Authors Guild, Inc. v. Hathitrust

Filing 159

AMICUS BRIEF, on behalf of Amicus Curiae Digital Humanities and Law Scholars, FILED. Service date 06/04/2013 by CM/ECF. [955881] [12-4547]--[Edited 06/05/2013 by JW]

12-4547-cv
United States Court of Appeals
for the

Second Circuit
AUTHORS GUILD, INC., AUSTRALIAN SOCIETY OF AUTHORS
LIMITED, UNION DES ECRIVAINES ET DES ECRIVAINS QUEBECOIS,
ANGELO LOUKAKIS, ROXANA ROBINSON, ANDRE ROY, JAMES
SHAPIRO, DANIELE SIMPSON, T.J. STILES, FAY WELDON,
(For Continuation of Caption See Inside Cover)
_______________________________
ON APPEAL FROM THE UNITED STATES DISTRICT COURT
FOR THE SOUTHERN DISTRICT OF NEW YORK

BRIEF OF DIGITAL HUMANITIES AND LAW SCHOLARS
AS AMICI CURIAE IN SUPPORT OF DEFENDANTSAPPELLEES AND AFFIRMANCE

On the Brief:
MATTHEW SAG*
ASSOCIATE PROFESSOR
LOYOLA UNIVERSITY OF
CHICAGO SCHOOL OF LAW

JASON SCHULTZ*
ASSISTANT CLINICAL PROFESSOR OF LAW
UC BERKELEY SCHOOL OF LAW
396 Simon Hall
Berkeley, California 94720
[phone]
[e-mail]
Attorneys for Amici Curiae

* Filed in their individual capacity and not on behalf of their institutions.

AUTHORS LEAGUE FUND, INC., AUTHORS’ LICENSING AND
COLLECTING SOCIETY, SVERIGES FORFATTARFORBUND, NORSK
FAGLITTERAER FORFATTERO OG OVERSETTERFORENING,
WRITERS’ UNION OF CANADA, PAT CUMMINGS, ERIK GRUNDSTROM,
HELGE RONNING, JACK R. SALAMANCA,
Plaintiffs-Appellants,
v.
HATHITRUST, CORNELL UNIVERSITY, MARY SUE COLEMAN, President,
University of Michigan, MARK G. YUDOF, President, University of California,
KEVIN REILLY, President, University of Wisconsin System,
MICHAEL MCROBBIE, President, Indiana University,
Defendants-Appellees,
NATIONAL FEDERATION OF THE BLIND, GEORGINA KLEEGE,
BLAIR SEIDLITZ, COURTNEY WHEELER,
Intervenor Defendants-Appellees.

TABLE OF CONTENTS

TABLE OF AUTHORITIES ......................................................................................................... iv
STATEMENT OF INTEREST OF AMICI..................................................................................... 1
SUMMARY OF ARGUMENT ...................................................................................................... 2
ARGUMENT .................................................................................................................................. 4
I. The Freedom to Make Non-expressive Use of Copyrighted Works is Vital to the “Progress of
Science” in the Digital Humanities ................................................................................................. 4
II. Copyright Law Does Not Protect Non-expressive Aspects of Works .................................. 14
A. The Idea/Expression Distinction ....................................................................................... 14
B. Section 102(b) ................................................................................................................... 15
C. Merger and Scènes à Faire................................................................................................ 16
D. Fact/Expression Distinction .............................................................................................. 17
E. Non-expressive Metadata Does Not Implicate the Statutory Rights of the Copyright
Holder ....................................................................................................................................... 18
F. Non-expressive Metadata Is Also Noninfringing Because It Does Not Allow the Public to
Perceive the Expressive Content of a Work ............................................................................. 22
III. Text Mining Creates Value by Facilitating the Advancement of Our Collective Knowledge;
To Protect That Value, Mass Digitization and Similar Intermediate Copying for Data Mining
and Other Non-expressive Purposes Should Be Considered "Fair Use" ...................................... 24
A. Non-expressive Copying to Expand Our Knowledge in the Digital Humanities Is An
Activity of the Sort that Copyright Law Should Favor, Through Fair Use .............................. 24
B. The Nature of the Works in Question Is Favorable to the Fair Use Analysis of Mass
Digitization for the Advancement of Digital Humanities Research and Scholarship .............. 27
C. To the Extent Relevant, Mass Digitization Uses a Reasonable “Amount and
Substantiality” of the Works in Question, in Light of the Socially Beneficial Purpose of
Facilitating Data Mining for the Advancement of the Digital Humanities .............................. 28
D. Allowing Intermediate Copying in Order to Enable Non-expressive Uses Does Not Harm
the Market for the Original Works in a Legally Cognizable Manner, As The Practice Does Not
Implicate the Works' Expressive Aspects in Any Way ............................................................ 29

ii

CERTIFICATE OF COMPLIANCE WITH FRAP 32(a) ............................................................ 32

iii

TABLE OF AUTHORITIES
Cases
A.V. ex rel. Vanderhye v. iParadigms, LLC,
562 F.3d 630 (4th Cir. 2009) ......................................................... 4, 29, 30
Basic Books, Inc. v. Kinko's Graphics Corp.,
758 F. Supp. 1522 (S.D.N.Y. 1991) ........................................................ 27
Bill Graham Archives v. Dorling Kindersley Ltd.,
448 F.3d 605 (2d Cir. 2006) .............................................................. 25, 27
Bond v. Blum,
317 F.3d 385 (4th Cir. 2003) ....................................................... 26, 27, 29
Campbell v. Acuff-Rose Music, Inc.,
510 U.S. 569 (1994) ..................................................................... 25, 28, 30
Cariou v. Prince, No. 11-1197-cv,___ F.3d __, slip op. at 13
(2d Cir., April 25, 2013) ......................................................................... 25, 29
Castle Rock Entm’t v. Carol Publishing Grp.,
150 F.3d 132 (2d Cir. 1998) ........................................................ 20, 21, 22
Davis v. United Artists, Inc.,
547 F. Supp. 722 (S.D.N.Y. 1982) .......................................................... 23
Eldred v. Ashcroft, 537 U.S. 186 (2003). ..................................................... 13
Feist Publ’ns, Inc. v. Rural Tel. Serv. Co., Inc.,
499 U.S. 340 (1991) ........................................................................... 17, 20
Fisher v. Dees,
794 F.2d 432 (9th Cir. 1986) ................................................................... 30
Fuld v. Nat’l Broad. Co., Inc.,
390 F. Supp. 877 (S.D.N.Y. 1975) .......................................................... 23
iv

Golan v. Holder,
132 S. Ct. 873 (2012) ............................................................................... 15
Harper & Row Publishers, Inc. v. Nation Enters.,
471 U.S. 539 (1985) ........................................................................... 14–15
Hasbro Bradley, Inc. v. Sparkle Toys, Inc.,
780 F.2d 189 (2d Cir. 1985) .................................................................... 21
Hoehling v. Universal City Studios, Inc.,
618 F.2d 972 (2d Cir. 1980) .............................................................. 16, 18
Kelly v. Arriba Soft Corp.,
336 F.3d 811 (9th Cir. 2002) ............................................................. 25, 29
Kregos v. Associated Press,
937 F.2d 700 (2d Cir. 1991) .................................................................... 16
Madrid v. Chronicle Books,
209 F. Supp. 2d 1227 (D. Wyo. 2002)..................................................... 23
MyWebGrocer, LLC v. Hometown Info, Inc.,
375 F.3d 190 (2d Cir. 2004) .................................................................... 17
Nat’l Basketball Ass’n v. Motorola, Inc.,
105 F.3d 841 (2nd Cir. 1997) ............................................................ 17, 18
New Era Publ’ns Int’l, ApS v. Carol Pub. Grp.,
904 F.2d 152 (2d Cir. 1990) ............................................................... 27-28
N.Y. Mercantile Exch., Inc. v. IntercontinentalExchange, Inc.,
497 F.3d 109 (2d Cir. 2007) .................................................................... 16
N.Y. Times Co. v. Tasini,
533 U.S. 483 (2001) ........................................................................... 22, 23
NXIVM Corp. v. Ross Inst.,
364 F.3d 471 (2d Cir. 2004) .................................................................... 26

