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`UNITED STATES PATENT AND TRADEMARK OFFICE
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`____________________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`
`____________________
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`SAMSUNG ELECTRONICS CO., LTD.; and
`SAMSUNG ELECTRONICS AMERICA, INC.
`Petitioners
`
`v.
`
`IMAGE PROCESSING TECHNOLOGIES, LLC
`Patent Owner
`
`____________________
`
`Patent No. 8,989,445
`____________________
`
`DECLARATION OF DR. JOHN C. HART
`IN SUPPORT OF PETITION FOR INTER PARTES REVIEW
`OF U.S. PATENT NO. 8,989,445
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`Page 1 of 206
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`SAMSUNG EXHIBIT 1002
`Samsung v. Image Processing Techs.
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`Declaration of Dr. John C. Hart
`Inter Partes Review of U.S. Patent No. 8,989,445
`TABLE OF CONTENTS
`
`INTRODUCTION .............................................................................................................. 1
`
`BACKGROUND AND EXPERIENCE ............................................................................. 1
`
`A.
`
`B.
`
`Qualifications .......................................................................................................... 1
`
`Previous Testimony ................................................................................................ 4
`
`TECHNOLOGICAL BACKGROUND.............................................................................. 5
`
`THE ’445 Patent ............................................................................................................... 11
`
`SUMMARY OF OPINIONS ............................................................................................ 22
`
`LEVEL OF ORDINARY SKILL IN THE ART .............................................................. 24
`
`
`I.
`
`II.
`
`III.
`
`IV.
`
`V.
`
`VI.
`
`VII. CLAIM CONSTRUCTION .............................................................................................. 24
`
`VIII. THE PRIOR ART TEACHES OR SUGGESTS EVERY STEP AND FEATURE
`OF THE CHALLENGED CLAIMS OF THE ’445 PATENT ......................................... 25
`
`A.
`
`Overview Of The Prior Art References ................................................................ 25
`
`1.
`
`2.
`
`3.
`
`4.
`
`U.S. Patent No. 5,481,622 (“Gerhardt”) (Ex. 1013) ................................. 25
`
`U.S. Patent No. 6,044,166 (“Bassman”) (Ex. 1014) ................................. 31
`
`U.S. Patent No. 5,764,786 (“Kuwashima”) (Ex. 1015) ........................... 34
`
`Berthold K.P. Horn and Brian G. Schunck, Determining Optical
`Flow, SPIE Techniques and Applications of Image Understanding
`281, 1981, at 319 (“Horn”) (Ex. 1016) ..................................................... 39
`
`5.
`
`U.S. Patent No. 5, 748,775 (“Tsuchikawa”) (Ex. 1017) ........................... 44
`
`B.
`
`Gerhardt In View Of Bassman Teaches Or Suggests Every Step And
`Feature Of Unchalleged Claims From Which Dependent Claims Depend .......... 49
`
`1.
`
`2.
`
`3.
`
`Reasons To Combine Gerhardt and Bassman ........................................... 49
`
`Elements Incorporated Into Claims 2, 3, 5, 7, 8, 10-17, And 19
`From Claims On Which These Claims Depend ........................................ 51
`
`Elements Incorporated Into Claims 26, 28, 29, And 30 From
`Claims On Which These Claims Depend ................................................. 58
`
`
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`Declaration of Dr. John C. Hart
`Inter Partes Review of U.S. Patent No. 8,989,445
`Ground 1: Gerhardt In View Of Bassman Renders Obvious Claims 3, 5, 7,
`8, 14-17, 26, 28, and 29 ........................................................................................ 62
`
`C.
`
`1.
`
`2.
`
`3.
`
`4.
`
`5.
`
`6.
`
`7.
`
`8.
`
`9.
`
`10.
`
`11.
`
`12.
`
`13.
`
`Reasons To Combine Gerhardt And Bassman .......................................... 62
`
`Claim 3 ...................................................................................................... 62
`
`Elements Incorporated Into Claims 5, 7, And 8 From Claims On
`Which these Claims Depend ..................................................................... 63
`
`Claim 5 ...................................................................................................... 65
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`Claim 7 ...................................................................................................... 66
`
`Claim 8 ...................................................................................................... 67
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`Claim 14 .................................................................................................... 68
`
`Claim 15 .................................................................................................... 69
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`Claim 16 .................................................................................................... 69
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`Claim 17 .................................................................................................... 70
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`Claim 26 .................................................................................................... 71
`
`Claim 28 .................................................................................................... 72
`
`Claim 29 .................................................................................................... 73
`
`D.
