`
`UNITED STATES PATENT AND TRADEMARK OFFICE
`____________________
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`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`
`____________________
`
`
`SAMSUNG ELECTRONICS CO., LTD; AND SAMSUNG ELECTRONICS
`AMERICA, INC
`Petitioner
`
`v.
`
`IMAGE PROCESSING TECHNOLOGIES, LLC
`Patent Owner.
`____________________
`
`CASE IPR2017-00353
`Patent No. 8,983,134
`____________________
`
`DECLARATION OF DR. ALAN BOVIK IN SUPPORT OF PATENT
`OWNER RESPONSE PURSUANT TO
`37 C.F.R. § 42.120
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`Exhibit 2007
`IPR2017-00353
`Petitioner- Samsung Electronics Co., Ltd., et al.
`Patent Owner- Image Processing Technologies LLC
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`IPR2017-00353
`Ex. 2007 - Declaration of Dr. Bovik
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`TABLE OF CONTENTS
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`I.
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`Introduction ............................................................................................................. 1
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`A. Background and Qualifications ....................................................................... 2
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`B. Materials Considered ....................................................................................... 7
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`C. Claim Construction .......................................................................................... 8
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`1. “forming at least one histogram . . . said at least one histogram referring to
`classes defining said target” ................................................................................ 9
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`2. “wherein forming the at least one histogram further comprises determining
`X minima and maxima and Y minima and maxima of boundaries of the target.”
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`14
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`II. Summary of Opinions ........................................................................................... 15
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`III. My Analysis of Claims 1 and 2 ............................................................................ 15
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`A. Summary ........................................................................................................ 15
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`B. Discussion of References .............................................................................. 16
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`1. Gilbert .........................................................................................................16
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`2. Hashima ......................................................................................................18
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`3. Ueno ............................................................................................................22
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`C. The Asserted References Do Not Teach or Suggest All Elements of
`the ’134 Patent ............................................................................................... 24
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`1. Gilbert Does Not Teach or Suggest Claim Elements [1a], [1b], and [1c] .25
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`2. Hashima Does Not Teach or Suggest Claim Elements [1a] and [1c] ........32
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`3. Ueno Does Not Teach or Suggest Claim Element [1c] ..............................37
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`D. A POSA Would Not Have Selected and Combined the Asserted
`References...................................................................................................... 40
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`1. A POSA Would Not Have Selected and Combined Gilbert and Hashima 40
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`2. A POSA Would Not Have Selected and Combined Gilbert and Ueno......45
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`IV. Concluding Statement ........................................................................................... 48
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`IPR2017-00353
`Ex. 2007 - Declaration of Dr. Bovik
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`LIST OF APPENDICES
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`APPENDIX A
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`Dr. Alan Bovik Curriculum Vitae
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`ii
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`IPR2017-00353
`Ex. 2006 - Declaration of Dr. Bovik
`I hereby declare that all the statements made in this Declaration are of my
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`own knowledge and true; that all statements made on information and belief are
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`believed to be true; and further that these statements were made with the
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`knowledge that willful false statements and the like so made are punishable by
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`fine or imprisonment, or both, under 18 U.S.C. § 1001 and that such willful false
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`statements may jeopardize the validity of the application or any patent issue
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`thereupon.
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`I declare under penalty of perjury under the laws of the United States of
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`America that the following is true and correct.
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`Dated: August 25, 2017
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`Respectfully Submitted
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`___________________
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`Alan Bovik
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`IPR2017-00353
`Ex. 2007 - Declaration of Dr. Bovik
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`I.
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`INTRODUCTION
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`1.
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`I have been retained by counsel for Image Processing Technologies
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`LLC (“Image Processing” or “Patent Owner”) as an expert consultant in regards to
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`inter partes review proceeding IPR2017-00353 for U.S. Patent No. 8,983,134.
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`2.
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`In IPR2017-00353, I understand that Petitioners, Samsung Electronics
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`Co., Ltd. and Samsung Electronics America, Inc. (“Samsung” or “Petitioners”)
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`challenged the validity of Claims 1 and 2 of the ’134 Patent.
