazingly similar to two known mathematical constants that appear in nature and fractal studies (Halsey et al. 1986). This suggests that the patent network (1976–1980) had fundamentally natural origins in its citation structure when accounting for ClassMatch in the

cumulative-combined Patent Rank model. In the above graphic, we describe the drift the value φ

with a conﬁdence band of √2 in gray. We overlay the mean scores ( SD) for the (mc) normal ± distributions for six model windows (1976–1980, 1981–1984, 1985–1990, 1991-1994, 1995–2000, and 2005-2009).

2This dilution may be a result of the annual increase in granted patents. 3This number φ is also frequently called the Golden mean.

139 APPENDIX H

FIRM-PATENT MATCHING: RED LIGHT/GREEN LIGHT

Matching any approved patent to a firm is a nontrivial task. Every patent must have at least one inventor, and most patents have at least one “assignee” which represents the corporate entity or firm that is assigned the intellectual property. Normally, inventors receive some royalty while the firm legally defends the exclusionary-rights from the patent. Patent data is messy, as there is not a unique identifier to match the assignee name on the patent document to a specific firm. This problem is further compounded because there are misspellings of the assignee name. For example, Patent # 6,639,807 is purportedly assigned to Sun Microsoft, Inc. How do such mistakes happen? From where do they originate? This is a question of interest that may be unanswerable. Is it a data-entry error at the Patent Office (by the examiner or staff)? Is it a careless mistake made by the appropriate patent agent assisting the firm with the application process (e.g., patent attorneys)? Or is it something else? We address this problem by doing a two-stage matching technique. Although imperfect, this process reduces systematic error.

Step 1: Expand the nomological net

To find all of the patents for a given firm (e.g., IBM), we need to consider all relevant search terms that will widen the search domain but account for most mispellings. For example, we search for “IBM” and “International Business” and review the results (although “International” may also be misspelled, we only search additionally for “Intl Business”). In addition, the USPTO search tool does not allow for ampersands (&) in the search, so to find a firm like “AT&T” a search for ”AT” had to be considered comprehensively.

140 Step 2: Red light/Green light from Assignee Name (Location)

Software was written to parse the search results from the USPTO and display the results based on unique assignee name and the location of each unique assignee name. From which, manual decisions are made using the name-location information to discern if the patent belongs to a specific firm. For example, Sun Microsoft, Inc. (Palo Alto, CA) is the assignee information for Patent # 6,639,807; from which we conclude that this patent belongs to Sun Microsystems. At the search level, two unique searches were performed. Manually, with the “Sun” search we would green light this patent, concluding it belongs to the firm (permno 10078); with the “Microsoft” search we would red light this patent, concluding it does not belong to the firm (permno 10107). For IBM (permno 12490), we list all assignees we green-lighted, meaning these patents were assigned to IBM; we utilize our knowledge that headquarters for IBM is in Armonk, NY:

Search Assignees Green-Lighted IBM IBM Intl Business IBM Business Machines Corporation International Business IBM Corp IBM Corp. IBM Corporation IBM Corporation of Armonk IBM International Business Machines Corporation IBM Japan Business Logistics Co., Ltd. IBM Japan Ltd. IBM Japan, Ltd. IBM Patent Operations IBM Thomas J. Watson Search Center International Business Machines International Business Machines - IBM International Business Machines Corp. International Business Machines Corporation International Business Machines, International Business

141 International Business [[AND]] Technology Corporation International Business Business Machines International Business Corporation International Business Corpration International Business Development Co. International Business Development Company International Business Development Inc. International Business MAchines Corporation International Business Machiens Corporation International Business Machin es Corporation International Business Machinces Corp. International Business Machinces Corporation International Business Machine International Business Machine Company International Business Machine Corp. International Business Machine Corp. International Property Law International Business Machine Corporation International Business Machined Corporation International Business Machines - Corporation International Business Machines Cirporation International Business Machines Coirporation International Business Machines Company International Business Machines Company Corporation International Business Machines Coporation International Business Machines Coproartion International Business Machines Coproation International Business Machines Coproration International Business Machines Coprporation International Business Machines Coroporation International Business Machines Cororation International Business Machines Corp International Business Machines Corp. International Business Machines Corpoartion International Business Machines Corpoation International Business Machines Corporaion International Business Machines Corporaiton International Business Machines Corporartion International Business Machines Corporataion International Business Machines Corporatiion International Business Machines Corporatin International Business Machines Corporatioin

142 International Business Machines Corporatiom International Business Machines Corporation International Business Machines Corporation Inc. International Business Machines Corporation Limited International Business Machines Corporation [ International Business Machines Corporation, International Business Machines Corporation, Inc. International Business Machines Corporation. International Business Machines Corporational International Business Machines Corporations International Business Machines Corporatoin International Business Machines Corporaton International Business Machines Corporatrion International Business Machines Corportaion International Business Machines Corportation International Business Machines Corportion International Business Machines Corproation International Business Machines Inc International Business Machines Inc. International Business Machines Inc. Corporation International Business Machines Incorp. International Business Machines Incorporated International Business Machines Incorporation International Business Machines Machine International Business Machines Machines International Business Machines Machines Corporation International Business Machines Operation International Business Machines corp. International Business Machines corporation International Business Machines for Corporation International Business Machines of Corporation International Business Machines, International Business Machines, Corp International Business Machines, Corp. International Business Machines, Corporation International Business Machines, Inc. International Business Machines, Incorporation International Business Machinesc Corporation International Business Machiness Corporation International Business Machins Corporation International Business Machnes Corporation International Business Machnies Corporation

143 International Business Machnines Corporation International Business Macines Corp. International Business Macines Corporation International Business Macjines Coporation International Business Madnine Corporation International Business Mahcines Corporation International Business Mahines Corporation International Business Mcahines Corporation International Business Relations Bureau Inc. International Business Systems, Incorporated International Business Technology Corporation International Business and Machines Corporation International Business and Technology Corporation International Business machines Corporation International business Machines Corporation International, Business Machines Corporation International;Business Machines Corporation international Business Machines Corporation

Future research can build off of this technique, merging this idea with traditional ideas of SOUNDEX (Trajtenberg et al. 2006). In addition, the nature of the network can be utilized to probabilistically assign patents more precisely to firms based on additional information (Lai et al. 2009). We manually consider mergers and acquisitions, joint ventures, and subsidiaries in our search. We search relevant1 websites that can identify if a firm is a subsidiary of a parent firm (e.g, Sony has several very different subsidiaries that carry the Sony brand; in addition, the DBA for Sony is different across the world). Whether the firm is capable of utilizing the patents across division- boundaries, within joint ventures, and so on, we posit that it represents a resource at the disposal of the firm to determine its product offerings (Penrose 1959). For example, Fuji Xerox is a joint venture between Fuji Film and Xerox. Although the ownership of this joint-venture is unbalanced

1http://investing.businessweek.com/research/stocks/private/snapshot.asp? privcapId=5719412 and http://investing.businessweek.com/research/stocks/private/ snapshot.asp?privcapId=5540899 are GE-related examples.

144 (75% for Fuji Film), we determine that patents from the joint venture belong to both firms. In addition, if two firms are assignees on a single patent, the patent is included into both firms’ patent-portfolios. Future reseach can merge the SDC data to firm identifiers. This will account for mergers, acquisitions, joint ventures, divestitures, and so on. This would be an ultimate solution to match patents to firms.

145 APPENDIX I

FIRM LISTS Sample from S&P 500

PERMNO Firm Name 76129 3COM CORP 22592 3M CO 55351 7-ELEVEN INC 20482 ABBOTT LABORATORIES 79057 ACE LTD 10353 ADAPTEC INC 50906 ADC TELECOMMUNICATIONS INC 75510 ADOBE SYSTEMS INC 61241 ADVANCED MICRO DEVICES 76712 AES CORP 80913 AFFILIATED COMPUTER SERVICES 57904 AFLAC INC 87432 AGILENT TECHNOLOGIES INC 56266 AHMANSON (H F) & CO 70308 AIRGAS INC 80094 AIRTOUCH COMMUNICATIONS INC 80303 AK STEEL HOLDING CORP 87299 AKAMAI TECHNOLOGIES INC 25662 AKZONA 42083 ALBERTO-CULVER CO 50032 ALBERTSON’S INC 24264 ALCAN INC 24643 ALCOA INC 63845 ALEXANDER & ALEXANDER 10137 ALLEGHENY ENERGY INC 43123 ALLEGHENY TECHNOLOGIES INC 75646 ALLERGAN INC 17304 ALLIED STORES 76887 ALLIED WASTE INDUSTRIES INC 10153 ALLIS-CHALMERS ENERGY INC 79323 ALLSTATE CORP 41443 ALLTEL CORP 75577 ALTERA CORP 13901 ALTRIA GROUP INC 64856 ALZA CORP 24555 AMALGAMATED SUGAR CO 10161 AMAX INC 84788 AMAZON.COM INC 76757 AMBAC FINANCIAL GP 60046 AMDAHL CORP 24985 AMEREN CORP 10233 AMERICAN BROADCASTING 85271 AMERICAN CAPITAL LTD 23341 AMERICAN CYANAMID CO 24109 AMERICAN ELECTRIC POWER CO 59176 AMERICAN EXPRESS CO 48397 AMERICAN GENERAL CORP 13056 AMERICAN GREETINGS -CL A 66800 AMERICAN INTERNATIONAL GROUP 46754 AMERICAN MEDICAL HOLDINGS

146 10321 AMERICAN MOTORS CORP 24141 AMERICAN NATURAL RESOURCES 11970 AMERICAN POWER CONVERSION CP 44652 AMERICAN STORES CO 86111 AMERICAN TOWER CORP 25873 AMERIFIN CORP 90880 AMERIPRISE FINANCIAL INC 81540 AMERISOURCEBERGEN CORP 65859 AMERITECH CORP 15763 AMF INC 14008 AMGEN INC 19553 AMOCO CORP 27051 AMP INC 84769 AMPHENOL CORP 21020 AMR CORP/DE 62770 AMSOUTH BANCORPORATION 10479 AMSTAR CORP 10487 AMSTED INDUSTRIES 70332 ANADARKO PETROLEUM CORP 60871 ANALOG DEVICES 14323 ANDREW CORP 59184 ANHEUSER-BUSCH COS INC 61735 AON CORP 39490 APACHE CORP 80711 APARTMENT INVT &MGMT -CL A 81138 APOLLO GROUP INC -CL A 14593 APPLE INC 27713 APPLIED BIOSYSTEMS INC 85522 APPLIED MICRO CIRCUITS CORP 10516 ARCHER-DANIELS-MIDLAND CO 64451 ARCHSTONE-SMITH TRUST 17566 ARMCO INC 19692 ARMSTRONG HOLDINGS INC 26649 ASA LTD 10364 ASARCO INC 80506 ASCEND COMMUNICATIONS INC 83440 ASSOC FST CAPITAL CP -CL A 10559 ASSOCIATED DRY GOODS CORP 90038 ASSURANT INC 10604 ATLANTIC RICHFIELD CO 85631 AUTODESK INC 44644 AUTOMATIC DATA PROCESSING 76282 AUTONATION INC 76605 AUTOZONE INC 80381 AVALONBAY COMMUNITIES INC 19019 AVATEX CORP 88587 AVAYA INC 44601 AVERY DENNISON CORP 25487 AVIS BUDGET GROUP INC 40416 AVON PRODUCTS 75034 BAKER HUGHES INC 58480 BALLY ENTERTAINMENT CORP 49656 BANK OF NEW YORK MELLON CORP 51772 BANKBOSTON CORP 48354 BANKERS TRUST CORP 46877 BARD (C.R.) INC 61284 BARNETT BANKS INC 11415 BARR PHARMACEUTICALS INC 71298 BARRICK GOLD CORP 17137 BASSETT FURNITURE INDS 85869 BATTLE MOUNTAIN GOLD CO 27887 BAXTER INTERNATIONAL INC 76754 BAY NETWORKS INC 147 71563 BB&T CORP 16651 BE HOLDINGS INC -CL A 68304 BEAR STEARNS COMPANIES INC 17953 BEATRICE COS INC 39642 BECTON DICKINSON & CO 77659 BED BATH & BEYOND INC 57488 BEKER INDUSTRIES 43772 BEMIS CO INC 10751 BENDIX CORP 83443 BERKSHIRE HATHAWAY 85914 BEST BUY CO INC 10989 BESTFOODS 10786 BETHLEHEM STEEL CORP 67467 BIG LOTS INC 76841 BIOGEN IDEC INC 18092 BIOMET INC 76240 BJ SERVICES CO 20220 BLACK & DECKER CORP 27087 BLOCKBUSTER ENMNT CORP 11976 BMC SOFTWARE INC 42606 BNS HOLDING CO 19561 BOEING CO 85058 BOSTON PROPERTIES INC 77605 BOSTON SCIENTIFIC CORP 18649 BRINKS CO 85963 BROADCOM CORP 83630 BROADVISION INC 40352 BROADWAY STORES INC 50737 BROCKWAY INC 19589 BRUNOS INC 10874 BRUNSWICK CORP 50227 BURLINGTON NORTHERN SANTA FE 17590 BURNS INTL SERVICES CORP 76285 C & S SOVRAN CORP 85459 C H ROBINSON WORLDWIDE INC 25778 CA INC 68857 CABLEVISION SYS CORP -CL A 76082 CABOT OIL & GAS CORP 59432 CALIBER SYSTEMS INC 83981 CALPINE CORP 25320 CAMPBELL SOUP CO 11041 CANADIAN PACIFIC RAILWAY LTD 21152 CANON INC 21371 CARDINAL HEALTH INC 89508 CARMAX INC 30365 CARNATION CO 59707 CATTLESALE COMPANY 53831 CENTEX CORP 76625 CEPHALON INC 38914 CERIDIAN CORP 10909 CERNER CORP 26622 CHAMPION SPARK PLUG 22753 CHARMING SHOPPES INC 58296 CHEMFIRST INC 78877 CHESAPEAKE ENERGY CORP 11164 CHICAGO PNEUMATIC TOOL CO 59192 CHUBB CORP 84519 CIENA CORP 64186 CIGNA CORP 23660 CINTAS CORP 76076 CISCO SYSTEMS INC 82686 CITRIX SYSTEMS INC 23210 CLEVELAND ELECTRIC ILLUM 148 46578 CLOROX CO/DE 11295 CLUETT PEABODY & CO 89626 CME GROUP INC 23229 CMS ENERGY CORP 47626 CNA FINANCIAL CORP 63108 CNW CORP 11308 COCA-COLA CO 86158 COGNIZANT TECH SOLUTIONS 39140 COLEMAN CO INC -OLD 15712 COLLINS & AIKMAN CORP -OLD 11332 COLTEC INDUSTRIES 11340 COLUMBIA ENERGY GROUP 19297 COLUMBIA PICTURES INDS 18999 COMBUSTION ENGINEERING INC 89525 COMCAST CORP 25081 COMERICA INC 68347 COMPAQ COMPUTER CORP 40125 COMPUTER SCIENCES CORP 60337 COMPUTERVISION CORP 78139 COMPUWARE CORP 10942 COMVERSE TECHNOLOGY INC 41929 CON-WAY INC 26332 CONCORD EFS INC 86496 CONEXANT SYSTEMS INC 67571 CONSECO FINANCE CORP 86799 CONSOL ENERGY INC 69796 CONSTELLATION BRANDS 24221 CONSTELLATION ENERGY GRP INC 39335 CONTEL CORP 86305 CONVERGYS CORP 21290 CORDANT TECHNOLOGIES INC 22293 CORNING INC 30912 CORROON & BLACK CORP 87055 COSTCO WHOLESALE CORP 34841 COVANTA ENERGY CORP 76619 COVENTRY HEALTH CARE INC 62164 CRAY RESEARCH 64207 CULLINET SOFTWARE INC 87127 CYPRUS AMAX MINERALS CO 49680 DANAHER CORP 57592 DATA GENERAL CORP 11081 DELL INC 86591 DELPHI CORP 11600 DENTSPLY INTERNATL INC 87137 DEVON ENERGY CORP 76708 DEVRY INC 43916 DIGITAL EQUIPMENT 49429 DILLARDS INC -CL A 89954 DIRECTV GROUP INC 26403 DISNEY (WALT) CO 30067 DSC COMMUNICATIONS CORP 86356 EBAY INC 31536 ECHLIN INC 83596 ELECTRONIC DATA SYSTEMS CORP 10147 EMC CORP/MA 55386 ENVIROTECH CORP 52476 EQUIFAX INC 86964 F5 NETWORKS INC 89003 FIDELITY NATIONAL INFO SVCS 77546 FIRST DATA CORP 91611 FIRST SOLAR INC 10696 FISERV INC 77150 FISHER SCIENTIFIC INTL INC 149 19617 FLAGSTAR CORP 47271 FLEMING COMPANIES INC 79265 FLIR SYSTEMS INC 15456 FOOT LOCKER INC 23887 FRONTIER COMMUNICATIONS CORP 37867 FUJIFILM HLDGS CORP 47941 GANNETT CO 59010 GAP INC 79973 GATEWAY INC 32141 GDV INC 12052 GENERAL DYNAMICS CORP 12060 GENERAL ELECTRIC CO 16109 GENERAL FOODS CORP 77644 GENERAL INSTRUMENT CORP 17144 GENERAL MILLS INC 21055 GENESCO INC 90319 GOOGLE INC 22568 GOULD INC 21004 GTE CORP 76090 HARRAHS ENTERTAINMENT INC 25582 HARRIS CORP 40484 HBO & CO 27828 HEWLETT-PACKARD CO 64231 HITACHI LTD 59555 HONDA MOTOR CO LTD 18374 HONEYWELL INC 48653 HUMANA INC 84020 IMS HEALTH INC 87664 INFINEON TECHNOLOGIES AG 12431 INGERSOLL-RAND PLC 59328 INTEL CORP 44792 INTERGRAPH CORP 12490 INTL BUSINESS MACHINES CORP 78975 INTUIT INC 79094 JABIL CIRCUIT INC 79879 JDS UNIPHASE CORP 42534 JOHNSON CONTROLS INC 86979 JUNIPER NETWORKS INC 46886 KLA-TENCOR CORP 86021 L-3 COMMUNICATIONS HLDGS INC 20415 LEUCADIA NATIONAL CORP 82643 LEXMARK INTL INC -CL A 10299 LINEAR TECHNOLOGY CORP 26294 LITTON INDUSTRIES INC 33785 LORAL CORP 50156 LOTUS DEVELOPMENT CORP 48267 LSI CORP 83332 LUCENT TECHNOLOGIES INC 39538 MATTEL INC 11896 MAXIM INTEGRATED PRODUCTS 77976 MCAFEE INC 81776 MEMC ELECTRONIC MATRIALS INC 79718 MERCURY INTERACTIVE CORP 78987 MICROCHIP TECHNOLOGY INC 53613 MICRON TECHNOLOGY INC 10107 MICROSOFT CORP 54181 MILLIPORE CORP 54827 MOLEX INC 22779 MOTOROLA INC 18980 MULTIGRAPHICS INC 84372 NCR CORP 82598 NETAPP INC

