Case 1:14-cv-02396-PGG-MHD Document 148-1 Filed 05/30/19 Page 1 of 32
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`UNITED STATES DISTRICT COURT
`SOUTHERN DISTRICT OF NEW YORK
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`NETWORK-1 TECHNOLOGIES, INC.,
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`Plaintiff,
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`v.
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`14 Civ. 2396 (PGG)
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`14 Civ. 9558 (PGG)
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`GOOGLE LLC and YOUTUBE, LLC,
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`Defendants.
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`DECLARATION OF PROFESSOR MICHAEL D. MITZENMACHER IN SUPPORT OF
`PLAINTIFF NETWORK-1 TECHNOLOGIES, INC.’S
`OPENING CLAIM CONSTRUCTION BRIEF
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`Case 1:14-cv-02396-PGG-MHD Document 148-1 Filed 05/30/19 Page 2 of 32
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`TABLE OF CONTENTS
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`I.
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`II.
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`INTRODUCTION .............................................................................................................. 1
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`QUALIFICATIONS ........................................................................................................... 1
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`III. MATERIALS CONSIDERED ........................................................................................... 2
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`IV.
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`V.
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`LEVEL OF ORDINARY SKILL IN THE ART ................................................................ 3
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`LEGAL PRINCIPLES INVOLVED IN MY ANALYSIS ................................................. 3
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`A. Claim Construction ............................................................................................................. 3
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`B. Claim Definiteness .............................................................................................................. 4
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`VI.
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`BACKGROUND ................................................................................................................ 5
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`A. Feature Extraction ............................................................................................................... 7
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`B. Building the Databases of Reference Works ...................................................................... 9
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`C. Comparing an Unknown Work with the Reference Works in the Database .................... 11
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`VII. PARTICULAR CLAIM TERMS ..................................................................................... 16
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`A. “non-exhaustive [. . .] search” ........................................................................................... 16
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`B. “correlation information” .................................................................................................. 24
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`C. “extracted features” and “extracting features” .................................................................. 26
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`I.
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`INTRODUCTION
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`1.
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`I have been retained as an expert in the above-captioned case by counsel for
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`Plaintiff Network-1 Technologies, Inc. I understand that Network-1 is currently asserting three
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`patents in this case: U.S. Patent Nos. 8,010,988 (“the ‘988 patent”), 8,205,237 (“the ‘237 patent”),
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`and 8,904,464 (“the ‘464 patent”) (collectively the “patents-in-suit”).
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`2.
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`These patents relate generally to systems and methods for identifying media content
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`and performing actions associated with the identified content. All three were invented by Professor
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`Ingemar J. Cox and all claim their priority to a provisional patent application filed on September
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`14, 2000.
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`II.
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`QUALIFICATIONS
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`3.
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`I am currently employed as a Professor of Computer Science at Harvard University.
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`Specifically, I am the Thomas J. Watson, Sr. Professor of Computer Science in the School of
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`Engineering and Applied Sciences. I joined the faculty of Harvard as an Assistant Professor in
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`January 1999. I was promoted to Associate Professor in 2002 and to Professor in 2005. In 2010,
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`I began a three-year term as Area Dean, which is essentially equivalent to what other schools call
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`Department Chair, of Computer Science, and held that position through June 2013. I am currently
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`serving as Area Co-Chair of Computer Science for the 2018-2019 academic year. My work
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`address is 33 Oxford Street, Cambridge, MA 02138. My primary research interests include design
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`and analysis of algorithms, networks and data transmission, and information theory.
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`4.
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`I received my undergraduate degree in Mathematics and Computer Science from
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`Harvard College in 1991. I received a Certificate of Advanced Study in Mathematics from
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`Cambridge University in 1992. I received a Ph.D. in Computer Science from the University of
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`California at Berkeley in 1996. From August 1996 to January 1999, I was employed as a Research
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`Scientist at Digital Systems Research Center, where my work included projects on algorithms for
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`the Internet.
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`5.
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`I am listed as an inventor or co-inventor on 19 issued patents, and am the co-author
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`of a textbook entitled “Probability and Computing” published by Cambridge University Press. I
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`am a Fellow of the Association for Computing Machinery (ACM).
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`6.
