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`Exhibit 27
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`UNITED STATES DISTRICT COURT
`SOUTHERN DISTRICT OF NEW YORK
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`14 Civ. 2396 (PGG)
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`14 Civ. 9558 (PGG)
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`NETWORK-1 TECHNOLOGIES, INC.,
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`Plaintiff,
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`v.
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`GOOGLE LLC and YOUTUBE, LLC,
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`Defendants.
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`DECLARATION OF MICHAEL MITZENMACHER IN SUPPORT OF PLAINTIFF
`NETWORK-1 TECHNOLOGIES, INC.’S BRIEF IN OPPOSITION TO
`MOTION FOR SUMMARY JUDGMENT
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`I, Michael Mitzenmacher, declare as follows:
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`1.
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`I have been retained as an independent expert witness in this matter to provide
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`analyses and opinions on behalf of Plaintiff, Network-1 Technologies, Inc.
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`2.
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`In connection with this matter, I have provided an expert report reflecting my
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`analyses and opinions regarding infringement of the asserted patents by Google, Inc.
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`3.
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`My report reflects my opinions and the bases therefore and I could and would testify
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`thereto if called as a witness. A true and correct copy of my report is attached hereto.
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`
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`I declare under penalty of perjury that the forgoing is true and correct. Executed this 15th
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`day of October 2020 at Lexington, Massachusetts.
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`
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`Michael Mitzenmacher
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`1
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`GOOGLE LLC and YOUTUBE, LLC,
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`vs.
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`Defendants.
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`Case 1:14-cv-02396-PGG-SN Document 241-2 Filed 11/12/20 Page 4 of 313
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`UNITED STATES DISTRICT COURT
`SOUTHERN DISTRICT OF NEW YORK
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`
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`NETWORK-1 TECHNOLOGIES, INC.,
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`
`Plaintiff,
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` Case No. 14-cv-2396
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`Case No. 14-cv-9558
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`
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`EXPERT REPORT OF MICHAEL MITZENMACHER, PH.D.
`REGARDING GOOGLE LLC AND YOUTUBE, LLC’S INFRINGEMENT
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`Page
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`CONFIDENTIAL OUTSIDE COUNSEL ONLY –
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`PROSECUTION/ACQUISITION BAR MATERIALS
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`
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`TABLE OF CONTENTS
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`
`
`1. INTRODUCTION ...................................................................................................................... 1
`1.1. Retention ......................................................................................................................... 1
`1.2. Qualifications .................................................................................................................. 1
`1.3.
`The Asserted Patents ....................................................................................................... 2
`1.4. Materials Considered ...................................................................................................... 8
`1.5.
`Legal Principles .............................................................................................................. 8
`1.6.
`Level of Ordinary Skill ................................................................................................. 11
`2. OVERVIEW OF DEFENDANTS’ CONTENT ID ACCUSED INSTRUMENTALITIES .... 12
`3. SUMMARY OF MY OPINIONS ............................................................................................ 18
`4. DEFENDANTS’ INFRINGEMENT BY THE CONTENT ID ACCUSED
`INSTRUMENTALITIES .............................................................................................................. 18
`4.1.
`’988 patent claim 17 ...................................................................................................... 19
`4.2.
`’237 patent claim 33 ...................................................................................................... 85
`4.3.
`’237 patent claim 34 .................................................................................................... 149
`4.4.
`’237 patent claim 35 .................................................................................................... 149
`4.5.
`’464 patent claim 1 ...................................................................................................... 150
`4.6.
`’464 patent claim 8 ...................................................................................................... 217
`4.7.
`’464 patent claim 10 .................................................................................................... 218
`4.8.
`’464 patent claim 16 .................................................................................................... 218
`4.9.
`’464 patent claim 18 .................................................................................................... 219
`4.10.
`’464 patent claim 25 ................................................................................................ 276
`4.11.
`’464 patent claim 27 ................................................................................................ 277
`4.12.