150 89393 NETFLIX INC 87128 NOKIA (AB) OY 58640 NORTEL NETWORKS CORP 24766 NORTHROP GRUMMAN CORP 90609 NOVELL INC 86580 NVIDIA CORP 10104 ORACLE CORP 87800 PALM INC 53727 PANASONIC CORP 75912 PARAMETRIC TECHNOLOGY CORP 61621 PAYCHEX INC 78083 PEOPLESOFT INC 42200 PERKINELMER INC 24459 PITNEY BOWES INC 76624 PMC-SIERRA INC 80266 QLOGIC CORP 77178 QUALCOMM INC 80470 QUINTILES TRANSNATIONAL CORP 80515 RATIONAL SOFTWARE CORP 24942 RAYTHEON CO 14090 RCA CORP 87184 RED HAT INC 60572 ROLM CORP 91547 SAIC INC 42999 SANDERS ASSOCIATES INC 82618 SANDISK CORP 83413 SAPIENT CORP 56186 SCA SERVICES INC 45671 SCIENTIFIC-ATLANTA INC 69607 SEAGATE TECHNOLOGY-OLD 70229 SHARED MEDICAL SYSTEMS CORP 83693 SIEBEL SYSTEMS INC 88935 SIEMENS AG 10791 SILICON GRAPHICS INC 75857 SOLECTRON CORP 14525 SPERRY CORP 39087 SPRINT NEXTEL CORP 12095 SPX CORP 45604 SQUIBB CORP 21450 STANDARD OIL CO 77702 STARBUCKS CORP 25216 STAUFFER CHEMICAL CO 14592 STERLING DRUG INC 20407 STOKELY VAN CAMP INC 58464 STORAGE TECHNOLOGY CP 27764 STRIDE RITE CORP 73139 STRYKER CORP 10078 SUN MICROSYSTEMS INC 33312 SUNAMERICA INC 10108 SUNGARD DATA SYSTEMS INC 75607 SYMANTEC CORP 73940 SYMBOL TECHNOLOGIES 74617 TANDEM COMPUTERS INC 40061 TEKTRONIX INC 75257 TELLABS INC 92293 TERADATA CORP 51369 TERADYNE INC 15579 TEXAS INSTRUMENTS INC 62092 THERMO FISHER SCIENTIFIC INC 38578 THOMAS & BETTS CORP 57234 TRACOR INC 81285 TRANE INC 45356 TYCO INTERNATIONAL LTD 59811 TYMSHARE INC 151 26921 UNIDYNAMICS CORP 10890 UNISYS CORP 85753 VERISIGN INC 80055 VERITAS SOFTWARE CORP 77173 VITESSE SEMICONDUCTOR CORP 65816 WANG LABS INC 82651 WATERS CORP 66384 WESTERN DIGITAL CORP 27983 XEROX CORP 76201 XILINX INC 83435 YAHOO INC

152 Most Proliﬁc Patenting Firms ID Firm Name random RANDOM SAMPLE 20482 ABBOTT LABORATORIES 61241 ADVANCED MICRO DEVICES 19553 AMOCO CORP 27051 AMP INC 10604 ATLANTIC RICHFIELD CO 27887 BAXTER INTERNATIONAL INC 39642 BECTON DICKINSON & CO 10751 BENDIX CORP 19561 BOEING CO THE 21152 CANON K K JP 68347 COMPAQ COMPUTER CORP 22293 CORNING INC 20aa8413c1ebcae99aba87698c95e3d7 DENSO CORP JP 43916 DIGITAL EQUIPMENT 37867 FUJIFILM CORP JP 42be629cee585662a72ab2548b10df71 FUJITSU LTD JP 21004 GTE CORP 25582 HARRIS CORP 27828 HEWLETT-PACKARD DEVELOPMENT CO L P 18374 HONEYWELL INC 59328 INTEL CORP 12490 INTERNATIONAL BUSINESS MACHINES CORP ba850363a2ad8d7f86b05b2fc306d805 LG ELECTRONICS INC KR 26294 LITTON INDUSTRIES INC 48267 LSI CORP 83332 LUCENT TECHNOLOGIES INC 53613 MICRON TECHNOLOGY INC 10107 MICROSOFT CORP 22779 MOTOROLA INC 84372 NCR CORP 24459 PITNEY BOWES INC 24942 RAYTHEON CO 14090 RCA CORP 74b83fbd610636ab0f13bcd511c88845 SAMSUNG ELECTRONICS CO LTD KR 69607 SEAGATE TECHNOLOGY dc9882eb9f5b3e79abbcb7267eb38d39 SEIKO EPSON CORP JP 51131 SONY CORP JP 14525 SPERRY CORP 21450 STANDARD OIL CO 25216 STAUFFER CHEMICAL CO 10078 SUN MICROSYSTEMS INC 40061 TEKTRONIX INC 15579 TEXAS INSTRUMENTS INC 76559 TOSHIBA CORP JP 27983 XEROX CORP

153 APPENDIX J

FINANCIAL PERFORMANCE: ALL MODELS

Models created using the actual patent stock

TOTAL (actual): The deciles below are created by annually considering the total patent stock.

Decilei Portfolio Less No Patent (NP ) Portfolio Difference Portfolios

(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP ) 1 − 2 − 3 − 4 − 5 − 6 − 7 − 8 − 9 − 10 −

NDecile 6314 4032 5147 5201 5034 5221 5070 5142 5100 5004

NPortfolio 348 348 348 348 348 348 348 348 348 348

adj. R2 0.02957 0.1085 0.1923 0.1063 0.06605 0.1381 0.05381 0.1466 0.04847 0.1077 p-value 0.00636 8.18e-09 6.062e-16 1.215e-08 1.579e-05 3.096e-11 0.0001258 5.964e-12 0.0003052 9.394e-09

Abnormal Return

αj 0.003472 0.004825 0.003153 0.003486 0.0001276 0.002148 -0.001207 -0.003716 -9.912e-05 0.0004139 (2.015) (1.867) (1.275) (1.771) (0.060) (1.117) (-0.520) (-1.960) (-0.052) (0.213)

CAPM

βj -0.006729 0.139188 0.123920 0.072249 0.0509701 0.121056 0.089884 0.127690 4.610e-02 0.0544010 (-0.271) (3.711) (3.477) (2.528) (1.652) (4.339) (2.665) (4.640) (1.670) (1.929)

SMB

sj 0.040864 0.131141 0.196390 0.122575 0.1294086 0.041336 0.091596 0.022053 1.088e-02 0.1034604 (1.101) (2.335) (3.690) (2.865) (2.802) (0.990) (1.814) (0.535) (0.263) (2.451)

HML

hj -0.113519 -0.162664 -0.270122 -0.145411 -0.1242788 -0.167959 -0.105002 -0.154984 -1.204e-01 -0.1551136 (-3.532) (-3.353) (-5.853) (-3.934) (-3.115) (-4.654) (-2.407) (-4.354) (-3.371) (-4.253)

UMD

uj -0.019814 0.056729 -0.131145 -0.090645 -0.0655574 -0.037733 -0.028814 -0.106969 -8.048e-02 -0.1358122 (-0.559) (1.066) (-2.581) (-2.236) (-1.498) (-0.954) (-0.602) (-2.741) (-2.056) (-3.396)

Annualized Return 4.247% 5.946% 3.850% 4.264% 0.153% 2.608% -1.439% -4.369% -0.119% 0.498%

154 DELTA (actual): The deciles below are created by considering annual changes in the total patent stock.

Decilei Portfolio Less No Patent (NP ) Portfolio Difference Portfolios

(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP ) 1 − 2 − 3 − 4 − 5 − 6 − 7 − 8 − 9 − 10 −

NDecile 6992 3366 5131 5201 5037 5207 5113 5126 5064 5028

NPortfolio 348 348 348 348 348 348 348 348 348 348

adj. R2 0.02237 0.1147 0.1407 0.1424 0.08604 0.07432 0.1535 0.07391 0.1009 0.1397 p-value 0.01913 2.61E-09 1.86E-11 1.34E-11 4.79E-07 3.77E-06 1.55E-12 4.06E-06 3.29E-08 2.26E-11

Abnormal Return

αj 0.004013 0.005221 0.004183 0.001213 0.003805 0.002135 -0.0006777 -0.001214 0.001232 -0.001233 (2.440) (2.028) (1.698) (0.573) (1.964) (1.045) (-0.304) (-0.610) (0.617) (-0.528)

CAPM

βj -0.027526 0.103121 0.119066 0.097448 0.031581 0.065375 0.1032195 0.107366 0.085206 0.075188 (-1.161) (2.760) (3.353) (3.170) (1.123) (2.204) (3.195) (3.717) (2.937) (2.219)

SMB

sj 0.011615 0.152881 0.193487 0.067675 0.095557 0.113252 0.1246931 0.007618 0.077199 0.154978 (0.328) (2.732) (3.649) (1.470) (2.269) (2.550) (2.577) (0.176) (1.777) (3.055)

HML

hj -0.104462 -0.173117 -0.150837 -0.19882 -0.173886 -0.130013 -0.2319867 -0.074592 -0.160035 -0.17803 (-3.406) (-3.582) (-3.280) (-5.000) (-4.779) (-3.389) (-5.551) (-1.997) (-4.265) (-4.062)

UMD

uj -0.011454 0.138159 -0.177984 -0.157237 -0.017711 -0.013417 -0.062845 -0.110123 -0.044467 -0.224936 (-0.339) (2.607) (-3.515) (-3.607) (-0.444) (-0.319) (-1.371) (-2.688) (-1.081) (-4.680)

Annualized Return 4.923% 6.448% 5.137% 1.465% 4.663% 2.592% -0.810% -1.447% 1.488% -1.470%

155 NORM (actual): The deciles below are created by considering annual changes in the total patent stock divided by the number of patents present in the patent network for the same year (within-ﬁrm normalization of patents per capita measure of the entire portfolio’s performance).

Decilei Portfolio Less No Patent (NP ) Portfolio Difference Portfolios

(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP ) 1 − 2 − 3 − 4 − 5 − 6 − 7 − 8 − 9 − 10 −

NDecile 6502 3786 5065 5026 5097 5023 5095 5039 5041 4919

NPortfolio 348 348 348 348 348 348 348 348 348 348

adj. R2 0.0306 0.1399 0.07681 0.03681 0.01267 0.1268 0.1341 0.1804 0.2368 0.1693 p-value 0.005415 2.165e-11 2.445e-06 0.002031 0.07874 2.661e-10 6.616e-11 7.002e-15 < 2.2e-16 6.649e-14

Abnormal Return

αj 0.003869 0.002202 -0.001129 0.003190 0.002234 8.343e-05 0.001447 -0.0004251 0.003191 -0.0004434 (2.306) (0.968) (-0.584) (1.712) (1.218) (0.043) (0.752) (-0.179) (1.255) (-0.173)

CAPM

βj -0.015675 0.074289 0.091338 0.034518 0.003965 9.164e-02 0.083206 0.1605239 0.183492 0.1714544 (-0.648) (2.251) (3.252) (1.285) (0.149) (3.277) (2.978) (4.650) (4.971) (4.619)

SMB

sj 0.007202 0.163907 -0.024531 -0.018224 -0.000603 -3.274e-03 0.086377 0.2045088 0.234390 0.2151742 (0.199) (3.316) (-0.583) (-0.454) (-0.015) (-0.078) (2.065) (3.956) (4.241) (3.871)

HML

hj -0.120018 -0.203857 -0.117009 -0.108857 -0.091552 -1.853e-01 -0.141837 -0.1826592 -0.251768 -0.1884764 (-3.836) (-4.774) (-3.220) (-3.133) (-2.658) (-5.123) (-3.925) (-4.090) (-5.273) (-3.925)

UMD

uj -0.028978 -0.159723 -0.092954 0.043954 -0.045769 5.380e-02 -0.175678 -0.0899822 -0.187216 -0.0317152 (-0.840) (-3.412) (-2.333) (1.148) (-1.212) (1.356) (-4.434) (-1.838) (-3.576) (-0.602)

Annualized Return 4.743% 2.675% -1.346% 3.896% 2.714% 0.100% 1.750% -0.509% 3.897% -0.531%

156 Z (actual): The deciles below are created by standardizing the normalized stock (presented previ-

x−µ ously) to create z-scores (z = σ ).

Decilei Portfolio Less No Patent (NP ) Portfolio Difference Portfolios

(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP ) 1 − 2 − 3 − 4 − 5 − 6 − 7 − 8 − 9 − 10 −

NDecile 5297 5087 5569 5218 5264 4522 5057 5159 5109 4983

NPortfolio 348 348 348 348 348 348 348 348 348 348

adj. R2 0.1544 0.1107 0.151 0.09362 0.06847 0.09557 0.05852 0.04006 0.08863 0.1089 p-value 1.293e-12 5.459e-09 2.518e-12 1.232e-07 1.549e-05 8.663e-08 5.697e-05 0.001205 3.016e-07 7.652e-09

Abnormal Return

αj 0.004362 -0.001657 0.006074 -0.0005026 0.004406 0.001502 -0.0007679 0.0002703 3.508e-05 0.0001741 (1.853) (-0.761) (2.825) (-0.252) (2.007) (0.761) (-0.417) (0.135) (0.018) (0.090)

CAPM

βj 0.086030 0.106770 0.111067 0.0997863 0.043372 0.065985 0.0643637 0.0712489 7.535e-02 0.0915013 (2.536) (3.402) (3.608) (3.451) (1.289) (2.303) (2.411) (2.446) (2.605) (3.261)

SMB

sj 0.054811 0.082553 0.088243 0.0983150 0.081718 0.120998 0.0665127 -0.0470394 8.961e-02 0.0534869 (1.082) (1.761) (1.912) (2.271) (1.765) (2.820) (1.664) (-1.079) (2.069) (1.273)

HML

hj -0.264272 -0.172449 -0.220015 -0.1191251 -0.071211 -0.116015 -0.0867774 -0.0980269 -1.275e-01 -0.1609868 (-6.022) (-4.243) (-5.563) (-3.185) (-1.772) (-3.130) (-2.513) (-2.602) (-3.408) (-4.435)

UMD

uj -0.187202 0.011949 -0.006189 -0.0298328 -0.190481 -0.128582 -0.0820714 -0.0478922 -1.072e-01 -0.0589473 (-3.871) (0.267) (-0.140) (-0.727) (-4.226) (-3.164) (-2.167) (-1.159) (-2.613) (-1.481)

Annualized Return 5.362% -1.970% 7.537% -0.601% 5.417% 1.817% -0.918% 0.325% 0.042% 0.209%

157 Correlations of Deciles across ‘actual patent stock’ models

TOTAL DELTA NORM Z

TOTAL 1.0000

DELTA 0.9513 1.0000

NORM 0.5746 0.6446 1.0000

Z 0.3608 0.4215 0.6118 1.0000

158 Models created using the expected patent stock

TOTAL (expected): The deciles below are created by annually considering the modeled expectation of total patent stock.