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`3.
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`I understand that the Board instituted an inter partes review on the
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`following Grounds: Claims 1 and 2 as obvious under 35 U.S.C. § 103(a) over
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`Gilbert in view of Hashima; Claims 1 and 2 as obvious under 35 U.S.C. § 103(a)
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`over Ueno in view of Gilbert. Paper No. 12 (Institution Decision) at 29.
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`4.
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`I was asked to consider whether the instituted claims of the U.S.
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`Patent No. 8,983,134 (“the ’134 Patent”) (Ex. 1001), which are claims 1 and 2,
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`would have been obvious to a person of ordinary skill in the art (“POSA”) as of the
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`date of the invention.
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`5.
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`Based on my analysis of the ’134 Patent and my understanding of the
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`state of the relevant prior art as well as the specific references relied upon by the
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`Petitioner for the ground that was instituted by the Board, it is my opinion that the
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`challenged claims would not have been obvious to a POSA as of the date of the
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`invention.
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`A. Background and Qualifications
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`6.
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`This is a summary of my background and qualifications. I set forth
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`my background in more detail in my Curriculum Vitae which is attached as
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`Appendix A.
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`7.
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`I hold a Ph.D. in in Electrical and Computer Engineering from the
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`University of Illinois, Urbana-Champaign (awarded in 1984). I also hold a
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`Master's degree in Electrical and Computer Engineering from the University of
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`Illinois, Urbana-Champaign (awarded in 1982).
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`8.
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`I am a tenured full Professor and I hold the Cockrell Family Regents
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`Endowed Chair at the University of Texas at Austin. My appointments are in the
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`Department of Electrical and Computer Engineering, the Department of Computer
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`Sciences, and the Department of Biomedical Engineering. I am also the Director
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`of the Laboratory for Image and Video Engineering (“LIVE”).
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`9. My research is in the general area of digital television, digital cameras,
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`image and video processing, computational neuroscience, and modeling of
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`biological visual perception. I have published over 800 technical articles in these
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`areas and hold seven U.S. patents. I am also the author of The Handbook of Image
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`and Video Processing, Second Edition (Elsevier Academic Press, 2005); Modern
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`Image Quality Assessment (Morgan & Claypool, 2006); The Essential Guide to
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`Image Processing (Elsevier Academic Press, 2009); and The Essential Guide to
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`Video Processing (Elsevier Academic Press, 2009); and numerous other
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`IPR2017-00353
`Ex. 2007 - Declaration of Dr. Bovik
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`publications.
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`10.
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`I will receive the 2017 Edwin H. Land Medal from the Optical
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`Society of America in September 2017 with citation: For substantially shaping the
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`direction and advancement of modern perceptual picture quality computation, and
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`for energetically engaging industry to transform his ideas into global practice. I
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`also 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
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`monitoring models and algorithms that are widely used throughout the global
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`broadcast, cable, satellite and internet Television industries.
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`11. Among other awards and honors, I have received the 2013 IEEE
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`Signal Processing Society’s “Society Award,” which is the highest honor accorded
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`by that technical society (“for fundamental contributions to digital image
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`processing theory, technology, leadership and education”). In 2005, I received the
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`Technical Achievement Award of the IEEE Signal Processing Society, which is
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`the highest technical honor given by the Society, for “broad and lasting
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`contributions to the field of digital image processing”; and in 2008 I received the
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`Education Award of the IEEE Signal Processing Society, which is the highest
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`education honor given by the Society, for “broad and lasting contributions to image
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`IPR2017-00353
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`processing, including popular and important image processing books, innovative
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`on-line courseware, and for the creation of the leading research and educational
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`journal and conference in the image processing field.”
