Case 1:14-cv-02396-PGG-MHD Document 158-5 Filed 07/19/19 Page 1 of 33
`Case 1:14-cv-02396—PGG-MHD Document 158-5 Filed 07/19/19 Page 1 of 33
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`EXHIBIT 4
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`EXHIBIT 4
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`Case 1:14-cv-02396-PGG-MHD Document 158-5 Filed 07/19/19 Page 2 of 33
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`UNITED STATES PATENT AND TRADEMARK OFFICE
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`
`Google Inc.
`Petitioner
`
`V.
`
`Network-1 Technologies, Inc.
`Patent Owner
`
`Trial No: Not Assigned
`Patent No. 8,904,464
`
`DECLARATION OF PIERRE MOULIN
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`Page 1 of 84
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`Google Exhibit 1003
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`1
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`Case 1:14-cv-02396-PGG-MHD Document 158-5 Filed 07/19/19 Page 3 of 33
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`Contents
`Introduction .................................................................................................................... 3
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`Qualifications ................................................................................................................. 4
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`I.
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`II.
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`Ill. Materials Reviewed ........................................................................................................ 5
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`IV.
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`Person Having Ordinary Skill in the Art .................................................................... 5
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`V.
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`General Background ..................................................................................................... 6
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`VI. Overview of the '464 Patent ........................................................................................ 8
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`VII. Claim Construction
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`..................................................................................................... 13
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`A.
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`B.
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`"near neighbor" /"neighbor"
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`......................................................................... 14
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`"tag" ................................................................................................................... 16
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`VIII. Comparison of References to Claims of '464 Patent ............................................. 26
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`A.
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`Ferris in combination with Lambert and Gionis ............................................. 26
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`1.
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`2.
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`Implementation of Ferris) Broadcaster 402 ...................................... 30
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`Implementation of Ferris) Comparison ............................................. 32
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`B.
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`C.
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`
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`Ferris in combination with Lambert, Gionis, and Phi/ymJJ .............................. 58
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`Ferris in combination with Lambert, Gionis, and Goldstein ........................... 61
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`IX.
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`Conclusion .................................................................................................................... 66
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`Page 2 of 84
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`2
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`Case 1:14-cv-02396-PGG-MHD Document 158-5 Filed 07/19/19 Page 4 of 33
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`automated systems to recognize audio, video, and/ or image content by analyzing the
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`intrinsic features of a video work.
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`16.
`
`The second technology
`
`is at issue in this proceeding. Computer-
`
`automated systems for recognizing audio, video, and/ or image content universally
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`relied on two widely known technologies:
`
`feature extraction and neighbor searching in
`
`a database.
`
`17.
`
`Feature extraction refers to quantifying a media work in a form that-
`
`unlike a raw video feed-admits
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`a compact representation
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`and is easily parsed by a
`
`computer. Being compact means that they occupy less memory on a computer
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`than
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`the corresponding video file did. Furthermore,
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`extracted features are typically
`
`structured
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`in a format that facilitates efficient search. In the context of a content
`
`identification
`
`system, each set of extracted features corresponding
`
`to a given media
`
`work is stored as an entry in a database. Within such a database, entries are typically
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`organized
`
`to facilitate efficient search. Such organization
`
`is sometimes known as
`
`"preprocessing.''
`
`18.
`
`Neighbor searching refers to algorithms for comparing a first set of
`
`extracted features with one or more additional sets of extracted features to locate a
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`close, but not necessarily exact, match. Because neighbor searching is computationally
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`intensive for large feature sets, content recognition schemes typically employed search
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`algorithms
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`that increased efficiency by intelligently searching only a subset of potential
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`matches (i.e., "non-exhaustive"
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`algorithms).
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`7
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`Case 1:14-cv-02396-PGG-MHD Document 158-5 Filed 07/19/19 Page 5 of 33
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`example, early computers utilized primitive processors
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`to generate data, and stored
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`and retrieved data on punch cards, drum memory, or other primitive storage devices.