v

Perfect 10, Inc. v. Amazon.com, Inc.,
508 F.3d 1146 (9th Cir. 2007) ....................................................... 4, 25, 29
Peter F. Gaito Architecture, LLC v. Simone Dev. Corp.,
602 F.3d 57 (2d Cir. 2010) ...................................................................... 15
Religious Tech. Ctr. v. Lerma,
908 F. Supp. 1362 (E.D. Va. 1995) ................................................... 26, 27
Reyher v. Children’s Television Workshop,
533 F.2d 87 (2d Cir. 1976) ...................................................................... 15
Sega Enters. Ltd. v. Accolade, Inc.,
977 F.2d 1510 (9th Cir. 1992) ....................................................... 4, 28, 30
Sony Computer Entm’t, Inc. v. Connectix Corp.,
203 F.3d 596 (9th Cir. 2000) ............................................................... 4, 28
Sony Corp. of Am. v. Universal City Studios, Inc.,
464 U.S. 417 (1984) ............................................................................ 14-15
Stromback v. New Line Cinema,
384 F.3d 283 (6th Cir. 2004) ................................................................... 23
Tufenkian Imp./Exp. Ventures, Inc. v. Einstein Moomjy, Inc.,
338 F.3d 127 (2d Cir. 2003) .................................................................... 15
Ty, Inc. v. Publ’ns Int’l Ltd.,
292 F.3d 512 (7th Cir. 2002) ................................................................... 21
Warner Bros. Entm’t Inc. v. RDR Books,
575 F. Supp. 2d 513 (S.D.N.Y. 2008) ......................................... 19, 20, 21
Walker v. Time Life Films, Inc.,
615 F. Supp. 430 (S.D.N.Y. 1985) .......................................................... 23
Statutes
17 U.S.C. § 102(a) (2006) ............................................................................. 20
17 U.S.C. § 102(b) (2006) ............................................................ 4, 14, 15, 16
vi

17 U.S.C. § 106(2) (2006) ............................................................................ 21
17 U.S.C. § 107 (2006) ................................................................................. 25
17 U.S.C. § 201(c) (2006) ............................................................................. 22
Constitutional Provisions
U.S. Const. Art I., Sec. 8 ............................................................................... 13
Other Authorities
Sophia Ananiadou et al., Text Mining and its Potential
Applications in Systems Biology,
24 TRENDS IN BIOTECHNOLOGY 571 (2006) .................................................... 5
Leif Isaksen, Elton Barker, Eric C. Kansa, Kate Byrne, GAP: A NeoGeo Approach
to Classical Resources, 45 LEONARDO 82-83 (2012) ..................................... 7
Christian Blaschke et al. Information Extraction in Molecular Biology, 3
BRIEFINGS IN BIOINFORMATICS 154 (2002) ..................................................... 5
Patricia Cohen, Digital Keys for Unlocking the Humanities’ Riches,
N.Y. TIMES, Nov. 17, 2010, at C1.................................................................. 7
James M. Hughes, et al., Quantitative Patterns of Stylistic Influence in the
Evolution of Literature, 109 PROC. OF THE NAT’L ACAD. OF SCI. OF
THE U.S. 7682 (2012) ............................................................................... 10-11
Matthew Jockers, MACROANALYSIS: DIGITAL METHODS FOR LITERARY
HISTORY (2013) ..................................................................................... 6, 7, 10
Brian Lavoie & Lorcan Dempsey, Beyond 1923: Characteristics of
Potentially In Copyright Print Books in Library Collections, 15 D-Lib Mag.,
http://www.dlib.org/dlib/november09/lavoie/11lavoie.html ........................ 28
Pierre N. Leval, Toward A Fair Use Standard,
103 HARV. L. REV. 1105 (1990) ................................................................... 26
Steve Lohr, Dickens, Austen and Twain, Through a Digital Lens,
N.Y. TIMES, Jan. 26, 2013, at BU3, available at
http://www.nytimes.com/2013/01/27/technology/literary-history-seen-through
-big-datas-lens.html?pagewanted=all&_r=2&. ............................................ 11
vii

MALLET: MAchine Learning for LanguagE Toolkit, http://mallet.cs.umass.edu/
(last visited May 31, 2012) ........................................................................... 10
Jean-Baptiste Michel, Yuan Kui Shen, Aviva Presser Aiden, Adrian Veres,
Matthew K. Gray, The Google Books Team, Joseph P. Pickett, Dale Hoiberg,
Dan Clancy, Peter Norvig, Jon Orwant, Steven Pinker, Martin A. Nowak, and
Erez Lieberman Aiden; Quantitative Analysis of Culture Using Millions of
Digitized Books. 331 SCIENCE 176 (2011) .................................................... 10
MONK: Metadata Offer New Knowledge, http://www.monkproject.org/
(last visited May 31, 2013) ........................................................................... 10
Franco Moretti, Graphs, Maps, Trees: Abstract Models for
Literary History (2005) ................................................................................... 6
Toshihide Ono et al., Automated Extraction of Information on Protein–Protein
Interactions from the Biological Literature,
17 BIOINFORMATICS 155 (2001) ...................................................................... 5
Matthew Sag, Copyright and Copy-Reliant Technology,
103 NW. U.L. REV. 1607 (2009) ..................................................................... 3
Matthew Sag, Orphan Works as Grist for the Data Mill,
27 BERKELEY TECH. L. J. 1503 (2012) ........................................................... 3
Software Environment for the Advancement of Scholarly Research (“SEASR”)
http://seasr.org (last visited May 31, 2013) .................................................. 10
Text Analysis Portal for Research (“TAPoR”), http://www.tapor.ca/portal/
portal (last visited July 2, 2012) .................................................................... 10
Tracking 18th-century “social network” through letters, STANFORD
UNIVERSITY (Dec. 14, 2009) (video), http://www.youtube.com/watch?v=
nw0oS-AOIPE ................................................................................................ 7

viii

STATEMENT OF INTEREST OF AMICI1
Amici are over 100 professors and scholars who teach, write, and research in
computer science, the digital humanities, linguistics or law, and two associations
that represent Digital Humanities scholars generally.2 Amici have an interest in this
case because of its potential impact on their ability to discover and understand,
through automated means, the data in and relationships among textual works.
Legal Scholar Amici also have an interest in the sound development of intellectual
property law. Resolution of the legal issue of copying for non-expressive uses has
far-reaching implications for the scope of copyright protection, a subject germane
to Amici’s professional interests and one about which they have great expertise.
Amici speak only to the issue of copying for non-expressive uses. A complete list
of individual amici is attached as Appendix A.

Pursuant to Fed. R. App. P. 29(a), (c)(4), (c)(5) and Rule 29.1 of the Local Rules
of the United States Court of Appeals for the Second Circuit, Amici hereby state
that none of the parties to this case nor their counsel authored this brief in whole or
in part; no party or any party’s counsel contributed money intended to fund
preparing or submitting the brief; and no one else other than Amici and their
counsel contributed money that was intended to fund preparing or submitting this
brief. Amici also hereby state that all parties have consented to the filing of this
brief, and we rely on that consent as our source of authority to file.