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`12. My technical articles have been widely recognized as well, including
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`the 2009 IEEE Signal Processing Society Best Journal Paper Award for the paper
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`“Image quality assessment: From error visibility to structural similarity,” published
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`in IEEE Transactions on Image Processing, volume 13, number 4, April 2004; this
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`same paper received the 2017 IEEE Signal Processing Society Sustained Impact
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`Paper Award as the most impactful paper published over a period of at least ten
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`years; the 2013 Best Magazine Paper Award for the paper “Mean squared error:
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`Love it or leave it?? A new look at signal fidelity measures,” published in IEEE
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`Transactions on Image Processing, volume 26, number 1, January 2009; the IEEE
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`Circuits and Systems Society Best Journal Paper Prize for the paper “Video quality
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`assessment by reduced reference spatio-temporal entropic differencing,” published
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`in the IEEE Transactions on Circuits and Systems for Video Technology, vol. 23,
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`no. 4, pp. 684-694, April 2013.
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`13.
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`I received the Google Scholar Classic Paper Award twice in 2017, for
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`the paper “Image information and visual quality,” published in the IEEE
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`Transactions on Image Processing, vol. 15, no. 2, pp. 430-444, February 2006 (the
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`main algorithm developed in the paper, called the Visual Information Fidelity (VIF)
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`Index, is a core picture quality prediction engine used to quality-assess all encodes
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`streamed globally by Netflix), and for “An evaluation of recent full reference
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`image quality assessment algorithms,” published in the IEEE Transactions on
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`Image Processing, vol. 15, no. 11, pp. 3440–3451, November 2006. (the picture
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`quality database and human study described in the paper, the LIVE Image Quality
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`Database, has been the standard development tool for picture quality research since
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`its first introduction in 2003). Google Scholar Classic Papers are very highly-cited
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`papers that have stood the test of time, and are among the ten most-cited articles in
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`their area of research over the ten years since their publication.
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`14.
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`I have also been honored by other technical organizations, including
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`the Society for Photo-optical and Instrumentation Engineers (SPIE), from which I
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`received the Technology Achievement Award (2013) “For Broad and Lasting
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`Contributions to the Field of Perception-Based Image Processing,” and the Society
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`for Imaging Science and Technology, which accorded me Honorary Membership,
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`which is the highest recognition by that Society given to a single individual, “for
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`his impact in shaping the direction and advancement of the field of perceptual
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`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
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`2006, and as a Fellow of SPIE for “pioneering technical, leadership, and
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`educational contributions to the field of image processing” in 2007.
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`15. Among other relevant research, I have worked with the National
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`Aeronautics and Space Administration (“NASA”) to develop high compression
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`image sequence coding and animated vision technology, on various military
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`projects for the Air Force Office of Scientific Research, Phillips Air Force Base,
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`the Army Research Office, and the Department of Defense. These projects have
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`focused on developing local spatio-temporal analysis in vision systems, scalable
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`processing of multi-sensor and multi-spectral imagery, image processing and data
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`compression tools for satellite imaging, AM-FM analysis of images and video, the
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`scientific foundations of image representation and analysis, computer vision
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`systems for automatic target recognition and automatic recognition of human
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`activities, vehicle structure recovery from a moving air platform, passive optical
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`modeling, and detection of speculated masses and architectural distortions in
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`digitized mammograms. My research has also recently been funded by Netflix,
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`Qualcomm, Texas Instruments, Intel, Cisco, and the National Institute of Standards
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`and Technology (NIST) for research on image and video quality assessment. I
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`have also received numerous grants from the National Science Foundation for
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`research on image and video processing and on computational vision.
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`16. Additional details about my employment history, fields of expertise,
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`and publications are further described in my curriculum vitae, which is attached as
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`IPR2017-00353
`Ex. 2007 - Declaration of Dr. Bovik
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`Exhibit A to this declaration.
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`B. Materials Considered
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`17. For time spent in connection with this case, I am being compensated
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`at my customary rate of $500/hour. My compensation is not dependent upon the
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`outcome of this petition or any issues involved in or related to the ’134 Patent, and
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`I have no other financial stake in this matter. I have no financial interest in, or
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`affiliation with, any of the real parties in interest or the Patent Owner.