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`24.
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`Based on my understanding,
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`the only technical sounding feature in any
`
`of the claims - "correlating
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`... using a non-exhaustive, near neighbor search," as
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`recited in claims 1 and 18 - is not novel or unobvious.
`
`25.
`
`I understand
`
`that the Patent Owner has argued that "non-exhaustive
`
`search" is defined as "search using an algorithm designed to locate a match without
`
`requiring the query to be compared
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`to every record in the reference data set being
`
`searched until a match is identified." See Ex. 1004 at 3. I do not agree that this is the
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`broadest reasonable definition of non-exhaustive. However, the analysis presented
`
`herein is valid under this narrow definition and any broader definition.
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`26. Non-exhaustive, near neighbor searches were well-known to those of
`
`skill in 2000. For example, a paper by Aristides Gionis, published in 1999 and entitled
`
`"Similarity Search in High Dimensions via Hashing," discusses a method for
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`approximate similarity searching in high-dimensional data such as image and video
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`databases, pattern recognition, and other data having a large number of relevant
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`features. Ex. 1008 at 518. Gionis discloses preprocessing a set of objects ('P') "so as to
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`efficiently answer queries by finding the point in P closest to a query point q." Ex.
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`1008 at 520.
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`27. Gionis contrasts its algorithm with those in the prior art by "introduc[ing]
`
`a new indexing method for approximate nearest neighbor." Id. at 519, col. 1, ,r 3. The
`12
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`Page 12 of 84
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`Case 1:14-cv-02396-PGG-MHD Document 158-5 Filed 07/19/19 Page 6 of 33
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`resulting algorithm
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`is "non-exhaustive" in that it does not require the query to be
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`compared
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`to every record in the reference data set being searched until a match is
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`identified. For example, page 521 of Gionis discusses performing a K-Nearest
`
`Neighbor Search for a query q, which outputs
`
`the "K points Pi closest to q" by
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`searching "until we either encounter at least c · l points (for c specified later) or use all
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`l indices." Id., col. 1, ,r 4 ( emphases added). In my opinion, because Gionis stops
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`searching after locating the "at least c · l points,"
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`the query q will not be compared
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`to
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`every point and is thus "non-exhaustive."
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`28. Moreover, while "correlating
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`... using a non-exhaustive, near neighbor
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`search" may sound technical and complex, it can be understood as what a human
`
`being would perform
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`in looking up a word in the dictionary. For example, if a reader
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`wanted to look up "chese," a misspelling of the word "cheese,"
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`the reader would
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`perform a "non-exhaustive" search by looking only in the "C" section of the dictionary,
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`and not by comparing
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`the word "chese"
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`to every single word in every section of the
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`dictionary. Moreover,
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`the user would perform a "near neighbor' search to locate the
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`closest word to "chese," ending up at "cheese" because the two words are a close but
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`not exact match that has a difference of only one letter.
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`VII. Claim Construction
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`29.
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`I have been advised that the first step of assessing the validity of a patent
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`claim is to interpret or construe
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`the meaning of the claim.
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`Page 13 of 84
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`Case 1:14-cv-02396-PGG-MHD Document 158-5 Filed 07/19/19 Page 7 of 33
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`518. Gionis discloses preprocessing a set of objects ('P') "so as to efficiently answer
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`queries by finding the point in P closest to a query point q." Ex. 1008 at 520.
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`67. Gionis contrasts its algorithm with those in the prior art by "introduc[ing]
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`a new indexing method for approximate nearest neighbor." Id. at 519, col. 1, ,r 3. The
`
`resulting algorithm is "non-exhaustive" in that it does not require the query to be
`
`compared
`
`to every record in the reference data set being searched until a match is
`
`identified. For example, page 521 of Gionis discusses performing a K-Nearest
`
`Neighbor Search for a query q, which outputs the "K points Pi closest to q" by
`
`searching "until we either encounter at least c · l points (for c specified later) or use
`
`all l indices." Ex. 1008 at 521, col. 1, ,r 4 ( emphases added).