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`I regularly serve on program committees for conferences in networking, algorithms,
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`and communication. For example, I have served on the program committee multiple times for the
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`SIGCOMM conference, which is the flagship annual conference of the ACM Special Interest
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`Group on Data Communication (SIGCOMM). I have also served on numerous program
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`committees related to algorithms, including the ACM Symposium on the Theory of Computing,
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`the International Colloquium on Automata, Languages, and Programming, and the International
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`Conference on Web Search and Data Mining.
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`7.
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`The field of endeavor at issue in this case is identification of electronic content
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`(such as video or audio content) using algorithmic search techniques. I have published over 200
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`research papers in computer science and engineering conferences and journals, many of which
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`have explored algorithms and data structures for algorithmic search techniques, including both
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`mathematical analysis and applications.
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`8.
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`A copy of my curriculum vitae is attached as Exhibit A to this Declaration. It
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`contains a more complete listing of my professional activities and background.
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`III. MATERIALS CONSIDERED
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`9.
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`In forming my opinions set forth in this declaration, I have reviewed, considered,
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`and/or had access to the patent specifications and claims and their prosecution histories. I have
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`also considered the parties’ respective proposed claim constructions. In addition, I have relied
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`upon my professional and academic experience, as well as a number of references (including
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`academic papers, other patents, and other publications) identified in the body of this declaration.
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`I reserve the right to consider additional materials or information as I become aware of them and
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`to revise my opinions accordingly in light of such additional information.
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`IV.
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`LEVEL OF ORDINARY SKILL IN THE ART
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`10.
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`It is my understanding that analysis of claim interpretation is to be undertaken from
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`the perspective of a person of ordinary skill in the art to which the patents are directed at the time
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`of the invention, here in September 2000. The patents-in-suit are directed to the field of
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`identification of electronic content (such as video or audio content) using algorithmic search
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`techniques. In my opinion, a person of ordinary skill in this art would have a Bachelor’s degree
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`in computer science, mathematics, or a similar discipline and two to three years of relevant
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`experience, or a graduate degree in the same area.
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`V.
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`LEGAL PRINCIPLES INVOLVED IN MY ANALYSIS
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`A.
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`11.
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`Claim Construction
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`I have been informed that in connection with patent claim interpretation, a Court’s
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`analysis begins with the language of the claims themselves and that the words of a claim are
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`generally given their ordinary and customary meaning, i.e., the meaning that the term would have
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`to a person of ordinary skill in the art in question at the time of the invention, i.e., as of the effective
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`filing date of the patent application.
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`12.
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`I have further been informed that the person of ordinary skill in the art is deemed
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`to read the claim term not only in the context of the particular claim in which the disputed term
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`appears, but in the context of the entire patent, including the specification and its prosecution
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`history.
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`13.
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`In general, I understand that the Court looks to the sources available to the public
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`that show what a person of skill in the art would have understood the disputed claim language to
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`mean in the context of the specification, and that those sources include the words of the claims
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`themselves, the remainder of the patent specification and prosecution history, as well as extrinsic
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`evidence concerning relevant scientific principles, the meaning of technical terms, and the state of
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`the art.
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`B.
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`Claim Definiteness
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`14.
`I also understand that the defendants in this case assert that some terms of the claims
`in the patents-in-suit render certain claims indefinite.
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`15.
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`I have been informed that a claim is indefinite only if the claim, read in light of the
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`specification delineating the patent, and the prosecution history, fails to inform, with reasonable
`certainty, those skilled in the art about the scope of the invention.
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`16.
`I have also been informed that the definiteness requirement mandates clarity, while
`recognizing that absolute precision is unattainable.
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`I have used these understandings in providing my opinions as set forth in this
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`17.
`declaration.
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`VI.
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`BACKGROUND
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`18.
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`There are three patents-in-suit about which I have been requested to provide my
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`expert opinion:
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`• U.S. Patent No. 8,010,988 (“the ’988 patent”, Exhibit 11).
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`• U.S. Patent No. 8,205,237 (“the ’237 patent”, Exhibit 2).
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`• U.S. Patent No. 8,904,464 (“the ’464 patent”, Exhibit 3).
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`Collectively, I refer to all three of these patents as the “patents-in-suit.”
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`19.