`
`Ground 2: Gerhardt In View Of Bassman And Further In View Of Horn
`And The Knowledge Of A POSA Teaches Or Suggests Every Step And
`Feature Of Claims 2, 10, And 11 .......................................................................... 75
`
`1.
`
`2.
`
`3.
`
`4.
`
`Reasons To Combine Gerhardt With Bassman And Further With
`Horn .......................................................................................................... 75
`
`Claim 2 ...................................................................................................... 77
`
`Claim 10 .................................................................................................... 78
`
`Claim 11 .................................................................................................... 79
`
`E.
`
`Ground 3: Gerhardt In View Of Bassman And Further In View Of
`Tsuchikawa And The Knowledge Of A POSA Teaches Or Suggests Every
`Step And Feature Of Claims 3, 12, and 13 ........................................................... 83
`
`1.
`
`Reasons To Combine Gerhardt With Bassman And Further With
`Tsuchikawa ............................................................................................... 83
`ii
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`Declaration of Dr. John C. Hart
`Inter Partes Review of U.S. Patent No. 8,989,445
`Claim 3 ...................................................................................................... 85
`
`Claim 12 .................................................................................................... 85
`
`Claim 13 .................................................................................................... 87
`
`2.
`
`3.
`
`4.
`
`F.
`
`Ground 4: Gerhardt In View Of Bassman And Further In View Of
`Kuwashima And The Knowledge Of A POSA Teaches Or Suggests Every
`Step And Feature Of Claims 19-22, and 30 .......................................................... 89
`
`1.
`
`2.
`
`3.
`
`4.
`
`5.
`
`6.
`
`7.
`
`Reasons To Combine Gerhardt With Bassman And Further With
`Kuwashima ............................................................................................... 89
`
`Claim 19 .................................................................................................... 90
`
`Claim 20 .................................................................................................... 93
`
`Claim 21 .................................................................................................... 97
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`Claim 22 .................................................................................................... 98
`
`Claim 23 .................................................................................................... 99
`
`Claim 30 .................................................................................................. 100
`
`IX.
`
`Detailed Application Of Gerhardt, Bassman, Tsuchikawa, And Kuwashima To
`The Challenged Claims ................................................................................................... 100
`
`X.
`
`CONCLUSION ............................................................................................................... 202
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`iii
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`Declaration of Dr. John C. Hart
`Inter Partes Review of U.S. Patent No. 8,989,445
`I, John C. Hart, declare as follows:
`
`1.
`
`I.
`
`INTRODUCTION
`2.
`
`I have been retained by Samsung Electronics Co., Ltd. and Samsung
`
`Electronics America, Inc. (collectively, “Petitioner”) as an independent expert
`
`consultant in this proceeding before the United States Patent and Trademark Office
`
`(“PTO”).
`
`3.
`
`I have been asked to consider whether certain references teach or
`
`suggest the features recited in Claims 2, 3, 5, 7, 8, 10-17, 19-23, 26, and 28-30 (the
`
`“Challenged Claims”) of U.S. Patent No. 8,989,445 (“the ’445 Patent”) (Ex. 1001),
`
`which I understand is allegedly owned by Image Processing Technologies, LLC
`
`(“Patent Owner”). My opinions and the bases for my opinions are set forth below.
`
`4.
`
`I am being compensated at my ordinary and customary consulting rate
`
`for my work.
`
`5. My compensation is in no way contingent on the nature of my
`
`findings, the presentation of my findings in testimony, or the outcome of this or
`
`any other proceeding. I have no other interest in this proceeding.
`
`II. BACKGROUND AND EXPERIENCE
`A. Qualifications
`6.
`I have more than 25 years of experience in computer graphics and
`
`image processing technologies. In particular, I have devoted much of my career to
`
`
`
`1
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`Declaration of Dr. John C. Hart
`Inter Partes Review of U.S. Patent No. 8,989,445
`researching and designing graphics hardware and systems for a wide range of
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`applications.
