UNITED STATES PATENT AND TRADEMARK OFFICE
`______________________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`______________________
`
`Canon Inc., Canon U.S.A., Inc., and Axis Communications AB,
`
`Petitioners,
`
`v.
`
`Avigilon Fortress Corporation,
`
`Patent Owner.
`______________________
`
`Case No. IPR2019-00311
`U.S. Patent No. 7,932,923
`______________________
`
`DECLARATION OF DR. ALAN BOVIK IN SUPPORT OF PATENT
`OWNER’S RESPONSES TO INTER PARTES REVIEW OF U.S. PATENT
`NO. 7,932,923
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`TABLE OF CONTENTS
`ASSIGNMENT .............................................................................................. 1
`I.
`II. QUALIFICATIONS AND EXPERIENCE ................................................. 1
`III. MATERIALS CONSIDERED ..................................................................... 7
`IV. PERSON OF ORDINARY SKILL IN THE ART ...................................... 9
`V. UNDERSTANDING OF PATENT LAW ................................................. 10
`A.
`Claim Construction ............................................................................. 10
`B.
`Anticipation ......................................................................................... 11
`C.
`Obviousness ......................................................................................... 12
`VI. SUMMARY OF U.S. PATENT NO. 7,932,923 ......................................... 13
`VII. CLAIM CONSTRUCTION ........................................................................ 15
`A.
`“attributes of the object” (Claims 1-7, 9-19, 22-28, 30-41);
`“attributes of each of the detected first and second objects”
`(Claims 8, 29); “attributes of the detected objects (Claims 20, 21)
` ............................................................................................................. 15
`“new user rule” (Claims 1-41) ............................................................ 15
`The Independence-Based Claim Elements .......................................... 17
`1.
`“applying” (Petitioners’ “Independence Argument (1)”
`Discussion) (Claims 1-41) ........................................................ 17
`“event” (Petitioners’ “Independence Argument (3)”
`Discussion) (Claims 1-41) ........................................................ 18
`“independent” (Petitioners’ “Independence Argument (2)”
`Discussion) (Claims 1-41) ........................................................ 19
`“wherein the applying the new user rule to the plurality of
`detected attributes comprises applying the new user rule to
`only the plurality of detected attributes” (Claims 1-9, 22-
`29); “wherein the analysis of the combination of the
`
`B.
`C.
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`2.
`
`3.
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`
`
`
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`4.
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`attributes to detect the event comprises analyzing only the
`combination of the attributes” (Claims 20-21); “wherein
`the applying the selected new user rule to the plurality of
`attributes stored in memory comprises applying the
`selected new user rule to only the plurality of attributes
`stored in memory” (Claims 30-41) ........................................... 20
`“a video device” (Claims 9, 20, and 30) ................................... 22
`5.
`VIII. PETITIONERS’ PROPOSED GROUNDS DO NOT DISCLOSE
`THE CLAIMS OF THE ’923 PATENT .................................................... 23
`A.
`Background On Asserted References .................................................. 23
`1.
`Kellogg ...................................................................................... 23
`2.
`Dimitrova .................................................................................. 24
`3.
`Brill ........................................................................................... 25
`B. Motivation To Combine ...................................................................... 27
`1.
`Kellogg and Brill ....................................................................... 27
`2.
`Dimitrova and Brill ................................................................... 30
`Kellogg Does Not Anticipate Claims 1-41 ......................................... 31
`1.
`Kellogg does not disclose “detecting an object in a video
`from a single camera” (Claims 1-41) ....................................... 31
`Kellogg does not disclose “detecting a plurality of
`attributes of the object by analyzing the video from said
`single camera” (Claims 1-41) ................................................... 37
`Kellogg does not disclose “identifying an event of the
`object that is not one of the detected attributes of the object
`by applying the new user rule to the plurality of detected
`attributes, wherein the applying the new user rule to the
`plurality of detected attributes comprises applying the new
`user rule to only the plurality of detected attributes”
`(Claims 1-41) ............................................................................ 37
`
`C.
`
`2.
`
`3.
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`
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`
`
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`D.
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`E.
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`4.
`
`5.
`
`6.
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`Kellogg does not disclose “the plurality of attributes that
`are detected are independent of which event is identified”
`(Claims 1-41) ............................................................................ 42
`Kellogg does not disclose “selecting the new user rule
`comprises selecting a subset of the plurality of attributes
`for analysis” (Claims 2, 4, 7, 11, 12, 13, 14, 16, 23, 25, 28,
`32, 33, 34, 35, 38) ..................................................................... 44
`Kellogg does not disclose “a video device” (Claims 9, 20,
`and 30) ....................................................................................... 45
`The Combination Of Kellogg And Brill Does Not Render
`Obvious Claims 1-41 ........................................................................... 46
`1.