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`The patents-in-suit generally share a common specification, so for ease of
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`reference, unless otherwise indicated, I will refer to the ’988 patent specification in citing to the
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`inventor’s description of his invention. I recognize that there are, however, some differences in
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`the specifications of the patents-in-suit. The “Summary of the Invention” (Section 2 in the text of
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`the specification) varies somewhat among the patents. Also, there are some additional discussions
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`of certain material from the literature that was incorporated by reference in the earlier specification
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`of the ’988 patent that is set forth in greater detail in the later patents, such as, for example, in
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`Section 4.2.1.1.3 of the specification. In the ’237 and ’464 patents, for example, the specification
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`contains more details drawn from one of the Yianilos references that was incorporated by
`reference. ’237 patent at 9:7-19; ’464 patent at 9:1-14.
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`20.
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`As set forth in the Detailed Description section of the patents-in-suit (Section 4),
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`the patents describe systems and methods “for identifying works without the need of embedding
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`signals therein. Once identified, such information can be used to determine a work-related action.”
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`’988 patent at 5:39-42. In simple terms, these systems can analyze an unknown digital “work”
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`1 All Exhibit references are to Exhibits that are attached to the Declaration of Amy E. Hayden in
`Support of Plaintiff Network-1 Technologies, Inc.’s Opening Claim Construction Brief, which I
`understand is to be filed concurrently with my declaration.
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`such as a piece of content (like an audio and/or a video file) using characteristics of that work, and
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`then compare it, using those characteristics, to a collection of known references to determine if the
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`unknown content matches one of the known references. See, e.g., id. at 6:58-62. If it does, the
`system can then take actions based on that identification.
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`21.
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`For example, assume that the unknown work is a video that includes the song
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`“White Christmas” by Bing Crosby. The patents-in-suit describe an apparatus and methods to (1)
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`process a video file to extract features, such as by sampling the audio portion of that file or
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`performing calculations based on information within the file, and (2) then compare those extracted
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`features to a database that includes features related to many songs (for our example, assume that
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`the database includes “White Christmas” as well as other songs). If the comparison process
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`(implemented by a matching algorithm) finds that the extracted features from the unknown video
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`match the features extracted from the reference file for “White Christmas,” then the system looks
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`to see what action the database provides for works that match “White Christmas.” That action is
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`then associated with the unknown video. Such actions might include providing or displaying an
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`advertisement, displaying a weblink, dialing a number, or performing an e-commerce transaction.
`See id. at 9:59-10:1.
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`22.
`An example of how this process may be carried out is illustrated in Figure 1 of the
`patens-in-suit:
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`The patents-in-suit explain: “FIG. 1 is a process bubble diagram of operations that may be
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`performed . . . in which intra-work information [information derived from the work itself, as
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`opposed to information added or appended to the work] is used to identify the work.” Id. at 6:34-
`37.
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`A.
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`23.
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`Feature Extraction
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`The process of Figure 1 begins with a feature extraction operation that can be used
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`to identify a known reference work. See id. at 7:11-8:2 (§ 4.2.1.1.1). The patents-in-suit explain
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`that examples of a work can include “an image, an audio file or some portion of an audio signal or
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`may be one or more frames or fields of a video signal, or a multimedia signal.” Id. at 7:18-20. In
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`the example of the music video, the reference work could be the audio file and/or portions of the
`audio signal of Bing Crosby’s “White Christmas.”
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`24.
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`The patents-in-suit explain that “[t]he purpose of the feature extraction operation is
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`to derive a compact representation of the work that can subsequently be used for the purpose of
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`recognition.” Id. at 7:20-23. The patents recognize that electronic works can be represented with
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`shorter “sketches” or “fingerprints” that require far less space to store in computer memory, and
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`far less computing resources to compare, but must be sufficiently complex that each sketch or
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`fingerprint represents the underlying content (the primary work) with a low likelihood that two
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`different primary works will have the same sketch or fingerprint. The patents refer to these so-
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`called “sketches” or “fingerprints” as “compact electronic representations,” “feature vectors,” or
`“extracted features.”
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`25.
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`The patent specification teaches numerous ways in which feature extraction can be
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`accomplished. See id. at 7:11-8:2 (§ 4.2.1.1.1). The specification explains that feature extraction
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`operations derive a representation of the work by, for example, using “a pseudo-random sample of
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`pixels” from a frame of a video. Id. at 7:23-26. In addition, feature extraction can be accomplished
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`through the use of a variety of mathematical operations including Fourier, wavelet, or cosine
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`transforms/decompositions or statistical methods like principle component analysis. See id. at
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`7:26-43.
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`B.
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`26.