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`7. My research has resulted in the publication of more than 80 peer-
`
`reviewed scientific articles, and more than 50 invited papers, and talks in the area
`
`of computer graphics and image processing.
`
`8.
`
`I have authored or co-authored several publications that are directly
`
`related to target identification and tracking in image processing systems. Some
`
`recent publications include:
`
`•
`
`•
`
`•
`
`P.R. Khorrami, V.V. Le, J.C. Hart, T.S. Huang. A System for
`
`Monitoring the Engagement of Remote Online Students using Eye
`
`Gaze Estimation. Proc. IEEE ICME Workshop on Emerging
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`Multimedia Systems and Applications, July 2014.
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`V. Lu, I. Endres, M. Stroila and J.C. Hart. Accelerating Arrays of
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`Linear Classifiers Using Approximate Range Queries. Proc. Winter
`
`Conference on Applications of Computer Vision, Mar. 2014.
`
`M. Kamali, E. Ofek, F. Iandola, I. Omer, J.C. Hart Linear Clutter
`
`Removal from Urban Panoramas. Proc. International Symposium on
`
`Visual Computing. Sep. 2011.
`
`9.
`
`From 2008-2012, as a Co-PI of the $18M Intel/Microsoft Universal
`
`Parallelism Computing Research Center at the University of Illinois, I led the
`
`
`
`2
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`Declaration of Dr. John C. Hart
`Inter Partes Review of U.S. Patent No. 8,989,445
`AvaScholar project for visual processing of images that included face
`
`identification, tracking and image histograms.
`
`10.
`
`11.
`
`I am a co-inventor of U.S. Patent No. 7,365,744.
`
`I have served as the Director for Graduate Studies for the Department
`
`of Computer Science, an Associate Dean for the Graduate College, and I am
`
`currently serving as the Executive Associate Dean of the Graduate College at the
`
`University of Illinois. I am also a professor in the Department of Computer
`
`Science at the University of Illinois, where I have served on the faculty since
`
`August 2000. As a professor I have taught classes on image processing and
`
`graphics technology and have conducted research into specific applications of
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`these technologies.
`
`12. From 1992 to 2000, I worked first as an Assistant Professor and then
`
`as an Associate Professor in the School of Electrical Engineering and Computer
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`Science at Washington State University.
`
`13. From 1991-1992, I was a Postdoctoral Research Associate at the
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`Electronic Visualization Laboratory at the University of Illinois at Chicago, and at
`
`the National Center for Supercomputing Applications at the University of Illinois
`
`at Urbana-Champaign.
`
`14.
`
`I earned a Doctor of Philosophy in Electrical Engineering and
`
`Computer Science from the University of Illinois at Chicago in 1991.
`
`
`
`3
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`Declaration of Dr. John C. Hart
`Inter Partes Review of U.S. Patent No. 8,989,445
`I earned a Master’s Degree in Electrical Engineering and Computer
`
`15.
`
`Science from the University of Illinois at Chicago in 1989.
`
`16.
`
`I earned a Bachelor of Science in Computer Science from Aurora
`
`University in 1987.
`
`17.
`
`I have been an expert in the field of graphics and image processing
`
`since prior to 1996. I am qualified to provide an opinion as to what a person of
`
`ordinary skill in the art (“POSA”) would have understood, known, or concluded as
`
`of 1996.
`
`18. Additional qualifications are detailed in my curriculum vitae, which I
`
`understand has been submitted as Exhibit 1003 in this proceeding.
`
`B.
`19.
`
`Previous Testimony
`
`In the previous five years, I have testified as an expert at trial or by
`
`deposition or have submitted declarations in the following cases:
`
`20. Certain Computing or Graphics Systems, Components Thereof, and
`
`Vehicles Containing Same, Inv. No. 337-TA-984.
`
`21. ZiiLabs Inc., Ltd v. Samsung Electronics Co. Ltd. et al., No. 2:14-cv-
`
`00203 (E.D. Tex. Feb. 4, 2016).
`
`22. Certain Consumer Electronics with Display and Processing
`
`Capabilities, Inv. No. 337-TA-884.
`
`23.