`
`68.
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`Because Gionis stops searching after locating the "at least c · l points,"
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`the query q will not be compared
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`to every point (the claimed
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`"non-exhaustive
`
`... search"). Gionis explains that its system has many advantages over
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`the prior art, such as a reduction
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`in disk accesses and a speed-up of the algorithm. See
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`id. at 519 (explaining that the algorithm is "significantly faster than the earlier
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`methods") and 526-28 (quantifying advantages of the algorithm). Accordingly, Gionis
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`discloses a "correlating, by the computer system using a non-exhaustive, near
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`neighbor search."
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`69.
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`In my opinion, it would merely be a matter of design choice and a mere
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`substitution of known elements to implement
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`the comparison
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`in Ferris as the
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`Page 33 of 84
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`Case 1:14-cv-02396-PGG-MHD Document 158-5 Filed 07/19/19 Page 8 of 33
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`non-exhaustive, near neighbor search in Gionis. Ferris and Gionis both relate to
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`determining matches between a particular piece of media, like video data, and stored
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`samples of possible matches. See Ex. 1006 at p. 11, ,r 3 - p. 12, ,r 1 ("continuously
`
`compares input from the various broadcast dwnnels with these samples, and uses a
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`commonly known algorithm
`
`...
`
`to determine when a 'match) has occurred") and Ex.
`
`1008 at p. 518, § 1 ("A similarity seanh problem involves a colledion ef o/jeds ( e.g.,
`
`documents,
`
`images) ... some examples are: ...
`
`image and video databases").
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`70. Moreover,
`
`in my opinion, such a substitution would create a more
`
`powerful and faster-operating
`
`system, which is a predictable result. See Ex. 1008 at
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`525-27.
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`71.
`
`Therefore,
`
`in my opinion, it would have been obvious to modify Fem·s to
`
`utilize a Broadcaster comprising a minicomputer, as in Lambert and to implement
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`the
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`comparison as a non-exhaustive, nearest neighbor search, as in Gionis.
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`72.
`
`As further detailed in the claim charts below 3
`
`, the combination of Ferris,
`
`Lambert, and Gionis teaches all elements of claims 1-11, 13-15, 18-28, and 30-32 of
`
`the '464 patent.
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`Non-limiting preamble.
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`[1.P] A method
`..
`compnsmg:
`[1.A] receiving, by a Ferris teaches a Central Processing System 420 and Broadcasters
`402 (the claimed "computer system including at least one
`computer system
`computer"). See) e.g., p. 10-11 and Figure 3. Broadcasters 402
`including at least
`one computer, a
`broadcast a transmission signal including live and taped video
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`3 All emphasis in the claim charts in this petition is added unless otherwise noted.
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`Page 34 of 84
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`Case 1:14-cv-02396-PGG-MHD Document 158-5 Filed 07/19/19 Page 9 of 33
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`first electronic
`media work;
`
`(the claimed "first electronic media work"). See) e.g., p. 10-11.
`Central Processing System 420 receives the transmission signal
`(the claimed "receiving, by a computer system"). See) e.g., Figure
`3. For example:
`
`"Broadcasters 402 generate content from a number of
`sources 403, depending on the broadcast medium in use.
`For example, a television broadcaster might utilise live
`feed from video cameras) and video plqyed from tape, as primary
`sources." P. 10, ,r 8 - p. 11, ,r 1.
`
`"In one envisaged embodiment of such a monitoring
`system, each central processing station contains a
`database of various audio and/ or video samples
`(supplied ahead of time) taken from the programmes
`(including advertisement and infomercials) which are to
`be augmented with data." P. 11, ,r 3.
