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`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
`
`____________________
`
`Patent No. 8,983,134
`____________________
`
`DECLARATION OF DR. JOHN C. HART
`IN SUPPORT OF PETITION FOR INTER PARTES REVIEW
`OF U.S. PATENT NO. 8,983,134
`
`
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`Page 1 of 186
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`SAMSUNG EXHIBIT 1002
`Samsung v. Image Processing Techs.
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`

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`Declaration of Dr. John C. Hart
`Inter Partes Review of Patent No. 8,805,001
`
`
`
`I.
`
`II.
`
`TABLE OF CONTENTS
`
`Contents
`INTRODUCTION ........................................................................................... 1
`
`BACKGROUND AND EXPERIENCE .......................................................... 1
`
`A. Qualifications ........................................................................................ 1
`
`B.
`
`Previous Testimony ............................................................................... 4
`
`III. TECHNOLOGICAL BACKGROUND .......................................................... 5
`
`IV. THE ’134 PATENT ....................................................................................... 11
`
`V.
`
`SUMMARY OF OPINIONS ......................................................................... 20
`
`VI. LEVEL OF ORDINARY SKILL IN THE ART ........................................... 21
`
`VII. CLAIM CONSTRUCTION .......................................................................... 22
`
`VIII. THE PRIOR ART TEACHES OR SUGGESTS EVERY STEP AND
`FEATURE OF THE CHALLENGED CLAIMS OF THE ’134
`PATENT ........................................................................................................ 22
`
`A. Overview Of The Prior Art References .............................................. 22
`
`1.
`
`2.
`
`3.
`
`U.S. Patent No. 5,481,622 (“Gerhardt”) (Ex. 1013) ................. 22
`
`U.S. Patent No. 6,044,166 (“Bassman”) (Ex. 1014) ................. 29
`
`Alton L. Gilbert et al., A Real-Time Video Tracking
`System, PAMI-2 No. 1 IEEE Transactions on Pattern
`Analysis and Machine Intelligence 47 (Jan. 1980)
`(“Gilbert”) (Ex. 1005) ............................................................... 32
`
`4.
`
`U.S. Patent No. 5,521,843 (“Hashima”) (Ex. 1006) ................. 44
`
`B.
`
`Gerhardt In View Of Bassman Renders Obvious Claims 3-6 ............ 50
`
`1.
`
`Reasons To Combine Gerhardt And Bassman ......................... 50
`
`i
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`Declaration of Dr. John C. Hart
`Inter Partes Review of Patent No. 8,893,134
`Elements Incorporated Into Claims 3-6 As Claims
`Dependent From An Independent Claim .................................. 53
`
`Claim 3: “The process according to claim 1, wherein said
`image processing system comprises at least one
`component selected from a memory, a temporal
`processing unit, and a spatial processing unit” ......................... 60
`
`Claim 4: “The process according to claim 1, wherein
`forming the at least one histogram further comprises
`successively increasing the size of a selected area until
`the boundary of the target is found” ......................................... 64
`
`Claim 5: “The process according to claim 4, wherein
`forming the at least one histogram further comprises
`adjusting a center of the selected area based upon a shape
`of the target until substantially the entire target is within
`the selected area” ...................................................................... 65
`
`Claim 6: “The process according to claim 5, wherein
`forming the at least one histogram further comprises
`setting the X minima and maxima and Y minima and
`maxima as boundaries in X and Y histogram formation
`units such that only pixels within the selected area will be
`processed by the image processing system” ............................. 68
`
`Gerhardt and Bassman Are Not Cumulative ............................ 69
`
`Detailed Application Of Gerhardt And Bassman To The
`Challenged Claims .................................................................... 70
`
`2.
`
`3.
`
`4.
`
`5.
`
`6.
`
`7.
`
`8.
`
`C.
`
`Gerhardt In View Of Gilbert And Further In View Of Hashima
`Renders Obvious Claims 3-6 .............................................................104
`
`1.
`
`2.
`
`3.