`Neither Kellogg nor Brill discloses “detecting an object in
`a video from a single camera” (Claims 1-41) ........................... 46
`Neither Kellogg nor Brill discloses “detecting a plurality
`of attributes of the object by analyzing the video from said
`single camera” (Claims 1-41) ................................................... 48
`Neither Kellogg nor Brill discloses “the plurality of
`attributes that are detected are independent of which event
`is identified” (Claims 1-41) ...................................................... 49
`Neither Kellogg nor Brill discloses “identifying an event
`of the object that is not one of the detected attributes of the
`object by applying the new user rule to the plurality of
`detected attributes; wherein the applying the new user rule
`to the plurality of detected attributes comprises applying
`the new user rule to only the plurality of detected
`attributes” (Claims 1-41) ........................................................... 49
`The Combination Of Dimitrova And Brill Does Not Render
`Obvious Claims 1-41 ........................................................................... 51
`1.
`Neither Dimitrova nor Brill discloses “identifying an event
`of the object that is not one of the detected attributes of the
`object by applying the new user rule to the plurality of
`detected attributes; wherein the applying the new user rule
`to the plurality of detected attributes comprises applying
`
`2.
`
`3.
`
`4.
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`3.
`
`2.
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`the new user rule to only the plurality of detected
`attributes” (Claims 1-41) ........................................................... 51
`Neither Dimitrova nor Brill discloses “the plurality of
`attributes that are detected are independent of which event
`is identified” (Claims 1-41) ...................................................... 54
`Neither Dimitrova nor Brill discloses “wherein selecting
`the new user rule comprises selecting a subset of the
`plurality of attributes for analysis” (Claims 2, 4, 7, 11, 12,
`13, 14, 16, 23, 25, 28, 32, 33, 34, 35, 38) ................................. 59
`IX. SECONDARY CONSIDERATIONS OF NON-OBVIOUSNESS .......... 61
`X. DECLARATION ......................................................................................... 63
`
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`I, Dr. Alan Bovik, being of legal affirm, hereby declare, affirm, and state the
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`Case No. IPR2019-00311
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`following:
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`I.
`
`ASSIGNMENT
`I have been retained by counsel for Avigilon Fortress Corporation (“Avigilon”
`
`or “Patent Owner”). I understand that the Patent Trial and Appeal Board (“PTAB”
`
`or “Board”) has instituted an inter partes review of Avigilon’s U.S. Patent No.
`
`7,932,923 (the “’923 patent”) in two related proceedings, IPR2019-00311 (“the 311
`
`IPR”) and IPR2019-00314 (“the 314 IPR”), based on petitions filed by Axis
`
`Communications AB, Canon Inc., and Canon U.S.A. Inc. (“Petitioners”). 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.
`
`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 experience
`
`relevant to the issues in this litigation.
`
`
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`1
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`I hold a Ph.D. in in Electrical and Computer Engineering from the University
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`
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`of Illinois, Urbana-Champaign (awarded in 1984). I also hold a Master's degree in
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`Electrical and Computer Engineering from the University of Illinois, Urbana-
`
`Champaign (awarded in 1982).
`
`I am a tenured full Professor and I hold the Cockrell Family Regents 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”).
`
`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.
`
`I received the 2017 Edwin H. Land Medal from the Optical Society of
`
`America in September 2017 with citation: For substantially shaping the direction
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`
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`and advancement of modern perceptual picture quality computation, and for
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`energetically engaging industry to transform his ideas into global practice.
`
`I received a Primetime Emmy Award for Outstanding Achievement in
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`Engineering Development, for the Academy of Television Arts and Sciences, in
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`October 2015, for the widespread use of my video quality prediction and monitoring
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`models and algorithms that are widely used throughout the global broadcast, cable,
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`satellite and internet Television industries.
`
`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
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`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
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`creation of the leading research and educational journal and conference in the image
`
`processing field.”
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`My technical articles have been widely recognized as well, including the 2009
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`
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`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 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.
`
`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
`4
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`picture quality prediction engine used to quality-assess all encodes streamed globally
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`by Netflix), and for “An evaluation of recent full reference image quality assessment
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`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, has been the standard
`
`development tool for picture quality research since its first introduction in 2005).
`
`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.
`
`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
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`Electronics Engineers (IEEE) “for contributions to nonlinear image processing” in
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`1995, a Fellow of the Optical Society of America (OSA) for “fundamental research
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`contributions to and technical leadership in digital image and video processing” in
`5
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`2006, and as a Fellow of SPIE for “pioneering technical, leadership, and educational
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`contributions to the field of image processing” in 2007.
