UNITED STATES PATENT AND TRADEMARK OFFICE
`
`________________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`
`________________
`
`AXIS COMMUNICATIONS AB,
`CANON INC., and CANON U.S.A., INC.,
`Petitioners,
`
`v.
`
`AVIGILON FORTRESS CORPORATION,
`Patent Owner.
`____________
`
`Case No.: IPR2018-00138
`U.S. Patent No. 8,564,661
`
`Case No.: IPR2018-00140
`U.S. Patent No. 8,564,661
`
`________________
`
`DECLARATION OF DR. ALAN BOVIK, PH.D. IN SUPPORT OF PATENT
`OWNER’S RESPONSES TO INTER PARTES REVIEWS OF U.S. PATENT
`NO. 8,564,661
`
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`Case Nos. IPR2018-00138 and IPR2018-00140
`U.S. Patent No. 8,564,661
`
`TABLE OF CONTENTS
`
`PAGE
`INTRODUCTION...........................................................................................1
`I.
`QUALIFICATIONS AND EXPERIENCE.....................................................1
`II.
`III. MATERIALS CONSIDERED........................................................................7
`IV.
`PERSON OF ORDINARY SKILL IN THE ART........................................10
`V.
`LEGAL STANDARDS.................................................................................12
`Claim Construction .............................................................................12
`Anticipation.........................................................................................14
`Obviousness ........................................................................................15
`VI. U.S. PATENT NO. 8,564,661.......................................................................16
`Disclosure of the ’661 Patent..............................................................16
`Re-Exam of U.S. Patent No. 7,868,912 ..............................................20
`Description of Courtney............................................................21
`The ’912 Patent Overcame Courtney, Brill, and Olson............24
`The ’912 and ’661 Patents Share the Independence-Based
`Claim Elements.........................................................................27
`VII. CORRECT CLAIM CONSTRUCTION.......................................................33
`The Independence-Based Claim Elements .........................................33
`“User Rule that Defines an Event” .....................................................38
`VIII. DESCRIPTION OF ASSERTED ART.........................................................40
`Kellogg................................................................................................40
`Kellogg does not teach object or attribute detection ................41
`Kellogg does not teach the independence-based claim
`elements ....................................................................................44
`Dimitrova ............................................................................................47
`Dimitrova does not teach the independence-based claim
`elements ....................................................................................47
`Dimitrova does not teach user rules..........................................55
`Brill......................................................................................................56
`i
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`I, Dr. Alan Bovik, being of legal age, hereby declare, affirm, and state the
`
`following:
`
`I.
`
`INTRODUCTION
`1.
`I have been retained as an independent expert by Patent Owner
`
`Avigilon Fortress Corporation (“Patent Owner” or “Avigilon”) for IPR2018-00138
`
`(“the 138 IPR”) and IPR2018-00140 (“the 140 IPR”), both involving U.S. Patent
`
`No. 8,564,661 (the “’661 patent”). I have personal knowledge of the facts and
`
`opinions set forth in this declaration, and, if called upon to do so, I would testify
`
`competently thereto. All of the opinions and conclusions found in this declaration
`
`are my own.
`
`2.
`
`I am being compensated at a rate of $500 per hour for my services. I
`
`am being paid regardless of the conclusions or opinions I reach. I have no personal
`
`or financial stake or interest in the outcome of the present proceedings, and my
`
`compensation is not dependent in any way upon the outcome of these proceedings.
`
`II. QUALIFICATIONS AND EXPERIENCE
`I expect to testify regarding my background, qualifications, and
`
`3.
`
`experience relevant to the issues in this litigation. I hold a Ph.D. in in Electrical and
`
`Computer Engineering from the University of Illinois, Urbana-Champaign (awarded
`
`in 1984). I also hold a Master's degree in Electrical and Computer Engineering from
`
`the University of Illinois, Urbana-Champaign (awarded in 1982).
`
`1
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`I am a tenured full Professor and I hold the Cockrell Family Regents
`
`4.