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`18. The materials I considered include the ’134 Patent and the prosecution
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`history for the ’134 Patent, the Petition from Samsung for inter partes review
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`(Paper No. 2), the Patent Trial and Appeal Board (“PTAB”) decision to institute
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`inter partes review in IPR2017-00353 (Paper No. 12), and IPT’s Preliminary
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`Response (Paper No. 6). I also considered the materials that I refer to and that I
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`cite in this declaration, and, to the extent I considered them relevant, the materials
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`provided by the Petitioner.
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`19.
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`In addition, I have drawn on my experience and knowledge, as
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`discussed above and described more fully in my CV.
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`20. The opinions I express herein are given from the point of view of a
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`person of ordinary skill in the art, as described above, at the time of the invention
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`of the ’134 Patent. Even if I do not repeat this explicitly, this is the perspective
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`that I applied in my analysis and in this declaration, unless I indicate otherwise.
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`C. Claim Construction
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`21.
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`I understand that the claims and specification of a patent must be read
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`and construed through the eyes of a person of ordinary skill in the art at the time of
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`the priority date of the claims.
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`22.
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`I further understand that the claim construction standard that applies
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`for the purposes of this proceeding is the Phillips standard, under which claim
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`terms are given the meaning that the term would have to a person of ordinary skill
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`in the art in question at the time of the invention. I understand further that a claim
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`term’s meaning can take into account both intrinsic evidence (the claims,
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`specification, and prosecution history) as well as extrinsic evidence, such as
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`dictionary definitions. I have applied this standard in claim constructions I have
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`set forth below.
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`23. The Board has construed term “forming at least one histogram of the
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`pixels in the one or more of a plurality of classes in the one or more of a plurality
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`of domains” not to be limited to “forming at least one histogram of the pixels in
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`two or more classes that are in two or more domains.” D.I. 12 at 10. I have
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`applied the Board’s construction in my analysis.
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`24.
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`In conducting my analysis of the challenged claims of the ’134 patent,
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`I have applied the claim constructions below consistent with the Phillips standard.
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`Elsewhere in my analysis, except when I state otherwise, I have applied the
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`ordinary meaning of claim terms as they are used in the specification, under the
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`IPR2017-00353
`Ex. 2007 - Declaration of Dr. Bovik
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`Phillips standard.
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`1.
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`“forming at least one histogram . . . said at least one
`histogram referring to classes defining said target”
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`25.
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`I understand that Patent Owner has proposed a claim construction for
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`the term “forming at least one histogram . . . said at least one histogram referring to
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`classes defining said target” in claims 1 and 2 of ’134 patent of “forming at least
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`one histogram . . . said at least one histogram being formed of pixels in the one or
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`more classes that define said target.” I understand that Patent Owner has construed
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`this term to mean the “at least one histogram” is composed only of pixels in at least
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`one class defining said target. I have reviewed and agree with this proposed
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`construction.1
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`26. Patent Owner’s proposed construction is consistent with my opinion
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`of how a POSA would have understood the claim term at the time of the patent as
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`well as the embodiments I reviewed in the specification of the ’134 patent.
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`27. A POSA would interpret a histogram “referring to classes defining
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`said target” to apply a limitation on the histogram being formed in claim 1.
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`Specifically, it is my opinion that, in light of the patent specification and claim 1 as
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`1 As discussed above in paragraph 23, I understand the Board has held that “one or
`more classes” is sufficient for claim 1. I have applied this construction in my
`analysis.
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`a whole, a POSA would construe this term to mean that the histogram must be
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`made up only of pixels belonging to classes which “define” the target, i.e. classes
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`IPR2017-00353
`Ex. 2007 - Declaration of Dr. Bovik
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`that correspond to target characteristics.
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`28. For example, Patent Owner’s proposed construction is consistent with
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`the surrounding claim language. For reference, the first part of claim 1 of the ’134
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`patent is copied below, with relevant portions bolded.