`
`Lambert teaches a broadcast system that comprises
`minicomputer 11 (the claimed "computer system including at
`least one computer"). See, e.g., Figure 1. For example:
`
`"Minicomputer 11 also provides s1JJitd1ing control signals over
`line 23 to video switches 24 that selectively couple a
`selected program source that may be a video tape
`cassette, disc or film source 25 or other television
`program source 26, such as scheduled programs from
`television broadcast stations being rebroadcast over the
`cable system, to designated ones ef television transmitters 14 for
`broadcast over a selelted lhannel determined ry s1JJitd1ing control
`si;!_nals on line 23." 2:34-42.
`[1.B] correlating, by Ferris teaches that Central Processing System 420 compares (the
`claimed "correlating, by the computer system") the received
`the computer
`transmission signal (the claimed "first electronic media work")
`system usmg a non-
`exhaustive, near
`to samples (the claimed "electronic media work identifier")
`to
`neighbor search, the determine which portions of the signal are to be augmented
`with data. See, e.g., pp. 11-12. The comparison may be
`first electronic
`media work with an
`performed using an algorithm (the claimed "search")
`technique
`to match the transmission signal with one or more samples. id.
`electronic media
`work identifier;
`For example:
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`Page 35 of 84
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`Case 1:14-cv-02396-PGG-MHD Document 158-5 Filed 07/19/19 Page 10 of 33
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`"In one envisaged embodiment of such a monitoring
`system, each central processing station contains a database
`ef various audio and/ or video samples (supplied ahead of
`time) taken from the programmes
`(including
`advertisement and infomercials) which are to be
`augmented with data. A matching engine then continuous!J
`compares input from the various broadcast d1annels JJJith these
`samples, and uses a commonly knoJJJn algorithm (such as a
`sliding-window, averaged, square-of-difference
`system
`with an activation threshold)
`to determine when a
`'match' has occurred." P. 11, ,r 3 - p. 12, ,r 1.
`
`Gionis teaches a method for approximate similarity searching
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`Page 36 of 84
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`Case 1:14-cv-02396-PGG-MHD Document 158-5 Filed 07/19/19 Page 11 of 33
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`that preprocesses a set of objects in order to efficiently perform
`a Nearest Neighbor Search (the claimed "near neighbor")4
`that
`does not require the query to be compared
`to every record in
`the reference data set being searched until a match is identified
`(the claimed "non-exhaustive")
`on the size of a searched
`
`4 In construing
`
`terms in the other patents related to the '464 patent, I understand
`
`that
`
`the Patent Owner argued that "non-exhaustive
`
`search" be construed
`
`to mean a
`
`"search using an algorithm designed to locate a match without requiring the query to
`
`be compared
`
`to every record in the reference data set being searched until a match is
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`identified,"
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`that "neighbor" be construed
`
`to mean a "close, but not necessarily exact
`
`or the closest, match of a feature vector, compact electronic representation, or set of
`
`extracted features to another,
`
`that has a distance or difference
`
`that falls within a
`
`defined threshold of a query," and that "non-exhaustive
`
`neighbor search" be
`
`construed
`
`to mean a "non-exhaustive
`
`search to identify a neighbor." See Ex. 1004 at 3.
`
`I also understand
`
`the Patent Owner contended
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`that "neighbor" and "near neighbor"
`
`have the same construction.
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`Id. Even if the Board adopts these constructions, Gionis
`
`still teaches the claimed elements. For example, page 521 of Gionis discusses
`
`performing a K-Nearest Neighbor Search for a query q, which outputs
`
`the "K points
`
`Pi closest to q" by searching "until we either encounter at least c · l points (for c
`
`specified later) or use all l indices." Ex. 1008 at 521, col. 1, 1 4. Because Gionis stops
`
`searching after locating the "at least c · l points,"
`
`the query q will not be compared
`
`to
`
`every point (the claimed "non-exhaustive
`
`... search").
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`Page 37 of 84
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`Case 1:14-cv-02396-PGG-MHD Document 158-5 Filed 07/19/19 Page 12 of 33
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`database. See, e.g.) p. 519-21. For example:
`
`"[W]e introduce a new indexing method for approximate
`nearest neighbor." P. 519, § 1.