`
`Endowed Chair at the University of Texas at Austin. My appointments are in the
`
`Department of Electrical and Computer Engineering, the Department of Computer
`
`Sciences, and the Department of Biomedical Engineering. I am also the Director of
`
`the Laboratory for Image and Video Engineering (“LIVE”).
`
`5.
`
`My research is in the general area of digital television, digital cameras,
`
`image and video processing, computational neuroscience, and modeling of
`
`biological visual perception. I have published over 800 technical articles in these
`
`areas and hold seven U.S. patents. I am also the author of The Handbook of Image
`
`and Video Processing, Second Edition (Elsevier Academic Press, 2005); Modern
`
`Image Quality Assessment (Morgan & Claypool, 2006); The Essential Guide to
`
`Image Processing (Elsevier Academic Press, 2009); and The Essential Guide to
`
`Video Processing (Elsevier Academic Press, 2009); and numerous other
`
`publications.
`
`6.
`
`I received the 2017 Edwin H. Land Medal from the Optical Society of
`
`America in September 2017 with citation: For substantially shaping the direction
`
`and advancement of modern perceptual picture quality computation, and for
`
`energetically engaging industry to transform his ideas into global practice.
`
`7.
`
`I received a Primetime Emmy Award for Outstanding Achievement in
`
`Engineering Development, for the Academy of Television Arts and Sciences, in
`
`2
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`October 2015, for the widespread use of my video quality prediction and monitoring
`
`models and algorithms that are widely used throughout the global broadcast, cable,
`
`satellite and internet Television industries.
`
`8.
`
`Among other awards and honors, I have received the 2013 IEEE Signal
`
`Processing Society’s “Society Award,” which is the highest honor accorded by that
`
`technical society (“for fundamental contributions to digital image processing theory,
`
`technology, leadership and education”). In 2005, I received the Technical
`
`Achievement Award of the IEEE Signal Processing Society, which is the highest
`
`technical honor given by the Society, for “broad and lasting contributions to the field
`
`of digital image processing”; and in 2008 I received the Education Award of the
`
`IEEE Signal Processing Society, which is the highest education honor given by the
`
`Society, for “broad and lasting contributions to image processing, including popular
`
`and important image processing books, innovative on-line courseware, and for the
`
`creation of the leading research and educational journal and conference in the image
`
`processing field.”
`
`9.
`
`My technical articles have been widely recognized as well, including
`
`the 2009 IEEE Signal Processing Society Best Journal Paper Award for the paper
`
`“Image quality assessment: From error visibility to structural similarity,” published
`
`in IEEE Transactions on Image Processing, volume 13, number 4, April 2004; this
`
`same paper received the 2017 IEEE Signal Processing Society Sustained Impact
`
`3
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`Paper Award as the most impactful paper published over a period of at least ten
`
`years; the 2013 Best Magazine Paper Award for the paper “Mean squared error:
`
`Love it or leave it?? A new look at signal fidelity measures,” published in IEEE
`
`Transactions on Image Processing, volume 26, number 1, January 2009; the IEEE
`
`Circuits and Systems Society Best Journal Paper Prize for the paper “Video quality
`
`assessment by reduced reference spatio-temporal entropic differencing,” published
`
`in the IEEE Transactions on Circuits and Systems for Video Technology, vol. 23,
`
`no. 4, pp. 684-694, April 2013; and the 2017 IEEE Signal Processing Letters Best
`
`Paper Award for the paper A. Mittal, R. Soundararajan and A.C. Bovik, “Making a
`
`‘completely blind’ image quality analyzer,” published in the IEEE Signal Processing
`
`Letters, vol. 21, no. 3, pp. 209-212, March 2013.
`
`10.
`
`I received the Google Scholar Classic Paper citation twice in 2017, for
`
`the paper “Image information and visual quality,” published in the IEEE
`
`Transactions on Image Processing, vol. 15, no. 2, pp. 430-444, February 2006 (the
`
`main algorithm developed in the paper, called the Visual Information Fidelity (VIF)
`
`Index, is a core picture quality prediction engine used to quality-assess all encodes
`
`streamed globally by Netflix), and for “An evaluation of recent full reference image
`
`quality assessment algorithms,” published in the IEEE Transactions on Image
`
`Processing, vol. 15, no. 11, pp. 3440-3451, November 2006. (the picture quality
`
`database and human study described in the paper, the LIVE Image Quality Database,
`
`4
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`has been the standard development tool for picture quality research since its first
`
`introduction in 2003). Google Scholar Classic Papers are very highly-cited papers
`
`that have stood the test of time, and are among the ten most-cited articles in their
`
`area of research over the ten years since their publication.