Decilei Portfolio Less No Patent (NP ) Portfolio Difference Portfolios

(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP ) 1 − 2 − 3 − 4 − 5 − 6 − 7 − 8 − 9 − 10 −

NDecile 6314 4056 5132 5195 5031 5195 5100 5150 5076 5016

NPortfolio 348 348 348 348 348 348 348 348 348 348

adj. R2 0.03016 0.1164 0.1806 0.1014 0.08168 0.1301 0.08728 0.09943 0.05274 0.1121 p-value 0.005805 1.881e-09 6.719e-15 3.022e-08 1.037e-06 1.418e-10 3.837e-07 4.305e-08 0.0001502 4.222e-09

Abnormal Return

αj 0.003398 0.003688 0.003540 0.002946 0.001267 0.002284 -0.0005283 -0.002689 -0.0002321 0.0001068 (1.969) (1.414) (1.403) (1.463) (0.644) (1.213) (-0.233) (-1.396) (-0.122) (0.052)

CAPM

βj -0.008357 0.148420 0.131804 0.068275 0.071507 0.100117 0.0960991 0.108863 0.0472338 0.0640966 (-0.336) (3.920) (3.626) (2.337) (2.506) (3.663) (2.920) (3.893) (1.712) (2.160)

SMB

sj 0.039874 0.134633 0.226498 0.104011 0.119858 0.052198 0.0914205 0.069212 0.0104709 0.0950420 (1.073) (2.375) (4.172) (2.377) (2.806) (1.276) (1.855) (1.653) (0.253) (2.139)

HML

hj -0.114274 -0.167701 -0.240384 -0.159596 -0.118927 -0.173865 -0.1450813 -0.113350 -0.1219134 -0.1667008 (-3.550) (-3.424) (-5.106) (-4.222) (-3.222) (-4.918) (-3.408) (-3.134) (-3.417) (-4.343)

UMD

uj -0.004754 0.060485 -0.092986 -0.093196 -0.034480 -0.037740 -0.0960858 -0.062416 -0.0917427 -0.1495629 (-0.134) (1.126) (-1.794) (-2.249) (-0.852) (-0.974) (-2.059) (-1.574) (-2.345) (-3.553)

Annualized Return 4.15% 4.52% 4.33% 3.59% 1.53% 2.78% -1.439% -3.18% -0.28% 0.13%

159 DELTA (expected): The deciles below are created by considering annual changes in the modeled expectation of total patent stock.

Decilei Portfolio Less No Patent (NP ) Portfolio Difference Portfolios

(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP ) 1 − 2 − 3 − 4 − 5 − 6 − 7 − 8 − 9 − 10 −

NDecile 8444 1932 5102 5194 5073 5172 5113 5143 5100 4992

NPortfolio 348 348 348 348 348 348 348 348 348 348

adj. R2 0.02728 0.06705 0.1095 0.1702 0.07617 0.1179 0.16 0.09948 0.07176 0.1462 p-value 0.00907 4.252e-05 6.824e-09 5.577e-14 2.731e-06 1.419e-09 4.273e-13 4.259e-08 5.89e-06 6.362e-12

Abnormal Return

αj 0.001984 0.001429 0.004058 0.001139 0.003796 0.002810 0.001204 -0.002544 0.000793 -7.291e-05 (1.318) (0.346) (1.852) (0.459) (2.021) (1.456) (0.546) (-1.217) (0.417) (-0.029)

CAPM

βj -0.031924 0.079153 0.138180 0.131205 0.067074 0.057617 0.107866 0.137075 0.079190 8.112e-02 (-1.471) (1.308) (4.345) (3.667) (2.461) (2.056) (3.369) (4.517) (2.871) (2.258)

SMB

sj -0.009874 0.351591 0.071314 0.196792 0.062662 0.164782 0.092480 0.069146 0.054805 1.550e-01 (-0.305) (3.996) (1.498) (3.683) (1.535) (3.927) (1.929) (1.522) (1.327) (2.881)

HML

hj -0.095042 -0.117987 -0.136304 -0.205781 -0.126738 -0.152340 -0.241480 -0.070025 -0.117683 -2.246e-01 (-3.386) (-1.531) (-3.314) (-4.441) (-3.595) (-4.202) (-5.831) (-1.784) (-3.298) (-4.833)

UMD

uj 0.017165 0.067685 -0.023751 -0.173148 -0.073641 -0.059393 -0.092591 -0.101682 -0.044242 -2.169e-01 (0.555) (0.820) (-0.527) (-3.393) (-1.905) (-1.494) (-2.039) (-2.363) (-1.131) (-4.256)

Annualized Return 2.41% 1.73% 4.98% 1.38% 4.65% 3.42% 1.45% -3.01% 0.96% -0.09%

160 NORM (expected): The deciles below are created by considering annual changes in the modeled expectation of total patent stock divided by the number of patents present in the patent network for the same year (within-ﬁrm normalization of patents per capita measure of the entire portfolio’s performance).

Decilei Portfolio Less No Patent (NP ) Portfolio Difference Portfolios

(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP ) 1 − 2 − 3 − 4 − 5 − 6 − 7 − 8 − 9 − 10 −

NDecile 7961 2327 5053 5053 5068 5038 5086 5014 5069 4924

NPortfolio 348 348 348 348 348 348 348 348 348 348

adj. R2 0.03657 0.07333 0.06038 0.05909 0.06216 0.1444 0.1461 0.1878 0.2001 0.15 p-value 0.002108 6.882e-06 4.159e-05 5.175e-05 3.071e-05 9.171e-12 6.569e-12 1.518e-15 < 2.2e-16 3.064e-12

Abnormal Return

αj 0.002374 -0.001918 0.001228 -0.001700 0.004397 0.002755 -0.0009932 0.001752 0.0004492 0.002850 (1.515) (-0.533) (0.631) (-0.938) (2.451) (1.454) (-0.498) (0.659) (0.179) (1.293)

CAPM

βj -0.023514 0.133388 0.092594 0.068123 0.014878 0.083561 0.1226886 0.180398 0.1566053 0.118422 (-1.040) (2.570) (3.278) (2.590) (0.571) (3.038) (4.234) (4.671) (4.305) (3.702)

SMB

sj -0.025124 0.318960 0.001444 0.023998 0.099446 0.038828 0.1221720 0.227761 0.2040488 0.136228 (-0.744) (4.119) (0.034) (0.609) (2.550) (0.943) (2.816) (3.938) (3.746) (2.845)

HML

hj -0.112837 -0.033552 -0.081649 -0.110629 -0.133084 -0.207029 -0.1419350 -0.195775 -0.2534962 -0.201503 (-3.860) (-0.489) (-2.235) (-3.252) (-3.951) (-5.819) (-3.787) (-3.919) (-5.388) (-4.870)

UMD

uj 0.016331 -0.037441 -0.088820 0.018066 -0.038811 -0.093570 -0.0999272 -0.167990 -0.1117257 -0.009741 (0.507) (-0.506) (-2.217) (0.484) (-1.051) (-2.398) (-2.432) (-3.067) (-2.166) (-0.215)

Annualized Return 2.89% -2.28% 1.48% -2.02% 5.41% 3.36% -1.19% 2.12% 0.54% 3.47%

161 Z (expected): The deciles below are created by standardizing the normalized stock (presented pre-

x−µ viously) to create z-scores (z = σ ).

Decilei Portfolio Less No Patent (NP ) Portfolio Difference Portfolios

(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP ) 1 − 2 − 3 − 4 − 5 − 6 − 7 − 8 − 9 − 10 −

NDecile 5282 5118 5089 5723 4963 4964 4890 5154 5092 4990

NPortfolio 348 348 348 348 348 348 348 348 348 348

adj. R2 0.1399 0.1098 0.1264 0.05634 0.02462 0.1184 0.09433 0.08502 0.1427 0.09777 p-value 2.169e-11 6.415e-09 2.867e-10 8.226e-05 0.01361 1.304e-09 1.083e-07 5.739e-07 1.267e-11 5.816e-08

Abnormal Return

αj 0.001723 -0.002327 0.004213 0.001540 -0.001694 -7.008e-05 0.001744 -0.0009315 0.001990 0.0005394 (0.739) (-1.141) (2.091) (0.822) (-0.951) (-0.034) (0.986) (-0.404) (0.955) (0.272)

CAPM

βj 0.103274 0.082578 0.103450 0.085303 0.040390 2.793e-02 0.072618 0.0729074 0.093588 0.0428641 (3.070) (2.789) (3.561) (3.137) (1.572) (0.934) (2.829) (2.179) (3.093) (1.487)

SMB

sj 0.158090 0.048191 0.041110 0.012335 0.006797 1.634e-01 0.031864 0.0973091 0.146260 0.1231465 (3.147) (1.087) (0.947) (0.303) (0.177) (3.652) (0.829) (1.942) (3.229) (2.853)

HML

hj -0.213393 -0.189514 -0.181740 -0.095729 -0.084871 -2.023e-01 -0.141225 -0.1825978 -0.162305 -0.1747890 (-4.905) (-4.949) (-4.836) (-2.721) (-2.554) (-5.231) (-4.253) (-4.219) (-4.147) (-4.687)

UMD

uj -0.047886 -0.060166 -0.087826 -0.025692 -0.038033 -1.377e-02 -0.076939 -0.0346914 -0.145791 -0.0291426 (-0.999) (-1.433) (-2.121) (-0.666) (-1.039) (-0.325) (-2.113) (-0.731) (-3.397) (-0.713)

Annualized Return 2.09% -2.76% 5.17% 1.86% -2.01% -0.08% 2.11% -1.11% 2.41% 0.65%

162 Correlations of Deciles across ‘expected patent stock’ models

TOTAL DELTA NORM Z

TOTAL 1.0000

DELTA 0.8706 1.0000

NORM 0.5273 0.7119 1.0000

Z 0.2981 0.4764 0.6684 1.0000

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178 LIST OF FIGURES

179 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10

1966 1968 1972 1976 1978 1981 1984 1988 1990 1993 Figure 1: Toy Example Patent Network. A very simple example of a directed patent graph to illustrate the patent network. Nodes represent patents, links represent citations between patents. The direction of the arrows deﬁnes the nature of the link—from incrementalness to radicalness. The temporal constraints of the network are represented in the timeline.

180 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10

Figure 2: Toy Example Patent Network with Super-node: We deﬁne all patents to have a full association with the super-node, the U.S. Patent Ofﬁce.

181 Our Patent Rank Approach (Del Corso et al. 2005, Bini et al. 2008) 7 Sort Partition Augment 0 0 0 0 0 0 0 0 0 0 0.1319 0 0 0 0 0 0 0 0 0 0 01111111111 0 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 0.1395   0 0 0 0 0 0 0 0 0 0 29/15 * Drop   0 0 0 0 0 0 0 0 0 0 10000000000 10000000000 0.1327       92      0 0 0 0 0 0 0 0 0 0   /45 Augmented   0 1 1 0 0 0 0 0 0 0  10000000000 10000000000  0.0910    0 0 0 0 0 0 0 0 0 0     35/18    1 1 0 0 0 0 0 0 0 0     10000000000   10000000000     0.0819     0 0 0 0 0 0 0 0 0 0   Row   Solve  4/3  Value    0 0 0 0 0 0 0 0 0 0  -   -  10000000000  -  10000000000  -   - 0.1274           6       0 1 1 0 0 0 0 0 0 0       /5     1 1 0 0 1 1 0 0 0 0     10110000000 Normalize 1/3 0 1/3 1/3 0 0 0 0 0 0 0  Linear  * Sort  0.0682     1 1 0 0 0 0 0 0 0 0       28/15     1 0 0 1 0 1 0 0 0 0     11100000000   1/3 1/3 1/3 0 0 0 0 0 0 0 0     0.0910          System  * 1-norm      1 0 0 1 1 0 0 0 0 0     1 1 1 1   1     0 0 1 0 0 0 0 0 0 0     11001100000   /4 /4 0 0 /4 /4 0 0 0 0 0     0.0682   1 1 0 1 0 1 0 0 0 0       4/3         1 1 1 1 1       0 0 0 0 0 1 0 1 0 0     11100110000   /5 /5 /5 0 0 /5 /5 0 0 0 0     0.0682     0 0 1 0 0 0 0 0 0 0       1         10010000000   1/2 0 0 1/2 0 0 0 0 0 0 0       0 0 0 1 0 0 1 0 0 0       1       1/3 1/3 1/3       10000010100   0 0 0 0 0 0 0 0       

Parent Patent P P P P P P P P P P → 1 2 3 4 5 6 7 8 9 10 P1 0000000000 P2 0000000000 TM P3 0 000000000 Google’s PageRank Approach (Brin and Page 1998, Page et al. 1999) P4 0 1 10000000 T P5 1 1 0 0000000 Id e P 0 0 0 0 000000 · 6 1 10 1 10 1 10 1 10 1 10 1 10 1 10 1 10 1 10 1 10 Child Patent 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 / / / / / / / / / / 1 1 1 1 1 1 1 1 1 1 P7 1 1 0 0 1 10000  0 0 0 0 0 0 0 0 0 0   1 1 1 1 1 1 1 1 1 1   1 1 1 1 1 1 1 1 1 1   /10 /10 /10 /10 /10 /10 /10 /10 /10 /10  0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10                1 1  P8 1 0 0 1 0 1 0000  0 1 1 0 0 0 0 0 0 0   0 0 0 0 0 0 0 0 0 0   0 1 1 0 0 0 0 0 0 0   0 /2 /2 0 0 0 0 0 0 0           1 1 0 0 0 0 0 0 0 0   0 0 0 0 0 0 0 0 0 0   1 1 0 0 0 0 0 0 0 0   1/2 1/2 0 0 0 0 0 0 0 0        Row   P9 0 0 1 0 0 0 0 000   +   =   -  1 1 1 1 1 1 1 1 1 1   0 0 0 0 0 0 0 0 0 0   1 1 1 1 1 1 1 1 1 1   1 1 1 1 1 1 1 1 1 1   /10 /10 /10 /10 /10 /10 /10 /10 /10 /10           1 1 0 0 1 1 0 0 0 0   0 0 0 0 0 0 0 0 0 0   1 1 0 0 1 1 0 0 0 0  Normalize 1/4 1/4 0 0 1/4 1/4 0 0 0 0  P10 0 0 0 0 0 1 0 1 00          1 0 0 1 0 1 0 0 0 0   0 0 0 0 0 0 0 0 0 0   1 0 0 1 0 1 0 0 0 0   1/3 0 0 1/3 0 1/3 0 0 0 0                   0 0 1 0 0 0 0 0 0 0   0 0 0 0 0 0 0 0 0 0   0 0 1 0 0 0 0 0 0 0   0 0 1 0 0 0 0 0 0 0           0 0 0 0 0 1 0 1 0 0   0 0 0 0 0 0 0 0 0 0   0 0 0 0 0 1 0 1 0 0   0 0 0 0 0 1/2 0 1/2 0 0          S         S address dangling nodes S ? 1 α T S − ? n ee S 0.0850 0.0850 0.085 0.0850 0.0850 0.0850 0.085 0.085 0.085 0.085 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1333  0.0850 0.0850 0.085 0.0850 0.0850 0.0850 0.085 0.085 0.085 0.085   0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015   0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000  Solve-  0.1454  S 0.0850 0.0850 0.085 0.0850 0.0850 0.0850 0.085 0.085 0.085 0.085 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1531               Power    0 0.4250 0.425 0 0 0 0 0 0 0   0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015   0.0150 0.4400 0.4400 0.0150 0.0150 0.0150 0.0150 0.0150 0.0150 0.0150   0.0878          S  0.4250 0.4250 0 0 0 0 0 0 0 0   0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015   0.4400 0.4400 0.0150 0.0150 0.0150 0.0150 0.0150 0.0150 0.0150 0.0150   0.0759        Method     +   =      0.0850 0.0850 0.085 0.0850 0.0850 0.0850 0.085 0.085 0.085 0.085   0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015   0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000   0.1277           0.2125 0.2125 0 0 0.2125 0.2125 0 0 0 0   0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015   0.2275 0.2275 0.0150 0.0150 0.2275 0.2275 0.0150 0.0150 0.0150 0.0150   0.0626  S          0.2833 0 0 0.2833 0 0.2833 0 0 0 0   0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015   0.2983 0.0150 0.0150 0.2983 0.0150 0.2983 0.0150 0.0150 0.0150 0.0150   0.0891                   0 0 0.850 0 0 0 0 0 0 0   0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015   0.0150 0.0150 0.8650 0.0150 0.0150 0.0150 0.0150 0.0150 0.0150 0.0150   0.0626  S          0 0 0 0 0 0.4250 0 0.425 0 0   0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015   0.0150 0.0150 0.0150 0.0150 0.0150 0.4400 0.0150 0.4400 0.0150 0.0150   0.0626          Sw         scale by damping factor α (= 0.85) address irreducibility

Figure 3: Comparison of Patent Rank to Google’s Page Rank: The introduction of the U.S. Patent Ofﬁce as a super-node is a more intuitive technique which simpliﬁes the computation using linear algebra.