`
`Reasons To Combine Gilbert, Gerhardt, And Hashima .........104
`
`Elements Incorporated Into Claims 3-6 As Claims
`Dependent From An Independent Claim ................................109
`
`Claim 3: “The process according to claim 1, wherein said
`image processing system comprises at least one
`
`
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`Declaration of Dr. John C. Hart
`Inter Partes Review of Patent No. 8,893,134
`component selected from a memory, a temporal
`processing unit, and a spatial processing unit” .......................119
`
`Claim 4: “The process according to claim 1, wherein
`forming the at least one histogram further comprises
`successively increasing the size of a selected area until
`the boundary of the target is found” .......................................123
`
`Claim 5: “The process according to claim 4, wherein
`forming the at least one histogram further comprises
`adjusting a center of the selected area based upon a shape
`of the target until substantially the entire target is within
`the selected area” ....................................................................128
`
`Claim 6: “The process according to claim 5, wherein
`forming the at least one histogram further comprises
`setting the X minima and maxima and Y minima and
`maxima as boundaries in X and Y histogram formation
`units such that only pixels within the selected area will be
`processed by the image processing system” ...........................130
`
`Gerhardt and Bassman Are Not Cumulative ..........................131
`
`Detailed Application Of Gilbert, Gerhardt, And Ueno To
`The Challenged Claims ...........................................................132
`
`4.
`
`5.
`
`6.
`
`7.
`
`8.
`
`IX. CONCLUSION ............................................................................................182
`
`
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`-iii-
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`Declaration of Dr. John C. Hart
`Inter Partes Review of Patent No. 8,893,134
`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 3 through 6 (the “Challenged Claims”) of
`
`U.S. Patent No. 8,983,134(“the ’134 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 Patent No. 8,893,134
`researching and designing graphics hardware and systems for a wide range of
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`applications.
`
`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
`
`Multimedia Systems and Applications, July 2014.
`
`• V. Lu, I. Endres, M. Stroila and J.C. Hart. Accelerating Arrays of
`
`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 Patent No. 8,893,134
`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
`
`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
`
`Science at Washington State University.
`
`13. From 1991-1992, I was a Postdoctoral Research Associate at the
`
`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 Patent No. 8,893,134
`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.
`
`4
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`Declaration of Dr. John C. Hart
`Inter Partes Review of Patent No. 8,893,134
`I have also submitted declarations in support of the following
`
`23.
`
`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.
`
`
`24.
`
`TECHNOLOGICAL BACKGROUND
`
`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
`
`5
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`Declaration of Dr. John C. Hart
`Inter Partes Review of Patent No. 8,893,134
`image processing, histograms are often used to count the number of pixels in an
`
`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
`
`
`
`6
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`Declaration of Dr. John C. Hart
`Inter Partes Review of Patent No. 8,893,134
`domain) and the number having each Y-coordinate value (i.e., the Y-coordinate
`
`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
`
`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.
`
`28. A more complex greyscale image is shown below, along with its
`
`luminance histogram (black = 0; white = 255):
`
`7
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`
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`29. Here, the peak in the dark luminance region (luminance = 0-50)
`
`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
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`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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`Declaration of Dr. John C. Hart
`Inter Partes Review of Patent No. 8,893,134
`brightness between 130 and 170 and plot them according to their x and y position,
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`one would get the following image:
`
`
`
`30. Taking projection histograms of this subset of pixels with luminance
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`between 130 and 170, then, provides an indication of location of the face in the
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`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
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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
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`wheel that indicates which “color” is bring represented, e.g. 0° = red, 60° = yellow,
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`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
`
`image.
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`10
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`Declaration of Dr. John C. Hart
`Inter Partes Review of Patent No. 8,893,134
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`
`
`IV. THE ’134 PATENT
`32. The ’134 Patent, entitled “Image Processing Method,” was filed on
`
`March 17, 2014, and issued on March 17, 2015. The ’134 Patent names Patrick
`
`Pirim as the sole inventor. I understand that the ’134 Patent claims a priority date
`
`of July 22, 1996.
`
`33. The ’134 Patent is generally directed to tracking a target using an
`
`image processing system. For example, in the Abstract, the ’134 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, ’134
`
`Patent at Abstract.
`
`34. The ’134 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
`
`e.g., Ex. 1001, ’134 Patent at Claim 1. The input signal employed in the ’134
`
`11
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`Inter Partes Review of Patent No. 8,893,134
`Patent is comprised of a “succession of frames, each frame having a succession of
`
`pixels.” Ex. 1001 at 3:32-34. Although the disclosed embodiments relate
`
`primarily to a video signal, the ’134 Patent also teaches that the input signal could
`
`correspond to other types of signals, for example “ultrasound, IR, Radar, tactile
`
`array, etc.” Ex. 1001, ’134 Patent at 9:27-32.
`
`35. The ’134 Patent teaches constructing a histogram showing the
`
`frequency of the pixels meeting a certain characteristic. In the ’134 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 ’134
`
`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
`
`4:5-9.