`
`11.
`
`I have also been honored by other technical organizations, including the
`
`Society for Photo-optical and Instrumentation Engineers (SPIE), from which I
`
`received the Technology Achievement Award (2013) “For Broad and Lasting
`
`Contributions to the Field of Perception-Based Image Processing,” and the Society
`
`for Imaging Science and Technology, which accorded me Honorary Membership,
`
`which is the highest recognition by that Society given to a single individual, “for his
`
`impact in shaping the direction and advancement of the field of perceptual image
`
`processing.” I was also elected as a Fellow of the Institute of Electrical and
`
`Electronics Engineers (IEEE) “for contributions to nonlinear image processing” in
`
`1995, a Fellow of the Optical Society of America (OSA) for “fundamental research
`
`contributions to and technical leadership in digital image and video processing” in
`
`2006, and as a Fellow of SPIE for “pioneering technical, leadership, and educational
`
`contributions to the field of image processing” in 2007.
`
`12. Among other relevant research, I have worked with the National
`
`Aeronautics and Space Administration (“NASA”) to develop high compression
`
`image sequence coding and animated vision technology, on various military projects
`
`5
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`for the Air Force Office of Scientific Research, Phillips Air Force Base, the Army
`
`Research Office, and the Department of Defense. These projects have focused on
`
`developing local spatio-temporal analysis in vision systems, scalable processing of
`
`multi-sensor and multi-spectral imagery, image processing and data compression
`
`tools for satellite imaging, AM-FM analysis of images and video, the scientific
`
`foundations of image representation and analysis, computer vision systems for
`
`automatic target recognition and automatic recognition of human activities, vehicle
`
`structure recovery from a moving air platform, passive optical modeling, and
`
`detection of speculated masses and architectural distortions
`
`in digitized
`
`mammograms. My research has also recently been funded by Netflix, Qualcomm,
`
`Facebook, Texas Instruments, Intel, Cisco, and the National Institute of Standards
`
`and Technology (NIST) for research on image and video quality assessment. I have
`
`also received numerous grants from the National Science Foundation for research
`
`on image and video processing and on computational vision.
`
`13.
`
`I have conducted extensive research throughout my career in the areas
`
`of image and video processing, analysis, and surveillance. For example in the time
`
`period of the ’661 patent I was funded by Freescale Semiconductor, Inc., to conduct
`
`research of video surveillance tools for industry perimeters and other outdoor areas.
`
`I was in the same time period funded by Advanced Digital Imaging Research (ADIR)
`
`to conduct research of face detection and recognition methods and systems, which
`
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`are common integral components of video surveillance systems. I have published
`
`extensively in these areas and authored a chapter in one of my books on image and
`
`video processing entitled “Video Surveillance.”
`
`14. Additional details about my employment history, fields of expertise,
`
`and publications are further described in my curriculum vitae, which is attached as
`
`Exhibit A to this report.
`
`III. MATERIALS CONSIDERED
`15.
`In forming the opinions set forth herein, I have considered and relied
`
`upon my education, knowledge in the relevant field, and my experience. I have also
`
`reviewed and considered the materials cited herein, including the following
`
`materials:
`
`Description
`
`Exhibit No.
`IPR2018-00138
`
`Exhibit No.
`IPR2018-00140
`
`U.S. Patent No. 8,564,661 (“the ’661
`patent”)
`Prosecution History of the ’661 patent
`Ex. 1003 “Visual Memory” by
`Christopher James Kellogg (“Kellogg”)
`“Motion Recovery for Video Content
`Classification” by N. Dimitrova et al.
`(“Dimitrova”)
`“Event Recognition and Reliability
`Improvements for the Autonomous Video
`Surveillance System” by Frank Brill et al.