`’464 patent claim 33 ................................................................................................ 277
`5. ALLEGED NON-INFRINGING ALTERNATIVES ............................................................ 278
`6. NON-COMPARABILITY OF LICENSES ........................................................................... 293
`7. CONCLUSION ...................................................................................................................... 295
`
`
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`ii
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`1.
`INTRODUCTION
`1.1. Retention
`
`1.
`I have been retained as an independent expert witness by the law firm of Russ August &
`Kabat on behalf of Network-1 Technologies, Inc. to testify as a technical expert in the
`following lawsuits concerning U.S. Patent Nos. 8,010,988 (“the ’988 patent”); 8,205,237 (“the
`’237 patent”); and 8,904,464 (“the ’464 patent”) (collectively, the “Asserted Patents”):
`
`Network-1 Technologies, Inc. v. Google LLC and YouTube, LLC, 14-cv-2396 (S.D.N.Y)
`Network-1 Technologies, Inc. v. Google LLC and YouTube, LLC, 14-cv-9558 (S.D.N.Y)
`
` I
`
` refer to Google LLC and YouTube, LLC as “Defendants” or “Google” in this report.
`
`
`2.
`In this expert report, I provide opinions regarding the Asserted Patents, and Defendants’
`infringement of the currently asserted claims of the Asserted Patents. I expect to testify at trial
`on these issues, as set forth in this report and in any supplemental reports or declarations that I
`may prepare for this litigation in the future. I also expect to testify at trial with respect to the
`matters addressed by any expert testifying on behalf of Defendants, if asked about these
`matters by the Court or by the parties’ counsel. I may also testify on other matters relevant to
`this case, if asked by the Court or by the parties’ counsel.
`
`3.
`To ensure that my opinions are complete and accurate, I reserve the right to supplement
`or amend this report if additional facts and information that affect my opinions become
`available. Such information may include, for example, materials produced in this litigation,
`and information and documents relevant to this case that Defendants has not yet disclosed. I
`may also supplement or amend my report or opinions in response to additional discovery or
`other events, and may rebut expert reports submitted by Defendants.
`
`4. My work in this case is being billed at my standard rate of $850 per hour, with
`reimbursement for actual expenses. My payment is not contingent upon my testimony or the
`outcome of the case. I have no personal interest in the outcome of the case.
`1.2. Qualifications
`
`5. My Curriculum Vitae, attached as Exhibit B, is a true and accurate listing of my
`qualifications. I summarize some of these qualifications below.
`
`6.
`I am currently employed as a Professor of Computer Science at Harvard University.
`Specifically, I am the Thomas J. Watson, Sr. Professor of Computer Science in the School of
`Engineering and Applied Sciences. I joined the faculty of Harvard as an Assistant Professor in
`January 1999. I was promoted to Associate Professor in 2002 and to Professor in 2005. In
`2010, I began a three-year term as Area Dean, which is essentially equivalent to what other
`schools call Department Chair, of Computer Science, and held that position through June 2013.
`I served as Area Co-Chair of Computer Science for the 2018-2019 academic year. My work
`address is 33 Oxford Street, Cambridge, MA 02138. My primary research interests include
`design and analysis of algorithms, networks and data transmission, and information theory.
`
`1
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`7.
`I received my undergraduate degree in Mathematics and Computer Science from Harvard
`College in 1991. I received a Certificate of Advanced Study in Mathematics from Cambridge
`University in 1992. I received a Ph.D. in Computer Science from the University of California
`at Berkeley in 1996. From August 1996 to January 1999, I was employed as a Research
`Scientist at Digital Systems Research Center, where my work included projects on algorithms
`for the Internet.
`
`8.
`I am listed as an inventor or co-inventor on 19 issued patents, and am the co-author of a
`textbook entitled “Probability and Computing” published by Cambridge University Press. I am
`a Fellow of the Association for Computing Machinery (ACM).
`
`9.