`
`I have also submitted declarations in support of the following
`
`
`
`4
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`Declaration of Dr. John C. Hart
`Inter Partes Review of U.S. Patent No. 8,989,445
`Petitions for Inter Partes Review in Samsung v. Image Processing Technologies,
`
`LLC:
`
`•
`
`•
`
`•
`
`•
`
`•
`
`IPR2017-00357 against the ’445 Patent, filed 11/30/2016.
`
`IPR2017-00336 against U.S. Patent No. 6,959,293, filed 11/29/2016.
`
`IPR2017-00355 against U.S. Patent No. 7,650,015, filed 11/30/2016.
`
`IPR2017-00347 against U.S. Patent No. 8,805,001, filed 11/29/2016.
`
`IPR2017-00353 against U.S. Patent No. 8,983,134, filed 11/30/2016.
`
`III. TECHNOLOGICAL BACKGROUND
`24.
`Image processing systems have long used histograms as a
`
`mathematical tool to identify and track image features and to adjust image
`
`properties. The use of histograms to identify and track image features dates back
`
`to well before 1997. D. Trier, A. K. Jain and T. Taxt, “Feature Extraction Methods
`
`for Character Recognition-A Survey”, Pattern Recognition, vol. 29, no. 4, 1996,
`
`pp. 641–662 (Ex. 1009) (citing M. H. Glauberman, “Character recognition for
`
`business machines,” Electronics, vol. 29, pp. 132(136), Feb. 1956(Ex. 1010))
`
`25. A digital image is represented by a number of picture elements, or
`
`pixels, where each pixel has certain properties, such as brightness, color, position,
`
`velocity, etc., which may be referred to as domains. For each pixel property or
`
`domain, a histogram may be formed. A histogram is a type of statistical tool. In
`
`image processing, histograms are often used to count the number of pixels in an
`
`
`
`5
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`Declaration of Dr. John C. Hart
`Inter Partes Review of U.S. Patent No. 8,989,445
`image in a certain domain of the pixel. Histograms have multiple bins, where each
`
`bin in the histogram counts the pixels that fall within a range for that domain. For
`
`example, for the continuous variable of luminance (also called brightness), the
`
`luminance value for each pixel can be sampled by a camera and then digitized and
`
`represented by an 8-bit value. Then, those luminance values could be loaded into a
`
`luminance histogram. The histogram would have one bin for each range of
`
`luminance values, and each bin would count the number of pixels in the image that
`
`fall within that luminance value range. As shown below, a luminance histogram
`
`may reveal certain properties of an image, such as whether it is properly exposed,
`
`based on whether an excessive number of pixels fall on the dark end or light end of
`
`the luminance range.
`
`
`
`26. Histograms of other pixel properties can also be formed. For
`
`example, the figure below illustrates two histograms formed by counting the
`
`number of black pixels having each X-coordinate value (i.e., the X-coordinate
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`domain) and the number having each Y-coordinate value (i.e., the Y-coordinate
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`
`
`6
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`domain).
`
`
`
`27. Such histograms are sometimes called “projection histograms”
`
`because they represent the image projected onto each axis. In the example above,
`
`the image was pure black and white, but projection histograms of a greyscale
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`image can also be formed in a similar manner by defining a luminance threshold
`
`and projecting, for example, only those pixels that have a luminance value lower
`
`than 100.
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`28. A more complex greyscale image is shown below, along with its
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`luminance histogram (black = 0; white = 255):
`
`
`
`7
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`29. Here, the peak in the dark luminance region (luminance = 0-50)
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`corresponds to the dark suit and tie and relatively dark background. The peak in
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`the light luminance region (luminance > 230) corresponds to the white shirt, while
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`the central peak (between luminance 130 and 170) corresponds largely to the
`
`medium brightness of the face. If one were to select only the subset of pixels with
`
`
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`8
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`brightness between 130 and 170 and plot them according to their x and y position,
`
`one would get the following image:
`
`
`
`30. Taking projection histograms of this subset of pixels with luminance
`
`between 130 and 170, then, provides an indication of location of the face in the
`
`image. On the left, below, is a projection of this subset of pixels onto the x axis,
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`and on the right is a similar projection onto the y axis.