1

See Association for Computers and the Humanities, http://www.ach.org/;
Canadian Society for Digital Humanities, http://csdh-schn.org.
2

.
1

SUMMARY OF ARGUMENT
Mass digitization, especially by libraries, is a key enabler of socially
valuable computational and statistical research (often called “data mining” or “text
mining”). While the practice of data mining has been used for several decades in
traditional scientific disciplines such as astrophysics and in social sciences like
economics, it has only recently become technologically and economically feasible
within the humanities. This has led to a revolution, dubbed “Digital Humanities,”
ranging across subjects like literature and linguistics to history and philosophy.
New scholarly endeavors enabled by Digital Humanities advancements are still in
their infancy but have enormous potential to contribute to our collective
understanding of the cultural, political, and economic relationships among various
collections (or corpora) of works—including copyrighted works—and with society.
The Court’s ruling in this case on the legality of mass digitization could
dramatically affect the future of work in the Digital Humanities.
This Court should affirm the decision of the district court below that library
digitization for the purpose of text mining and similar non-expressive uses present
2

no legally cognizable conflict with the statutory rights or interests of the copyright
holders. Where, as here, the output of a database—i.e., the data it produces and
displays—is noninfringing, this Court should find that the creation and operation
of the database itself is likewise noninfringing. The copying required to convert
paper library books into a searchable digital database is properly considered a
“non-expressive use” because the works are copied for reasons unrelated to their
protectable expressive qualities — the copies are intermediate and, as far as is
relevant here, unread.
The type of non-expressive use at issue here – statistical analysis of text – is
common among copy-reliant technologies: for example, Internet search engines
and plagiarism detection software do not read, understand, or enjoy copyrighted
works, nor do they deliver these works directly to the public. Such platforms copy
the works only incidentally, in order to process them as “grist for the mill”—raw
materials that feed various algorithms and indices. See Matthew Sag, Copyright
and Copy-Reliant Technology, 103 NW. U.L. REV. 1607 (2009); Matthew Sag,
Orphan Works as Grist for the Data Mill, 27 BERKELEY TECH. L. J. 1503 (2012).
Further, generating data about a copyrighted work (often called “metadata”)
does not infringe the original work because, as has been recognized for over a
century, copyright law protects only an author’s original expression, not facts. That
a “fact” might pertain to or describe an expressive work does not change its factual
3

character—or render it an author’s exclusive intellectual property under the law.
Indeed, making such factual information freely available to all is crucial to the
harmony between copyright law and the First Amendment—hence the existence of
rules like the “idea/expression” distinction (see 17 U.S.C. § 102(b)), the doctrine of
scenes à faire, and the “merger” principle.
The act of copying works into a database in order to enable the generation of
metadata about those works should thus be deemed noninfringing. As numerous
courts have found, making intermediate copies that enable socially beneficial
noninfringing uses and/or outputs constitutes a protected “fair use” under Section
107 of the Copyright Act. See, e.g., A.V. ex rel. Vanderhye v. iParadigms, LLC,
562 F.3d 630, 645 (4th Cir. 2009); Perfect 10, Inc. v. Amazon.com, Inc., 508 F.3d
1146, 1168 (9th Cir. 2007); Sony Computer Entm’t, Inc. v. Connectix Corp., 203
F.3d 596, 609 (9th Cir. 2000); Sega Enters. Ltd. v. Accolade, Inc., 977 F.2d 1510,
1527-28 (9th Cir. 1992). Similarly, the mass digitization of books for text-mining
purposes is a form of incidental or “intermediate” copying that enables ultimately
non-expressive, non-infringing, and socially beneficial uses without unduly
treading on any expressive—i.e., legally cognizable—uses of the works. The Court
should find such copying to be fair use.
ARGUMENT
I.

The Freedom to Make Non-expressive Use of Copyrighted Works is
Vital to the “Progress of Science” in the Digital Humanities
4

Where large-scale electronic text collections are available, advances in
computational power and a proliferation of new text-mining and visualization tools
offer scholars of the humanities the chance to do what biologists, physicists, and
economists have been doing for decades—analyze massive amounts of data.
“Digital Humanities” scholars fervently believe that text mining and the
computational analysis of text are vital to the progress of human knowledge in the
current Information Age. The potential of these non-expressive uses of text has
already been revealed in the life sciences, where researchers routinely use a variety
of text-mining tools to facilitate the search for relevant research across disparate
fields and to uncover previously unnoticed “correlations or associations such as
protein-protein interactions and gene-disease associations.” See Sophia Ananiadou
et al., Text Mining and its Potential Applications in Systems Biology, 24 TRENDS IN
BIOTECHNOLOGY 571, 571 (2006) (citing Toshihide Ono et al., Automated
Extraction of Information on Protein–Protein Interactions from the Biological
Literature, 17 BIOINFORMATICS 155 (2001) and Christian Blaschke et al.
Information Extraction in Molecular Biology, 3 BRIEFINGS IN BIOINFORMATICS 154
(2002)).
Similar breakthroughs are on the horizon in the humanities. Traditionally,
literary scholars have relied upon the close and often anecdotal study of select
works. Modern computing power, advances in computational linguistics and
5

natural language processing, and the mass digitization of texts now permit
investigation of the larger literary record.
Digitization enhances our ability to process, mine, and ultimately better
understand individual texts, the connections between texts, and the evolution of
literature and language. As University of Nebraska Professor Matthew Jockers
explains, by exploring the literary record writ large, researchers can better
understand the context in which individual texts exist, and thereby better
understand the texts themselves. See Matthew Jockers, MACROANALYSIS: DIGITAL
METHODS FOR LITERARY HISTORY (2013). Along similar lines, Stanford University
Professor Franco Moretti has noted that “a field this large cannot be understood by
stitching together separate bits of knowledge about individual cases, because it
isn’t a sum of individual cases: it’s a collective system, that should be grasped as
such, as a whole . . . .” Franco Moretti, GRAPHS, MAPS, TREES: ABSTRACT MODELS
FOR LITERARY HISTORY

4 (2005) (emphasis in original).

Researchers working in the field of information retrieval frequently use text
mining and computer-aided classification to identify and retrieve relevant
documents. Using similar techniques, researchers in the Digital Humanities are
able to identify and retrieve relevant texts, often from unlikely places. Humanities
researchers can thereby expand their traditional study of a few canonical works to a
study of several million in the larger archive of literary history—an archive that
6

has hitherto remained hidden because of the limitations of humans’ reading
capacity. As part of this process, such non-expressive uses often leads to additional
expressive uses, expanding the audience (and the potential market) for enjoyment
of individual works.3
Mass digitization also results in the creation of data that enables scholars to
reimagine relationships between texts—for example, by linking texts with maps.
Thus, Google’s “Ancient Places Project” links the text of public domain books like
Gibbon’s Decline and Fall of the Roman Empire to a map of the ancient world.4
The interface allows the user to browse the books, including the full text, at the
same time as she browses a map. The places mentioned are marked on the map and
hyperlinked.5 Similar maps could be made with reference to works still under

For example, Matthew Jockers used text mining and computer aided
classification to identify an overlooked tradition of whaling fiction predating (and
arguably informing) Melville’s writing of Moby Dick. See Jockers, supra.

3

See Leif Isaksen, Elton Barker, Eric C. Kansa, Kate Byrne, GAP: A NeoGeo
Approach to Classical Resources, 45 LEONARDO 82-83 (2012).