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`Building the Databases of Reference Works
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`In the example illustrated in Figure 1, the patents-in-suit contemplate assembling a
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`database of these sketches or fingerprints of reference works and associated actions that are
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`connected to the individual reference works. See id. at 8:4-59 (§ 4.2.1.1.2). This process is
`illustrated in the following excerpt of Figure 1:
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`In the example referenced earlier, “WORK @t1” could be the reference work, Bing Crosby’s
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`“White Christmas.” This reference is used in generating the reference database. In Step 122, one
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`or more “feature extraction operation(s)” are performed. These operations extract features from
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`the reference work (in this example “White Christmas”) to generate one or more sketches or
`fingerprints that can be used to identify this work.
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`27.
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`In Step 124, the extracted features can also be associated to a work identification
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`number or reference called a “work id.” Each work id is associated with a specific work, but may
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`also be associated with one or more extracted features from the “WORK @t1.” The feature(s)
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`(vector) and work ids are tied together to form the database 110 referred to as “WID Information”
`in Figure 1.
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`28.
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`As part of the same or a different database, the work ids can be linked with
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`“associated information” 136 (such as an action to be performed with respect to any works linked
`to the work id). This process is shown in the following excerpt also from Figure 1:
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`Thus, the patents describe a system in which the operator could create/maintain a database of
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`known references. For example, this database might include popular songs from major record
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`companies, and television programs from major studios. The full versions of the reference items
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`in the database might be very large (for example, the electronic file for a single song might be
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`more than 1 megabyte (1 million bytes of data) and a film might require more than 1 gigabyte (1
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`billion bytes of data)). Storing such references in their full length would require huge amounts of
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`storage for a large database. Even more problematic, searching by comparing a reference to the
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`entirety of a reference work would be very difficult and require significant computing resources
`and time.
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`29.
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`The patents contemplate a system where each reference work in the database is
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`represented by a “sketch” or “fingerprint.” Although these sketches or fingerprints may be far
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`more compact than the complete media file, they still need to be sufficiently complex that
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`numerous different reference works will each be very unlikely to have the same sketch or
`fingerprint.
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`C.
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`30.
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`Comparing an Unknown Work with the Reference Works in the Database
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`To compare an unknown video to the database of reference works, the patents
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`explain that one can obtain a sketch or fingerprint of the unknown video and then search for a
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`match in the database. The following excerpt from Figure 1 illustrates an example of this process
`with reference to “WORK @t2:”
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`In the working example discussed earlier, the “WORK @t2” could be an uploaded video
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`containing at least a portion of the song “White Christmas.” Thus, in this portion of the process,
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`the same feature extraction operations that were run on the reference works used to generate the
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`WID Information database 110 could now be run in Step 140 on the unknown work (“WORK
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`@t2”)—the unknown video containing White Christmas in the example above. The extracted
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`features from the unknown work can then be matched with features in the WID Information
`database to identify a matching work.
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`31.
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`The patents explain that the matching of these sketches or fingerprints is not
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`equivalent to looking up a word in a dictionary. Id. at 8:63-9:5. Looking up a word in a dictionary
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`is a search for an exact, or identity, match in an ordered set of data. All of the words in the
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`dictionary have been pre-processed by organizing them in alphabetical order. If one is searching
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`for a particular word in the dictionary, it is possible to search for an exact match very efficiently
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`because of the pre-processing organization of the data set. As the patents explain, the kind of
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`matching involved here is different both because the comparisons are not looking for exact
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`matches, and because the data set involves high dimensional data that is not ordered in the way a
`dictionary can be ordered.
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`32.
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`As explained in the patents, its comparisons are not necessarily looking for an exact
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`match because there can be, for example, noise or distortions in the unknown video. Id. This
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`could be a consequence of using imperfect recording technology, recording a video from a signal
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`that had some static in it (like a weak television broadcast), recording a video using a video
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`recorder pointed at a television showing a program, or many other possible reasons. Similarly, the
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`unknown video might be altered slightly from the reference. For example, a reference television
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`program might be 22 minutes and 30 seconds long, but an uploaded recording of the same
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`television program might have started recording a few seconds early so that the recording is 22
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`minutes and 45 seconds long. In each of these cases, the unknown video would not be identical to
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`the reference work, but it is still desirable for the system to identify the two files as matching.