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`
`
`9
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`Declaration of Dr. John C. Hart
`Inter Partes Review of U.S. Patent No. 8,989,445
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`31. Histograms may also be formed of pixel color properties in much the
`
`same way. Color is typically represented by three values: hue, saturation and
`
`luminance. Hue (aka “tone”) is an angle ranging from 0° to 360° around a color
`
`wheel that indicates which “color” is bring represented, e.g. 0° = red, 60° = yellow,
`
`120° = green, 180° = cyan, 240° = blue, and 300° = magenta. Saturation, which
`
`may also range from 0 to 255, represents how “brilliant” the color is. For example,
`
`if a color with a saturation of 255 represents red, then a saturation of 128 would
`
`represent pink and a saturation of 0 would represent gray. Luminance ranges from
`
`0 to 255 and represents the “brightness” of the color. If luminance = 0, then the
`
`color is black, regardless of the other values. Given a color image, the luminance
`
`values of the pixels would yield the “black-and-white” or grayscale version of the
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`image.
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`10
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`Declaration of Dr. John C. Hart
`Inter Partes Review of U.S. Patent No. 8,989,445
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`
`
`IV. THE ’445 Patent
`32. The ’445 Patent, entitled “Image Processing Apparatus And Method,”
`
`was filed on August 13, 2014, and issued on March 24, 2015. The ’445 Patent
`
`names Patrick Pirim as the sole inventor. I understand that the ’445 Patent claims
`
`a priority date of July 22, 1996.
`
`33. The ’445 Patent is generally directed to tracking a target using an
`
`image processing system. For example, in the Abstract, the ’445 Patent notes that
`
`the invention relates to “[a] method and apparatus for localizing an area in relative
`
`movement and for determining the speed and direction thereof.” Ex. 1001, ’445
`
`Patent at Abstract.
`
`34. The ’445 Patent uses the pixels in a frame of an image of a video
`
`signal to form one or more histograms in order to identify and track a target. See
`
`
`
`11
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`e.g., Ex. 1001, ’445 Patent at Claim 1. The input signal employed in the ’445
`
`Patent is comprised of a “succession of frames, each frame having a succession of
`
`pixels.” Ex. 1001 at 3:34-37. Although the disclosed embodiments relate
`
`primarily to a video signal, the ’445 Patent also teaches that the input signal could
`
`correspond to other types of signals, for example “ultrasound, IR, Radar, tactile
`
`array, etc.” Ex. 1001, ’445 Patent at 9:29-34. The ’445 Patent teaches a process
`
`for smoothing the input signal on a pixel-by-pixel basis using a time constant that
`
`is modified depending on whether there is significant variation between such a
`
`pixel and the same pixel in a previous frame. Ex. 1001, 4:62-5:3.
`
`35. The ’445 Patent teaches constructing a histogram showing the
`
`frequency of the pixels meeting a certain characteristic. In the ’445 Patent, these
`
`characteristics—such as luminance or speed—are referred to as “domains.”
`
`Histograms may be constructed in a variety of domains, for example, the ’445
`
`Patent teaches that examples of possible domains include pixel data such as “i)
`
`luminance, ii) speed (V), iii) oriented direction (D1), (iv) time constant (CO), v)
`
`hue, vi) saturation, and vii) first axis (x(m)), and viii) second axis (y(m)).” Id. at
`
`4:9-13.
`
`36. The histograms include a plurality of “classes” within a given
`
`domain. Figure 14a (and the accompanying text) illustrates an example of
`
`“classes” within a domain:
`
`
`
`12
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`Declaration of Dr. John C. Hart
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`FIG. 14 a shows an example of the successive classes C1
`
`C2 . . . Cn−1 Cn, each representing a particular velocity, for
`
`a hypothetical velocity histogram, with their being
`
`categorization for up to 16 velocities (15 are shown) in
`
`this example. Also shown is envelope 38, which is a
`
`smoothed representation of the histogram.
`
`Ex. 1001, ’445 Patent at 20:51-56.
`
`37. The hypothetical histogram in Figure 14a would be constructed using
`
`histogram formation block 25 in Figure 11. In this figure, various histogram
`
`processors (numbered 24-29) are shown that allow creation of histograms in
`
`various domains. Block 25 is disclosed as creating velocity histograms. Id. at
`
`17:7-13.