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`1. A process of tracking a target in an input signal implemented
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`using a system comprising an image processing system, the input
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`signal comprising a succession of frames, each frame comprising a
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`succession of pixels, the target comprising pixels in one or more of
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`a plurality of classes in one or more of a plurality of domains, the
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`process performed by said system comprising, on a frame-by-frame
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`basis:
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`forming at least one histogram of the pixels in the one or more of
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`a plurality of classes in the one or more of a plurality of
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`domains, said at least one histogram referring to classes
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`defining said target; and
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`Ex. 1001 at 26:36–46 (emphasis added).
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`29. The target comprises pixels in one or more of a plurality of classes in
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`one or more of a plurality of domains. A histogram is then formed in “the one or
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`more of a plurality of classes in the one or more of a plurality of domains”
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`(emphasis added). The word “the” in the claim to refers back to the first reference
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`of the pixels in a plurality of classes which make up the target. A POSA would
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`thus interpret the claim language to mean that the histogram is formed only in the
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`classes that comprise the target.
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`30. The plain meaning of the claim language thus supports Patent
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`Owner’s construction of “said at least one histogram referring to classes defining
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`said target” requiring the pixels making up the histogram to be in classes defining
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`the target.
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`31. Patent Owner’s proposed construction also appears consistent with the
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`specification of the patent. For example, this limitation in forming a histogram can
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`be seen in Figure 17, copied below.
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`Ex. 1001 at Fig. 17.
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`32. As seen above, Figure 17 is a “diagram illustrating histograms formed
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`on the shape of the head of a participant in a video conference.” Ex. 1001 at 8:66–
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`67. I understand from the specification that the target of these histograms is a
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`user’s head. See Ex. 1001 at 22:32–43. The histograms used to identify the user’s
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`face V rely on movement, where DP=1. Ex. 1001 at 22:44–54.
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`33.
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`I understand from Figure 17 and the accompanying specification text
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`that a class defining the target is based on movement. As seen in Figure 17, the
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`user’s face V comprises edge pixels which are in the histogram and non-edge
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`pixels which are not. As explained in the specification, this is because the greatest
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`movement occurs at the peripheral edges of the head. Ex. 1001 at 22:44–45.
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`34. The histograms used to identify the face end up being formed based
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`on pixels in classes defining the target face, but not all pixels of the face are in
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`these classes. Pixels that are not in classes defining the target are not included.
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`35. Another example of this limitation in the embodiments of the ’134
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`patent can be seen in Figure 12 copied below, a “two-dimensional histogram of a
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`moving area.” Ex. 1001 at 8:54–55.
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`Ex. 1001 at Fig. 12.
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`
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`36. As explained by the specification, the target in Figure 12 is defined by,
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`for example, “significant speeds.” See Ex. 1001 at 21:37–40. A POSA would
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`recognize from the diagram of Figure 12 that the histograms are formed from
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`pixels having significant speeds, illustrated in Figure 12 by black dots representing
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`pixels. The black blob labeled 40 has white portions in it, indicating pixels not
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`falling into classes defining the target.
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`37.
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`It is my opinion that a POSA would recognize that these handful of
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`pixels completely surrounded by pixels of significant speeds are nevertheless likely
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`to be pixels comprising the target. Consistent with Patent Owner’s proposed
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`construction therefore, the histogram only includes values in the target classes, but
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`the target may comprise pixels not in these classes.
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`2.
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`“wherein forming the at least one histogram further
`comprises determining X minima and maxima and Y
`minima and maxima of boundaries of the target.”
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`38.
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`I understand that Patent Owner has proposed a claim construction for
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`the term “wherein forming the at least one histogram further comprises
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`determining X minima and maxima and Y minima and maxima of boundaries of
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`the target” in claims 1 and 2 of ’134 patent wherein this term requires that the
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`determination of X and Y boundaries of the target is done as a part of forming or
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`creating the histogram. I have reviewed and agree with this proposed construction.
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`39. Patent Owner’s proposed construction
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`is consistent with my
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`understanding of how a POSA would understand the plain meaning of the above
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`language as well as consistent with dictionary definitions of the word “forming.”