4

In a similar vein, researchers at Stanford University have mapped thousands of
letters exchanged during the Enlightenment and thereby devised a theory of how
these individual networks fit into a coherent whole, which the scholars refer to as
the “Republic of Letters.” See Tracking 18th-century “social network” through
letters, STANFORD UNIVERSITY (Dec. 14, 2009) (video),
http://www.youtube.com/watch?v=nw0oS-AOIPE. Such aggregation yields
surprising insights: for example, “the common narrative is that the Enlightenment
started in England and spread to the rest of Europe,” but the relatively low volume
of correspondence between London and Paris suggests otherwise. See Patricia
7

5

copyright—importantly, without ever making the text of the book available for free
viewing. Extracting such data from texts to create these maps is a quintessential
non-expressive use of the underlying texts that does not implicate any copyrightprotected use—let alone infringe the copyrights of—the works in question.
Google’s “Ngram” tool provides another example of a non-expressive use
enabled by mass digitization—this time easily visualized. Figure 1, below, is an
Ngram-generated chart that compares the frequency with which authors of texts in
the Google Book Search database refer to the United States as a single entity (“is”)
as opposed to a collection of individual states (“are”). As the chart illustrates, it
was only in the latter half of the Nineteenth Century that the conception of the
United States as a single, indivisible entity was reflected in the way a majority of
writers referred to the nation. This is a trend with obvious political and historical
significance, of interest to a wide range of scholars and even to the public at large.
But this type of comparison is meaningful only to the extent that it uses as raw data
a digitized archive of significant size and scope.6

Cohen, Digital Keys for Unlocking the Humanities’ Riches, N.Y. TIMES, Nov. 17,
2010, at C1.
6

Google Ngram is available at http://books.google.com/ngrams.
8

Figure 1: Google Ngram Visualization Comparing Frequency of
“The United States is” to “The United States are”7

To be absolutely clear, 1) the data used to produce this visualization can only
be collected by digitizing the entire contents of the relevant books, and 2) not a
single sentence of the underlying books has been reproduced in the finished
product. In other words, this type of non-expressive use only adds to our collective

Figure 1 is a reconstruction of data generated using Google Ngram, sampled at
five-year intervals. The y-axis is scaled to 1/100,000 of a percent, such that 1 =
0.00001%.
7

9

knowledge and understanding, without in any way replacing, damaging the value
of, or interfering with the market for, the original works.8
Google Ngram is just the tip of the iceberg.9 In Macroanalysis: Digital
Methods and Literary History, Professor Jockers draws on a corpus of Nineteenth
Century novels to demonstrate how literary style changes over time. See generally
Jockers, supra. Examining word frequencies, syntactic patterns, and thematic
markers in the metadata-enriched context of author nationality, author gender, and
time period, opens up literary study to an entirely new perspective.10 Trendsetters

For additional examples of Ngram’s uses, see, e.g., Jean-Baptiste Michel, Yuan
Kui Shen, Aviva Presser Aiden, Adrian Veres, Matthew K. Gray, The Google
Books Team, Joseph P. Pickett, Dale Hoiberg, Dan Clancy, Peter Norvig, Jon
Orwant, Steven Pinker, Martin A. Nowak, and Erez Lieberman Aiden;
Quantitative Analysis of Culture Using Millions of Digitized Books. 331 SCIENCE
176 (2011) (a study of linguistic and cultural changes in over five million digitized
books).

8

9 The

toolkit available to Digital Humanities researchers is becoming increasingly
sophisticated. See, e.g., Text Analysis Portal for Research (“TAPoR”),
http://portal.tapor.ca/portal/portal (last visited May 21, 2013) (tools to map word
usage over time, including peaks, density, collocations, and types); MALLET:
MAchine Learning for LanguagE Toolkit, http://mallet.cs.umass.edu/ (last visited
May 31, 2013) (a Java-based package for statistical natural language processing,
document classification, clustering, topic modeling, information extraction, and
other machine learning applications to text); MONK: Metadata Offer New
Knowledge, http://www.monkproject.org/ (last visited May 31, 2013) (a digital
environment designed to help humanities scholars discover and analyze patterns in
the texts); Software Environment for the Advancement of Scholarly Research
(“SEASR”), http://seasr.org (last visited May 31, 2013).
A recently published study, led by mathematicians at Dartmouth, makes a
similar point. See James M. Hughes et al., Quantitative Patterns of Stylistic
10
10

and outliers are revealed, as when Jockers’ text mining and computational analysis
demonstrated that Harriet Beecher Stowe’s fiction is far more similar to the work
of male authors of her generation than to the female-authored works of
“sentimental fiction” among which her work has traditionally been categorized.11
Figure 2 provides another fascinating example of Professor Jockers’ research.
The chart shows the extent to which British, American, and Irish authors focused
on the theme of American slavery during the Nineteenth Century, based on a
corpus of 3,450 novels from that time period. Although it comes as no surprise that
slavery was most often addressed by American authors, the strong Irish reaction to
the American Civil War (note the spike in the light gray line beginning in 1860)
compared with the decidedly muted response by British authors invites—indeed,
demands—further investigation.

Influence in the Evolution of Literature, 109 PROC. OF THE NAT’L ACAD. OF SCI. OF
THE U.S. 7682 (2012).
Steve Lohr, Dickens, Austen and Twain, Through a Digital Lens, N.Y. TIMES,
Jan.
26,
2013,
at
BU3,
available
at
http://www.nytimes.com/2013/01/27/technology/literary-history-seen-through-bigdatas-lens.html?pagewanted=all&_r=2&.
11

11

Figure 2: American Slavery in American, English, and Irish Literature, 18001899.

As Jockers’ work reveals, “macroanalysis” of text archives has the potential
to provide insight into historical literary questions, such as the place of individual
texts, authors, and genres in relation to a larger literary context; literary patterns
and lexicons employed over time, across periods, within regions, or within
demographic groups; the cultural and societal forces that impact literary style and
the evolution of style; the waxing and waning of literary themes; and the tastes and
preferences of the literary establishment—and whether those preferences
correspond to general tastes and preferences. However, realizing this potential
requires access to digitized texts.
12

If libraries, research universities, non-profit organizations, and commercial
entities are prohibited from making non-expressive use of copyrighted material,
literary scholars, historians, and other humanists are restricted to becoming 19thcenturyists; slaves not to history, but to the public domain. History does not end in
1923.12 But if copyright law prevents Digital Humanities scholars from using
more recent materials, 1923 will be the effective end date of the work these
scholars can do.
In short, the possibility of mining huge digital archives and manipulating the
data collected in the process has inspired many scholars to re-conceptualize the
very nature of humanities research. For others, it has played the more modest—but
still valuable—role of providing new tools for testing old theories, or suggesting
new areas of inquiry. None of this, however, can be done in the modern context if
scholars cannot make non-expressive uses of underlying copyrighted texts, which
(as shown above) will frequently number in the thousands, if not millions. Given
copyright law’s objective of promoting “the Progress of Science,”13 it would be
perversely counterintuitive if the promise of Digital Humanities were extinguished
in the name of copyright protection.
12 Due

to repeated extensions of the copyright term, U.S. copyrights after 1923 do
not automatically expire on an annual basis; thus, most modern works are still
copyrighted. See Eldred v. Ashcroft, 537 U.S. 186 (2003).

13 U.S.

Const. Art I., Sec. 8. “Science,” as used in the Constitution, referred to
knowledge and learning.
13

II.

COPYRIGHT LAW DOES NOT PROTECT NON-EXPRESSIVE
ASPECTS OF WORKS
Fortunately, this Court need not contemplate such a scenario, as non-

expressive aspects of copyrighted works—e.g., the facts and ideas contained within
the work and concerning it—are not protected by copyright. Such fundamental
legal principles as the “idea/expression” distinction (reflected in Section 102(b) of
the Copyright Act), the “merger” doctrine, the rule of “scènes à faire,” and the
“fact/expression” distinction all reflect this basic tenet. Metadata—information
about copyrighted works collected through data mining and used by Digital
Humanities scholars in the research described above—either does not implicate
copyright protection at all, or is inoculated by the aforementioned doctrines that
limit authors’ rights to their works’ expressive content.
A.

The Idea/Expression Distinction

Copyright gives authors the right to set the terms upon which their original
expression is made available to the public. But this right is not unlimited. As one of
the fundamental—and Constitutional—limitations on those rights, the ideaexpression distinction strikes a balance between “the interests of authors . . . in the
control and exploitation of their writings . . . on the one hand, and society’s
competing interest in the free flow of ideas, information, and commerce on the
other hand.” Harper & Row Publishers, Inc. v. Nation Enters., 471 U.S. 539 (1985)
(quoting Sony Corp. of Am. v. Universal City Studios, Inc., 464 U.S. 417, 429
14

(1984)); see also Golan v. Holder, 132 S. Ct. 873, 890 (2012) (describing the ideaexpression distinction as one of copyright’s “built-in First Amendment
accommodations”). Copyright law protects only expressive use: “It is an axiom of
copyright law that the protection granted to a copyrightable work extends only to
the particular expression of an idea and never to the idea itself.” Reyher v.
Children’s Television Workshop, 533 F.2d 87, 90 (2d Cir. 1976).
B.