`(“Brill”)
`
`Ex. 1001
`
`Ex. 1002
`
`Ex. 1003
`
`-
`
`Ex. 1001
`
`Ex. 1002
`
`-
`
`Ex. 1003
`
`Ex. 1004
`
`Ex. 1004
`
`7
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`Description
`
`Exhibit No.
`IPR2018-00138
`
`Exhibit No.
`IPR2018-00140
`
`Ex. 1005
`
`-
`
`Ex. 1006
`
`Declaration of John R. Grindon, D.Sc.
`(138 IPR)
`Declaration of John R. Grindon, D.Sc.
`(140 IPR)
`Curriculum Vitae of John R. Grindon,
`D.Sc.
`Declaration of Emily R. Florio (138 IPR) Ex. 1007
`Declaration of Emily R. Florio (140 IPR)
`Reference Numbering for Challenged
`Claims 1-32
`’912 ex parte reexamination - Applicant
`Response of Oct. 30, 2013
`’912 inter partes reexamination -
`Applicant Response of June 11, 2012
`Declaration of Christopher James Bailey-
`Kellogg
`90/012,878 Reexamination, Final Action
`(March 27, 2014)
`U.S. Patent No. 6,628,835, to Brill
`“Moving Object Detection and Event
`Recognition Algorithms for Smart
`Cameras,” Thomas J. Olson and Frank Z.
`Brill, Proceedings of the 1997 Image
`Understanding Workshop, New Orleans,
`May 1997, pp. 159-175
`U.S. Patent No. 7,868,912
`90/012,878 Reexamination, Declaration
`of Dr. Kenneth Zeger (Oct. 30, 2013)
`U.S. Patent App. 11/828,842 (issued as
`the ’661 patent), IDS Form by Applicant
`(March 21, 2012)
`
`Ex. 1008
`
`Ex. 1009
`
`Ex. 1010
`
`Ex. 1011
`
`Ex. 2001
`
`Ex. 2002
`Ex. 2003
`
`Ex. 2004
`Ex. 2005
`
`-
`
`8
`
`-
`
`Ex. 1005
`
`Ex. 1006
`
`Ex. 1007
`
`Ex. 1008
`
`Ex. 1009
`
`Ex. 1010
`
`-
`
`Ex. 2001
`
`Ex. 2002
`Ex. 2003
`
`Ex. 2004
`Ex. 2005
`
`Ex. 2006
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`
`Description
`
`Declaration of Dr. Alan Bovik in Support
`of Patent Owner’s Response
`U.S. Patent No. 5,969,755 to Courtney
`Deposition Transcript of Dr. John
`Grindon (Aug. 15, 2018)
`“Automatic video indexing via object
`motion analysis,” Pattern Recognition,
`30(4), April 1997, by J.D. Courtney
`
`Exhibit No.
`IPR2018-00138
`Ex. 2007
`
`Exhibit No.
`IPR2018-00140
`Ex. 2007
`
`Ex. 2008
`Ex. 2009
`
`Ex. 2010
`
`Ex. 2008
`Ex. 2009
`
`Ex. 2010
`
`16.
`
`I have also considered the papers filed in each of the 138 IPR and the
`
`140 IPR, including the Petition, the preliminary response, and the institution
`
`decision.
`
`17.
`
`I have also considered other background references, of which I had
`
`been previously aware, and not cited herein, that a person of ordinary skill in the art
`
`(“POSA”) at the time of the invention would have recognized as being related to the
`
`subject matter of the ’661 patent.
`
`18.
`
`I have reviewed Dr. Zeger’s declaration from the ’878 Re-Exam,
`
`submitted in these trials as Exhibit 2005. I agree with Dr. Zeger’s conclusions, and
`
`incorporate the following paragraphs from Exhibit 2005 into my declaration in their
`
`entirety: 31-35, 66-82, 88-93, 99-110, and 115.
`
`19.