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`Likewise, if one is searching for a song, an unknown sample might include only a portion of the
`song in it, but it still might be desirable to identify the unknown song as a match to the reference.
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`33.
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`The system needs to be designed to recognize the two exemplary similar videos
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`discussed above as a match, even though they are not identical. Comparing the entirety of the files
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`would be very difficult. For example, a single video or audio file might be many millions of bytes
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`(megabytes) long. Comparing all of the millions of individual data bytes in the entire file to all of
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`the millions of individual data bytes of another (reference) file to see how similar they are would
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`be extremely time consuming and difficult. The patents discuss using feature vectors of, compact
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`electronic representations of, and sets of features extracted from the files to help simplify the
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`comparisons. Shorter representations of the electronic works are easier to compare than the entire
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`files, but the representations must be sufficiently complex that each compact representation is very
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`unlikely to be the same for more than one primary work. For example, if one wanted to represent
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`a song, a measure of the tempo, such as the beats per minute of the song, would be a simple way
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`to represent the song, but many songs could have the same tempo, so that representation by itself
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`would not be very helpful for use in comparisons to identify an unknown work. Rather, one could
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`capture snapshots of or “sample” particular values (for example the pitch and intensity values2) at
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`multiple times during the song. See id. at 7:11-42 (describing various feature extraction
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`methodologies); see also infra ¶¶ 63-65. Each of these independent features could be compared
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`to the same features of a reference work. If the values for many of the features were close to the
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`same features for a reference work, it might be possible to infer that the two primary works (the
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`works represented by the compact representations) were also similar. One would say that the two
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`representations are close both in the feature space (the compact representations are similar) and
`close in the primary space (the works represented by the compact representations are also similar).
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`2 Although these exemplary features are “human-recognizable features,” in practice the features
`may be defined via a mathematical process so they are recognizable to a computer processor, but
`are not necessarily recognizable by a human.
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`34.
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`The patents explain that the matching discussed could use such things as a statistical
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`comparison of the compact representations to determine similarity. ’988 patent at 9:3-5. The
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`patents give several examples of such statistical comparisons including linear correlation,
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`correlation coefficients, mutual information, Euclidean distance, and Lp-norms. Id. at 9:5-8. Each
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`of these examples are types of comparisons that can be done between two feature vectors to try to
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`measure not only whether they are the same, but whether they are sufficiently similar to
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`characterize them (and the underlying works that the sketches or fingerprints represent) as
`matching.
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`35.
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`As noted above, the patent points out that these types of comparisons are different
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`than simply looking up a word in a dictionary. Words in a dictionary have been pre-processed so
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`that they are an ordered data set. Likewise, the search for a word in the dictionary is an identity
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`search. When looking up a particular word (for example, the word “banjo”), the search is for an
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`exact match, and the data set can be pre-processed (i.e., put in alphabetical order) to make the
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`search straightforward so that one can quickly locate the word without looking at every page of
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`the dictionary or every word on every page. If, however, one were searching for something
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`different, a different kind of search might be required. First, consider the same word, “banjo,” but
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`instead of looking for the word in the dictionary (a well-ordered data set), consider looking for it
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`in the text of a novel—a more random, non-ordered data set that is not pre-processed. It would no
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`longer be possible to simply turn to the pages where words beginning with the letter “b” were
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`located. A different kind of search, such as looking at every word, might be necessary in that kind
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`of search. Another, still different kind of search might be needed if one were to search for not only
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`exact matches to the word “banjo,” but also words that were “similar” by some statistical measure.
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`For example, consider a measure of similarity where a word might be considered similar to “banjo”
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`when it has approximately the same number of letters and the proximity of each letter to other
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`letters on a keyboard is also considered. In this example, the (hypothetical) word “darko” might
`be considered similar when each letter is considered as shown in the table below:
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`“banjo”
`b
`a
`n
`j
`o
`
`
`This kind of similarity search would not be simplified by the kind of pre-ordering of the data set
`
`“darko”
`D
`A
`R
`K
`O
`
`keyboard distance
`3 (b to d)
`0
`4 (n to r)
`1 (j to k)
`0
`
`that was utilized in the dictionary. Organizing the list of words alphabetically does not necessarily
`
`allow for easy pruning of the data set to limit the number of comparisons that need to be made.
`
`36.