`
`36. A domain can be further subdivided into classes, each class consisting
`
`of the subset of pixels with similar domain values. Figure 14a (and the
`
`accompanying text) illustrates an example of “classes” within a domain:
`
`
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`12
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`Declaration of Dr. John C. Hart
`Inter Partes Review of Patent No. 8,893,134
`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, ’134 Patent at 20:49-54.
`
`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
`
`17:4-10.
`
`
`
`13
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`Declaration of Dr. John C. Hart
`Inter Partes Review of Patent No. 8,893,134
`A detailed depiction of histogram block 25 is shown in Figure 13:
`
`
`
`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
`
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`Declaration of Dr. John C. Hart
`Inter Partes Review of Patent No. 8,893,134
`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
`
`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 ’134 Patent discloses that its teachings are applicable to a broad
`
`range of applications. For example, in one embodiment, the ’134 Patent performs
`
`“automatic framing of a person . . . during a video conference.” Ex. 1001, ’134
`
`Patent at 22:5-6. 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, ’134 Patent
`
`at 22:44-54. By this method, the ’134 Patent teaches the system is able to
`
`determine the boundaries of the target based on peaks in the histograms generated.
`
`Ex. 1001, ’134 Patent at 10:33-61. This application and result are shown in Figure
`
`16 and 17, reproduced below:
`
`15
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`Declaration of Dr. John C. Hart
`Inter Partes Review of Patent No. 8,893,134
`
`
`
`Ex. 1001, Figure 16
`
`
`
`Ex. 1001, Figure 17
`
`
`40. Other related applications disclosed by the ’134 Patent include using a
`
`mounted spotlight or camera to automatically track a target, for example using a
`
`spotlight mounted on a helicopter to track a target on the ground or using
`
`automated stage lights to track a performer during a performance. Ex. 1001, ’134
`
`Patent at 23:35-40. The ’134 Patent also teaches that “[t]he invention would
`
`similarly be applicable to weapons targeting systems.” Ex. 1001, ’134 Patent at
`
`23:39-40.
`
`41.
`
`In each of these embodiments, the ’134 Patent finds the X- and Y-
`
`minima and maxima of the histograms and uses these points to determine a center
`
`point of the target. The patent states that the “XMIN and XMAX” for the X-projection
`
`histogram and “YMIN and YMAX” for the y projection histogram, are “key
`
`characteristics” of the histogram “which include the minimum (MIN) of the
`
`histogram” and “the maximum (MAX) of the histogram.” Id. at 19:41-50. It
`
`16
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`Inter Partes Review of Patent No. 8,893,134
`teaches that these key characteristics are computed by the condition:
`
`For each pixel with a validation signal V2 of “l”:
`
`(a) if the data value of the pixel<MIN (which is initially
`
`set to the maximum possible value of the histogram),
`
`then write data value in MIN,
`
`(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 19:51-57. Hence the ‘134 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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`42. After determining the X- and Y-minima and maxima of the
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`histograms, the ’134 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, ’134
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`Patent at 24:46-51. 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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`Inter Partes Review of Patent No. 8,893,134
`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 ’134 Patent.
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`43. The X- and Y- minima and maxima is used to draw a “tracking box”
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`around the target. Figure 23 shows an example of the tracking box in a frame:
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`Inter Partes Review of Patent No. 8,893,134
`In addition, the ’134 Patent teaches that the image processing system
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`44.
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`may include a memory, a temporal processing unit, and a spatial processing unit.
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`The ’134 Patent states:
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`Referring to FIG. 2, image processing system 11 includes
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`a first assembly 11a, which consists of a temporal
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`processing unit 15 having an associated memory 16, a
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`spatial processing unit 17 having a delay unit 18 and
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`sequencing unit 19, and a pixel clock 20, which generates
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`a clock signal HP, and which serves as a clock for
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`temporal processing unit 15 and sequencing unit 19.
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`Clock pulses HP are generated by clock 20 at the pixel
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`rate of the image, which is preferably 13.5 MHZ.
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`Ex. 1001, ’134 Patent at 10:8-15.
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`45. The temporal processing unit 15 of the ’134 Patent “smooth[s] the
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`video signal and generate[s] a number of outputs that are utilized by spatial
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`processing unit 17.” Ex. 1001, ’134 Patent at 10:16-19. Specifically, the temporal
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`processing unit of the ’134 Patent “generates a binary output signal DP for each
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`pixel, which identifies whether the pixel has undergone significant variation, and a
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`digital signal CO, which represents the updated calculated value of time constant
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`C.” Ex. 1001, ’134 Patent at 10:28-32.