182 EMI E M I Patent # 3,778,614 Patent # 3,924,129

Measuring Radiation Radiography (Imaging)

0 0

5 5

1 1 wwwwww w

ww ww

w 0 0

0 0

1 1

w *

* *

* w

* wwwwwww

www

w *

* w

0 0 w

w w ------wwwwww

- - - - -

5 5 w

- w

- w w

- * *

- *

w *

- *

* *

w * w *

* *

w *

* w *

s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s ws s s s*s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s 0 0 * *

1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 (a) Rank #1 (b) Rank #2

EMI Stanford Patent # 3,881,110 Patent # 4,029,963

Liquid Medium Stable Imaging System

0 0

5 5

1 1

0 0

w 1 1

w w

0 0

5 5 w

wwwwwww ww

ww w

w w

w *

www *

ww *

* w

w * *

w *

w * *

* *

w *

* *

w w

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w *

* *

w *

* ------

s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s wsws ws s s s s s*s s s s s s s s s s s s s s s s s s s s s s s s s s 0 0 ------*- - - - * * * * **

1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 (c) Rank #3 (d) Rank #4

Figure 4: Diffusion patterns of the top CT-scanner innovations from Trajtenberg (1990a): Rankings are based on WPC1981; corresponding rankings for PR2009 are 1 (101.93), 4 (32.60), 3 (32.89), 2 (35.16) with respective scores in parentheses.

183

WPC as Proxy for Adoption

w 0

0 w

6 w

0 w

w 0

0 w

3 w

w 0

0 w

w 2

w 0

0 w

w 0 w

1975 1980 1985 1990 1995 2000 2005 2010

Year

Figure 5: WPCt as a Proxy for Adoption: We report the number of adopters of the CT-scanner and the number of weighted patent counts, cumulative over time.

184 Israel Kirzner Joseph Schumpeter

Kirznerian Entrepreneur Schumpeterian Entrepreneur

Alert Entrepreneur Key Character Creative Entrepreneur

incremental and continuous Innovation radical and discontinuous

often Frequency rare

equilibriates Market Process disequilibriates

value appropriation value creation

market-sensing, market-making, customer-linking customer-driving Day (1994) Kumar et al. (2000)

discovers/exploits Entrepreneurial Opportunities creates

entrepreneur can be capitalist Entrepreneur/Capital entrepreneur is mutually capitalist can be entrepreneur exclusive from capitalist

Arbitrageur Leadership Innovator

Imitator Pioneer

Competitor Captain of Industry

Homo agens Motivation Unternehmergeist

competition creation

execution vision

proﬁt-generating strategies strategies for potential proﬁts

short-term long-term

extrinsic intrinsic

economic growth Outcome economic development

Figure 6: Entrepreneurial Innovation: Two competing views on entrepreneurial activity from the Austrian school; the Kirznerian Entrepreneur and the Schumpeterian Entrepreneur.

185 Kirznerian Innovation Schumpeterian Innovation

Incremental Radical

Figure 7: Continuum of Entrepreneurial Innovation: Schumpeterian entrepreneurial activity occurs less frequently (y-axis as frequency) and inherently represents changes that are more radical (x-axis as impact of innovation on society).

186 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10

Figure 8: Patent Rank: Using patent-citation analysis, we deﬁne the patent network and ascertain every patent’s value based on its citation ancestry (backward citations) and heritage (forward citations).

187 Intensity

Volume

Equilibrium

Duration

Figure 9: Schumpeterian shocks: A shock can be uniquely described based on its intensity, time of intensity, duration, and total volume.

188 Our Patent Rank Approach (Del Corso et al. 2005, Bini et al. 2008) 7 Sort Partition Augment 0 0 0 0 0 0 0 0 0 0 0.1319 0 0 0 0 0 0 0 0 0 0 01111111111 0 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 0.1395   0 0 0 0 0 0 0 0 0 0 29/15 * Drop   0 0 0 0 0 0 0 0 0 0 10000000000 10000000000 0.1327       92      0 0 0 0 0 0 0 0 0 0   /45 Augmented   0 1 1 0 0 0 0 0 0 0  10000000000 10000000000  0.0910    0 0 0 0 0 0 0 0 0 0     35/18    1 1 0 0 0 0 0 0 0 0     10000000000   10000000000     0.0819     0 0 0 0 0 0 0 0 0 0   Row   Solve  4/3  Value    0 0 0 0 0 0 0 0 0 0  -   -  10000000000  -  10000000000  -   - 0.1274           6       0 1 1 0 0 0 0 0 0 0       /5     1 1 0 0 1 1 0 0 0 0     10110000000 Normalize 1/3 0 1/3 1/3 0 0 0 0 0 0 0  Linear  * Sort  0.0682     1 1 0 0 0 0 0 0 0 0       28/15     1 0 0 1 0 1 0 0 0 0     11100000000   1/3 1/3 1/3 0 0 0 0 0 0 0 0     0.0910          System  * 1-norm      1 0 0 1 1 0 0 0 0 0     1 1 1 1   1     0 0 1 0 0 0 0 0 0 0     11001100000   /4 /4 0 0 /4 /4 0 0 0 0 0     0.0682   1 1 0 1 0 1 0 0 0 0       4/3         1 1 1 1 1       0 0 0 0 0 1 0 1 0 0     11100110000   /5 /5 /5 0 0 /5 /5 0 0 0 0     0.0682     0 0 1 0 0 0 0 0 0 0       1         10010000000   1/2 0 0 1/2 0 0 0 0 0 0 0       0 0 0 1 0 0 1 0 0 0       1       1/3 1/3 1/3       10000010100   0 0 0 0 0 0 0 0       

Figure 10: Comparison of Patent Rank to Google’s Page Rank: The introduction of the U.S. Patent Ofﬁce as a super-node is a more intuitive technique which simpliﬁes the computation using linear algebra.

189 Network Size (5-Year Moving Window) 0 . 3 5 . 2 ) s n o i l l i 0 . m

2 n i (

k r o w t 5 . e 1 N

n I

s t n e 0 t . a 1 P 5 . 0 0 . 0

1975 1980 1985 1990 1995 2000 2005 2010

(a) NetworkYear Size

(b) Adjacency Matrix

Figure 11: Summary of Data Inputs: We report the size of the network over time and the structure of the adjacency matrix.

190 Year Ending 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

3,778,614 4,054,595 3,950,357 4,024,163 4,098,888 4,237,224 4,278,793 4,226,898 4,318,043 4,358,535 National Takeda Hoechst Energy University of EMI Gist-Brocades Merck Research Chemical Stanford Aktiengesellschaft Conversion Devices Berkeley Washington (1973) (1977) (1976) (1977) (1978) (1980) (1980) (1980) (1982) (1982) MAX = 40 CT-scanner prostaglandins antibiotic synthetic cephalosporin (oral) DNA chimera cephem amorphous nuclear DNA diagnostics (Trajtenberg 1990a) (treats asthma) insecticides (bacterial infections) (bacterial infections) semiconductor magnetic resonance (medical treatment)

Year Ending 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

4,399,209 3,694,412 4,258,264 2,495,286 4,880,804 4,683,202 4,683,195 5,045,417 4,723,129 4,418,068

Mead Shell Fuji DuPont DuPont Cetus Cetus Hitachi Canon Eli Lilly (1983) (1972) (1981) (1950) (1989) (1987) (1987) (1991) (1988) (1983) MAX = 113 photocopier high melting X-rays interpolymer benzimidazoles DNA ampliﬁcation DNA cloning microminiaturization ink jet benzothiophenes (interpolymer) imaging system (treating hypertension) semiconductor (liquid ﬂows) (breast cancer)

Year Ending 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

4,463,359 5,572,643 4,740,796 4,558,333 4,345,262 5,523,520 5,640,343 5,850,009 5,536,637 5,367,109 Goldsmith Pioneer Genetics Pioneer Canon Judson Canon Canon Canon Seeds IBM Hi-Bred Institute Hi-Bred (1984) (1996) (1988) (1985) (1982) (1996) (1997) (1998) (1996) (1994) MAX = 130 ink jet Web browser ink jet ink jet ink jet Dwarﬁsm gene nonvolatile magnetic Inbred corn cloning cDNAs Inbred corn (drop on demand) (simple structure) (precise, durable) (original) (smaller petunias) random access memory (PH0HC) (breast cancer) (PHHB9)

Figure 12: Shock Patterns for Top Entrepreneurial Innovations: Using marginal Patent Rank scores, we identify Schumpeterian shocks for exception patent innovations.

191 Random sample Dangling nodes Hi-tech sample

Patterns

Random Sample (ms) Dangling Nodes (ms) High Technology (ms) 0 0 0 0 0 0 0 8 5 5 2 2 0 0 0 0 0 0 0 0 2 2 0 6 0 0 0 0 5 5 y y y 1 1 c c c n n n 0 e e e 0 u u u 0 q q q 4 e e e r r r F F F 0 0 0 0 0 0 1 1 0 0 0 2 0 0 0 0 5 5 0 0 0

0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 Time to Maximum Intensity Time to Maximum Intensity Time to Maximum Intensity Time to Maximum Intensity

Random Sample (ms) Dangling Nodes (ms) High Technology (ms) 0 0 0 6 0 0 0 0 0 2 0 0 0 0 8 0 5 0 0 0 0 0 0 5 0 0 1 0 4 6 y y y c c c 0 n n n 0 e e e 0 0 u u u 0 3 q 0 q q 0 e 0 e e 0 r r r 0 1 F F F 4 0 0 0 2 0 0 0 0 0 0 5 0 2 0 0 1 0 0 0

0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 Maximum Intensity Maximum Intensity Maximum Intensity Maximum Intensity

Random Sample (ms) Dangling Nodes (ms) High Technology (ms) 0 0 0 0 0 0 0 5 7 0 1 0 0 3 0 0 0 6 0 0 0 5 0 2 0 0 0 5 0 0 0 0 0 1 0 0 0 2 y y y 0 c c c 0 n n n 4 e e e 0 u u u 0 q q q 0 e e e 0 r r r 5 0 F F F 1 0 3 0 0 0 0 0 0 0 5 0 0 0 1 2 0 0 0 0 0 0 5 1 0 0 0

0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 Volume Volume Volume Volume

Figure 13: Nontrivial Patent Rank scores: Summaries of the portion of the sample that has some minimal level of diffusion.

192 Distribution of raw (ms) Patent Rank scores Distribution of log transformation 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 y 0 y 0 c c 8 8 n n e e u u 0 0 q q 0 0 e e r 0 0 r 0 F 0 F 6 6 0 0 0 0 0 0 0 0 4 4 0 0 0 0 0 0 0 0 2 2 0 0

1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0

Patent Rank score x*=ln(Patent Rank score) (a) Nontrivial Raw scores (b) Logarithmic Transformation

Distribution of double-log transformation 0 0 0 0 4 0 0 0 0 3 y c n e u q 0 e 0 r 0 F 0 2 0 0 0 0 1 0

-6 -4 -2 0 2

x=ln(x*)=ln(ln(Patent Rank score)) (c) Double Log Transformation

Figure 14: Distribution Patterns: Histogram of nontrivial Patent Rank scores and transforma- tions.

193 Firm Patent Portfolio Firm Patent Portfolio By Count 'First Generation CT−scanner' Longitudinal Volume

1200 0 EMI EMI - 27% 2 General Electric (120) 3 Philips 4 Siemens Stanford - 1% 1000 − OTHER (6) 800 Siemens - 11% (50) General Electric - 11% 600 (51)

Shibaura - 4% (17) 400 Ohio Nuclear - 2% Picker - 2% (10) (11) 200

Philips - 11% 0 (50) 1980 1985 1990 1995 2000 2005 2010 OTHER - 30% (135)

Figure 15: Competitive Landscape: We report the ﬁrms who secured patent protection for CT- scanner technologies and the total value of these ﬁrms’ portfolios over time.

194 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10

Figure 16: Patent Rank: Using patent-citation analysis, we deﬁne the patent network and ascertain every patent’s value based on its citation ancestry (backward citations) and heritage (forward citations).

195 Intensity

Volume

Equilibrium

Duration

Figure 17: Schumpeterian shocks: A shock can be uniquely described based on its intensity, time of intensity, duration, and total volume.

196 Year Ending 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

Year Ending 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

4,399,209 3,694,412 4,258,264 2,495,286 4,880,804 4,683,202 4,683,195 5,045,417 4,723,129 4,418,068

Year Ending 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Figure 18: Shock Patterns for Top Entrepreneurial Innovations: Using marginal Patent Rank scores, we identify Schumpeterian shocks for exception patent innovations.

197 Intensity

● ● ● ● ● ● ● ● ● ● ● ● ● ● ^ ● ● β = 101.644 ● ● ●

●

● Volume maturation stage * ^ growth stage δ = 0.6644 ● Patent Lifetime Value (PLV) Value Lifetime Patent

P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 ●

● Equilibrium ● ● ^τ = 1984.85 Duration Network is formed at time Radical innovation as Schum- Diffusion patternTime of total vol- t using (mc) Patent Rank peterian shock is observed up ume is modeled to identify scores. to and including time t. the growth δ, time of maximum growth τ and the expected patent lifetime value β at time t.

Figure 19: Overview of Process: Measuring a patent’s lifetime value is a function of its Patent Rank score, its actual diffusion pattern, and its modeled diffusion pattern.

198 1983 1984 1985 1986 1987 1988 1989 1990 1991

Intensity Intensity Intensity Intensity Intensity Intensity Intensity Intensity Intensity

Volume Volume Volume Volume Volume Volume Volume Volume Volume shock

Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium

Duration Duration Duration Duration Duration Duration Duration Duration Duration

● ● ● ● ● ● β^ = ● ● ● β^ = ● 92.086 β^ = 90.169 ^ 87.377 ● ● β = 83.464 ● ● ● ^ β = 78.345 ● ^ ● ● ● ● ● β = 72.199 ^ ● β = 65.266 ● ● ● ● ● ●

^ β = 53.539 ^ ^ δ^ = δ = 0.8707 ● ● ● ● ^ ● δ = 0.9773 ● 0.9135 ●* ●* ^ δ = 1.0714 * * ^ δ = 1.2046 *

growth ^ δ = β = 39.814 ^ 1.3794 * δ = 1.583 * Patent Lifetime Value (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent ^ (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent δ = 1.957 * ^ ● ● ● ● ● ● ● ● ● δ = 2.1622 *

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ^τ = 1982.96 ^τ = 1983.23 ^τ = 1983.52 ^τ = 1983.71 ^τ = 1983.9 ^τ = 1984.07 ^τ = 1984.21 ^τ = 1984.32 ^τ = 1984.39

Time Time Time Time Time Time Time Time Time

1992 1993 1994 1995 1996 1997 1998 1999 2000

Intensity Intensity Intensity Intensity Intensity Intensity Intensity Intensity Intensity

Volume Volume Volume Volume Volume Volume Volume Volume Volume shock

Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium

Duration Duration Duration Duration Duration Duration Duration Duration Duration

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ^ ● ^ ● ● ● ● ^ ● ^ ● ^ ● β^ = ● β = 98.122 ● β = 98.584 ● ● ^ ● ^ ● β = ● β = 96.659 ● β = 97.186 ● 97.662 ● ● β^ = β = 94.438 β = 95.279 96.024 ● 93.427 ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ●

^ ^ ^ ^ ^ ^ δ^ = δ^ = δ^ = δ = 0.7485 δ = 0.7386 δ = 0.7287 δ = 0.8411 δ = 0.8188 δ = 0.8003 * 0.784 * 0.7702 * 0.7587 * * * * ●* ●* ● ● ● ● ● ● ● growth Patent Lifetime Value (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent

● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ^τ = 1984.45 ^τ = 1984.5 ^τ = 1984.53 ^τ = 1984.57 ^τ = 1984.6 ^τ = 1984.62 ^τ = 1984.64 ^τ = 1984.67 ^τ = 1984.69

Time Time Time Time Time Time Time Time Time

2001 2002 2003 2004 2005 2006 2007 2008 2009

Intensity Intensity Intensity Intensity Intensity Intensity Intensity Intensity Intensity

Volume Volume Volume Volume Volume Volume Volume Volume Volume shock

Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium

Duration Duration Duration Duration Duration Duration Duration Duration Duration

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ^ ● ● ^ ● ● ^ ● ● ● ● ● ● ● ● ^ ● ● ^ ● ● ^ ● ● β = ● ● β = ● ● β = 103.025 ● ^ ● ^ ● β^ = ● β = ● β = 101.141 ● β = 101.644 ● 102.131 ● 102.596 ● ● β = 99.07 ● β = 99.579 ● 100.099 ● 100.622 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ●

^ ^ ^ ^ ^ ^ δ^ = δ^ = δ^ = δ = δ = 0.6544 δ = 0.6448 δ = 0.636 δ = 0.7183 * δ = 0.7076 * 0.6966 * 0.6856 * 0.6748 * 0.6644 * * * * ● ● ● ● ● ● ● ● ● growth Patent Lifetime Value (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent (PLV) Value Lifetime Patent

● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ^τ = 1984.71 ^τ = 1984.74 ^τ = 1984.77 ^τ = 1984.79 ^τ = 1984.82 ^τ = 1984.85 ^τ = 1984.87 ^τ = 1984.9 ^τ = 1984.92

Time Time Time Time Time Time Time Time Time

Figure 20: Filmstrip Shock/Growth: An example how the diffusion model for a patent innovation updates as more information about the patent’s intrinsic value is available.