`
`is defined as
`"The nearest neighbor search problem
`follows: Definition 1 (Nearest Neighbor Seanh (NNS))
`Given a set P of n objects represented as points in a
`normed space zg, preprocess P so as to efficiently answer
`queries by finding the point in P closest to a query point
`q." P. 520, col. 1, ,r 2.
`
`the K points Pi
`
`"For Approximate K-NNS, we output
`closest to q." P. 521, col. 1, ,r 4.
`[1.C] storing, by the Ferris teaches using a known algorithm
`the
`to compare
`that a
`transmission
`signal with stored samples, and determining
`computer system,
`is reached. See,
`correlation
`match occurs only when a particular threshold
`e.f!,., pp. 11-12. In determining whether
`information
`that threshold was
`associating the first
`reached, Central Processing Station 420 stores the result of the
`in memory 5 (the claimed "storing, by the computer
`electronic media
`comparison
`work and the
`system, correlation
`information associating the first electronic
`electronic media
`media work and the electronic media work identifier"). For
`work identifier;
`example:
`
`"each central processing station contains a database of
`various audio and/ or video samples (supplied ahead of
`time) taken from the programmes
`(including
`advertisement and infomercials) which are to be
`augmented with data. A matd1ing engine then continuously
`compares
`input from the various broadcast channels
`with these samples, and uses a commonly known
`algorithm (such as a sliding-window, averaged, square-
`system JJJith an adivation threshold; to determine
`of-difference
`
`5 The result must be at least temporarily stored, e.g., in random access memory or
`
`buffer memory, because computers necessarily store calculations and other data in
`
`memory.
`
`Page 38 of 84
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`Case 1:14-cv-02396-PGG-MHD Document 158-5 Filed 07/19/19 Page 13 of 33
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`JJJhen a 'matd/ has ocatrred." P. 11, ,r 3 - p. 12, ,r 1.
`[1.D] accessing, by
`Ferris teaches that Central Processing Station 420 (the claimed
`"computer system") may determine
`that a display message is
`the computer
`due to be sent to a user device, and may retrieve the display
`system, associated
`information
`related message received from sponsors 401 from a database (the
`to an action to be
`claimed "accessing
`... associated information
`related to an
`performed
`in
`action"). See, e.g., p. 12. The display message may be an action
`association with one
`such as an advertisement
`to purchase an item (the claimed
`"action to be performed"). See, e.g., Figs. 2A and 2C. The
`or more electronic
`media works
`message may be determined
`to be transmitted based on the
`comparison between the samples and the transmission signal
`to
`corresponding
`the electronic media
`(the claimed "action to be performed
`in association with one or
`work identifier;
`more electronic media works corresponding
`to the electronic
`media work identifier"). See, e.g., p. 12. For example:
`
`"Commercial broadcasters will also have programmes
`and segments (such as infomenials and commenials) provided
`f?y sponsors 401, for insertion." P. 10, ,r 8 - p. 11, ,r 1.
`
`"A matching engine then continuously compares input
`from the various broadcast channels with these samples,
`and uses a commonly known algorithm (such as a
`sliding-window, averaged, square-of-difference
`system
`to determine JJJhen a 'match' has
`with an activation threshold)
`occurred." P. 11, ,r 3 - p. 12, ,r 1.
`
`"When the next display message is due to be transmitted, as
`may be deteded at the PAD sd1eduler 411 using either a
`polling or, preferably, an interrupt mechanism,
`it is
`retrieved from a PAD database 408, given a unique
`identification number (P ADUID), and sent to a
`transmission gateway 413, which may be physically
`remote, where it is translated into the correct format to
`be sent over a radio transmission service 414." P. 12, ,r 3.