Section 102(b)

Recognizing the importance of access to ideas within expressive works,
Congress has placed statutory limits on the rights of copyright holders through
Section 102(b) of the Copyright Act, which provides: “In no case does copyright
protection for an original work of authorship extend to any idea . . . concept,
principle, or discovery, regardless of the form in which it is described, explained,
illustrated, or embodied in such work.” 17 U.S.C. § 102(b) (2006). This provision
has played a key role in modern copyright cases, ensuring that access to nonexpressive aspects of works is not inhibited. See, e.g., Peter F. Gaito Architecture,
LLC v. Simone Dev. Corp., 602 F.3d 57, 67 (2d Cir. 2010) (holding that the
principle behind § 102(b) required the court “to determine whether . . . ‘similarities
are due to protected aesthetic expressions original to the allegedly infringed work,
or whether the similarity is to something in the original that is free for the taking’ ”
(quoting Tufenkian Imp./Exp. Ventures, Inc. v. Einstein Moomjy, Inc., 338 F.3d
15

127, 134-35 (2d Cir. 2003))). As noted above, text mining extracts and compiles
ideas, concepts, and principles in copyrighted works into metadata. This process
generates the very types of “discovery” that § 102(b) envisions.
C.

Merger and Scènes à Faire

The policy of excluding non-expressive elements from copyright protection
is so strong that—even in situations where expressive and non-expressive elements
intertwine—doctrines like that of “merger” and “scènes à faire” preclude copyright
protection for expression “in those instances where there is only one or so few
ways of expressing an idea that protection of the expression would effectively
accord protection to the idea itself.” Kregos v. Associated Press, 937 F.2d 700, 705
(2d

Cir.

1991);

see

also

New

York

Mercantile

Exch.,

Inc.

v.

IntercontinentalExchange, Inc., 497 F.3d 109, 118 (2d Cir. 2007). The “merger”
doctrine is built upon the same principle as the idea/expression distinction: the
protection of expressive elements of a work cannot, for Constitutional and practical
reasons, interfere with the public’s “free access to ideas.” New York Mercantile
Exch., Inc., 497 F.3d. at 116. Relatedly, elements of a work that are scènes à
faire—that is, “incidents, characters or settings which are as a practical matter
indispensable, or at least standard, in the treatment of a given topic”—are not
protectable. Hoehling v. Universal City Studios, Inc., 618 F.2d 972, 979 (2d Cir.

16

1980); see also MyWebGrocer, LLC v. Hometown Info, Inc., 375 F.3d 190, 194 (2d
Cir. 2004).
D.

Fact/Expression Distinction

Finally, the monopoly rights of authors cannot extend to factual elements
that “do not owe their origin to an act of authorship.” Feist Publ’ns, Inc. v. Rural
Tel. Serv. Co., Inc., 499 U.S. 340, 347 (1991). “The distinction is one between
creation and discovery: The first person to find and report a particular fact has not
created the fact; he or she has merely discovered its existence.” Id. The Supreme
Court in Feist made clear that if an “author clothes facts with an original
collocation of words, he or she may be able to claim a copyright in this written
expression;” nevertheless, “[o]thers may copy the underlying facts from the
publication . . . .” Id. at 348.
In National Basketball Association v. Motorola, Inc., 105 F.3d 841 (2d Cir.
1997), for example, a sports reporting service distributing real-time game statistics
based on a data feed from reporters was held non-infringing. This Court reasoned
that “[b]ecause [the service reproduced] only factual information culled from the
broadcasts and none of the copyrightable expression of the games, appellants did
not infringe the copyright of the broadcasts.” Id. at 847. This Court has similarly
held that one has “the right to avail himself of the facts contained in [another’s]
book and to use such information, whether correct or incorrect, in his own literary
17

work.” Hoehling, 618 F.2d at 979. In other words, copyright law clearly
distinguishes between expressive and non-expressive content, and deems only
expressive content protectable.
E.

Non-expressive Metadata Does Not Implicate the Statutory Rights
of the Copyright Holder

Metadata about a copyrighted work does not implicate any legally
cognizable interest of the copyright holder. Metadata may contain facts about the
works themselves, might capture (in different terminology) the ideas contained
within the text, or may convey information such as the number of times a given
word appears in a particular text, how often a particular author uses a specific
literary device, or the essence of what the work is about. Though it is true that
metadata would not exist but for the underlying work, it does not contain the
expression of the work.
Consider, for example, two facts about Moby Dick: first, that the word
“whale” appears 1119 times; second, that the word “dinosaur” appears 0 times.
While a whale is certainly central to the expression contained in Moby Dick, this
data is not. Rather, metadata of this sort—a simplified version of the metadata
surveyed in Section I—is factual and non-expressive, and incapable of infringing
the rights of copyright holders.

18

The same principle can be illustrated using a recent decision of the court
below, Warner Brothers Entertainment Inc. v. RDR Books, 575 F. Supp. 2d 513
(S.D.N.Y. 2008). Consider the following four statements:
[1] “Goblin-made armour does not require cleaning, simple girl.
Goblins’ silver repels mundane dirt, imbibing only that which
strengthens it.”
[2] “goblin-made armor does not require cleaning, because
goblins’ silver repels mundane dirt, imbibing only that which
strengthens it, such as basilisk venom.”
[3] “Statement [1] contains twenty words, and other than
‘Goblin’, no word in expression [1] is repeated.”
[4] “Statement [2] is strikingly similar to Statement [1].”
Statement [1] originates with J.K. Rowling, the author of the Harry Potter
novels. See Warner Bros., 575 F. Supp. 2d at 527 (quoting J.K. Rowling, Harry
Potter and the Deathly Hallows 303 (2007)). Statement [2] was held out as
originating with a contributor to the Harry Potter Lexicon (a reference work for the
“Harry Potter universe”), which was found to infringe because too much of its
contents consisted of direct quotations or close paraphrases of vivid passages in the
Harry Potter books, as the comparison between [1] and [2] illustrates. Id. at 527.
Statements [3] and [4], by contrast, are classic metadata; they would not exist but
19

for the underlying work, and yet neither passage is substantially similar—or indeed,
bears any resemblance at all—to the expressive elements of the underlying work.
Even more importantly, this metadata does not originate with the author of
the underlying work. As the Supreme Court held in Feist Publications, “copying of
constituent elements of the work that are original” is an essential element of a
copyright infringement claim. 499 U.S. at 361 (emphasis added); see also 17 U.S.C.
§ 102(a) (2006).
Amici wish to emphasize that metadata is not the same thing as so-called
“invented facts.” J.K. Rowling’s conception and description of goblin armor and
thousands of other details in the Harry Potter series could be regarded as “invented
facts” because, quite simply, she made them up. As laid out in the case law, if
such facts and their associated expressive descriptions are reproduced in sufficient
quantity, they may “constitute creative expression protected by copyright because
characters and events spring from the imagination of the original authors.” Warner
Bros., 575 F. Supp. 2d at 536 (quoting Castle Rock Entm’t Inc. v. Carol Publ’g
Grp., Inc., 150 F.3d 132, 139 (2d Cir. 1998)). Metadata, however, cannot be
accurately characterized as “invented facts,” but only as facts about “invented
facts.” The distinction is significant: once again, facts are not eligible for
copyright protection.