199 Top Patenting Firms (by Quantity)

5000 IBM Samsung Microsoft Canon Sony

4000 LG Toshiba Fujitsu Seiko Epson Intel 3000 FujiFilm HP Xerox Micron Patents Granted Patents

2000 Denso 1000 0

1980 1985 1990 1995 2000 2005

Year

Figure 21: Top Patenting Firms (by Quantity): From our sample, we report the top-15 patenting ﬁrms for 1976–2006.

200 Top Patenting Firms (by Quality)

200000 IBM Canon Motorola Toshiba Xerox Fujitsu

150000 Sony Texas Instruments HP Intel FujiFilm Micron

100000 Samsung Lucent Patents Granted Patents AMD 50000 0

1980 1985 1990 1995 2000 2005

Year

Figure 22: Top Patenting Firms (by Quality): From our sample, we report the top-15 patenting ﬁrms for 1976–2006.

201 Top Patenting Firms (by Quality, Changes)

IBM Canon Toshiba

20000 Motorola Sony Fujitsu HP Xerox

15000 Samsung Intel Texas Instruments Micron Lucent FujiFilm 10000 Patents Granted Patents Microsoft 5000 0

1980 1985 1990 1995 2000 2005

Year

Figure 23: Top Patenting Firms (by Quality, Changes): From our sample, we report the top-15 patenting ﬁrms for 1976–2006.

202 Top Patenting Firms (by Quality, Changes Per Patent)

Compaq DEC

2.0 Lucent Motorola Sun AMD LSI IBM 1.5 Texas Instruments NCR Toshiba AMP Xerox 1.0 Intel Patents Granted Patents Canon 0.5 0.0

1980 1985 1990 1995 2000 2005

Year

Figure 24: Top Patenting Firms (by Quality, Changes Per Patent): From our sample, we report the top-15 patenting ﬁrms for 1976–2006.

203 Kirznerian Innovation Schumpeterian Innovation

Incremental Radical

Figure 25: Continuum of Entrepreneurial Innovation: Schumpeterian entrepreneurial activity occurs less frequently and inherently represents changes that are more radical.

204 Product/Market Process/Market Innovation Innovation

AnsoffA (1957)nsoff (1957) HendersonHen andderso Clarkn and Cl (1990)ark (1992) d w e e n r N u t r e v O

Market Diversification Modular Radical Development Innovation Innovation s t p s e t c e n k o r a C

e M r o

Market C Incremental Architectural Product Penetration Innovation Innovation Development d e c g r n o i f t s n i i e x

E Products R Linkages (Core Concepts / Components) Existing New Unchanged Changed

Marketing Concept ⇓ ⇓ Chandy andChand Tellisy and Te (1998)llis (1998) Kumar, Scheer,Kumar, S andcheer Kotler, and Kotl (2000)er (2000) h p a g r i e l

a H s l l u o o u n D i

t r n e Radical o

Market c Value Market P s i Innovation t n

Breakthrough D Innovation Driving n o i e t i m s l o l i p f l o u r F P

t e n d e e u l e m e a N Incremental v Incremental

o Technological V Architectural r r Innovation Development p e Breakthrough Innovation m I m

s o u t o s u u n i t C n w o o

L Newness of Technology C Business System Low High Existing Plus Unique

Figure 26: Theoretical conceptualizations of Radical Innovation: Product/Process Innovation in relation to the marketing concept

205 Intensity

Volume

Equilibrium

Duration

Figure 27: Schumpeterian shocks: A shock can be uniquely described based on its intensity, time of intensity, duration, and total volume.

206 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10

Figure 28: Patent Rank: Using patent-citation analysis, we deﬁne the patent network and ascertain every patent’s value based on its citation ancestry (backward citations) and heritage (forward citations).

207 Intensity

● ● ● ● ● ● ● ● ● ● ● ● ● ● ^ ● ● β = 101.644 ● ● ●

●

● Volume maturation stage * ^ growth stage δ = 0.6644 ● Patent Lifetime Value (PLV) Value Lifetime Patent

P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 ●

Figure 29: Overview of Process: Measuring a patent’s lifetime value is a function of its Patent Rank score, its actual diffusion pattern, and its modeled diffusion pattern.

208 1983 1984 1985 1986 1987 1988 1989 1990 1991

Intensity Intensity Intensity Intensity Intensity Intensity Intensity Intensity Intensity

Volume Volume Volume Volume Volume Volume Volume Volume Volume shock

Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium

Duration Duration Duration Duration Duration Duration Duration Duration Duration

^ β = 53.539 ^ ^ δ^ = δ = 0.8707 ● ● ● ● ^ ● δ = 0.9773 ● 0.9135 ●* ●* ^ δ = 1.0714 * * ^ δ = 1.2046 *

Time Time Time Time Time Time Time Time Time

1992 1993 1994 1995 1996 1997 1998 1999 2000

Intensity Intensity Intensity Intensity Intensity Intensity Intensity Intensity Intensity

Volume Volume Volume Volume Volume Volume Volume Volume Volume shock

Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium

Duration Duration Duration Duration Duration Duration Duration Duration Duration

● ● ● ● ● ● ● ● ●

Time Time Time Time Time Time Time Time Time

2001 2002 2003 2004 2005 2006 2007 2008 2009

Intensity Intensity Intensity Intensity Intensity Intensity Intensity Intensity Intensity

Volume Volume Volume Volume Volume Volume Volume Volume Volume shock

Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium Equilibrium

Duration Duration Duration Duration Duration Duration Duration Duration Duration

● ● ● ● ● ● ● ● ●

Time Time Time Time Time Time Time Time Time

Figure 30: Filmstrip Shock/Growth: An example how the diffusion model for a patent innovation updates as more information about the patent’s intrinsic value is available.

209 LIST OF TABLES

210 Who What Measure(s) and How Used Findings

Sorescu, Chandy, and Prabhu (2003) forward-patent citations and R&D expen- positive financial consequence of radi- ditures used to define technology product cal innovation among pharmaceuticals— support (using PCA) technology product support influences NPV (Model 3)

Wuyts, Dutta, and Stremersch (2004) simple patent counts and forward cita- relationship of patent stock was “surpris- tions are a control to represent “resident ingly” not significant in describing in- knowledge” terfirm relationships within incremental innovation; posthoc analysis identifies a quadratic effect and concludes that “the role of patents requires further research.”

Prabhu, Chandy, and Ellis (2005) simple patent counts and technological knowledge breadth, depth, and intensity classiﬁcations of relevant patents are beneﬁcial to extract value from mergers and acquisitions

Narasimhan, Rajiv, and Dutta (2006) patents with both forward and backward knowledge stores as absorptive capac- citations, technological classification of ity is different across high-tech firms relevant patents, and normalization of (demonstrated using several patent met- forward citations across a firm’s patent rics) portfolio and time

Chandy, Hopstaken, Narasimhan, and Prabhu (2006) patent ﬁling dates with forward and back- important ideas matter, experience mat- ward citations where the forward-citation ters, and speed can kill in the conversion- counts are ‘bump’ corrected based on the ability of pharmaceutical products FDA-approval date

Sorescu, Chandy, and Prabhu (2007) simple patent counts, forward-citation product support and development assets counts, and patent-inventor experience as essential for acquisition success human capital ﬁve years before acquisition

Aboulnasr, Narasimhan, Blair, and Chandy (2008) “radical product innovations in the phar- competitive responses to radical inno- maceutical industry are typically pro- vation (e.g., Purohit (1994) originally tected by patents” describes this incumbency/clone context (Chandy and Tellis 2000) with outcomes of leapfrogging and shelving dependent on the nature of the innovation and the structure of the competitive landscape)

Rao, Chandy, and Prabhu (2008) simple patent counts, forward-citation alliances for new ventures is essential counts as “know-how” on the FDA- to establish legitimacy based on “know- approval date how” which increases gains from innovations

Sood and Tellis (2009) patents as an event in the commercializa- abnormal stock-market returns for the tion process “entire innovation project”

Tellis, Prabhu, and Chandy (2009) forward citations as “codiﬁed know-how” “commercialization of radical innovations is a stronger predictor of ﬁnan- cial performance than other popular measures, such as patents”

Table 1: Recent use of patent-related data in marketing science: For each article, we report the patent-valuation metric used and the patent-data impact on the ﬁndings.

211 Child Patent P P P P P P P P P P → 10 9 8 7 6 5 4 3 2 1 P al 2: Table 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 P aetNetwork Patent 2 P 3 P 4 aua omo o Example Toy of Form Tabular : aetPatent Parent 212 P 5 P 6 P 7 P 8 P 9 P 10 1976–2000 1976–2000 1976–2000 1976–2010 Patent Patent Patent forward- forward- Rank Rank Patent Title Number citation citation score count count

1 217.395 Process for amplifying nucleic acid sequences 4,683,202 732 2256 2 208.643 Process for amplifying, detecting, and/or-cloning nucleic acid sequences 4,683,195 748 2018 3 150.947 Process for producing biologically functional molecular chimeras 4,237,224 214 286 4 130.271 Crystalline Zeolite ZSM-5 and method of preparing the same 3,702,886 393 558 5 122.930 Arrangement of writing mechanisms for writing on paper with a colored liquid 3,747,120 148 297 6 116.960 Specific DNA probes in diagnostic microbiology 4,358,535 309 427 7 110.004 Novel amorphous metals and amorphous metal articles 3,856,513 193 206 8 103.333 Bubble jet recording method and apparatus in which a heating element generates bubbles 4,723,129 1006 1955 in a liquid flow path to project droplets 9 98.040 Droplet generating method and apparatus thereof 4,463,359 929 1691 10 89.447 Antibiotics 3,950,357 88 91 11 87.834 Method for the direct analysis of sickle cell anemia 4,395,486 47 70 12 86.202 Method of producing tumor antibodies 4,172,124 118 136 13 85.657 Mask for manufacturing semiconductor device and method of manufacture thereof 5,045,417 148 168 14 85.559 Chiral smectic C or H liquid crystal electro-optical device 4,367,924 412 463 15 85.216 Method and apparatus for measuring x- or .gamma.-radiation absorption or transmission at 3,778,614 133 143 plural angles and analyzing the data 16 84.583 Software version management system 4,558,413 295 619 17 84.103 Bubble jet recording method and apparatus in which a heating element generates bubbles 4,740,796 879 1656 in multiple liquid flow paths to project droplets 18 83.301 Microorganisms having multiple compatible degradative energy-generating plasmids and 3,813,316 15 16 preparation thereof 19 82.645 Apparatus and method for producing images corresponding to patterns of high energy ra- 3,859,527 108 152 diation 20 80.806 Ink jet recording method 4,345,262 853 1550

Table 3: Top-20 in 2000: We report the top-20 patent innovations with a description for the (1976– 2000) patent network.

213 Year Ending 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

1 3,778,614 3,778,614 3,950,357 3,950,357 3,950,357 3,950,357 3,950,357 3,856,513 3,856,513 3,702,886 2 3,950,357 4,054,595 3,778,614 3,778,614 3,856,513 3,856,513 3,856,513 3,702,886 3,702,886 3,856,513 3 3,856,513 3,950,357 3,835,176 3,835,176 3,778,614 3,702,886 3,702,886 3,950,357 3,950,357 3,950,357 4 3,835,176 3,835,176 4,054,595 3,856,513 3,835,176 3,778,614 3,778,614 3,778,614 3,778,614 4,237,224 5 4,038,222 3,856,513 3,856,513 4,054,595 3,702,886 3,835,176 3,835,176 4,046,889 4,237,224 3,778,614 6 2,813,048 4,038,222 4,038,222 3,702,886 3,642,746 4,046,889 4,046,889 3,835,176 4,046,889 4,046,889

Patent Rank 7 3,622,615 3,878,046 3,642,746 3,642,746 4,054,595 3,642,746 3,642,746 3,642,746 3,835,176 3,835,176 8 3,827,237 3,827,237 4,024,163 4,024,163 4,046,889 4,098,888 4,098,888 4,098,888 4,064,521 4,064,521 9 3,760,171 3,803,124 3,702,886 4,046,889 4,024,163 4,024,163 4,024,163 4,064,521 3,642,746 4,098,888 10 3,816,393 3,760,171 4,046,889 4,038,222 4,098,888 4,054,595 4,105,776 4,237,224 4,098,888 3,642,746

Year Ending 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

1 3,702,886 3,702,886 3,702,886 3,702,886 3,702,886 3,702,886 3,702,886 4,237,224 4,683,195 4,683,202 2 3,856,513 3,856,513 3,856,513 4,237,224 4,237,224 4,237,224 4,237,224 3,702,886 4,683,202 4,683,195 3 4,237,224 4,237,224 4,237,224 3,856,513 3,856,513 3,856,513 3,856,513 3,856,513 4,237,224 4,237,224 4 3,950,357 3,950,357 3,950,357 3,950,357 3,950,357 3,950,357 3,950,357 4,683,195 3,702,886 3,702,886 5 3,778,614 3,778,614 3,778,614 3,778,614 3,778,614 3,778,614 3,778,614 4,683,202 3,856,513 4,358,535 6 4,046,889 4,046,889 4,046,889 4,046,889 3,859,527 3,859,527 3,747,120 3,950,357 4,358,535 3,856,513

Patent Rank 7 3,835,176 3,859,527 3,859,527 3,859,527 4,046,889 4,046,889 3,859,527 4,358,535 3,747,120 3,747,120 8 4,064,521 4,064,521 4,064,521 4,064,521 4,064,521 3,747,120 4,046,889 3,778,614 3,950,357 3,950,357 9 4,098,888 3,835,176 3,835,176 3,747,120 3,747,120 4,064,521 4,683,202 3,747,120 3,778,614 3,778,614 10 3,642,746 3,642,746 3,642,746 3,642,746 3,642,746 3,813,316 4,358,535 3,859,527 3,859,527 4,367,924

Year Ending 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

1 4,683,202 4,683,202 4,683,202 4,683,202 4,683,202 4,683,202 4,683,202 4,683,202 4,683,202 4,683,202 2 4,683,195 4,683,195 4,683,195 4,683,195 4,683,195 4,683,195 4,683,195 4,683,195 4,683,195 4,683,195 3 4,237,224 4,237,224 4,237,224 4,723,129 4,723,129 4,723,129 4,723,129 4,723,129 4,723,129 4,723,129 4 3,702,886 3,747,120 3,747,120 3,747,120 3,747,120 3,747,120 3,747,120 3,747,120 3,747,120 3,747,120 5 3,747,120 3,702,886 4,723,129 4,237,224 4,463,359 4,463,359 4,463,359 4,463,359 4,463,359 4,463,359 6 4,358,535 4,723,129 4,463,359 4,463,359 4,237,224 4,237,224 4,740,796 4,740,796 4,740,796 4,740,796

Patent Rank 7 3,856,513 4,358,535 3,702,886 3,702,886 4,740,796 4,740,796 4,237,224 4,237,224 4,558,333 4,558,333 8 4,723,129 4,463,359 4,358,535 4,740,796 4,558,333 4,558,333 4,558,333 4,558,333 4,345,262 4,345,262 9 4,463,359 3,856,513 4,740,796 4,358,535 4,345,262 4,345,262 4,345,262 4,345,262 4,313,124 4,313,124 10 3,950,357 4,740,796 4,558,333 4,558,333 3,702,886 4,313,124 4,313,124 4,313,124 4,237,224 4,237,224

Table 4: Annual Top-10 Patent Innovations: For each year, we report the top-10 patents (by patent number) according to our computations of Patent Rank.