`[1.E] generating, by Ferris teaches that Central Processing system 420 may retrieve a
`the computer
`display message from a database and assign (the claimed
`"generating, by the computer system") a PADUID to the
`system, a tag
`associated with the
`display message (the claimed "tag associated with the first
`electronic media work"). See, e.g., p. 12. For example:
`first electronic
`media work;
`
`"When the next display messaJ!_e
`is due to be transmitted, as
`39
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`Case 1:14-cv-02396-PGG-MHD Document 158-5 Filed 07/19/19 Page 14 of 33
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`[1.F] providing,
`from the computer
`system to a user
`electronic device,
`the first electronic
`media work and the
`associated
`tag;
`
`may be detected at the PAD scheduler 411 using either a
`it
`polling or, preferably, an interrupt mechanism,
`. ... " P.
`is . .. given a unique identification number (P ADUID)
`12, if 3.
`Ferris teaches that Central Processing system 420 may send the
`display message and PADUID to a remote control device 417
`(the claimed "providing,
`from the computer system to a user
`tag") See, e.g., p. 12. Ferris
`electronic device, ...
`the associated
`also discloses that Broadcasters 402 may send the transmission
`signal to remote control device 417, which is associated with a
`broadcast receiver 405, via broadcast receiver 405 (the claimed
`"providing,
`from the computer system ...
`the first electronic
`media work"). See, e.g., Figure 3 and pp. 12-13. Remote control
`device 417 may receive the signal via broadcast receiver 405
`using a microphone. See, e.g., pp. 6-7. For example:
`
`"When the next displqy message is due to be transmitted, as
`may be detected at the PAD scheduler 411 using either a
`it
`polling or, preferably, an interrupt mechanism,
`is . .. sent to a transmission gateJJJqJ 413, which may be
`physically remote, where it is translated
`into the correct
`format to be sent over a radio transmission
`service 414."
`P. 12, if 3.
`
`"Broadcasters 402 generate content from a number of
`sources 403, depending on the broadcast medium in use.
`For example, a television broadcaster might utilise live
`feed from video cameras, and video played from tape, as
`primary sources. Commercial broadcasters will also have
`programmes and segments (such as infomercials and
`commercials) provided by sponsors 401, for insertion. In
`the normal course of events this combined content
`stream is fed into a transmission med1anism 404 for broadcast
`to into a user's home 416 where the content carrier is acquired and
`the content reconstmded and displqyed using a broadcast receiver
`405." P. 10, if 8 - p. 11, if 1.
`
`"The apparatus, for example a remote control can
`determine whether a channel is selected on a broadcast
`receiver. In the case of a remote control this can be by
`way of selection by a user. A confirmation of the correct
`
`40
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`Page 40 of 84
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`Case 1:14-cv-02396-PGG-MHD Document 158-5 Filed 07/19/19 Page 15 of 33
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`channel can be obtained by receiving a signal from the
`broadcast receiver, for example using a microphone and comparing
`the signal JJJith a predetermined signal or a signal received at this
`apparatus." P. 6, ,r 5.
`
`"For now, simply note that the device 417 will display
`the incoming PAD display data to the user at the
`appropriate cue point, and may accept interaction from
`the user on the basis of the information so displayed.
`Details ef Stl{h interaltion, where relevant, may be transmitted
`balk to the central control station 420, together with the
`id (HUUID) and PADUID ef the
`unique handset/user
`initial display data." P. 13, ,r 3.
`Ferris teaches Central Processing Station 420 receiving a return
`signal from remote control 417 (the claimed "obtaining, by the
`computer system from the user electronic device, a request").
`See, e.g., Figure 3 and p. 14. The return signal may include, for
`example, the PADUID and a command (the claimed "a request
`related to the associated tag"). See, e.g., p. 13. For example:
`
`"For now, simply note that the device 417 will display the
`incoming PAD display data to the user at the appropriate
`cue point, and may accept interaction from the user on
`the basis of the information so displayed. Details ef Stl{h
`interadion, where relevant, may be transmitted balk to the
`central control station 420, together with the unique
`id (HUUID) and PADUID ef the initial
`handset/user
`displqy data." P. 13, ,r 3.