20

Nor does metadata infringe the author’s right “to prepare derivative works
based upon the copyrighted work[.]” 17 U.S.C. § 106(2) (2006). As the court
below held in Warner Brothers, an analytical work that provides insight into a
copyrighted work but does not “recast, transform, or adapt” that work does not
violate the derivative work right. 575 F. Supp. 2d at 539; see also Ty, Inc. v.
Publ'ns Int’l Ltd., 292 F.3d 512, 520 (7th Cir. 2002) (holding that collectors’ guide
to certain copyrighted works did not violate 17 U.S.C. § 106(2) because the guides
did not “recast, transform, or adapt the things to which they are guides”).
Amici urge the Court to carefully distinguish the facts of the instant case
from those in Castle Rock Entertainment v. Carol Publishing Group, 150 F.3d 132
(2d Cir. 1998). In Castle Rock, this Court held that a quiz book based on the
popular television series “Seinfeld” was, quantitatively and qualitatively,
substantially similar to that series, considered as a whole. Id. at 138–39. The quiz
book in that case, however, was not an analytical work; rather, it essentially recast
“Seinfeld’s” copyrightable characters into a new format, as if the defendant had
made miniature dolls of those same characters. See Hasbro Bradley, Inc. v. Sparkle
Toys, Inc., 780 F.2d 189, 192-93 (2d Cir. 1985) (upholding copyrightability of
“Transformer” robotic action figures as sculptural works). The supposed “facts”
conveyed in the “Seinfeld” quiz book were not truly facts about the television

21

program; they were “in reality fictitious expression created by Seinfeld’s authors.”
Castle Rock Entm’t, 150 F.3d at 139.
By contrast, the many forms of metadata produced by the library digitization
at the heart of this litigation do not merely recast copyrightable expression from
underlying works; rather, the metadata encompasses numerous uncopyrightable
facts about the works, such as author, title, frequency of particular words or
phrases, and the like.
F.

Non-expressive Metadata Is Also Noninfringing Because It Does
Not Allow the Public to Perceive the Expressive Content of a
Work
The significance of public perception runs deep in copyright law. Indeed,
controlling authority suggests that the copyright holder’s exclusive rights are
limited to the right to communicate the expressive aspects of her work to the public.
For example, in New York Times Co. v. Tasini, 533 U.S. 483 (2001), a case about
the scope of the 17 U.S.C. § 201(c) “privilege” of the copyright owner to
reproduce and distribute individual contributions “as part of [a] collective work,”
the Supreme Court held that “[i]n determining whether the Articles [at issue] have
been reproduced and distributed as part of a revision of the collective works in
issue, we focus on the Articles as presented to, and perceptible by, the user[s] of
the Databases [containing the Articles].” 533 U.S. at 499 (emphasis added; internal
quotation marks and citations omitted). The Court elaborated: “the question is not
whether a user can generate a revision of a collective work from a database, but
22

whether the database itself perceptibly presents the author’s contribution as part of
a revision of the collective work.” Id. at 504 (emphasis added).
This point is especially evident in cases where plaintiffs have argued that,
although a defendant’s final product does not support an allegation of infringement,
the defendant has violated the Copyright Act by making a reproduction of the
plaintiff’s work that is merely intermediate and imperceptible to the reading public.
In Davis v. United Artists, Inc., for example, the court below rejected out of hand
the allegation that the defendant’s unpublished screenplays were substantially
similar to plaintiff’s novel, refusing to “consider the preliminary scripts” because
“the ultimate test of infringement must be the film as produced and broadcast” to
the public. 547 F. Supp. 722, 724 n.9 (S.D.N.Y. 1982). See also Fuld v. Nat’l
Broad. Co., Inc., 390 F. Supp. 877, 882 n.4 (S.D.N.Y. 1975) (“[T]he ultimate test
of infringement must be the television film as produced and broadcast — and not
the preliminary scripts . . . .”); Walker v. Time Life Films, Inc., 615 F. Supp. 430,
434 (S.D.N.Y. 1985) (“The Court considers the works as they were presented to
the public.”).14
14 Courts

in other circuits have adopted the same view. See, e.g., Stromback v. New
Line Cinema, 384 F.3d 283, 299 (6th Cir. 2004) (“In deciding infringement claims,
courts have held that only the version of the alleged infringing work presented to
the public should be considered”); Madrid v. Chronicle Books, 209 F. Supp. 2d
1227, 1234 (D. Wyo. 2002) (“Since a court considers the works as they were
presented to the public, discovery in this case . . . would be pointless”) (internal
quotation marks omitted).
23

III.

Text Mining Creates Value by Facilitating the Advancement of Our
Collective Knowledge; To Protect That Value, Mass Digitization and
Similar Intermediate Copying for Data Mining and Other Nonexpressive Purposes Should Be Considered "Fair Use"
As demonstrated above, non-expressive metadata itself is noninfringing.

However, Amici recognize that this Court must also consider the legality of the
process of making copies to generate that metadata. Fortunately, numerous courts
have held that copying to enable purely non-expressive uses, such as the automated
extraction of data, does not infringe the statutory rights of the copyright holder.
Like copying employed for other transformative purposes, such as parody,
criticism, and reverse engineering, intermediate copying for the purpose of
extracting non-expressive metadata is fair use.
A.

Non-expressive Copying to Expand Our Knowledge in the Digital
Humanities Is An Activity of the Sort that Copyright Law Should
Favor, Through Fair Use

First among the statutory factors relevant to a fair use analysis is “the
purpose and character of the use, including whether such use is of a commercial
nature or is for nonprofit educational purposes.” 17 U.S.C. § 107(1). Like more
traditional expressive transformative uses, the more “non-expressive” the use of a
copyrighted work, the less it substitutes for the author’s original expression. As
such, non-expressive uses are properly considered equivalent to (or a subset of)
highly transformative uses: their “purpose and character” is such that they do not
24

merely supersede the objects of the original creation. Campbell v. Acuff-Rose
Music, Inc., 510 U.S. 569, 583 (1994). See also Cariou v. Prince, No. 11-1197cv,___ F.3d __, slip op. at 13 (2d Cir., April 25, 2013) (finding that defendant’s
“composition, presentation, scale, color palette, and media are fundamentally
different and new compared to [Plaintiff’s] photographs, as is the expressive nature
of [defendant]’s work.”); Perfect 10, Inc. v. Amazon.com, Inc., 508 F.3d 1146,
1165 (9th Cir. 2007) (holding that search engines are “highly transformative”
because “[a]lthough an image may have been created originally to serve an
entertainment, aesthetic, or informative function, a search engine transforms the
image into a pointer directing a user to a source of information”); Kelly v. Arriba
Soft Corp., 336 F.3d 811, 818 (9th Cir. 2002) (holding that use of images in search
engine was transformative because they served “as a tool to help index and
improve access to images on the internet and their related web sites” and their use
was “unrelated to any aesthetic purpose”); Bill Graham Archives v. Dorling
Kindersley Ltd., 448 F.3d 605, 609 (2d Cir. 2006) (finding critical to fair use
analysis that publisher’s use of copyrighted images of concert posters in book was
“plainly different from the original purpose for which they were created”). As the
process of digitization for text mining is intermediate and non-expressive, and its
purpose is to produce non-expressive metadata, this factor favors fair use.

25

Moreover, “there is a strong presumption that factor one [in the fair use
analysis] favors the defendant if the allegedly infringing work fits the description
of uses described in [17 U.S.C.] § 107,” which includes “scholarship” and
“research.” NXIVM Corp. v. Ross Institute, 364 F.3d 471, 477 (2d Cir. 2004). The
crucial role that mass digitization plays in promoting the progress of research and
scholarship in the Digital Humanities weighs heavily in favor of fair use here. See
also Pierre N. Leval, Toward A Fair Use Standard, 103 HARV. L. REV. 1105, 1111
(1990) (“If [a] secondary use adds value to the original – if the quoted matter is
used as raw material, transformed in the creation of new information, new
aesthetics, new insights and understandings – this is the very type of activity that
the fair use doctrine intends to protect for the enrichment of society.”)
Similarly, courts have ruled in favor of fair use when copying allowed
defendants or third parties to use facts from copyrighted works in news reporting
or court proceedings. See, e.g., Bond v. Blum, 317 F.3d 385, 395 (4th Cir. 2003)
(holding that “the narrow purpose of defendants’ use of the manuscript . . . for the
evidentiary value of its content” weighed “heavily” against a finding of
infringement); Religious Tech. Ctr. v. Lerma, 908 F. Supp. 1362, 1366 (E.D. Va.
1995) (finding fair use in part because documents were copied for “news gathering,
news reporting and responding to litigation,” not to “scoop” copyright owner).
Significantly, both the Bond and Religious Tech. Ctr. courts’ fair use holdings went
26

further than the text mining at issue here, because the users in those cases had to
glean the necessary facts by reading the materials, rather than mining the text with
computers. Bond, 317 F.3d at 393; Religious Tech. Ctr., 908 F. Supp. at 1364-65.
If a human’s reading of copyrighted expression to extract non-expressive material
is fair use, the result should be the same when a computer performs the extraction.
B.