214 Year Ending 1980 1985 1990 1995 2000 2005 2009

Patents (N) 1083196 1759719 2451518 3132481 3959151 4886372 5608070

Correlation(WPCt,PRt) 0.872 0.867 0.849 0.828 0.782 0.746 0.716

Table 5: Convergent Correlations: Diminishing Correlations between forward-citation counts (WPC) and Patent Rank scores over time.

215 Year ∆WTW Firms Brands Adopters WPCt+3 PRt+3 P P 1973 2.99 638 3 1 16 33 41.71 1974 11.70 7564 8 2 90 128 115.82 1975 13.21 9067 12 6 306 303 245.23 1976 17.99 15026 13 17 623 494 340.29 1977 18.93 16023 14 31 951 830 463.39 1978 19.05 16102 11 37 1162 1145 585.94 1979 19.19 16175 9 42 1371 1361 658.52 1980 19.26 16205 8 44 1548 1533 716.18 1981 19.44 16284 8 47 1649 1741 776.35 1982 19.64 16371 8 55 . 1984 838.70

Table 6: Cumulative Measures from Trajtenberg (1990a) with 3-year reverse lags: weighted patent counts WPCt+3 and Patent Rank scores PRt+3.

216 Trajtenberg (1990a) Our Cumulative Approach Approach

WPC WPCt+3 PRt+3

∆W 0.755P 0.7863P 0.8537 Social Economic Value TW 0.685 0.8104 0.8747

Table 7: Comparison of Results: An application of principle 4 identiﬁes major improvement by appropriately comparing measures to social value cumulatively. Even within this improved comparison, Patent Rank has more information about the network, and is superior to the traditional measure.

217 Year Ending 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

1 3,778,614 4,054,595 3,950,357 3,950,357 4,054,595 4,054,595 4,237,224 4,237,224 4,318,043 4,318,043 2 3,950,357 3,950,357 4,054,595 4,054,595 4,098,888 4,098,888 4,278,793 4,226,898 4,237,224 4,237,224 3 3,856,513 3,856,513 4,024,163 4,024,163 3,950,357 4,237,224 4,222,903 4,127,405 4,226,898 4,358,535 4 3,835,176 3,778,614 4,038,222 4,105,776 4,105,776 4,105,776 4,127,405 4,278,793 4,127,405 4,258,264 5 4,038,222 3,835,176 4,046,889 4,098,888 4,024,163 4,024,163 3,702,886 4,217,374 4,196,265 4,196,265

Rank 6 2,813,048 4,038,222 3,835,176 4,046,889 3,702,886 3,702,886 4,064,521 4,265,991 4,217,374 4,532,248 7 3,622,615 3,878,046 3,856,513 3,702,886 4,237,224 4,002,155 4,226,898 4,318,043 4,278,793 4,172,124 8 3,827,237 3,803,124 4,105,776 3,856,513 4,064,521 4,064,521 4,105,776 4,196,265 4,172,124 4,226,898 9 3,760,171 3,816,393 3,642,746 4,076,853 4,046,889 4,166,115 4,217,374 4,222,903 4,063,220 4,367,924 10 3,816,393 4,024,163 4,076,853 4,064,521 4,002,155 4,128,658 4,196,265 4,064,521 4,143,054 4,063,220

Year Ending 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

1 4,399,209 3,694,412 3,694,412 3,694,412 4,880,804 4,880,804 4,683,195 4,683,195 4,683,195 4,683,195 2 4,358,535 4,399,209 4,399,209 4,399,209 3,694,412 4,683,202 4,683,202 4,683,202 4,683,202 4,683,202 3 4,532,248 4,258,264 4,258,264 2,495,286 4,826,808 4,683,195 4,880,804 5,045,417 5,045,417 4,723,129 4 4,258,264 4,358,535 2,495,286 4,826,808 4,720,480 5,045,417 5,045,417 4,880,804 4,723,129 4,418,068 5 3,694,412 4,532,248 4,358,535 4,258,264 4,683,202 4,367,924 4,723,129 4,723,129 4,812,599 4,463,359

Rank 6 4,237,224 4,425,120 4,660,166 4,720,480 4,742,018 4,723,129 4,367,924 4,740,796 4,463,359 4,740,796 7 4,318,043 4,353,086 4,720,480 4,880,804 4,683,195 4,720,480 4,740,796 4,463,359 4,418,068 4,812,599 8 4,425,120 4,440,846 4,826,808 4,660,166 4,660,166 4,742,018 4,463,359 4,558,333 4,740,796 4,133,814 9 4,440,871 4,367,924 4,343,993 4,367,924 4,367,924 4,826,808 3,929,992 4,367,924 4,558,333 4,345,262 10 4,440,846 2,495,286 4,315,086 4,358,535 4,723,129 3,694,412 4,558,333 4,345,262 4,133,814 4,558,333

Year Ending 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

1 4,683,195 4,683,195 4,683,195 4,723,129 4,723,129 4,723,129 5,523,520 5,523,520 5,523,520 5,523,520 2 4,683,202 4,683,202 4,683,202 4,683,195 4,683,195 4,683,195 4,723,129 5,536,637 5,536,637 5,536,637 3 4,723,129 4,723,129 4,723,129 4,683,202 4,683,202 4,683,202 4,683,202 5,850,009 5,850,009 5,850,009 4 4,463,359 5,572,643 5,572,643 4,463,359 4,463,359 4,463,359 4,683,195 5,367,109 5,367,109 5,367,109 5 4,740,796 4,463,359 4,463,359 4,740,796 4,740,796 4,740,796 5,640,343 5,304,719 5,304,719 5,304,719

Rank 6 4,345,262 4,740,796 4,740,796 4,558,333 4,558,333 4,558,333 4,740,796 4,683,202 4,683,202 5,968,830 7 4,812,599 4,558,333 4,558,333 4,345,262 4,345,262 5,523,520 4,463,359 4,723,129 5,968,830 4,683,202 8 4,558,333 4,345,262 4,345,262 4,313,124 4,313,124 4,345,262 5,103,459 4,683,195 4,683,195 4,683,195 9 4,313,124 4,313,124 4,313,124 4,459,600 4,459,600 5,640,343 5,643,826 5,103,459 5,103,459 4,731,499 10 4,459,600 4,459,600 4,459,600 5,572,643 5,572,643 4,313,124 4,558,333 5,640,343 6,413,802 4,658,084

Table 8: Top 10 (ms) Innovations: For each year, we report the top-10 patents (by patent number) according to our computations of Patent Rank.

218 Formation

Structure-Only Combined

Cumulative (cs) (cc)

Marginal (ms) (mc) Temporal

Table 9: Summary of Basic Models: Temporal constraints (cumulative vs. marginal) and Network formation (structure vs. combined) as basic manifestations of the generalized Patent Rank model.

219 Patents Time To Grant Backward Citations Inventors Claims Complexity

Year N Diffused Percent Q1 median Q3 Q1 median Q3 Q1 median Q3 Q1 median Q3 Q1 median Q3 By 2008? Diffused

1976 402 373 93% 1.266 1.623 2.034 3 4 7 1 1 2 5 8 12 10 18 32 1977 373 357 96% 1.227 1.534 1.937 3 5 7 1 1 2 4 7 12 9 17 31 1978 378 363 96% 1.305 1.595 1.956 3 5 7 1 1 2 5 8 13 10 17 29 1979 279 256 92% 1.323 1.66 2.033 3 5 7 1 1 2 5 8 13 10 18 30 1980 355 344 97% 1.647 1.882 2.256 3 5 7 1 1 2 5 9 13 10 18 30 1981 378 357 94% 1.57 1.892 2.25 3 5 7.75 1 1 2 5 8 13 9 16 30 1982 332 314 95% 1.651 2.005 2.329 3 5 7 1 1 2 4 8 13 9 17 34.25 1983 325 302 93% 1.742 2.145 2.507 3 5 8 1 1 2 5 8 13 11 18 30 1984 386 353 91% 1.605 2.082 2.56 3 5 8 1 1 2 5 8 13 10 18 31 1985 410 390 95% 1.419 1.933 2.605 3 5 8.75 1 1 2 6 9 14 11 20 35 1986 407 384 94% 1.419 1.816 2.325 3 5 9 1 1 2 5 9 14 12 20 33 1987 476 459 96% 1.332 1.76 2.2 3 6 9 1 1 2 6 9 15 12 20 38 1988 443 422 95% 1.277 1.666 2.13 3 5 8 1 2 3 5 9 14 11 20 36.5 1989 547 515 94% 1.193 1.523 1.975 3 6 10 1 2 3 6 9 15 11 20 34 1990 515 489 95% 1.229 1.529 1.964 3 6 10 1 2 3 6 10 17 12 21 39 1991 553 522 94% 1.222 1.584 1.997 3 6 10 1 2 3 6 9 16 12 21 37 1992 558 526 94% 1.209 1.574 2.061 4 6 10 1 2 3 6 10 16.75 13 22 38 1993 564 531 94% 1.244 1.647 2.182 4 7 10 1 2 3 6 11 18 12 23 42.25 1994 581 552 95% 1.277 1.622 2.052 4 6 11 1 2 3 7 11 17 12 23 44 1995 581 551 95% 1.299 1.614 2.112 4 7 11 1 2 3 6 10 18 14 25 45 1996 626 590 94% 1.374 1.722 2.151 4 7 12 1 2 3 7 12 19 15 28 49 1997 644 597 93% 1.523 1.896 2.316 4 7 13 1 2 3 7 12 19 14 29 52.25 1998 847 790 93% 1.527 1.989 2.552 4 7 13 1 2 3 8 13 20 15.5 29 50.5 1999 880 809 92% 1.57 2.012 2.491 4 7 12 1 2 3 8 14 21 15 28 49.25 2000 902 801 89% 1.612 2.129 2.638 4 7 13 1 2 3 8 14 21 15 27 49 2001 953 849 89% 1.6 2.049 2.74 4 8 14 1 2 3 8 14 20 15 28 50 2002 959 808 84% 1.567 2.041 2.71 4 7 14 1 2 3 8 14 21 15 27 49 2003 967 782 81% 1.689 2.225 3.004 4 8 14 1 2 3 9 16 23 15.5 30 56 2004 941 684 73% 1.742 2.301 3.126 4 8 15 1 2 3 8 16 23 16 29 53 2005 825 551 67% 1.874 2.444 3.46 4 8 15 1 2 3 9 16 24 18 31 58 2006 1000 637 64% 2.047 2.881 4.017 5 8 17 1 2 3 9 16 23 20 34 59 2007 902 390 43% 2.298 3.142 4.302 4 8 16 1 2 3 10 16 23 18 33 60.5 2008 904 244 27% 2.361 3.347 4.403 4 8 15 1 2 3 9 15 21 19 34 56.25

Table 10: Descriptive Statistics for Random Sample: For each year, we report the number of patents in the random sample, the number of patents that have diffused (received at least one forward citation by the end of 2008), the percentage of patents that diffused. We also report descriptions of the patents (how long to get a ﬁled patent approved, in years; the number of backward citations; the number of inventors; the number of unique claims, the complexity deﬁned as the number of times a Table/Figure/Example was referenced in the patent).

220 Yg Yx 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 %

1976 5.97 30.1 44.03 54.23 61.94 66.67 70.65 74.88 76.37 78.61 79.6 80.85 82.84 83.58 84.33 84.83 85.57 87.31 87.81 88.31 88.81 88.81 89.55 90.05 90.55 90.8 91.29 92.04 92.29 92.54 92.79 92.79 92.79 1977 5.36 24.13 40.48 51.21 57.37 63 67.83 71.85 73.99 76.68 78.02 79.89 81.77 83.65 85.25 86.06 87.67 88.74 89.28 90.08 90.62 91.42 92.23 92.76 93.57 93.83 94.1 94.64 95.17 95.44 95.71 95.71 1978 3.17 24.07 45.77 60.05 65.61 71.43 75.4 78.31 82.8 84.13 85.71 87.57 88.36 88.62 89.42 89.68 90.74 91.53 91.8 92.33 93.39 93.39 93.65 94.18 94.71 94.97 94.97 95.24 95.5 95.5 95.5 1979 1.08 21.86 38.35 48.75 56.27 62.37 66.31 70.61 73.12 76.34 80.65 81.72 82.8 84.95 85.3 86.02 87.1 87.46 87.46 88.89 88.89 89.25 89.61 89.61 90.32 91.04 91.04 91.4 91.4 91.4 1980 2.82 21.69 37.18 49.58 59.44 66.76 73.24 77.75 80.28 82.82 84.51 85.35 86.48 87.61 88.17 89.58 90.14 91.83 92.11 92.96 93.8 95.21 95.49 95.49 95.49 96.06 96.34 96.62 96.9 1981 4.5 25.4 43.39 54.5 62.7 69.31 73.81 76.19 80.42 84.13 85.98 87.04 88.62 89.95 90.48 90.48 90.74 91.8 92.59 92.86 92.86 92.86 93.12 93.12 93.65 93.65 93.65 94.44 1982 5.42 28.01 48.49 58.73 67.77 72.59 75.6 78.31 80.42 83.73 84.94 87.35 88.86 89.76 90.36 90.66 91.27 92.47 92.77 93.67 93.98 93.98 93.98 94.58 94.58 94.58 94.58 1983 4.31 27.69 46.77 60.31 68 73.23 76.92 80 81.85 83.69 84.62 84.92 86.46 87.69 88.62 89.54 90.15 90.46 90.46 90.46 91.38 91.38 92 92.62 92.92 92.92 1984 5.18 27.46 48.19 57.25 65.28 70.47 75.13 77.72 81.61 82.9 85.49 87.05 88.08 88.6 89.12 89.12 89.38 89.38 89.9 89.9 90.41 90.41 90.67 90.67 91.19 1985 6.1 30 46.59 61.22 71.46 76.59 80.73 83.41 86.59 87.56 88.29 89.27 90.73 90.73 91.46 91.46 92.68 92.93 93.9 93.9 93.9 94.15 94.15 94.39 1986 7.62 34.89 50.61 62.16 69.53 75.68 80.84 82.56 85.01 86.73 88.7 89.43 90.66 90.91 91.89 92.38 93.12 94.1 94.1 94.35 94.35 94.35 94.35 1987 6.3 36.34 53.57 63.66 72.9 78.36 82.14 86.55 88.24 89.92 90.97 91.39 92.86 93.07 93.7 94.54 96.01 96.01 96.22 96.22 96.43 96.43 1988 8.35 36.79 55.53 63.43 71.56 75.4 78.33 81.72 84.88 87.81 90.07 91.42 92.1 92.55 93.68 93.91 94.13 94.13 94.81 95.03 95.26 1989 8.23 34.92 56.86 66.54 73.31 78.79 82.27 84.64 86.47 87.75 89.03 90.31 90.86 91.41 91.96 92.14 92.69 92.69 93.05 93.42 1990 8.16 35.92 55.92 67.18 75.53 79.22 82.33 85.63 87.18 88.16 89.9 91.07 92.04 92.62 93.2 93.4 93.98 93.98 94.17 1991 7.96 36.35 55.52 67.99 76.31 80.11 83.91 85.9 88.61 89.69 90.6 91.32 92.04 92.59 93.13 93.49 93.67 94.21 1992 8.96 39.25 57.89 69 75.45 80.47 84.59 87.1 88.53 90.14 90.68 91.22 91.76 92.65 93.19 93.55 93.91 1993 10.64 41.31 57.8 68.97 77.3 80.67 84.57 86.88 88.65 90.6 91.49 92.02 92.55 93.09 93.26 93.79 1994 6.71 35.8 53.87 68.33 76.42 81.93 85.71 88.64 90.71 91.91 92.77 93.63 94.66 94.66 95.01 1995 9.98 35.63 60.24 72.63 80.55 86.06 88.47 90.53 92.25 93.46 93.46 93.8 94.49 94.66 1996 8.47 39.46 62.14 76.36 83.07 85.78 88.34 90.73 92.01 92.65 93.29 93.77 93.77 1997 10.4 41.3 57.76 68.32 76.09 80.59 82.92 85.56 87.42 89.13 89.29 90.37 1998 14.17 43.09 64.82 75.21 82.29 86.07 88.31 89.61 91.15 91.74 92.68 1999 12.27 43.41 62.61 74.43 82.84 86.36 88.41 89.43 90.34 91.14 2000 16.63 44.24 60.86 72.06 79.05 81.93 84.04 85.37 87.69 2001 13.75 44.7 61.8 71.88 77.54 82.27 84.99 87.09 2002 12.41 39.52 57.25 65.69 73.51 78.1 81.44 2003 14.27 37.54 52.74 64.74 71.66 76.73 2004 9.67 32.2 49.63 61.11 68.54 2005 8.73 30.67 46.55 56.73 2006 10.5 30 44.1 2007 8.65 28.27 2008 8.85

Table 11: Absolute Chance of Diffusion over Time: For each year, we report the probability as chance (e.g., 8.57% chance is a probability of 0.0857) that a patent has diffused.