`I regularly serve on program committees for conferences in networking, algorithms, and
`communication. For example, I have served on the program committee multiple times for the
`SIGCOMM conference, which is the flagship annual conference of the ACM Special Interest
`Group on Data Communication (SIGCOMM). I have also served on numerous program
`committees related to algorithms, including the ACM Symposium on the Theory of
`Computing, the International Colloquium on Automata, Languages, and Programming, and the
`International Conference on Web Search and Data Mining.
`
`10. The field of endeavor at issue in this case is identification of electronic content (such as
`video or audio content) using algorithmic search techniques. I have published over 200
`research papers1 in computer science and engineering conferences and journals, many of which
`have explored algorithms and data structures for algorithmic search techniques, including both
`mathematical analysis and applications.
`1.3. The Asserted Patents
`
`11. The Asserted Patents generally share a common specification, so for ease of reference,
`unless otherwise indicated, I will refer to the ’988 patent specification in citing to the
`inventor’s description of his invention. I recognize that there are, however, some differences in
`the specifications of the Asserted Patents. The “Summary of the Invention” (Section 2 in the
`text of the specification) varies somewhat among the patents. Also, there are some additional
`discussions of certain material from the literature that was incorporated by reference in the
`earlier specification of the ’988 patent that is set forth in greater detail in the later patents, such
`as, for example, in Section 4.2.1.1.3 of the specification. In the ’237 and ’464 patents, for
`example, the specification 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.
`
`12. As set forth in the Detailed Description section of the Asserted Patents (Section 4), the
`patents describe systems and methods “for identifying works without the need of embedding
`signals therein. Once identified, such information can be used to determine a work-related
`action.” ’988 patent at 5:39-42. In simple terms, these systems can analyze an unknown
`digital “work” such as a piece of content (like an audio and/or a video file) using
`
`
`
`1 I note that in several comments in the source code Google produced in this case related to the
` I describe in detail below, there is reference to one of my publication on this
`topic. See, e.g., GOOG-NETWORK-SC-00000564; GOOG-NETWORK-SC-00000607.
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`2
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`characteristics of that work, and then compare it, using those characteristics, to a collection of
`known references to determine if the 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.
`Such actions might include providing or displaying an advertisement, displaying a weblink,
`dialing a number, or performing an e-commerce transaction. See id. at 9:59-10:1.
`
`13. An example of how this process may be carried out is illustrated in Figure 1 of the
`patens-in-suit:
`
`
`The Asserted Patents explain: “FIG. 1 is a process bubble diagram of operations that may be
`performed . . . in which intra-work information [information derived from the work itself, as
`opposed to information added or appended to the work] is used to identify the work.” Id. at
`6:34-37.
`1.3.1. Feature Extraction
`
`14. The process of Figure 1 begins with a feature extraction operation that can be used to
`identify a known reference work. See id. at 7:11-8:2 (§ 4.2.1.1.1). The Asserted Patents
`explain that examples of a work can include “an image, an audio file or some portion of an
`audio signal or may be one or more frames or fields of a video signal, or a multimedia signal.”
`Id. at 7:18-20. In the example of a music video, the reference work could be the audio file
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`and/or portions of the audio signal, the video file and/or portions of the video signal, or some
`combination.
`
`15. The Asserted Patents explain that “[t]he purpose of the feature extraction operation is to
`derive a compact representation of the work that can subsequently be used for the purpose of
`recognition.” Id. at 7:20-23. The patents recognize that electronic works can be represented
`with shorter “sketches” or “fingerprints” that require far less space to store in computer
`memory, and far less computing resources to compare, but must be sufficiently complex that
`each sketch or fingerprint represents the underlying content (the primary work) with a low
`likelihood that two different primary works will have the same sketch or fingerprint. The
`patents refer to these so-called “sketches” or “fingerprints” as “compact electronic
`representations,” “feature vectors,” or “extracted features.”