The Nature of the Works in Question Is Favorable to the Fair Use
Analysis of Mass Digitization for the Advancement of Digital
Humanities Research and Scholarship

When the purpose of a secondary use is socially beneficial, the second fair
use factor, “the nature of the copyrighted work,” is rarely dispositive. See, e.g.,
Bill Graham, 448 F.3d at 612 (“The second factor may be of limited usefulness
where the creative work of art is being used for a transformative purpose.”) This is
especially true in “intermediate copying” cases like this one, where the material
ultimately reaching the user is not the expressive content of the copyrighted work
at all, but rather ideas contained within it or facts about it.
Nevertheless, to the extent that the second fair use factor is relevant here, it
weighs in favor of fair use. Looking to this factor, “[c]ourts generally hold that ‘the
scope of the second fair use is greater with respect to factual than non-factual
works’. . . . [F]ictional works, on the other hand, . . . require more protection.”
Basic Books, Inc. v. Kinko's Graphics Corp., 758 F. Supp. 1522, 1533 (S.D.N.Y.
1991) (quoting New Era Publications Int'l, ApS v. Carol Pub. Group, 904 F.2d 152,
27

157 (2d Cir. 1990)). A detailed study of the copyrighted works in the collections
from which Google has created its digitized corpus have concluded that the
“overwhelming majority – 92 Percent . . . – were non fiction.” Brian Lavoie &
Lorcan Dempsey, Beyond 1923: Characteristics of Potentially In Copyright Print
Books in Library Collections, 15 D-Lib Mag.,
http://www.dlib.org/dlib/november09/lavoie/11lavoie.html.
Furthermore, as one court explained, the second fair use factor weighs in
favor of fair use where humans “cannot gain access to the unprotected ideas and
functional concepts contained in [the copyrighted work] without . . . making
copies.” Sega, 977 F.2d at 1525. This is effectively the case for Digital Humanities
scholars, as there are no plausible ways to conduct analyses of the sort described in
Section I other than mass digitization and algorithmic analysis, both of which
require making intermediate copies.

C.

To the Extent Relevant, Mass Digitization Uses a Reasonable
“Amount and Substantiality” of the Works in Question, in Light
of the Socially Beneficial Purpose of Facilitating Data Mining for
the Advancement of the Digital Humanities

The third fair use factor asks whether the amount and substantiality used are
“reasonable in relation to the purpose of the copying.” Campbell, 510 U.S. at 586–
87. Because the metadata created here does not contain any infringing material, the
third factor “is of very little weight.” See, e.g., Connectix, 203 F.3d at 606. This is
28

true even where many intermediate copies are made. Id. at 601. Moreover, as
Section I shows, it is not only reasonable to use mass digitization of an entire set of
works to enable the creation of noninfringing metadata about those works, it is a
practical necessity, as there is no equivalent human means of doing so. In order for
Digital Humanities research and scholarship to be as accurate and complete as
possible, every word or image in a copyrighted work must be mined.
Other courts have relied upon similar rationales to support full copying in
intermediate and non-expressive fair use cases. See, e.g., Cariou v. Prince, No. 111197-cv,___ F.3d __, slip op. (2d Cir., April 25, 2013); Vanderhye, 562 F.3d at
642 (finding mass digitization of entire student essays to be fair use when
reasonable as a means to check for plagiarism); Perfect 10, 508 F.3d at 1167-68
(finding thumbnail reproduction of entire photographs reasonable in light of
defendant’s use of the images to improve access to information on the internet
versus artistic expression); Kelly, 336 F.3d 820-21 (same); Bond, 317 F.3d at 396
(noting that “[t]he use of the copyrighted material [as evidence in a custody
proceeding], even the entire manuscript, does not undermine the protections
granted by the [Copyright] Act”). In light of practical necessity and ample
precedent in support, the Court should find that the “amount and substantiality”
factor favors the making of intermediate copies for non-expressive use.
D.

Allowing Intermediate Copying in Order to Enable Nonexpressive Uses Does Not Harm the Market for the Original
29

Works in a Legally Cognizable Manner, As The Practice Does Not
Implicate the Works' Expressive Aspects in Any Way
The fourth statutory fair use factor is “the effect of the use upon the potential
market for or value of the copyrighted work.” In the case of expressive uses such
as parody, and non-expressive uses such as reverse engineering, courts have
consistently held that the protection that copyright affords is limited to certain
cognizable markets. Campbell, 510 U.S. at 591-92 (“[W]hen a lethal parody, like a
scathing theater review, kills demand for the original, it does not produce a harm
cognizable under the Copyright Act.”); Sega, 977 F.2d at 1523-24. Transformative
expressive uses do not usually affect the market in any relevant sense because the
second author’s expression does not substitute for that of the original author.
Campbell, 510 U.S. at 591; Fisher v. Dees, 794 F.2d 432, 438 (9th Cir. 1986)
(“This is not a case in which commercial substitution is likely . . . . The two works
do not fulfill the same demand.”). As illustrated by the examples in Section I,
above, non-expressive uses have no potential substitution effect on any legally
cognizable market for copyrighted works, because copyright only protects markets
for expression, and not markets for discoveries, ideas, facts, principles, or concepts.
See, e.g., Vanderhye, 562 F.3d at 644 (“[N]o market substitute was created by
[defendants], whose archived student works do not supplant the plaintiffs’
works . . . so much as merely suppress demand for them . . . In our view, then, any
harm here is not of the kind protected against by copyright law.”) Indeed, in many
30

instances, the use of metadata made by scholars could actually enhance the market
for the underlying work, by causing researchers to revisit the original work and
reexamine it in more detail.
In short, there is no reason to disallow the digitization of libraries, whether
by libraries themselves, or commercial search engine companies, so long as that
digitization is for non-expressive use. Non-expressive uses such as those practiced
in the Digital Humanities hold great promise for Amici, other scholars, society at
large—and copyright owners, too.

DATED: June 4, 2013
New York, New York

Respectfully Submitted,

/s/ Jason Schultz
Jason Schultz
UC Berkeley School of Law
Counsel for Amici (with Matthew Sag)

31

CERTIFICATE OF COMPLIANCE WITH FRAP 32(A)
1. This brief complies with the type-volume limitation of Fed. R. App. P.
32(a)(7)(B) and 29(d) because this brief contains 6984 words, excluding the parts
of the brief exempted by Fed. R. App. P. 32(a)(7)(B)(iii).
2. This brief complies with the typeface requirements of Fed. R. App. P. 32(a)(5)
and the type style requirements of Fed. R. App. P. 32(a)(6) because this brief has
been prepared in a proportionally spaced typeface using Microsoft Word in Times
New Roman, 14 point font.
/s/ Jason M. Schultz