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`19
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`Inter Partes Review of Patent No. 8,893,134
`46. Thus, the “temporal processing unit” of the ’134 Patent generates
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`signal based on the information obtained by two (or more) frames in the video
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`signal representing images at different times.
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`47. The spatial processing unit of the ’134 Patent receives input from the
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`temporal processing unit, and determines the parameters relating to the movement
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`of the target. Ex. 1001, ’134 Patent at 15:31-55.
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`48. The ’134 Patent also teaches a method by which the system will
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`“process pixels only within a user-defined area.” Ex. 1001, ’134 Patent at 21:12-
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`24. For example, the system can receive user input instructing it to “process pixels
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`only in a defined rectangle by setting the XMIN and XMAX, and YMIN and
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`YMAX values as desired.” Id. The size of the area may be incrementally
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`increased until the box bounding the processed area overlaps the boundary of the
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`target. Ex. 1001, ’134 Patent at 24:35-38 (“This process is continued until the
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`histogram formed by either of histogram formation units 28 and 29 contains
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`meaningful information, i.e., until the box overlaps the boundary of the target.”).
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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 1001-1022 and other materials referred to herein in connection
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`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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`Inter Partes Review of Patent No. 8,893,134
`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 ’134 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.
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`50. Based on my experience and expertise, it is my opinion that certain
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`references teach or suggest all the features recited in Claims 3-6 of the ’134 Patent,
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`as explained in detail below. Specifically, it is my opinion that Claims 5-13 are
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`disclosed by U.S. Patent No. 5,481,622 (“Gerhardt”) in combination with U.S.
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`Patent No. 6,044,166 (“Bassman”). It is also my opinion that Claims 3-6 are
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`disclosed by Alton L. Gilbert et. al., A Real-Time Video Tracking System
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`(“Gilbert”) in combination with Gerhardt, and further in combination with U.S.
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`Patent No. 5,521,843 (“Hashima”).
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`VI. LEVEL OF ORDINARY SKILL IN THE ART
`51. Based on my review of the ’134 Patent specification, claims, file
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`history, and prior art, I believe one of ordinary skill in the art around the time of
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`the alleged invention of the ’134 Patent would have had either (1) a Master’s
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`Degree in Electrical Engineering or Computer Science or the equivalent plus at
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`least a year of experience in the field of image processing, image recognition,
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`machine vision, or a related field or (2) a Bachelor’s Degree in Electrical
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`Inter Partes Review of Patent No. 8,893,134
`Engineering or Computer Science or the equivalent plus at least three years of
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`experience in the field of image processing, image recognition, machine vision, or
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`a related field. Additional education could substitute for work experience and vice
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`versa.
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`52.
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`In determining the level of ordinary skill in the art, I was asked to
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`consider, for example, the type of problems encountered in the art, prior art
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`solutions to those problems, the rapidity with which innovations are made, the
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`sophistication of the technology, and the educational level of active workers in the
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`field.
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`53. My opinions concerning the ’134 Patent claims are from the
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`perspective of a person of ordinary skill in the art (“POSA”), as set forth above.
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`VII. CLAIM CONSTRUCTION
`54. For my analysis, I have interpreted all claim terms according to their
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`plain and ordinary meaning.
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`VIII. THE PRIOR ART TEACHES OR SUGGESTS EVERY STEP AND
`FEATURE OF THE CHALLENGED CLAIMS OF THE ’134 PATENT
`A. Overview Of The Prior Art References
`1.
`U.S. Patent No. 5,481,622 (“Gerhardt”) (Ex. 1013)
`55. Lester A. Gerhardt and Ross M. Sabolcik, researchers at Rensselaer
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`Polytechnic Institute, disclosed using a histogram of pixel characteristics to
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`identify and track a target in digitized visual input in U.S. Patent No. 5,481,622
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`Declaration of Dr. John C. Hart
`Inter Partes Review of Patent No. 8,893,134
`(“Gerhardt”). Gerhardt issued on January 2, 1996.
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`56. Gerhardt’s system tracks the position of a user’s pupil to generate
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`input to the computer. This allows one to interact with a computer without using
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`one’s hands. In one example, Gerhardt’s system uses a video camera mounted on
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`a helmet, as shown in Figures 1 and 2.
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`
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`57. Gerhardt’s system receives an input signal from a “camera means for
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`acquiring a video image” and a “frame grabber means [that is] coupled to the
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`camera means.” E