`
`This declaration is made based on information currently available to
`
`me. I intend to continue my investigation and study, which may include a review of
`
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`documents and information that may yet be produced, as well as deposition
`
`testimony from depositions for which transcripts are not yet available or that may
`
`yet be taken in this proceeding. Therefore, I expressly reserve the right to expand or
`
`modify my opinions as my investigation and study continue, and to supplement my
`
`opinions in response to any additional information that becomes available to me, any
`
`matters raised by Patent Owner and/or other opinions provided by Patent Owner’s
`
`expert(s), or in light of any relevant orders from the Patent Trial and Appeal Board
`
`or other authoritative body.
`
`IV. PERSON OF ORDINARY SKILL IN THE ART
`20.
`I have been asked for the purposes of this declaration to opine on the
`
`knowledge and understanding of one of ordinary skill in the art as of October 24,
`
`2000, or before.
`
`21.
`
`I have been informed and understand that a person of ordinary skill in
`
`the art (“POSA”) is a hypothetical person who is presumed to have been familiar
`
`with the relevant art at the time of the invention. I have also been informed and
`
`understand that, in determining the level of skill in the art, one may consider: the
`
`type of problems encountered in the art, prior art solutions to those problems, the
`
`rapidity with which innovations are made, the sophistication of the technology, and
`
`the educational level of active workers in the field.
`
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`I understand that Petitioners and their retained expert Dr. Grindon have
`
`22.
`
`alleged that appropriate level of skill for a POSA in the art of the ’661 patent “would
`
`have (i) a Bachelor of Science degree in electrical engineering, computer
`
`engineering, or computer science, with approximately two years of experience or
`
`research related to video processing and/or surveillance systems or (ii) equivalent
`
`training and work experience in computer engineering and video processing and/or
`
`surveillance systems.” I understand that Petitioners and Dr. Grindon further claim
`
`that a POSA “would be knowledgeable and familiar with the video processing and
`
`information extraction concepts and techniques recited in the claims of the ’661
`
`patent, as they were well known in 2000, as demonstrated above.” Ex. 1005 (’138
`
`IPR), ¶¶98-99
`
`23.
`
`I disagree with Petitioners’ level of skill for a POSA. In my opinion, a
`
`POSA for the ’661 patent would have practical experience working with and
`
`designing video surveillance systems. Accordingly, a POSA in the art of the ’661
`
`patent would have (i) a Bachelor of Science degree in electrical engineering,
`
`computer engineering, or computer science, with approximately two years of
`
`experience or research in the field of video surveillance systems or (ii) equivalent
`
`training and work experience in the field of video surveillance systems. In addition,
`
`a POSA would be knowledgeable and familiar with video processing, including
`
`segmentation and information extraction known at the time of the ’661 patent.
`
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`V.
`
`LEGAL STANDARDS
`24.
`In preparing and expressing my opinions and considering the subject
`
`matter of the ’661 patent, I am relying on certain basic legal principles that counsel
`
`have explained to me. These principles are discussed below.
`
`Claim Construction
`I understand that the first step in determining the validity of a claim is
`
`25.
`
`for the claim to be properly construed. I have been further advised that, in an inter
`
`partes review proceeding, the claims of an unexpired patent are to be given their
`
`broadest reasonable interpretation in view of the specification. I have also been
`
`informed that claim terms are typically given their ordinary and customary meaning
`
`as would be understood by one of ordinary skill in the art in the context of the patent
`
`disclosure (specification), unless the specification or the file history of the patent
`
`provides a specific definition. I understand that a patentee can be his own
`
`lexicographer, and that any special definition for a claim term must be set forth in
`
`the specification with reasonable clarity and precision.
`
`26.
`
`I understand that claim elements may be expressed as a means for
`
`performing a recited function as set out in 35 U.S.C. § 112, ¶ 6. I understand that
`
`construing such terms is a two-step process. First the function of the means-plus-
`
`function term must be identified. Second, the corresponding structure to that
`
`function must be identified. To be corresponding structure, the specification must
`
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`adequately disclose this structure based on the understanding of one skilled in the
`
`art, and the disclosed structure must be clearly linked with the recited function.
`
`However it is not enough that one of skill in the art would be capable of
`
`implementing a structure but rather that one of skill in the art would understand that
`
`the specification itself discloses the structure.
`
`27.