32

APPENDIX A

APPENDIX A
The Association for Computers and the Humanities
http://www.ach.org
The Canadian Society for Digital Humanities
http://csdh-schn.org
Andrew Adams
Student
Stanford University
Kristin W. Andrews
Social Sciences & Humanities Librarian
University of North Carolina Wilmington
Jonathan Askin
Professor
Brooklyn Law School
Founder/Director Brooklyn Law Incubator & Policy Clinic
Elton Barker
Reader
Open University
Christina Bell
Humanities Librarian
Bates College
Michael Black
Graduate Student
University of Illinois at Urbana-Champaign
Chris Bourg
Assistant University Librarian for Public Services
Stanford University
Daniel Boyarin
Hermann P. and Sophia Taubman Professor of Talmudic Culture
Departments of Near Eastern Studies and Rhetoric
34

University of California at Berkeley
Collin Gifford Brooke
Associate Professor of Rhetoric and Writing
Syracuse University
Susan Brown
Professor
University of Guelph/University of Alberta
Patrick J. Burns
Senior Teaching Fellow
Fordham University
Kate Byrne
Research Fellow
School of Informatics, University of Edinburgh
David Carroll
Associate Professor
Parsons The New School for Design
Carol Chiodo
PhD candidate
Yale University
Margaret Chon
Donald and Lynda Horowitz Professor for the Pursuit of Justice
Seattle University School of Law
Eve V. Clark
Professor
Stanford University
Dr. Daniel Cohen
Executive Director of the Digital Public Library of America
Digital Innovation Fellow, American Council of Learned Societies
James Coltrain
Assistant Professor of History
35

University of Nebraska
Paul Conway
Associate Professor
University of Michigan School of Information
Ryan Cordell
Assistant Professor of English
Northeastern University
Brian Croxall
Digital Humanities Strategist and Lecturer of English
Emory University
Michael Scott Cuthbert
Homer A. Burnell Associate Professor of Music
MIT
Johanna Drucker
Bernard and Martin Breslauer Professor of Bibliography
Department of Information Studies at the Graduate School of Education and
Information Studies
UCLA
G. Cory Duclos
Assistant Professor
Spring Hill College
Hoyt N. Duggan
Professor emeritus
University of Virginia
Morris Eaves
Professor of English and Turner Prof. of Humanities
University of Rochester
Co-Editor, William Blake Archive (www.blakearchive.org)
Penelope Eckert
Albert Ray Lang Professor of Humanities and Sciences
Professor by Courtesy of Anthropology
36

Stanford University
Jacob Eisenstein
Assistant Professor
Georgia Institute of Technology
James Evans
Graduate Student
CUNY Graduate Center
Dr. Marco Forlivesi
Director of the Digital Archive of Inaugural Lectures at Renaissance and Early
Modern Universities
Università degli Studi di Chieti e Pescara, Italy
Rosemary Franklin
Research Librarian
Langsam Library
University of Cincinnati
Bernard Frischer
Professor
Departments of Art History and Classics
University of Virginia
Shubha Ghosh
Professor
University of Wisconsin
Alex Gil
Digital Scholarship Coordinator
Columbia University
Melissa Girard
Assistant Professor of English
Loyola University Maryland
Matthew K. Gold
Associate Professor of English and Digital Humanities
City Tech and Graduate Center, CUNY
37

Les Harrison
Associate Professor
Virginia Commonwealth University
Charles van den Heuvel
Professor
Royal Netherlands Academy of Arts and Sciences
Huygens Institute for the History of the Netherlands
Jeremy Hunsinger
Center for Digital Discourse and Culture
Virginia Polytechnic Institute and State University (Virginia Tech)
Dan Hunter
Professor
New York Law School
Director of the Institute for Information Law & Policy
Leif Isaksen
Deputy Director, Web Science Doctoral Training Centre
University of Southampton
Matthew Jockers
Assistant Professor of English
Fellow, Center for Digital Humanities Research
University of Nebraska, Lincoln
Dr. Eric Kansa
UC Berkeley School of Information
Alexandria Archive Institute
Lead Developer, Open Context (www.opencontext.org)
Dennis S. Karjala
Jack E. Brown Professor of Law
Sandra Day O'Connor College of Law, Arizona State University
Matthew Kirschenbaum
Associate Professor of English
University of Maryland
38

Hubertus Kohle
Professor
University of Munich, Germany
Kari Kraus
Assistant Professor
University of Maryland
Lore Kuehnert
Instructor, History
Hagerstown Community College
John Laudun
Associate Professor
University of Louisiana
Konrad M. Lawson
Max Weber Postdoctoral Fellow
European University Institute
William R. Leben
Professor of Linguistics Emeritus
Stanford University
Jarom McDonald
Associate Research Professor
Brigham Young University
Director, BYU Office of Digital Humanities
Erin McKean
Founder
Wordnik.com
Mark P. McKenna
Professor of Law, Notre Dame Presidential Fellow
Notre Dame Law School
Elijah Meeks
Digital Humanities Specialist
39

Stanford University Libraries
Richard Menke
Associate Professor of English
University of Georgia
David Mimno
Postdoctoral Researcher
Princeton University
Sally Moffitt
Reference Librarian and Bibliographer
University of Cincinnati
Franco Moretti
Professor of English and Comparative Literature
Stanford University
Paige Morgan
PhD Student & Instructor
University of Washington
Brian D. Moss
Reference Coordinator
University of Kansas Libraries
Dr. James Murphy
Ira Steven Nathenson
Associate Professor
St. Thomas University School of Law
Bethany Nowviskie
Director, Digital Research & Scholarship
University of Virginia
Director of the UVA Library Scholars’ Lab
President of the Association for Computers & the Humanities
Dr. Julianne Nyhan
University College London
40

Amy V. Ogden
Associate Professor
University of Virginia
Piotr Organisciak
University of Illinois at Urbana-Champaign
Moacir P. de Sá Pereira
PhD Candidate
University of Chicago
Dorothy Porter
Curator, Digital Research Services
University of Pennsylvania
Todd Presner
Professor and Chair
Digital Humanities Program
UCLA
Kenneth M. Price
Hillegass University Professor
University of Nebraska-Lincoln
Co-director of the Center for Digital Research in the Humanities.
Adam Rabinowitz
Assistant Professor
University of Texas
Dean Rehberger
Michigan State University
Director of MATRIX (a digital humanities center)
Kevin Reilly, MSN, RN
Doctoral Student
Graduate School of Eduction & Psychology, Pepperdine University
Allen Riddell
PhD Student
41

Duke University
Augusta Rohrbach
Associate Professor
Washington State University
Editor ESQ: A Journal of the American Renaissance
Brian Rosenblum
Head, Center for Faculty Initiatives
University of Kansas Libraries
Ivan A. Sag
Sadie Dernham Patek Professor in Humanities and Professor of Linguistics
Stanford University
Mark Sample
Associate Professor
George Mason University
Jeffrey T. Schnapp
Professor and faculty director of metaLAB (at) Harvard
Harvard University
Dr. Christof Schöch
University of Würzburg, Germany
Susan Schreibman
Professor
Trinity College Dublin
Kris Shaffer
Assistant Professor of Music Theory
Charleston Southern University
Crandall Shifflett
Professor Emeritus of History
Virginia Tech
Stéfan Sinclair
Associate Professor
42

McGill University
Timothy Tangherlini
Professor
The Scandinavian Section and Department of Asian Languages and Cultures
UCLA
2010-2011 Digital Innovation Fellow, American Council of Learned Societies
Laurie Taylor
Digital Humanities Librarian
University of Florida
Dennis Tenen
Assistant Professor
Columbia University
Rebecca Tushnet
Professor
Georgetown University, School of Law
Ted Underwood
Associate Professor of English
University of Illinois, Urbana-Champaign
Christopher Warren
Assistant Professor of English
Carnegie Mellon University
Project Director and co-principle investigator for Six Degrees of Francis Bacon
Scott Weingart
Indiana University
Andrew Whalen
Researcher
University of California at Berkeley
Roger Whitson
Assistant Professor of English
Washington State University
43

Matthew Wilkens
Assistant Professor of English
University of Notre Dame
Glen Worthey
Digital Humanities Librarian
Stanford University
Vika Zafrin
Institutional Repository Librarian
Boston University

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