221 Yg Yx 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 %

1976 5.97 25.66 19.93 18.22 16.85 12.42 11.94 14.41 5.94 9.47 4.65 6.1 10.39 4.35 4.55 3.17 4.92 12.07 3.92 4.08 4.26 0 6.67 4.76 5 2.63 5.41 8.57 3.12 3.23 3.33 0 0 1977 5.36 19.83 21.55 18.02 12.64 13.21 13.04 12.5 7.62 10.31 5.75 8.54 9.33 10.29 9.84 5.45 11.54 8.7 4.76 7.5 5.41 8.57 9.38 6.9 11.11 4.17 4.35 9.09 10 5.56 5.88 0 1978 3.17 21.58 28.57 26.34 13.91 16.92 13.89 11.83 20.73 7.69 10 12.96 6.38 2.27 6.98 2.5 10.26 8.57 3.12 6.45 13.79 0 4 8.33 9.09 5 0 5.26 5.56 0 0 1979 1.08 21.01 21.1 16.86 14.69 13.93 10.48 12.77 8.54 12 18.18 5.56 5.88 12.5 2.38 4.88 7.69 2.78 0 11.43 0 3.23 3.33 0 6.9 7.41 0 4 0 0 1980 2.82 19.42 19.78 19.73 19.55 18.06 19.49 16.84 11.39 12.86 9.84 5.45 7.69 8.33 4.55 11.9 5.41 17.14 3.45 10.71 12 22.73 5.88 0 0 12.5 7.14 7.69 8.33 1981 4.5 21.88 24.11 19.63 18.02 17.73 14.66 9.09 17.78 18.92 11.67 7.55 12.24 11.63 5.26 0 2.78 11.43 9.68 3.57 0 0 3.7 0 7.69 0 0 12.5 1982 5.42 23.89 28.45 19.88 21.9 14.95 10.99 11.11 9.72 16.92 7.41 16 11.9 8.11 5.88 3.12 6.45 13.79 4 12.5 4.76 0 0 10 0 0 0 1983 4.31 24.44 26.38 25.43 19.38 16.35 13.79 13.33 9.23 10.17 5.66 2 10.2 9.09 7.5 8.11 5.88 3.12 0 0 9.68 0 7.14 7.69 4.17 0 1984 5.18 23.5 28.57 17.5 18.79 14.93 15.79 10.42 17.44 7.04 15.15 10.71 8 4.35 4.55 0 2.38 0 4.88 0 5.13 0 2.7 0 5.56 1985 6.1 25.45 23.69 27.4 26.42 17.95 17.71 13.92 19.12 7.27 5.88 8.33 13.64 0 7.89 0 14.29 3.33 13.79 0 0 4 0 4.17 1986 7.62 29.52 24.15 23.38 19.48 20.16 21.21 8.97 14.08 11.48 14.81 6.52 11.63 2.63 10.81 6.06 9.68 14.29 0 4.17 0 0 0 1987 6.3 32.06 27.06 21.72 25.43 20.16 17.48 24.71 12.5 14.29 10.42 4.65 17.07 2.94 9.09 13.33 26.92 0 5.26 0 5.56 0 1988 8.35 31.03 29.64 17.77 22.22 13.49 11.93 15.62 17.28 19.4 18.52 13.64 7.89 5.71 15.15 3.57 3.7 0 11.54 4.35 4.55 1989 8.23 29.08 33.71 22.46 20.22 20.55 16.38 13.4 11.9 9.46 10.45 11.67 5.66 6 6.38 2.27 6.98 0 5 5.26 1990 8.16 30.23 31.21 25.55 25.44 15.08 14.95 18.68 10.81 7.58 14.75 11.54 10.87 7.32 7.89 2.86 8.82 0 3.23 1991 7.96 30.84 30.11 28.05 25.99 16.03 19.09 12.36 19.23 9.52 8.77 7.69 8.33 6.82 7.32 5.26 2.78 8.57 1992 8.96 33.27 30.68 26.38 20.81 20.44 21.1 16.28 11.11 14.06 5.45 5.77 6.12 10.87 7.32 5.26 5.56 1993 10.64 34.33 28.1 26.47 26.86 14.84 20.18 14.94 13.51 17.19 9.43 6.25 6.67 7.14 2.56 7.89 1994 6.71 31.18 28.15 31.34 25.54 23.36 20.95 20.48 18.18 12.96 10.64 11.9 16.22 0 6.45 1995 9.98 28.49 38.24 31.17 28.93 28.32 17.28 17.91 18.18 15.56 0 5.26 11.11 3.12 1996 8.47 33.86 37.47 37.55 28.38 16.04 17.98 20.55 13.79 8 8.7 7.14 0 1997 10.4 34.49 28.04 25 24.51 18.83 12 15.45 12.9 13.58 1.43 10.14 1998 14.17 33.7 38.17 29.53 28.57 21.33 16.1 11.11 14.77 6.67 11.43 1999 12.27 35.49 33.94 31.61 32.89 20.53 15 8.82 8.6 8.24 2000 16.63 33.11 29.82 28.61 25 13.76 11.66 8.33 15.91 2001 13.75 35.89 30.93 26.37 20.15 21.03 15.38 13.99 2002 12.41 30.95 29.31 19.76 22.8 17.32 15.24 2003 14.27 27.14 24.34 25.38 19.65 17.88 2004 9.67 24.94 25.71 22.78 19.13 2005 8.73 24.04 22.9 19.05 2006 10.5 21.79 20.14 2007 8.65 21.48 2008 8.85

Table 12: Conditional Chance of Diffusion over Time: For each year, we report the probability as chance (e.g., 8.57% chance is a probability of 0.0857) that a patent has diffused.

222 Percentiles for Volume Parameter β

1 2 Year Granted 10% 25% 33 3 % 50% 66 3 % 75% 90%

1976 1.01 2.03 2.82 4.04 6.76 8.32 13.83 1977 0.96 2.10 2.49 4.17 6.08 7.88 14.00 1978 1.07 2.05 2.63 4.04 6.15 7.62 12.19 1979 0.79 1.93 2.65 3.89 5.70 6.54 12.64 1980 0.86 1.74 2.37 3.64 5.64 7.23 12.95 1981 0.98 1.86 2.55 4.24 6.30 8.15 14.18 1982 0.85 2.25 2.67 4.15 6.54 8.34 13.39 1983 0.89 2.12 2.99 4.70 6.85 9.08 14.17 1984 0.78 1.67 2.17 3.63 5.41 6.53 12.16 1985 0.78 1.65 2.39 4.21 6.74 8.55 13.44 1986 0.96 1.99 2.54 4.10 6.78 8.63 16.48 1987 0.81 1.72 2.24 3.93 6.34 7.72 12.39 1988 0.73 1.76 2.43 3.69 5.63 6.98 12.61 1989 0.81 1.92 2.60 3.78 5.93 7.60 12.83 1990 0.77 1.75 2.32 3.57 5.56 7.49 13.78 1991 0.82 1.85 2.35 3.75 5.79 7.00 11.98 1992 0.77 1.73 2.19 3.59 5.51 7.10 12.51 1993 0.77 1.85 2.49 3.88 6.04 7.47 13.82 1994 0.59 1.46 1.89 3.15 4.96 5.93 11.00 1995 0.71 1.65 2.21 3.38 5.20 6.14 10.76 1996 0.65 1.64 2.08 3.43 5.10 6.17 10.62 1997 0.59 1.50 2.08 2.99 4.51 5.54 9.89 1998 0.55 1.31 1.77 2.83 4.35 6.05 12.05 1999 0.47 1.07 1.42 2.18 3.37 4.36 8.87 2000 0.47 0.99 1.37 2.43 3.74 4.71 8.83 2001 0.44 0.88 1.12 1.77 2.84 3.72 7.04 2002 0.31 0.63 0.81 1.34 2.22 2.77 5.32 2003 0.29 0.56 0.76 1.21 2.02 2.75 4.95 2004 0.22 0.45 0.61 0.95 1.49 2.03 3.87 2005 0.19 0.37 0.47 0.69 1.11 1.38 2.72 2006 0.10 0.25 0.33 0.55 0.84 1.01 1.55

Table 13: Volume Parameter β: Percentiles observed in 2008

223 Percentiles for Growth Parameter δ

1 2 Year Granted 10% 25% 33 3 % 50% 66 3 % 75% 90%

1976 0.143 0.217 0.265 0.366 0.531 0.675 0.908 1977 0.149 0.213 0.261 0.394 0.644 0.772 0.912 1978 0.153 0.239 0.295 0.413 0.639 0.782 0.936 1979 0.158 0.233 0.290 0.391 0.657 0.743 0.908 1980 0.189 0.270 0.316 0.464 0.625 0.731 0.903 1981 0.191 0.263 0.323 0.425 0.595 0.745 0.908 1982 0.177 0.278 0.315 0.443 0.634 0.732 0.907 1983 0.212 0.300 0.332 0.449 0.604 0.664 0.895 1984 0.213 0.288 0.357 0.498 0.688 0.804 0.908 1985 0.229 0.312 0.359 0.455 0.624 0.749 0.907 1986 0.230 0.301 0.346 0.452 0.629 0.742 0.900 1987 0.238 0.313 0.351 0.469 0.642 0.742 0.908 1988 0.254 0.347 0.402 0.504 0.656 0.743 0.908 1989 0.263 0.357 0.408 0.502 0.645 0.747 0.897 1990 0.297 0.383 0.421 0.515 0.645 0.748 0.889 1991 0.328 0.411 0.462 0.548 0.669 0.766 0.908 1992 0.353 0.441 0.489 0.574 0.692 0.780 0.905 1993 0.367 0.455 0.499 0.602 0.706 0.771 0.897 1994 0.408 0.495 0.551 0.648 0.754 0.833 0.937 1995 0.419 0.531 0.577 0.658 0.764 0.827 0.944 1996 0.449 0.552 0.599 0.677 0.766 0.827 0.932 1997 0.492 0.585 0.628 0.702 0.794 0.858 0.953 1998 0.530 0.625 0.666 0.742 0.816 0.865 0.960 1999 0.563 0.668 0.708 0.786 0.870 0.913 0.996 2000 0.611 0.722 0.758 0.833 0.901 0.939 1.020 2001 0.693 0.770 0.803 0.869 0.934 0.978 1.167 2002 0.747 0.831 0.862 0.932 1.007 1.065 1.317 2003 0.802 0.897 0.926 1.005 1.151 1.269 1.741 2004 0.925 1.014 1.081 1.229 1.386 1.573 1.780 2005 1.130 1.272 1.275 1.566 1.769 1.780 1.876 2006 1.519 1.619 1.684 1.765 1.774 1.833 2.144

Table 14: Growth Parameter δ: Percentiles observed in 2008

224 Percentiles for Velocity Parameter τ

1 2 Year Granted 10% 25% 33 3 % 50% 66 3 % 75% 90%

1976 7.5 9.7 11.0 13.8 16.9 19.2 26.1 1977 6.7 9.2 10.6 13.4 17.4 20.1 26.1 1978 5.4 8.3 9.7 12.0 15.5 18.2 23.1 1979 6.3 8.9 10.0 12.7 15.4 17.9 24.3 1980 6.5 8.6 9.3 11.6 14.7 16.5 22.7 1981 5.9 8.0 9.1 11.0 13.9 15.8 20.7 1982 5.9 7.7 8.8 11.0 14.0 15.9 20.5 1983 6.1 7.9 8.9 10.8 13.1 15.2 20.2 1984 5.3 7.2 8.2 10.1 12.8 14.4 18.3 1985 6.1 7.5 8.4 10.0 11.8 13.1 17.8 1986 5.7 7.6 8.5 10.3 12.4 13.7 18.1 1987 5.4 7.2 8.1 10.1 11.9 13.3 17.0 1988 5.2 6.8 8.1 9.9 11.9 13.0 16.0 1989 5.6 7.3 8.4 9.5 11.2 12.4 15.6 1990 5.6 7.3 8.0 9.3 10.7 11.8 14.4 1991 5.4 7.0 7.8 9.1 10.5 11.4 14.1 1992 5.3 6.8 7.4 8.5 9.7 10.7 12.8 1993 5.5 6.7 7.1 8.2 9.6 10.4 12.3 1994 5.3 6.5 7.0 8.1 9.1 9.8 12.1 1995 5.3 6.3 6.8 7.7 8.5 9.1 10.8 1996 5.0 6.0 6.4 7.2 8.1 8.7 10.2 1997 5.0 6.0 6.4 7.2 8.1 8.6 10.3 1998 4.8 5.6 6.0 6.8 7.6 8.1 9.5 1999 4.8 5.5 5.8 6.5 7.2 7.6 8.6 2000 4.4 5.1 5.5 6.1 6.7 7.1 7.9 2001 4.0 4.8 5.2 5.8 6.4 6.6 7.2 2002 4.0 4.8 5.0 5.5 5.8 5.9 6.4 2003 3.8 4.4 4.5 4.9 5.1 5.2 5.7 2004 3.4 3.7 3.9 4.0 4.2 4.3 4.7 2005 2.6 2.9 3.0 3.1 3.3 3.3 3.6 2006 1.9 2.0 2.1 2.2 2.3 2.4 2.7

Table 15: Speed Parameter τ : Percentiles observed in 2008

225 Race Horses Mules Show Ponies

Rank Number Patent Title Number Patent Title Number Patent Title

1 4,598,327 Servo control system using servo pattern time of 4,578,720 Self-clocking code demodulator with error detect- 4,589,790 Method and apparatus for controlling escapement ﬂight for read/write head positioning in a magnetic ing capability recording system

2 4,595,990 Eye controlled information transfer 4,630,941 Tubular squeeze bearing apparatus with rotational 4,570,090 High-speed sense ampliﬁer circuit with inhibit ca- restraint pability

3 4,568,277 Apparatus for heating objects to and maintaining 4,623,244 Copy production machines 4,595,836 Alignment correction technique them at a desired temperature

4 4,591,120 Tiltable and/or rotatable support for display device 4,632,294 Process and apparatus for individual pin repair in a 4,630,194 Apparatus for expediting sub-unit and memory dense array of connector pins of an electronic pack- communications in a microprocessor implemented aging structure data processing system having a multibyte system bus that utilizes a bus command byte

5 4,589,042 Composite thin ﬁlm transducer head 4,571,699 Optical mark recognition for controlling input de- 4,577,099 Apparatus for proximity detection of an opaque pat- vices, hosts, and output devices tern on a translucent substrate

Table 16: IBM’s 1986 Patent Inventory: IBM’s winners and losers for patents granted in 1986.

226 Race Horses Mules Show Ponies

Rank Number Patent Title Number Patent Title Number Patent Title

1 5,562,770 Semiconductor manufacturing process for low dis- 5,490,149 Tactical read error recovery strategy based on dy- 5,530,251 Inductively coupled dual-stage magnetic deﬂection location defects namic feedback yoke

2 5,588,199 Lapping process for a single element magnetoresis- 5,548,572 Spare and calibration sector management for opti- 5,576,877 Automated system, and corresponding method, for tive head cal WORM media measuring receiver signal detect threshold values of electro-optic modules

3 5,534,359 Calibration standard for 2-D and 3-D proﬁlometry 5,550,970 Method and system for allocating resources 5,581,073 Machine and human readable label in the sub-nanometer range and method of producing it

4 5,579,471 Image query system and method 5,550,351 Process and apparatus for contamination-free processing of semiconductor parts

5 5,533,072 Digital phase alignment and integrated multichan- 5,524,989 Print element assignment in printing apparatus nel transceiver employing same

Table 17: IBM’s 1996 Patent Inventory: IBM’s winners and losers for patents granted in 1996.

227 Race Horses Mules Show Ponies

Rank Number Patent Title Number Patent Title Number Patent Title

1 7,023,064 Temperature stable metal nitride gate electrode 7,103,846 Collaborative application with indicator of concur- 7,073,166 Conformance of computer programs with predeter- rent users mined design structures

2 7,099,257 Data overwriting in probe-based data storage de- 7,088,418 Liquid crystal display device and method of fabri- 6,983,453 Method and system for obtaining performance data vices cating the same from software compiled with or without trace hooks

3 7,010,776 Extending the range of lithographic simulation in- 7,000,194 Method and system for proﬁling users based on 7,060,624 Deep ﬁlled vias tegrals their relationships with content topics

4 7,003,286 System and method for conference call line drop 7,007,125 Pass through circuit for reduced memory latency in 7,071,539 Chemical planarization performance for recovery a multiprocessor system copper/low-k interconnect structures

5 6,998,684 High mobility plane CMOS SOI 7,027,073 Virtual cameras for digital imaging

Table 18: IBM’s 2006 Patent Inventory: IBM’s winners and losers for patents granted in 2006.