`
`16. The patent specification teaches numerous ways in which feature extraction can be
`accomplished. See id. at 7:11-8:2 (§ 4.2.1.1.1). The specification explains that feature
`extraction operations derive a representation of the work by, for example, using “a pseudo-
`random sample of pixels” from a frame of a video. Id. at 7:23-26. In addition, feature
`extraction can be accomplished through the use of a variety of mathematical operations
`including Fourier, wavelet, or cosine transforms/decompositions or statistical methods like
`principle component analysis. See id. at 7:26-43.
`
`1.3.2. Building the Databases of Reference Works
`
`17.
`In the example illustrated in Figure 1, the Asserted Patents contemplate assembling a
`database of these sketches or fingerprints of reference works and associated actions that are
`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:
`
`
`Here, “WORK @t1” is the reference work, such as the music video I discuss above. This
`reference is used in generating the reference database. In Step 122, one or more “feature
`extraction operation(s)” are performed. These operations extract features from the reference
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`work (such as a music video) to generate one or more sketches or fingerprints that can be used
`to identify this work.
`
`18.
`In Step 124, the extracted features can also be associated to a work identification number
`or reference called a “work id.” Each work id is associated with a specific work, but may also
`be associated with one or more extracted features from the “WORK @t1.” The feature(s)
`(vector) and work ids are tied together to form the database 110 referred to as “WID
`Information” in Figure 1.
`
`19. As part of the same or a different database, the work ids can be linked with “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:
`
`
`Thus, the patents describe a system in which the operator could create/maintain a database of
`known references. For example, this database might include popular songs from major record
`companies, and television programs from major studios. The full versions of the reference
`items in the database might be very large (for example, the electronic file for a single song
`might be more than 1 megabyte (1 million bytes of data) and a film might require more than 1
`gigabyte (1 billion bytes of data)). Storing such references in their full length would require
`huge amounts of storage for a large database. Even more problematic, searching by comparing
`a reference to the entirety of a reference work would be very difficult and require significant
`computing resources and time.
`
`20. The patents contemplate a system where each reference work in the database is
`represented by a “sketch” or “fingerprint.” Although these sketches or fingerprints may be far
`more compact than the complete media file, they still need to be sufficiently complex that
`numerous different reference works will each be very unlikely to have the same sketch or
`fingerprint.
`
`1.3.3. Comparing an Unknown Work with the Reference Works in the Database
`
`21. To compare an unknown video to the database of reference works, the patents explain
`that one can obtain a sketch or fingerprint of the unknown video and then search for a 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
`containing at least a portion of the song underlying the music video. Thus, in this portion of
`the process, the same feature extraction operations that were run on the reference works used to
`generate the WID Information database 110 could now be run in Step 140 on the unknown
`work (“WORK @t2”)—the unknown video containing the same song as the reference work.
`The extracted features from the unknown work can then be matched with features in the WID
`Information database to identify a matching work.
`
`22. The patents explain that the matching of these sketches or fingerprints is not equivalent to
`looking up a word in a dictionary. Id. at 8:63-9:5. Looking up a word in a dictionary is a
`search for an exact, or identity, match in an ordered set of data. All of the words in the
`dictionary have been pre-processed by organizing them in alphabetical order. If one is
`searching for a particular word in the dictionary, it is possible to search for an exact match very
`efficiently because of the pre-processing organization of the data set. As the patents explain,
`the kind of matching involved here is different both because the comparisons are not looking
`for exact matches, and because the data set involves high dimensional data that is not ordered
`in the way a dictionary can be ordered.
`
`23. As explained in the patents, the comparisons of works are not necessarily looking for an
`exact match because there can be, for example, noise or distortions in the unknown video. Id.
`This could be a consequence of using imperfect recording technology, recording a video from a
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`signal that had some static in it (like a weak television broadcast), recording a video using a
`video recorder pointed at a television showing a program, or many other possible reasons.
`Similarly, the unknown video might be altered slightly from the reference. For example, a
`reference television program might be 22 minutes and 30 seconds long, but an uploaded
`recording of the same television program might have started recording a few seconds early so
`that the recording is 22 minutes and 45 seconds long. In each of these cases, the unknown
`video would not be identical to the reference work, but it is still desirable for the system to
`identify the two files as matching. 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.