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`
`
`13
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`Declaration of Dr. John C. Hart
`Inter Partes Review of U.S. Patent No. 8,989,445
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`A detailed depiction of histogram block 25 is shown in Figure 13:
`
`
`
`
`
`14
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`Declaration of Dr. John C. Hart
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`38.
`
`In Figure 13, velocity data for a pixel is input into a memory address
`
`and into classifier 25b. The classifier contains registers 106 that correspond to
`
`classes within the particular domain. Thus, for a classifier in a velocity histogram
`
`formation block, the classifier would have a register for each velocity class.
`
`Because the histogram will only be incremented for pixels satisfying the
`
`classification criteria that, when met, outputs a classification signal of “1,” the
`
`histogram in Figure 14a would be constructed using a classifier 25b that has each
`
`of the velocity-class registers set to “1.” In this example, each pixel that is input
`
`into classifier 25b would generate a classification signal of “1.” The histogram
`
`would then be updated to include the input pixel, which it would do by
`
`
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`15
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`Declaration of Dr. John C. Hart
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`incrementing the histogram bin corresponding to the appropriate velocity class. In
`
`other examples, a classifier may output a classification signal of “1” for only
`
`specific classes of a domain, rather than for all of the classes in a domain as in
`
`Figure 14a. Thus, for example, the classifier could choose pixels with only
`
`specific velocities for consideration in subsequent histograms. This feature may be
`
`used in conjunction with the output from other classification units to create
`
`histograms identifying only pixels meeting multiple classification criteria in a
`
`variety of domains.
`
`39. The ’445 Patent discloses that its teachings are applicable to a broad
`
`range of applications. For example, in one embodiment, the ’445 Patent performs
`
`“automatic framing of a person . . . during a video conference.” Ex. 1001, ’445
`
`Patent at 22:6-8. In this application, histograms are constructed in the X- and Y-
`
`domains and count the number of pixels between successive frames where the
`
`differences in luminance are above certain threshold values. Ex. 1001, ’445 Patent
`
`at 22:47-57. By this method, the ’445 Patent teaches the system is able to
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`determine the boundaries of the target based on peaks in the histograms generated.
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`Ex. 1001, ’445 Patent at 10:35-63. This application and result are shown in Figure
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`16 and 17, reproduced below:
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`16
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`Declaration of Dr. John C. Hart
`Inter Partes Review of U.S. Patent No. 8,989,445
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`Ex. 1001, Fig. 16
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`
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`Ex. 1001, Fig. 17
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`
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`40. The system may use a mounted spotlight or camera to automatically
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`track a target, for example using a spotlight mounted on a helicopter to track a
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`target on the ground or using automated stage lights to track a performer during a
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`performance. Ex. 1001, ’445 Patent at 23:38-43. The ’445 Patent also teaches that
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`“[t]he invention would similarly be applicable to weapons targeting systems.” Ex.
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`1001, ’445 Patent at 23:42-43.
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`41. Upon acquiring the target and determining its center, the camera or
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`spotlight may be adjusted to point toward the center of the target. Id. at 25:10-24.
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`As the target moves, the system will constantly update to keep the camera pointed
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`at the target and continually display the target and an appropriately sized tracking
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`box on the screen. Id.
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`42.
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`In each of these embodiments, the ’445 Patent finds the X- and Y-
`17
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`Inter Partes Review of U.S. Patent No. 8,989,445
`minima and maxima of the histograms and uses these points to determine a center
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`point of the target. The patent states that the “XMIN and XMAX” for the X-projection
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`histogram and “YMIN and YMAX” for the y projection histogram, are “key
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`characteristics” of the histogram “which include the minimum (MIN) of the
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`histogram” and “the maximum (MAX) of the histogram.” Id. at 19:43-50. It
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`teaches that these key characteristics are computed by the condition:
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`For each pixel with a validation signal V2 of “l”:
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`(a) if the data value of the pixel<MIN (which is initially
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`set to the maximum possible value of the histogram),
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`then write data value in MIN,
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`(b) if the data value of the pixel>MAX (which is initially
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`set to the minimum possible value of the histogram), then
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`write data value in MAX
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`Id. at 19:53-59. Hence the ‘445 Patent defines the minimum of the X-projection
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`histogram is the smallest X-coordinate of any pixel in the image region whose
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`validation signal is “1.” Similarly the maximum is the largest X-coordinate of any
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`pixel in the image region whose validation signal is “1.” The same holds true for
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`the Y-projection histogram where the maximum and minimum are computed in the
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`same way, but using the y coordinate axis.