`
`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 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
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`recovery from a moving air platform, passive optical modeling, and detection of
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`speculated masses and architectural distortions in digitized mammograms. My
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`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
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`numerous grants from the National Science Foundation for research on image and
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`video processing and on computational vision.
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`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
`
`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.”
`
`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
`In forming the opinions set forth herein, I have considered and relied upon the
`
`following:
`
`• the ’923 patent (Ex. 1001)
`• the ’923 patent’s file history (Exs. 1002, 1008-1020)
`• “Visual Memory” by Christopher James Kellogg (“Kellogg”) (Ex.
`1003)
`• “Event Recognition and Reliability Improvements for the Autonomous
`Video Surveillance System” by Frank Brill et al. (“Brill”) (Ex. 1004)
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`• “Motion Recovery for Video Content Classification” by N. Dimitrova
`et al. (“Dimitrova”) (Ex. 1006)
`• the Declaration of John R. Grindon D.Sc. (as far as it is relevant to the
`instituted grounds) (Ex. 1005)
`• the corrected Declaration of Emily R. Florio (Ex. 1007)
`• U.S. Patent No. 5,969,755 entitled “Motion Based Event Detection
`System and Method” (“Courtney”) (Ex. 1021)
`• “Object-Oriented Conceptual Modeling of Video Data” by Young
`Francis Day et al. (“Day-I) (Ex. 1022)
`• Deposition Transcript of John R. Grindon D.Sc. (Oct. 2, 2019) (Ex.
`2018)
`• the Petition for inter partes review (as far as it is relevant to the
`instituted grounds) (Paper 1)
`• the Patent Owner’s Preliminary Responses (Paper 9 for both the ’311
`and ’314 IPRs)
`• the PTAB decision to institute inter partes review (Paper 13)
`In addition, I have drawn on my experience and knowledge, as discussed
`
`above and described more fully in my CV. 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 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
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`that becomes available to me or in light of any relevant orders from the Patent Trial
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`and Appeal Board or other authoritative body.
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`IV. PERSON OF ORDINARY SKILL IN THE ART
`I have been asked for the purposes of this declaration to opine on the
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`knowledge and understanding of one of ordinary skill in the art as of October 24,
`
`2000, or before.
`
`I have been informed and understand that a person of ordinary skill in the art
`
`(“POSITA”) 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.
`
`I understand that Petitioners and Petitioners’ expert Dr. Grindon have alleged
`
`that appropriate level of skill for a POSITA in the art of the ’923 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
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`experience in computer engineering and video processing and/or surveillance
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`systems.” I understand that Petitioners and Dr. Grindon further claim that a POSITA
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`“would be knowledgeable and familiar with the video processing and information
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`extraction concepts and techniques recited in the claims of the ’923 patent, as they
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`were well known in 2000, as demonstrated above.” Ex. 1005 ¶ 77-78.
`
`I disagree with Petitioners’ level of skill for a POSITA. In my opinion, a
`
`POSITA for the ’923 patent would have practical experience working with and
`
`designing video surveillance systems. Accordingly, a POSITA in the art of the ’923
`
`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 POSITA would be knowledgeable and familiar with video processing known at
`
`the time of the ’923 patent. Regardless, my opinions that the challenged claims of
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`the ’923 patent are not invalid in light of the references raised by Petitioners would
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`not change even if Petitioners’ level of skill was adopted.
`
`V. UNDERSTANDING OF PATENT LAW
`In preparing and expressing my opinions and considering the subject matter
`
`of the ’923 patent, I am relying on certain basic legal principles that counsel have
`
`explained to me. These principles are discussed below.
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`A. Claim Construction
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`I understand that the first step in determining the validity of a claim is for the
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`claim to be properly construed. I have been further advised that, in an inter partes
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`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
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`specific definition. I understand that a patentee can be his own lexicographer, and
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`that any special definition for a claim term must be set forth in the specification with
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`reasonable clarity and precision.
`
`B. Anticipation
`I have been informed that a patent claim is anticipated under pre-AIA 35
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`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
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`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
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`disclosure under 35 U.S.C. § 102, the reference must enable the claimed invention
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`such that a POSITA could implement the claimed invention without undue
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`experimentation.
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`C. Obviousness
`I have been informed that a claim may be invalid as obvious if the subject
`
`matter described by the claim as a whole would have been obvious to a POSITA 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.
`
`I have also been informed that a determination of obviousness involves an
`
`analysis of the scope and content of the prior art, the similarities between the claimed
`
`invention and the prior art, and the level of ordinary skill in the art. I have been
`
`informed and understand that a prior art reference should be viewed as a whole.