`
`"In any event, the return signal is picked up by the
`receiver network 415 of a radio service provider (which
`may or may not be the same as provider 414), and
`forwarded
`to a reception gateway 412. This gateway
`formats the interaction data in a manner suitable for
`processing by the rest of the system, and arranges for the
`translated message to be transported to the appropriate central
`processing site 420, which may be physically remote." P. 14,
`,r 2.
`
`"FIGS. 2] and 2L are given to illustrate some other
`potential uses of the device, less directly connected with
`
`41
`
`[1.G] obtaining, by
`the computer
`system from the
`user electronic
`device, a request
`related to the
`associated tag;
`
`Page 41 of 84
`
`

`

`Case 1:14-cv-02396-PGG-MHD Document 158-5 Filed 07/19/19 Page 16 of 33
`
`programme-associated material In FIG 2J the user is
`prompted 306 to enter a palkage tralking code
`307 . ... Similarly, FIG. 2L illustrates a 'shop from
`home' usage, in which the user is prompted 310 to enter a
`joint produd vendor/ identification code 311." P. 27, ,r 2.
`
`[1.H] generating,
`using the computer
`system, machine-
`readable
`instructions based
`upon the associated
`information
`to be
`used in performing,
`at the user
`electronic device,
`the action; and
`
`Ferris teaches that Central Processing Station 420 receives a
`request from a user, and generates a response
`to enable
`receiving apparatus 417 to take further action. See, e.g., Figures
`2A, 2C, and 2L. For example, Central Processing Station 402
`may receive a request including a product/vendor
`identification
`code and generate a message that includes information about
`the requested product (the claimed "generating, using the
`computer system, machine-readable
`instructions based upon
`the associated
`information
`to be used in performing, at the user
`electronic device, the action"). 6 See, e.g., .Figures 2A and 2L, and
`p. 27. For example:
`
`"FIG. 2L illustrates a 'shop from home' usage, in which
`the user is prompted 310 to enter a joint pro dud/ vendor
`identification code 311. This will initiate a remote query to
`diJplqy irifof'fnation about the produd so identified, in a manner
`similar to that used ry PAD prodult effers (as shoJJJn in FIG.
`2A) for example)) if this is successful the user may initiate a
`purchase, as with the PAD example discussed
`previously." P. 27, ,r 2.
`Ferris teaches that Central Processing Station 420 may respond
`to a request from a user. See, e.g., Figures 2A, 2C, and 2L. For
`example, Central Processing Station 420 may generate and send
`(the claimed "providing,
`from the computer system") a
`message to remote control 417 (the claimed "to the user
`electronic device") that includes information about a requested
`product
`(the claimed "the machine-readable
`instructions
`to
`to the request"). Id. For
`perform
`the action in response
`
`[1.I] providing,
`from the computer
`system to the user
`electronic device,
`the machine-
`readable
`.
`.
`to
`instructions
`perform
`the action
`
`6 The message sent by Central Processing Station 402 comprises "machine-readable
`
`instructions" because the information
`
`is received by and displayed on receiving
`
`apparatus 417 (i.e., a machine).
`
`42
`
`Page 42 of 84
`
`

`

`Case 1:14-cv-02396-PGG-MHD Document 158-5 Filed 07/19/19 Page 17 of 33
`
`in response to the
`request.
`
`example:
`
`"FIG. 2L illustrates a 'shop from home' usage, in which
`the user is prompted 310 to enter a joint pro dud/ vendor
`identification code 311. This will initiate a remote query to
`displqy information about the produd so identified, in a manner
`similar to that used ry PAD prodult effers (as shoJJJn in FIG.
`2A) for example)) if this is successful the user may initiate a
`purchase, as with the PAD example discussed
`previously." P. 27, ,r 2.
`Ferris teaches that Central Processing Station 420 may generate
`and send a message to remote control 417 that includes
`information about a requested product (the claimed "associated
`information
`is related to one or more products or services").