`
`In this example, the total character distance between the two words could be
`
`represented as 7 (2 + 0 + 4 + 1 + 0). If it was decided that two words with a total character distance
`
`of 10 or less were sufficiently similar to be called a “match,” then these two words would be a
`
`match. Alternatively, one could decide that a total character distance of no more than 4 was needed
`
`to identify a match, so “darko” would not be a match, but the (hypothetical) word “banko” would
`
`be close enough. To find this kind of a match, even in a dictionary, is far more complicated than
`simply looking up the word “banjo,” and a different search strategy would be needed.
`
`37.
`
`The patents further explain that other information can be stored with the reference
`
`works. For example, additional information can be connected with a reference work about actions
`
`that are to be performed in connection with that work. Id. at 6:34-60. This kind of action
`
`information could be stored in a single database as part of the same record for the reference work,
`
`or as part of a separate database identified with a common “key” that is also connected to the
`
`record for the reference work. Id. These actions could include various things like displaying an
`
`
`
`15
`
`
`
`

`

`Case 1:14-cv-02396-PGG-MHD Document 148-1 Filed 05/30/19 Page 18 of 32
`
`advertisement, displaying a weblink, or initiating an e-commerce transaction. See, e.g., id. at 9:65-
`10:1.
`
`38.
`
`The patents go on to explain that when an unknown work is identified as a “match”
`
`to a reference work in the database, then the action that is connected with the reference work can
`
`also be performed in connection with the newly-identified uploaded work. Id. at 9:59-10:4. Thus,
`
`for example, if an advertisement or weblink (or both) are to be displayed with a particular reference
`
`work, then those same actions can be performed with the identified uploaded work once it is
`identified.
`
`VII. PARTICULAR CLAIM TERMS
`
`A.
`
`39.
`
` “non-exhaustive [. . .] search”
`
`I understand that Defendants contend this term is indefinite, and Network-1 offers
`
`the following construction: “a search designed to locate a [near] neighbor without comparing to
`
`all possible matches (i.e., all records in the reference data set), even if the search does not locate a
`
`[near] neighbor.” I further understand that this claim term appears in the claims asserted from the
`
`’988 patent (claim 17) and the ’464 patent (claims 1, 8, 10, 16, 18, 25, 27, and 33).
`
`40.
`
`The term “non-exhaustive search” is a term that is well understood by skilled
`
`artisans in the field of the patents, and was well understood at the time of the invention. In my
`
`view, it is not indefinite.
`
`41.
`
`In my expert opinion, Network-1’s proposed definition accurately reflects the
`
`ordinary meaning of this term that would have been understood by persons of ordinary skill in the
`
`art at the time of the patents. Skilled artisans then and now understand that the difference between
`
`a search being exhaustive and non-exhaustive is not a question of whether or not the search can
`
`find a match, but rather as turning on the methodology employed in the search itself. Specifically,
`
`
`
`16
`
`
`
`

`

`Case 1:14-cv-02396-PGG-MHD Document 148-1 Filed 05/30/19 Page 19 of 32
`
`the difference between exhaustive and non-exhaustive searches turns on the number of
`
`comparisons that must be performed between the query and the reference data set to be searched.
`
`The patents discuss this concept in the specification, for example at columns 8-9 and 21-22 of the
`
`’988 patent.
`
`42.
`
`Essentially, the patents explain that an exhaustive search may involve comparing a
`
`query to every record in the data set to be searched until a match is found. The patents note that
`
`on average, where you can halt a search after the match is found (and there is only a single match
`
`for each query), this would require N/2 comparisons (where N is the number of records in the data
`
`set). See, e.g., id. at 9:24-28. This means that, if you can stop after finding a match, over a number
`
`of searches, you will, on average, find the match after comparing the query to half of the total
`
`number of records in the set. This stands to reason (assuming that the queries are random in
`
`relation to the data set) because some matches might be found after being compared to only a small
`
`percentage of the set, while others might require comparisons to a much higher percentage of the
`
`set before finding matches. Collectively, though, the average is expected to be N/2. As a
`
`simplified example, we can consider a hypothetical search of a database of people’s names and
`
`social security numbers. Assume that the database is sorted only by the social security numbers,
`
`and that there are no repeated names in the database. If one has a name and is looking for the
`
`social security number associated with that name, the search might need to compare the name to
`
`every name in the database until a match is found. The first search might find a name after only a
`
`small number of comparisons, but the next search might not find a matching name until it had
`
`comp