`
`I have been informed that in considering whether an invention for a claimed
`
`combination would have been obvious, I may assess whether there are apparent
`
`reasons to combine known elements in the prior art in the manner claimed in view
`
`of interrelated teachings of multiple prior art references, the effects of demands
`
`known to the design community or present in the market place, and/or the
`
`background knowledge possessed by a POSITA. I also understand that other
`
`principles may be relied on in evaluating whether a claimed invention would have
`
`been obvious.
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`I have been informed that, in making a determination as to whether or not the
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`claimed invention would have been obvious to a POSITA, one may consider certain
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`objective indicators of non-obviousness if they are present, such as: commercial
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`success of product(s) practicing the claimed invention; long-felt but unsolved need;
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`teaching away; unexpected results; copying; and praise by others in the field. I
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`understand that for such objective evidence to be relevant to the non-obviousness of
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`a claim, however, there must be a causal relationship (called a “nexus”) between the
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`claim and the evidence. I also understand that this nexus must be based on a novel
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`element of the claim rather than something available in the prior art.
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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. SUMMARY OF U.S. PATENT NO. 7,932,923
`The ’923 patent describes a system for automatic video surveillance
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`employing video primitives. See Ex. 1001 at 1:18-19. As the patent explains,
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`conventional video surveillance systems available at the time generated large
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`amounts of data that became difficult to use after the data was recorded. Id. at 2:29-
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`34. This is because, in the words of the patent, those prior art “systems use[d] large
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`volumes of video imagery as the primary commodity of information interchange.”
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`U.S. Patent No. 7,932,923
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`Id. at 5:10-12. The ’923 patent describes a unique solution to this problem that “uses
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`video primitives as the primary commodity.” Id. at 5:12-14. Those video primitives,
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`which are described in the patent as observable attributes of an object, are extracted
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`in real time from the source video without reprocessing the video. Id. at 7:6-7, 9:25-
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`26. The system then uses event discriminators, i.e. interactions between video
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`primitives, to identify a desired event. Id. at 5:30-32, 7:5-7, 8:50-58. The automatic
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`system detects object attributes and can identify an event occurrence in real time by
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`applying a new user rule. Id. at 9:13-17, 6:64-67
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`The patent describes event identification as an analysis based upon
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`combinations of video primitives. Id. at 6:29-36. Once an event is identified, a new
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`user rule may trigger a response, such as activating an alert or forwarding data to
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`another computer system. Id. at 8:37-49. The action taken occurs in real time as
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`appropriate. Id. at 9:17-22. For example, “[a]n example of an event discriminator
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`for an object, a spatial attribute, and a temporal attribute associated with a response
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`include: a person enters an area between midnight and 6:00 a.m., and a security
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`service is notified.” Id. at 9:9-12.
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`This system is reflected in the claims. Claim 1 of the ’923 patent requires
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`“detecting an object in a video from a single camera.” Ex. 1001, Claim 1. The next
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`step involves “detecting a plurality of attributes of the object by analyzing the video
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`from said single camera” where the plurality of attributes includes “at least one of a
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`physical attribute and a temporal attribute, each attribute representing a
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`characteristic of the detected object.” Id. After the attributes are detected, a “new
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`user rule” is selected. Id. Then, an event is identified “that is not one of the detected
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`attributes of the object by applying the new user rule to the plurality of detected
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`attributes” wherein the new user rule is only applied to the plurality of detected
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`attributes. Id. The plurality of attributes are independent of the identified event, the
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`event is identified without reprocessing the video, and the identified event “refers to
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`the object engaged in an activity.” Id. Independent claims 9 and 30 further require
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`the system to include a video device with the means for carrying out the described
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`system. See id., claims 9, 30.
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`VII. CLAIM CONSTRUCTION
`I have been asked to provide my opinion regarding the correct claim
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`construction of certain terms contained in the challenged claims of the ’923 patent.
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`A.
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`“attributes of the object” (Claims 1-7, 9-19, 22-28, 30-41);
`“attributes of each of the detected first and second objects”
`(Claims 8, 29); “attributes of the detected objects (Claims 20, 21)
`I agree with the Board’s institution decisions in Axis Comm’ns v. Avigilon
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`Fortress Corp., IPR2018-00138 and IPR2018-00140 that no construction of the
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`“attributes” terms is necessary.
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`B.
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`“new user rule” (Claims 1-41)
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`Dr. Grindon states that the “corresponding feature” for the “new user rule” is
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`
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`the “[e]vent discriminators [that] define events by using attributes of objects to
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`specify a particular event.” Ex. 1005 ¶¶ 93-94. He further opines that the “new user
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`rule” is a “specified combination of a set of attributes for identifying an event.” I
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`disagree. Under Dr. Grindon’s construction, a “new user rule” is equal to a query.
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`But, as Dr. Grindon explains, a query is a “specified combination of a set of attributes
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`for identifying an event,