`See, e.g., p. 27 and Figures 2A and 2L. For example:
`
`"FIG. 2L illustrates a 'shop from home' usage, in which
`the user is prompted 310 to enter a joint pro dud/ vendor
`identification code 311. This will initiate a remote query to
`displqy information about the produd so identified, in a manner
`similar to that used by PAD product offers (as shown in
`FIG. 2A, for example) .... " P. 27, ,r 2.
`Ferris teaches that Central Processing Station 420 may generate
`and send a message to remote control 417 that includes the
`name of a requested product (the claimed "associated
`information
`is related to names of the one or more products or
`services"). See, e.g., pp. 23 and 27 and Figures 2A and 2L. For
`example:
`
`"FIG. 2L illustrates a 'shop from home' usage, in which
`the user is prompted 310 to enter a joint pro dud/ vendor
`identification code 311. This will initiate a remote query to
`displqy information about the produd so identified, in a manner
`similar to that used by PAD product offers (as shown in
`FIG. 2A, for example) .... " P. 27, ,r 2.
`
`"FIG. 2A illustrates the offer screen of the example
`from FIG. 4, after some interaction. Line 101 contains a
`brief description ef the produd, and line 103 gives pricing
`information." P. 23, ,r 4; see also Fig. 2A ("B&F Power
`Drill').
`
`43
`
`[2] The method of
`claim 1, wherein the
`associated
`is
`information
`related to one or
`more products or
`services.
`
`[3] The method of
`claim 2, wherein the
`associated
`is
`information
`related to names of
`the one or more
`products or
`services.
`
`Page 43 of 84
`
`

`

`Case 1:14-cv-02396-PGG-MHD Document 158-5 Filed 07/19/19 Page 18 of 33
`
`Ferris teaches that Central Processing Station 420 may receive a
`[4] The method of
`claim 2, wherein the message from remote control 417 requesting
`to purchase an
`associated
`album that contains a song that is currently playing, and that
`is
`information
`Central Processing Station 420 may send back information
`for
`displaying confirmation
`screens for purchase of the album that
`related to a product
`contains
`the song (the claimed "associated
`information
`is
`category associated
`with the one or
`related to a product category associated with the one or more
`products or services"). See, e.g., p. 27 and Figures 1 and 2I.
`more products or
`services.
`For example:
`
`to the 'BUY' label
`the button corresponding
`"Pressing
`305, or pressing the 'BUY NOW button (9 on FIG. 1)
`will initiate a purchase ef the album or single current!J plqying.
`In this case, the user will be prompted with further
`confirmation screens (not shown in detail here)." P. 27, ,r 1.
`Ferris teaches that Central Processing Station 420 may generate
`and send a message to remote control 417 that includes the
`manufacturer of a requested product, such as a manufacturer of
`a product or an artist who wrote a song (the claimed
`"associated
`information
`is related to a manufacturer of the one
`or more products or services"). See Figures 2A, 21, and pp. 23
`and 26-27. For example:
`
`"FIG. 2A illustrates the offer screen of the example
`from FIG. 4, after some interaction. Line 101 contains a
`brief description ef the produd, and line 103 gives pricing
`information." P. 23, ,r 4; see also Fig. 2A ("B&F Power
`Drill").
`
`"The system may be used with a number of different
`types of broadcast receiver, and FIG. 21 shows the sort
`of display that might be shown to accompany radio
`broadcasts. Details of the current station are shown 301,
`and an indication of the receiver type 312. Brief details ef
`the current!J plqying song, together with the current time, are
`given 303." P. 26, ,r 4 - p. 27, ,r 1; see also Fig. 21 ("Oasis:
`Wonderwall").
`
`"FIG. 2L illustrates a 'shop from home' usage, in which
`the user is prompted 310 to enter a joint pro dud/ vendor
`identification code 311. This will initiate a remote query to
`
`44
`
`is
`
`l 5 J The method of
`claim 2, wherein the
`associated
`information
`related to a
`manufacturer of the
`one or more
`products or
`services.
`
`Page 44 of 84
`
`