`
`24. The system needs to be designed to recognize the two exemplary similar videos discussed
`above as a match, even though they are not identical. Comparing the entirety of the files would
`be very difficult. For example, a single video or audio file might be many millions of bytes
`(megabytes) long. Comparing all of the millions of individual data bytes in the entire file to all
`of the millions of individual data bytes of another (reference) file to see how similar they are
`would be extremely time consuming and difficult. The patents discuss using feature vectors of,
`compact electronic representations of, and sets of features extracted from the files to help
`simplify the comparisons. Shorter representations of the electronic works are easier to
`compare than the entire files, but the representations must be sufficiently complex that each
`compact representation is very unlikely to be the same for more than one primary work. For
`example, if one wanted to represent a song, a measure of the tempo, such as the beats per
`minute of the song, would be a simple way to represent the song, but many songs could have
`the same tempo, so that representation by itself would not be very helpful for use in
`comparisons to identify an unknown work. Rather, one could capture snapshots of or “sample”
`particular values (for example the pitch and intensity values2) at multiple times during the
`song. See id. at 7:11-42 (describing various feature extraction methodologies). Each of these
`independent features could be compared to the same features of a reference work. If the values
`for many of the features were close to the same features for a reference work, it might be
`possible to infer that the two primary works (the works represented by the compact
`representations) were also similar. One would say that the two 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).
`
`25. The patents explain that the matching discussed could use such things as a statistical
`comparison of the compact representations to determine similarity. ’988 patent at 9:3-5. The
`patents give several examples of such statistical comparisons including linear correlation,
`correlation coefficients, mutual information, Euclidean distance, and Lp-norms. Id. at 9:5-8.
`Each of these examples are types of comparisons that can be done between two feature vectors
`to try to measure not only whether they are the same, but whether they are sufficiently similar
`to characterize them (and the underlying works that the sketches or fingerprints represent) as
`matching.
`
`
`
`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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`26. 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 advertisement, displaying a weblink, or initiating an e-commerce transaction.
`See, e.g., id. at 9:65-10:1.
`
`27. 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.
`
`28. The following are the Asserted Claims:
`
`• U.S. Pat. No. 8,010,988 (“the ’988 patent”), claim 17;
`
`• U.S. Pat. No. 8,205,237 (“the ’237 patent”), claims 33-35; and
`
`• U.S. Patent No. 8,904,464 (“the ’464 patent), claims 1, 8, 10, 16, 18, 25, 27, and 33.
`
`1.4. Materials Considered
`
`29.
`In preparation for this report and for expert testimony that I may be called upon to
`provide, I have considered and may rely on documents identified in this report or those
`referenced in the exhibits attached to this report. This includes among other materials the
`Asserted Patents and their prosecution histories, Network-1’s infringement contentions, the
`Court’s claim construction, discovery and publicly available information regarding the
`patented subject matter and the accused systems, third-party information, deposition testimony
`and deposition exhibits, other discovery responses, and my interaction with the accused
`instrumentalities. In addition to the materials explicitly reference in my report, I have also
`considered the materials listed in Exhibit A to this report. My opinions are based on these
`sources of information, together with my education, training, and experience.
`
`30.
`In testifying, I may use some or all of the information referenced above, additional
`information identified in discovery, as well as any materials relied upon by Defendants’
`experts, to support or summarize my opinions. In addition, I may prepare summaries and
`demonstrative exhibits to assist my presentation of testimony to the Court.
`
`1.5. Legal Principles
`
`31.
`I have been informed that the infringement analysis consists of two steps. The first step
`is claim construction, in which the Court determines the scope and meaning of certain claim
`
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`terms. My understanding is that all unconstrued claim terms are to be given their plain and
`ordinary meaning to a person of ordinary skill in the art.
`
`32.
`I have been informed that the second step in an infringement analysis is to compare the
`accused products with the properly construed claims. A conclusion of infringement is proper if
`every element in an asserted claim is found in the accused product, either literally or by
`doctrine of equivalents. To establish literal infringement, every limitation set forth in a claim
`must be found in an accused instrumentality.