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`43. After determining the X- and Y-minima and maxima of the
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`Inter Partes Review of U.S. Patent No. 8,989,445
`histograms, the ’445 Patent teaches that a center point of the target image may be
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`found at the coordinates of (XMIN+XMAX)/2 and (YMIN+YMAX)/2. Ex. 1001, ’445
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`Patent at 24:48-53. This is only one possible way of computing a center point of
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`an object. Indeed, for an irregularly shaped object like those shown in Figure 20,
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`below, there is not one clear center point. Rather, various center points such as
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`center of mass, center-of-area, and center of gravity might all give different
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`coordinates, but would nevertheless accomplish the purpose of the ’445 Patent.
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`44. Once the center of the target is determined, the center is used to adjust
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`the camera or spotlight to be directed to the moving target:
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`
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`Having acquired the target, controller 206 controls
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`servomotors 208 to maintain the center of the target in
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`the center of the image. . . .
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`19
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`Inter Partes Review of U.S. Patent No. 8,989,445
`It will be appreciated that as the target moves, the
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`targeting box will move with the target, constantly
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`adjusting the center of the targeting box based upon the
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`movement of the target, and enlarging and reducing the
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`size of the targeting box. The targeting box may be
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`displayed on monitor 212, or on another monitor as
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`desired to visually track the target.
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`Ex. 1001, ’445 Patent at 25:10-24.
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`45.
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`In addition, the ’445 Patent teaches that as part of the identification
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`and tracking process, the imaging system may place a tracking box around the
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`target that “may be displayed on monitor 212, or on another monitor as desired to
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`visually track the target.” Ex. 1001, ’445 Patent at 25:10-24. For example, Figure
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`23 shows an example of the tracking box in a frame:
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`
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`Ex. 1001 at Fig. 23
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`20
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`Declaration of Dr. John C. Hart
`Inter Partes Review of U.S. Patent No. 8,989,445
`In addition to teaching a process to automatically identify a target, the
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`46.
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`’445 Patent also teaches that a user may manually input information to select a
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`specific target. Ex. 1001, ’445 Patent at 23:65-24:3. For example, a user may use
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`a computer input, such as a mouse, to select a specific target. Id. In that case, the
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`pixel selected is used as a starting position for tracking the target. Id. After
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`receiving this input, the system processes the pixels immediately adjacent to the
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`starting pixel in successively larger areas surrounding the starting pixel until the
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`edges of the target have been determined and the entire target is within the tracking
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`box. Id. Figures 21-23 demonstrate this tracking box growing method:
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`21
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`Declaration of Dr. John C. Hart
`Inter Partes Review of U.S. Patent No. 8,989,445
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`47. Relatedly, the ’445 Patent teaches a method whereby the system will
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`“process pixels only within a user-defined area.” Ex. 1001, ’445 Patent at 21:14-
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`26. Thus, a user can create a rectangular window that defines the pixels to be
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`considered in each frame. Id.
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`48. Although the ’445 Patent only teaches tracking a single target, or
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`selecting a single target from among multiple targets, it contemplates that its
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`methods may be adapted for tracking multiple targets simultaneously: “[W]hile the
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`invention has been described with respect to tracking a single target, it is foreseen
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`that multiple targets may be tracked, each with user-defined classification criteria,
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`by replicating the various elements of the invention.” Ex. 1001, ’445 Patent at
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`25:56-61.
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`V.
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`SUMMARY OF OPINIONS
`49.
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`In preparing this declaration, I have reviewed at least the documents
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`labeled Exhibits 1001-1023 and other materials referred to herein in connection
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`Declaration of Dr. John C. Hart
`Inter Partes Review of U.S. Patent No. 8,989,445
`with providing this declaration. In addition to these materials, I have relied on my
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`education, experience, and my knowledge of practices and principles in the
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`relevant field, e.g., image processing. My opinions have also been guided by my
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`appreciation of how one of ordinary skill in the art would have understood the
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`claims and specification of the ’445 Patent around the time of the alleged
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`invention, which I have been asked to assume is the earliest claimed priority date
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`of July 22, 1996.