228 Year Ending 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

Year Ending 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Year Ending 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Table 19: Top 10 (cs) Innovations: For each year, we report the top-10 patents (by patent number) according to our computations of (cs) Patent Rank.

229 Year Ending 1981 1982 1983 1984 1985 1986 1987 1988 1989

1 3,778,614 3,778,614 3,950,357 3,950,357 3,950,357 3,950,357 3,950,357 3,950,357 3,950,357 3,950,357 2 3,950,357 4,054,595 3,778,614 3,778,614 3,778,614 3,778,614 3,778,614 3,702,886 3,856,513 3,702,886 3 4,038,222 3,950,357 3,835,176 3,835,176 3,835,176 3,856,513 3,856,513 3,856,513 3,702,886 3,856,513 4 3,835,176 3,835,176 4,054,595 3,856,513 3,856,513 3,835,176 3,702,886 3,778,614 3,778,614 4,237,224 5 3,856,513 3,856,513 3,856,513 4,054,595 3,642,746 3,702,886 4,046,889 4,046,889 4,237,224 3,778,614

Rank 6 3,827,237 3,878,046 4,038,222 3,642,746 4,046,889 3,642,746 3,835,176 3,835,176 4,046,889 4,046,889 7 3,622,615 4,038,222 3,642,746 4,046,889 3,702,886 4,046,889 3,642,746 3,642,746 3,642,746 3,642,746 8 3,760,171 3,803,124 4,046,889 4,038,222 4,098,888 4,098,888 4,098,888 4,098,888 3,835,176 3,835,176 9 2,813,048 3,827,237 3,878,046 3,702,886 4,054,595 4,024,163 4,237,224 4,237,224 4,098,888 4,098,888 10 3,772,519 3,845,394 4,024,163 4,024,163 4,024,163 4,054,595 4,024,163 4,064,521 4,064,521 4,064,521

Year Ending 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

1 4,237,224 4,237,224 4,237,224 4,237,224 4,237,224 4,237,224 4,237,224 4,237,224 4,683,202 4,683,202 2 3,702,886 3,702,886 3,702,886 3,702,886 3,702,886 3,702,886 3,702,886 3,702,886 4,237,224 4,683,195 3 3,950,357 3,950,357 3,950,357 3,950,357 3,950,357 3,950,357 3,950,357 4,683,202 4,683,195 4,237,224 4 3,856,513 3,856,513 3,856,513 3,856,513 3,856,513 3,856,513 3,856,513 3,950,357 3,702,886 3,702,886 5 3,778,614 3,778,614 3,778,614 3,778,614 3,778,614 3,778,614 3,813,316 3,813,316 4,358,535 4,358,535

Rank 6 4,046,889 4,046,889 3,813,316 3,813,316 3,813,316 3,813,316 3,778,614 4,683,195 3,813,316 3,747,120 7 3,642,746 3,859,527 4,046,889 3,859,527 3,859,527 3,859,527 3,859,527 3,856,513 3,747,120 3,813,316 8 3,813,316 3,813,316 3,859,527 4,046,889 4,046,889 4,046,889 3,747,120 4,358,535 3,950,357 3,950,357 9 3,835,176 3,642,746 3,642,746 3,642,746 3,747,120 3,747,120 4,683,202 3,747,120 3,856,513 3,856,513 10 4,098,888 4,098,888 3,747,120 3,747,120 3,642,746 3,642,746 4,046,889 3,778,614 3,778,614 4,395,486

Year Ending 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

1 4,683,202 4,683,202 4,683,202 4,683,202 4,683,202 4,683,202 4,683,202 4,683,202 4,683,202 4,683,202 2 4,683,195 4,683,195 4,683,195 4,683,195 4,683,195 4,683,195 4,683,195 4,683,195 4,683,195 4,683,195 3 4,237,224 4,237,224 4,237,224 3,747,120 3,747,120 3,747,120 3,747,120 3,747,120 3,747,120 3,747,120 4 3,747,120 3,747,120 3,747,120 4,237,224 4,237,224 4,237,224 4,723,129 4,723,129 4,723,129 4,723,129 5 4,358,535 4,358,535 4,358,535 4,723,129 4,723,129 4,723,129 4,237,224 4,463,359 4,463,359 4,463,359

Rank 6 3,702,886 3,702,886 4,723,129 4,463,359 4,463,359 4,463,359 4,463,359 4,237,224 4,237,224 4,237,224 7 3,813,316 3,813,316 4,463,359 4,358,535 4,358,535 4,345,262 4,345,262 4,345,262 4,345,262 4,345,262 8 4,395,486 4,463,359 3,702,886 4,345,262 4,345,262 4,558,333 4,558,333 4,558,333 4,558,333 4,558,333 9 3,856,513 4,395,486 3,813,316 4,558,333 4,558,333 4,358,535 4,313,124 4,313,124 4,313,124 4,313,124 10 3,950,357 4,723,129 4,395,486 4,313,124 4,313,124 4,313,124 4,358,535 4,358,535 4,459,600 4,459,600

Table 20: Top 10 (cc) Innovations: For each year, we report the top-10 patents (by patent number) according to our computations of (cc) Patent Rank.

230 Year Ending 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

Year Ending 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Year Ending 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Table 21: Top 10 (ms) Innovations: For each year, we report the top-10 patents (by patent number) according to our computations of (ms) Patent Rank.

231 Year Ending 1981 1982 1983 1984 1985 1986 1987 1988 1989

1 4,098,888 3,950,357 3,950,357 4,098,888 4,098,888 4,237,224 4,237,224 4,318,043 4,318,043 2 4,054,595 4,054,595 4,054,595 4,054,595 4,054,595 4,222,903 4,226,898 4,237,224 4,237,224 3 4,237,224 4,038,222 4,024,163 3,950,357 4,237,224 4,278,793 4,265,991 4,226,898 4,358,535 4 4,105,776 4,046,889 4,098,888 4,105,776 4,105,776 4,265,991 4,318,043 4,196,265 4,196,265 5 4,024,163 4,024,163 4,105,776 4,024,163 4,024,163 4,196,265 4,196,265 4,172,124 4,172,124

Rank 6 3,702,886 3,835,176 4,046,889 4,237,224 3,702,886 3,939,928 4,278,793 4,063,220 4,367,924 7 4,002,155 3,642,746 4,064,521 3,702,886 4,002,155 4,064,521 4,222,903 4,358,535 4,063,220 8 4,166,115 4,105,776 4,076,853 4,046,889 4,166,115 4,226,898 4,217,374 4,269,891 4,258,264 9 4,064,521 4,098,888 3,702,886 4,064,521 4,064,521 4,105,776 4,064,521 4,278,793 4,532,248 10 4,128,658 3,856,513 3,856,513 4,002,155 4,128,658 4,217,374 4,063,220 4,217,374 4,400,719

Year Ending 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

1 4,358,535 3,694,412 3,694,412 3,694,412 4,880,804 4,880,804 4,683,195 4,683,195 4,683,195 4,683,195 2 4,399,209 4,358,535 4,399,209 4,720,480 4,826,808 4,683,202 4,683,202 4,683,202 4,683,202 4,683,202 3 4,237,224 4,258,264 4,358,535 4,826,808 3,694,412 4,683,195 4,880,804 5,045,417 4,812,599 4,812,599 4 4,532,248 4,399,209 4,258,264 4,880,804 4,720,480 4,742,018 5,045,417 4,880,804 5,045,417 4,723,129 5 4,318,043 4,425,120 4,660,166 4,660,166 4,683,202 4,720,480 4,723,129 4,723,129 4,723,129 4,463,359

Rank 6 4,425,120 4,353,086 4,720,480 4,399,209 4,742,018 5,045,417 4,463,359 4,463,359 4,463,359 4,418,068 7 4,258,264 4,532,248 4,826,808 4,258,264 4,660,166 4,826,808 4,742,018 4,558,333 4,418,068 4,345,262 8 3,694,412 4,629,676 4,629,676 4,358,535 4,683,195 4,367,924 4,367,924 4,345,262 4,558,333 4,558,333 9 4,172,124 4,343,993 2,495,286 2,495,286 4,367,924 4,723,129 4,558,333 4,313,124 4,345,262 4,313,124 10 4,353,086 4,440,846 4,343,993 4,683,202 4,700,457 4,575,330 4,345,262 4,812,599 4,313,124 4,965,188

Year Ending 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

1 4,683,195 4,683,195 4,683,202 4,683,202 4,723,129 4,683,202 5,523,520 5,523,520 5,523,520 5,523,520 2 4,683,202 4,683,202 4,683,195 4,683,195 4,683,202 4,723,129 4,683,202 5,536,637 5,536,637 5,536,637 3 4,812,599 5,572,643 5,572,643 4,723,129 4,683,195 4,683,195 4,723,129 5,367,109 5,367,109 5,367,109 4 4,723,129 4,723,129 4,723,129 4,558,333 4,463,359 5,523,520 5,640,343 5,304,719 5,304,719 5,304,719 5 4,463,359 4,463,359 4,558,333 4,463,359 4,558,333 4,558,333 4,683,195 5,850,009 5,850,009 5,850,009

Rank 6 4,345,262 4,558,333 4,463,359 4,345,262 4,345,262 4,463,359 4,463,359 4,683,202 4,683,202 4,683,202 7 4,558,333 4,345,262 4,345,262 4,313,124 4,459,600 4,345,262 4,558,333 5,640,343 6,413,802 5,968,830 8 4,313,124 4,313,124 4,313,124 4,459,600 4,313,124 4,459,600 4,345,262 4,723,129 4,683,195 6,413,802 9 4,459,600 4,459,600 5,530,852 5,572,643 4,740,796 4,313,124 4,313,124 4,683,195 5,350,836 4,683,195 10 5,572,643 4,812,599 4,459,600 4,740,796 5,801,154 5,640,343 5,103,459 5,103,459 5,103,459 5,276,263

Table 22: Top 10 (mc) Innovations: For each year, we report the top-10 patents (by patent number) according to our computations of (mc) Patent Rank.

232 Rank Description of Innovation Relevant Patent Innovations Respective Patent Ranks (1976–2009) PBS / Wharton

1 Internet/Broadband/World Wide Web 5,530,852; 5,774,660; 5,347,632; 5,159,592 48; 88; 93; 146 2 PC/Laptop Computers 5,640,343; 5,167,024; 3,905,023 52; 104; 110 3 Mobile Phones 5,103,459; 3,906,166; 5,056,109; 3,663,762; 5,101,501 13; 40; 42; 61; 64 4 E-Mail 5,608,786; 4,837,798 134; 210 5 DNA Testing and Sequencing/Human Genome Mapping 4,683,202; 4,683,195; 4,237,224; 4,358,535; 4,395,486 1; 2; 6; 11; 16 6 Magnetic Resonance Imaging - - 7 Microprocessors 5,045,417 27 8 Fiber Optics 3,864,018; 3,455,625; 3,861,781 158; 230; 286 9 Ofﬁce Software 5,181,162 279 10 Non-Invasive Laser/Robotic Surgery - -

11 Open Source Software and Services - - 12 Light Emitting Diode products (LEDs) 3,621,321 86 13 Liquid Crystal Displays (LCDs) 3,840,695; 4,367,924; 3,862,360; 5,598,285; 5,643,826 41; 49; 98; 144; 164 14 GPS 5,223,844; 5,043,736 229; 270 15 Online Shopping/E-Commerce/Auctions 4,799,156; 3,573,747; 4,992,940 55; 118; 282 16 Media File Compression 4,464,650; 4,558,302 34; 263 17 Microﬁnance - - 18 Photovoltaic Solar Energy 4,064,521 117 19 Large Scale Wind Turbines - - 20 Social Networking via Internet - -

21 Graphic User Interface (GUI) 5,572,643; 5,335,320 19 22 Digital Photography/Videography - - 23 RFID and Applications - - 24 Genetically Modiﬁed Plants 5,523,520; 4,812,599; 5,367,109; 4,731,499; 5,304,719 12; 15; 63; 69; 83 25 Biofuels - - 26 Bar Codes and Scanners - - 27 ATMs 5,220,501 152 28 Stents 3,868,956; 4,733,665; 4,503,569; 4,655,771; 4,776,337 32; 58; 80; 142; 200 29 SRAM/Flash Memory 3,859,638 143 30 Anti-Retroviral Treatment for AIDS 4,520,113 66

Table 23: Experts rankings compared to Patent Rank: “Top 30 Innovations of the Last 30 Years” as deﬁned by PBS’s viewing audience and ranked by a panel of Wharton experts

233 Basic Portfolios

Difference (Patents LESS ALL Patents No Patents No Patents)

NGroup 83457 51265 32192 – NPortfolio 348 348 348 348

adj. R2 0.3959 0.4207 0.3059 0.2107 p-value < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16

Abnormal Return αj 0.002202 0.002108 0.001499 0.0006092 (0.941) (0.842) (0.640) (0.485)

CAPM βj 0.390948 0.426672 0.34583 0.0808427 (11.595) (11.825) (10.237) (4.467)

SMB sj 0.089689 0.115009 0.046178 0.0688316 (1.781) (2.134) (0.915) (2.546)

HML hj -0.1804 -0.242742 -0.092741 -0.1500008 (-4.136) (-5.201) (-2.122) (-6.407)

UMD uj -0.228157 -0.236166 -0.186222 -0.0499442 (-4.747) (-4.592) (-3.867) (-1.936)

Annualized Return 2.675% 2.559% 1.814% 0.733%

Table 24: Basic Portfolios: In each column, we report calendar-time portfolio results using the Fama-French/Carhart model. For each coefﬁcient, we subsequently report its t-value below in parentheses. Since these portfolios have monthly returns m, we also report the annualized abnormal return, calculated as follows: (1 + m)12 1. −

234 Decilei Portfolio Less No Patent (NP ) Portfolio Difference Portfolios

(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP )(D NP ) 1 − 2 − 3 − 4 − 5 − 6 − 7 − 8 − 9 − 10 −

NDecile 6992 3366 5131 5201 5037 5207 5113 5126 5064 5028 NPortfolio 348 348 348 348 348 348 348 348 348 348

adj. R2 0.02237 0.1147 0.1407 0.1424 0.08604 0.07432 0.1535 0.07391 0.1009 0.1397 p-value 0.01913 2.61E-09 1.86E-11 1.34E-11 4.79E-07 3.77E-06 1.55E-12 4.06E-06 3.29E-08 2.26E-11

Abnormal Return αj 0.004013 0.005221 0.004183 0.001213 0.003805 0.002135 -0.0006777 -0.001214 0.001232 -0.001233 (2.440) (2.028) (1.698) (0.573) (1.964) (1.045) (-0.304) (-0.610) (0.617) (-0.528)

CAPM βj -0.027526 0.103121 0.119066 0.097448 0.031581 0.065375 0.1032195 0.107366 0.085206 0.075188 (-1.161) (2.760) (3.353) (3.170) (1.123) (2.204) (3.195) (3.717) (2.937) (2.219)

SMB sj 0.011615 0.152881 0.193487 0.067675 0.095557 0.113252 0.1246931 0.007618 0.077199 0.154978 (0.328) (2.732) (3.649) (1.470) (2.269) (2.550) (2.577) (0.176) (1.777) (3.055)

HML hj -0.104462 -0.173117 -0.150837 -0.19882 -0.173886 -0.130013 -0.2319867 -0.074592 -0.160035 -0.17803 (-3.406) (-3.582) (-3.280) (-5.000) (-4.779) (-3.389) (-5.551) (-1.997) (-4.265) (-4.062)

UMD uj -0.011454 0.138159 -0.177984 -0.157237 -0.017711 -0.013417 -0.062845 -0.110123 -0.044467 -0.224936 (-0.339) (2.607) (-3.515) (-3.607) (-0.444) (-0.319) (-1.371) (-2.688) (-1.081) (-4.680)

Annualized Return 4.923% 6.448% 5.137% 1.465% 4.663% 2.592% -0.810% -1.447% 1.488% -1.470%

Table 25: Decile Portfolios Compared to No Patents Portfolio: We report portfolio results using the Fama-French/Carhart model in each column. For each coefﬁcient, we subsequently report its t-value below in parentheses. Since these portfolios have monthly returns m, we also report the annualized abnormal return, calculated as follows: (1 + m)12 1. −

235