`
`33. Doctrine of Equivalents: If a claim limitation is not literally present in the accused
`product, infringement may still be found under the doctrine of equivalents. An accused
`product will be found infringing under the doctrine of equivalents if insubstantial differences
`exist between the accused product and the limitations of the asserted claim (i.e., they are
`substantially the same). While no particular linguistic framework controls the inquiry, the
`insubstantial differences inquiry may be guided by, for example, determining whether the
`accused product performs substantially the same function in substantially the same way to
`obtain substantially the same result as the claim limitation.
`
`34.
`I understand that a dependent claim contains all of the limitations of the independent
`claim from which it depends. Thus, to establish infringement, every limitation set forth in a
`dependent claim as well as the independent claim from which it depends must be found in an
`accused instrumentality.
`
`35.
`I have been informed that an entity that uses the claimed methods of the Asserted Patents
`can be found liable for infringement. To directly infringe a method claim, the accused
`infringer must perform all the steps of the claimed method, either personally or through another
`acting under his direction or control.
`
`36. Claim Construction: I understand that claim construction is a question of law for the
`Court. I also understand that the Court has not yet issued an order on claim construction.
`Because the Court has not yet issued a claim construction order, I reserve the right to
`supplement my report once the Court does so. I also reserve the right to provide infringement
`opinions under the doctrine of equivalents if it is necessary to do so in light of the construction
`of one or more claim terms.
`
`37.
`In rendering my opinions below, I have applied the parties’ agreed construction where
`there is such an agreed construction. Where the parties dispute a construction, I have applied
`both constructions in my analysis. Finally, I understand there are two terms that Defendants
`assert are indefinite; for those terms, because Defendants have not proposed any construction, I
`have applied the construction proposed by Network-1.
`
`Agreed Constructions
`
`
`9
`
`
`
`

`

`Case 1:14-cv-02396-PGG-SN Document 241-2 Filed 11/12/20 Page 15 of 313
`CONFIDENTIAL OUTSIDE COUNSEL ONLY –
`
`PROSECUTION/ACQUISITION BAR MATERIALS
`
`Claim Term
`“sublinear” [search]
`
`“neighbor”
`“near neighbor”
`
`
`“near neighbor
`search”
`
`
`“approximate nearest
`neighbor search”
`
`“machine-readable
`instructions”
`
`
`
`Agreed Construction
`“A search whose execution time scales with a less than linear
`relationship to the size of the data set to be searched, assuming
`computing power is held constant.”
`“A close, but not necessarily exact or the closest, match of a feature
`vector, compact electronic representation, or set of extracted features
`to another, wherein the distance or difference between the two feature
`vectors, compact electronic representations, or sets of extracted
`features falls within a defined threshold.”
`“A search using an algorithm designed to identify a close, but not
`necessarily exact or the closest, match of a feature vector, compact
`electronic representation, or set of extracted features to another,
`wherein the distance or difference between the two feature vectors,
`compact electronic representations, or sets of extracted features falls
`within a defined threshold.”
`“A search using an algorithm designed to identify a close, but not
`necessarily exact or the closest, match of a feature vector, compact
`electronic representation, or set of extracted features to another,
`wherein the distance or difference between the two feature vectors,
`compact electronic representations, or sets of extracted features falls
`within a defined threshold.”
`“code or pseudocode that is executed using a computer processor, i.e.,
`that is discernable by a computer processor and dictates steps to be
`carried out by one or more computer processors”
`
`Disputed Constructions
`
`
`
`
`10
`
`

`

`Case 1:14-cv-02396-PGG-SN Document 241-2 Filed 11/12/20 Page 16 of 313
`CONFIDENTIAL OUTSIDE COUNSEL ONLY –
`
`PROSECUTION/ACQUISITION BAR MATERIALS
`
`Claim Term
`
`“non-exhaustive
`[. . .]