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`40. Based on my understanding of the ’134 patent and its specification, it
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`is my opinion that a POSA would understand “forming the histogram” to mean
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`“adding data to the histogram.” The specification describes, for example, “forming
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`a histogram for pixels of the output signal within the classes selected by the
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`classifier within each domain selected by the validation signal.” Ex. 1001 at 6:11–
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`14. A POSA would understand this to mean that forming the histogram involves
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`adding data based on the pixel data of the output signal.
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`41. The patent specification goes on to say that the process “further
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`includes the steps of forming histograms along coordinate axes for the pixels
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`within the classes selected by the classifier within each domain selected by the
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`validation signal.” Ex. 1001 at 6:15–18. A POSA would understand that forming
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`a histogram along coordinate axes for pixels within classes involves adding data to
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`the histogram, for example the pixel data for the pixels falling within those classes.
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`42.
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`It is my opinion that A POSA at the time of the invention would thus
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`understand that forming a histogram is the same as creating a histogram.
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`II.
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`SUMMARY OF OPINIONS
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`43.
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`In this Section I present a summary of my opinions. The full
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`statement of my opinions and the bases for my opinions are contained in the
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`appropriate sections of my declaration. I give this summary, however, for the
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`convenience of the reader.
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`44. Based on my analysis of the ’134 Patent, my knowledge and
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`experience, any references cited in this declaration, as well as the specific
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`references relied upon by the Petitioner for the ground that was instituted by the
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`Board, it is my opinion that the challenged claims (1 and 2) would not have been
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`obvious to a POSA as of the date of the invention. I have considered the date of
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`the invention of the ’134 patent to be 1996.
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`III. MY ANALYSIS OF CLAIMS 1 AND 2
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`A.
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`Summary
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`45.
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`In my opinion Gilbert and Hashima, and Ueno and Gilbert do not
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`disclose several elements of the ’134 patent claims. Gilbert does not disclose
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`elements [1a], [1b], or [1c]; Hashima does not disclose element [1a] or [1c]; and
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`Ueno does not disclose element [1c].
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`46.
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`It is also my opinion that a POSA would not have selected and
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`combined Gilbert and Hashima or Ueno and Gilbert to arrive at the invention of
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`claims 1 and 2 of the’134 patent.
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`B. Discussion of References
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`1. Gilbert
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`47. Gilbert is entitled “A Real-Time Video Tracking System”. Gilbert
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`states in the first paragraph of the section Introduction:
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`IMAGE PROCESSING methods constrained to operate on sequential
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`images at a high repetition rate are few. Pattern recognition techniques
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`are generally quite complex, requiring a great deal of computation to
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`yield an acceptable classification. Many problems exist, however,
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`where such a time-consuming technique is unacceptable. Reasonably
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`complex operations can be performed on wide-band data in real time,
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`yielding solutions to difficult problems in object identification and
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`tracking.
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`Ex. 1005 (Gilbert) at 47.
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`48. Gilbert states that the image consist of m x n pixels, and that, “pixel
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`intensity is digitized and quantized into eight bits (256 gray levels), counted into
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`one of six 256-level histogram memories, and then converted by a decision
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`memory to a 2-bit code indicating its classification (target, plume, or background).
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`Ex. 1005 at 47–48.
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`49. Gilbert discloses that a tracking window is placed around the target
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`image, and:
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`The tracking window frame is partitioned into a background region
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`(BR) and a plume region (PR). The region inside the frame is called
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`the target region (TR) as shown in Fig. 2. During each field, the
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`feature histograms are accumulated for the three regions of each
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`tracking window.
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`Ex. 1005 (Gilbert) at 47.
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`50. Figure 2 of Gilbert shows the tracking window:
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`51. Gilbert describes that, based on intensity of the pixels, the image is
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`binarized into “target” and “non-target” pixels:
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`
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`The video processor described above separates the target image from
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`the background and generates a binary picture, where target presence
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`is represented by a “1” and target absence by a “0.”