`
`I understand that claim elements that recite the word “means” as a
`
`structural element for performing one or more recited functions are presumed to be
`
`covered by 35 U.S.C. § 112, ¶ 6. I further understand that for such a means-plus-
`
`function element, the element is to be construed to cover only the structure or
`
`structures described in the patent specification for per-forming the exact function
`
`recited by the element and structural equivalents thereof. I further understand that
`
`structures are deemed equivalent if they are insubstantially different. One way of
`
`determining whether structures are equivalent is to determine whether each performs
`
`the identical recited function in a substantially similar way to obtain a substantially
`
`similar result. I understand that a structural equivalence analysis must be supported
`
`by specific evidence, and that a conclusory statement alleging that a structure within
`
`an accused product or process is equivalent to structure disclosed in the patent for
`
`performing the identical recited function is insufficient. I further understand that a
`
`structure may be equivalent only if it was available at the time of the issuance of the
`
`claim including the means-plus-function element being considered.
`
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`I also understand that when construing means-plus-function limitations
`
`28.
`
`that concern a general purpose computer or a microprocessor that is specially
`
`programmed to carry out an algorithm, the corresponding structure is to be construed
`
`as the general purpose computer and the algorithm disclosed in the patent
`
`specification by which the processor performs the claimed function. I understand
`
`that in this scenario, an algorithm must be disclosed unless the claimed functions can
`
`be achieved by any general purpose computer, without special programming, which
`
`I understand is a narrow exception that only applies in rare circumstances. I
`
`understand that an algorithm must be disclosed if the claimed function requires more
`
`than merely plugging in a general purpose computer. I also understand that if the
`
`algorithm is missing, then the claim is invalid as indefinite.
`
`Anticipation
`I have been informed that a patent claim is anticipated under pre-AIA
`
`29.
`
`35 U.S.C. § 102 if each and every element of the claim, as properly construed, is
`
`found either explicitly or inherently in a single prior art reference. I also understand
`
`that an element of a patent claim is inherent in a prior art reference if the element
`
`must necessarily be present and such would be recognized by one of ordinary skill
`
`in the art. However, I understand that inherency cannot be established by mere
`
`probabilities or possibilities. I also understand that to qualify as a sufficient
`
`disclosure under 35 U.S.C. § 102, the reference must enable the claimed invention
`
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`such that a POSA could implement the claimed invention without undue
`
`experimentation.
`
`Obviousness
`I have been informed that a claim may be invalid as obvious if the
`
`30.
`
`subject matter described by the claim as a whole would have been obvious to a POSA
`
`at the time the claimed invention was made. I understand that the standard for
`
`obviousness in an inter partes review proceeding is by a preponderance of the
`
`evidence.
`
`31.
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`I have also been informed that a determination of obviousness involves
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`an analysis of the scope and content of the prior art, the similarities between the
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`claimed invention and the prior art, and the level of ordinary skill in the art. I have
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`been informed and understand that a prior art reference should be viewed as a whole.
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`32.
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`I have been informed that in considering whether an invention for a
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`claimed combination would have been obvious, I may assess whether there are
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`apparent reasons to combine known elements in the prior art in the manner claimed
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`in view of interrelated teachings of multiple prior art references, the effects of
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`demands known to the design community or present in the market place, and/or the
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`background knowledge possessed by a POSA. I also understand that other principles
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`may be relied on in evaluating whether a claimed invention would have been
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`obvious.
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`Case Nos. IPR2018-00138 and IPR2018-00140
`U.S. Patent No. 8,564,661
`I have been informed that, in making a determination as to whether or
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`33.
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`not the claimed invention would have been obvious to a POSA, one may consider
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`certain objective indicators of non-obviousness if they are present, such as:
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`commercial success of product(s) practicing the claimed invention; long-felt but
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`unsolved need; teaching away; unexpected results; copying; and praise by others in
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`the field. I understand that for such objective evidence to be relevant to the non-
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`obviousness of a claim, however, there must be a causal relationship (called a
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`“nexus”) between the claim and the evidence. I also understand that this nexus must
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`be based on a novel element of the claim rather than something available in the prior
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`art.