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`Ex. 1005 (Gilbert) at 48.
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`52. The binary image is analyzed:
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`The target location, orientation, and structure are characterized by the
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`pattern of 1 entries [that is, pattern of “1” versus “0” values] in the
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`binary picture matrix, and the target activity is characterized by a
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`sequence of picture matrices.
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`Ex. 1006 (Gilbert) at 60 (explanation added in brackets for clarity).
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`53. One analysis of the binary image is via projection histograms, for
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`example as shown in Figure 4:
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`Ex. 1006 (Gilbert) at 51.
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`54. A “projection histogram” as used in Gilbert is, generally speaking, a
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`histogram of a parameter such as based on x-axis location or y-axis location.
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`2. Hashima
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`55. Hashima is entitled “System for and Method of Recognizing and
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`Tracking Target Mark”.
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`56. Hashima states in the first paragraph of the section Technical Field:
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`The present invention relates to a system for and a method of
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`recognizing and tracking a target mark using a video camera, and
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`more particularly to a system for and a method of recognizing and
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`tracking a target mark for detecting the position and attitude of the
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`target mark by processing an image of the target mark produced by a
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`video camera, detecting a shift of the position of the target mark from
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`a predetermined position, and controlling the position and attitude of a
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`processing mechanism based on the detected shift.
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`Ex. 1006 (Hashima) at 1:5–16.
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`57. The system of Hashima is designed to operate in real-time based on
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`the benefit of knowledge of the shape and color of the target (which is known in
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`advance), and the ability of the robot hand or grip to move in six dimensions of
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`freedom, such that Hashima can control the position and attitude of the object with
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`respect to the video camera. Ex. 1006 (Hashima) at 1:3–12, 2:64–3:13.
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`58. Hashima is directed to tracking a predefined mark mounted on an
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`object with known, contrasting colors, in order for a robot to grip the object.
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`Ex. 1006 (Hashima) at 1:7–16; 7:45–65. Hashima relies on knowing, for example,
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`that the target is a triangle that rests on a post and thus is offset from a circular
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`background of a contrasting color, Ex. 1006 (Hashima) at Figures 2 & 4 and 7:66–
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`8:17, and uses that knowledge to track the target in three-dimensional space in
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`order to guide the robot towards the target.
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`Ex. 1006 (Hashima) at Fig. 2.
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`
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`Ex. 1006 (Hashima) at Fig. 4.
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`
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`59. For example, Hashima computes the target’s three-dimensional shift
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`by moving the camera along the x- and y-axes, in addition to rotating the camera
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`about the x-, y-, and z-axes of the target coordinate system. Ex. 1006 (Hashima) at
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`16:50–17:5. Hashima describes that changes in position between the target mark
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`and the camera are “relatively small” because Hashima controls the camera
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`position with respect to the target with six degrees of freedom. Ex. 1006 (Hashima)
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`at 17:6–11.
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`60. Figure 5 of Hashima is a flowchart illustrating a sequence for
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`detecting the target mark. Ex. 1006 (Hashima) at 5:18–19.
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`Ex. 1006 (Hashima) at Fig. 5.
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`61. Figure 6 of Hashima is a diagram showing the X- and Y- histograms
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`of the target mark. Ex. 1006 (Hashima) at 5:20–22.
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`Ex. 12 (Hashima) at Fig. 6.
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`3.
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`Ueno
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`62. Ueno is entitled “System for and Method of Recognizing and
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`Tracking Target Mark”. Ex. 1007 (Ueno) at Title.
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`63. Ueno states in the first paragraph of the section Field of the Invention:
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`The present invention relates to an apparatus for encoding video
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`signals used for a teleconference or videophone.
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`Ex. 1007 (Ueno) at 1:8–11.
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`64. The system of Ueno operates in a fixed, assumed background of a
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`teleconference or videophone, and requires the assumption that (i) there is one
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`person in the video signal; (ii) the person is moving and so can be detected by
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`movement; (iii) the person has a particular head-on-top-of-shou