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`34.
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`I have also been informed and understand that when considering the
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`obviousness of a patent claim, one should consider whether a reason or motivation
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`existed for combining the elements of the references in the manner claimed, and that
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`the prior art must create a reasonable expectation of success in producing the claimed
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`subject matter.
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`VI. U.S. PATENT NO. 8,564,661
`Disclosure of the ’661 Patent
`The ’661 patent is directed to the field of video surveillance systems.
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`35.
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`Video surveillance systems are used to monitor areas of interest and detect whether
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`objects are engaged in activities, also known as events. Prior to the ’661 patent,
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`U.S. Patent No. 8,564,661
`video surveillance systems used “large volumes of video imagery as the primary
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`commodity of information exchange.” Ex. 1001, 6:19-21. These early systems
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`required extensive human viewing, and thus these systems recorded and stored all
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`video regardless of whether the footage contained an event so that a human could
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`later review the video. Ultimately, these early systems required large amounts of
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`storage and analysis time.
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`36.
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`Later video surveillance systems prior to the ’661 patent digitally
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`analyzed video to determine video primitives that could be used as a short hand to
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`describe certain aspects of what was captured on video so that event detection could
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`become an automated process. An example of a video primitive is the color of a
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`shirt worn by a person captured on video, where a value of “red” is referred to as an
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`“attribute” of the shirt object. In other words, attributes are observable
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`characteristics of an object in a video. Some examples of attributes are:
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`size, shape, perimeter, position, trajectory, speed and direction of motion,
`motion salience and its features, color, rigidity, texture, and/or classification.
`The object descriptor may also contain some more application and type
`specific information: for humans, this may include the presence and ratio of
`skin tone, gender and race information, some human body model de-scribing
`the human shape and pose; or for vehicles, it may include type (e.g., truck,
`SUV, sedan, bike, etc.), make, model, license plate number. The object
`descriptor may al-so contain activities, including, but not limited to, carrying
`an object, running, Walking, standing up, or raising arms. Some activities,
`such as talking, fighting or colliding, may also refer to other objects. The
`object descriptor may also contain identification information, including, but
`not limited to, face or gait.
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`U.S. Patent No. 8,564,661
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`Ex. 1001, 19:10-25.
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`37.
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`These systems prior to the ’661 patent and its family looked specifically
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`for primitives that were necessary to satisfy event queries regarding events that were
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`predetermined or predefined by the system. An exemplary predefined rule is to send
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`an alert when the event of a person wearing a red shirt enters a particular door. These
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`systems were limited by system rules that defined events such that these systems
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`were constrained in capability to only collect metadata (primitives) that were
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`associated with the events that had been predefined by the system.
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`38.
`
`The ’661 patent describes a system that detects, collects, and stores
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`video primitives that correlate to attributes of objects in a scene. Ex. 1001, 5:51-6:2.
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`While the ’661 patent specification discusses databases generally, the claims do not
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`require that any attributes are stored in a database, nor do the claims attempt to claim
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`any database specific functionality. “Attributes” are information about objects,
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`while “events” are determined by analyzing attributes to determine what an object
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`in a scene is doing. Id., 6:3-14. More precisely, the ’661 patent teaches that after
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`multiple attributes have been detected and collected, they can be analyzed to
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`determine whether or not an event has occurred, including events that were not
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`contemplated when the attributes were detected and collected. Id., 13:51-62.
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`39.
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`The ’661 patent further describes the process of “tasking” the system.
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`Specifically, tasking allows a user to create user rules, whereby the user specifies
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`U.S. Patent No. 8,564,661
`one or more “event discriminators.” Ex. 1001, 16:25-30. “Event discriminators are
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`identified with one or more objects (whose descriptions are based on video
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`primitives), along with one or more optional spatial attributes, and/or one or more
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`optional temporal attributes.” Id., 6:3-14. For example, a user can define an event
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`discriminator (called a “loitering” event in this example) as a “person” object in the
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`“automatic teller machine” space for “longer than 15 minutes” and “between 10:00
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`p.m. and 6:00 a.m.” Id., 6:4-12; see also 18:16-19. The user can also defin