Samsung Electronics Co., Ltd. et al., Petitioner
`
`v.
`
`Image Processing Technologies, LLC, Patent Owner
`
`IPR2017-00353
`Patent No. 8,983,134
`
`Patent Owner’s Demonstratives
`February 21, 2018 Oral Argument
`
`
`
`v.
`
`Image Processing Technologies, LLC, Patent Owner
`
`IPR2017-00353
`Patent No. 8,983,134
`
`Patent Owner’s Demonstratives
`February 21, 2018 Oral Argument
`
`
`
`Two Instituted Grounds
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`
`2
`Paper 12 at 29
`
`
`
`’134 Patent Claim 1
`
`[1pre] A process of tracking a target in an input signal
`implemented using a system comprising an image processing
`system, the input signal comprising a succession of frames, each
`frame comprising a succession of pixels, the target comprising
`pixels in one or more of a plurality of classes in one or more of a
`plurality of domains, the process performed by said system
`comprising, on a frame-by-frame basis:
`
`[1a] forming at least one histogram of the pixels in the one or more
`of a plurality of classes in the one or more of a plurality of
`domains, said at least one histogram referring to classes defining
`said target; and
`
`[1b] identifying the target in said at least one histogram itself,
`
`[1c] wherein forming the at least one histogram further comprises
`determining X minima and maxima and Y minima and maxima of
`boundaries of the target.
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`
`3
`Ex. 1001 at 26:36–50
`
`
`
`’134 Patent Claim 1
`
`[1pre] A process of tracking a target in an input signal
`implemented using a system comprising an image processing
`system, the input signal comprising a succession of frames, each
`frame comprising a succession of pixels, the target comprising
`pixels in one or more of a plurality of classes in one or more of a
`plurality of domains, the process performed by said system
`comprising, on a frame-by-frame basis:
`
`[1a] forming at least one histogram of the pixels in the one or more
`of a plurality of classes in the one or more of a plurality of
`domains, said at least one histogram referring to classes defining
`said target; and
`
`[1b] identifying the target in said at least one histogram itself,
`
`[1c] wherein forming the at least one histogram further comprises
`determining X minima and maxima and Y minima and maxima of
`boundaries of the target.
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`
`4
`Ex. 1001 at 26:36–50
`
`
`
`Definition of “Forming”
`
`American Heritage College Dictionary (1997)
`Form (v):
`“to give form to; shape.”
`“to shape or mold (dough, for example) into
`a particular form.”
`
`The Random House Webster’s College Dictionary (1998)
`Form (v.t.): “to construct or frame.”
`“to make or produce.”
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`
`5
`Ex. 2008; Ex. 2009; Paper 17 at 14
`
`
`
`Effect of Wherein Language
`
`[1pre] A process of tracking a target in an input signal
`implemented using a system comprising an image processing
`system, the input signal comprising a succession of frames, each
`frame comprising a succession of pixels, the target comprising
`pixels in one or more of a plurality of classes in one or more of a
`plurality of domains, the process performed by said system
`comprising, on a frame-by-frame basis:
`
`[1a] forming at least one histogram of the pixels in the one or more
`of a plurality of classes in the one or more of a plurality of
`domains, said at least one histogram referring to classes defining
`said target; and
`
`[1b] identifying the target in said at least one histogram itself,
`
`[1c] wherein forming the at least one histogram further comprises
`determining X minima and maxima and Y minima and maxima of
`boundaries of the target.
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`
`6
`Ex. 1001 at 26:36–50
`
`
`
`’134 Patent Fig. 20
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`
`7
`Ex. 1001 at Fig. 20; Ex. 1002 at ¶ 41
`
`
`
`’134 Patent Fig. 21
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`8
`Ex. 1001 Fig. 21; Ex. 1011 at 13:3–19:11; Paper 22 at 10–11
`
`
`
`’134 Patent Fig. 22
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`9
`Ex. 1001 at Fig. 22; Ex. 1011 at 19:3–35:2; Paper 22 at 10–11
`
`
`
`’134 Patent Fig. 23
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`10
`Ex. 1001 at Fig. 23; Ex. 1011 at 19:3–35:2; Ex. 1002 at ¶ 42; Paper 22 at 10–11
`
`
`
`’134 Patent
`
`Col. 19, lines 41–50
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`
`11
`Ex. 1001 at 19:41–50; Ex. 1002 at ¶¶ 40–41
`
`
`
`’134 Patent
`
`Col. 24, lines 1–7
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`12
`Ex. 1001 at 24:1–7; Ex. 1011 at 12:22–20:24; see Paper 22 at 10–11
`
`
`
`’134 Patent
`
`Col. 24, lines 35–46
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`13
`Ex. 1001 at 24:35–46; Ex. 1011 at 12:22–20:24; see Ex. 1002 at ¶¶ 41–42; Paper 22 at 10–11
`
`
`
`Bovik Testimony re: Claim 1
`
`Q. Okay. So Figure 22 shows this embodiment we've
`been discussing where the area being processed has been
`enlarged; correct?
`
`A. So, you know, just for clarity, you know,
`Figure 22 is described further down in this embodiment,
`in this column. And it describes, you know, this as
`a -- you know, the area under consideration begins
`across the borders of target 218. So, I mean, this is
`a -- a later stage in this iterative process of --
`of forming the histogram that is -- that this embodiment
`targets in, you know, explaining claim 1.
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`
`14
`Ex. 1011 at 14:18–15:3
`
`
`
`Bovik Testimony re: Claim 1
`
`Q. Okay.· And that part you're reading from relates to Figure 23; right?
`A. Yeah, but it's all --
`Q. Not Figure 22?
`A. But -- well, they're not -- they're not really separate.· It's all part of the same
`iterative process…In each stage, it's clear that you determine whether the entire target
`is bounded….If it's not…there is…a further iteration…prior to further enlarging the
`area under consideration, in lines 42 and 43. …
`Q. Okay.
`A. And, you know, it says (as read):· Will be adjusted based upon the content of the
`histogram, but, you know --
`Q. Okay.
`A. … It's part of computing the final histogram.…you go to element 1(c), you know, I
`mean, wherein forming the "at least one histogram" further comprises determining X
`minima and maxima and Y minima and maxima boundaries in the target.· …
`boundaries of the target … are being…iteratively calculated….And… it's critical
`that that be part of the formation of the histogram. It can't be -- it can't be after the
`formation of the histogram.
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`
`15
`Ex. 1011 at 37:3–38:15
`
`
`
`Gilbert
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`
`16
`Ex. 1005 at Figs. 2, 4; Paper 17 at 15–18
`
`
`
`Gilbert
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`
`17
`Ex. 1005 at 50 (Right Column); Paper 17 at 29–30
`
`
`
`Hashima
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`
`18
`Ex. 1006 at Figs. 6–7, 15; Paper 17 at 22–23, 40
`
`
`
`Hashima
`
`Col. 11, lines 13–25
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`
`19
`Ex. 1006 at 11:13–25, Fig. 15; Paper 17 at 31
`
`
`
`Hashima
`
`Col. 11, lines 13–25
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`
`20
`Ex. 1006 at 11:13–25, Fig. 15; Paper 17 at 31–32
`
`
`
`Hashima
`
`Col. 12, lines 8–11
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`21
`Ex. 1006 at 12:8–11, Fig. 19; Paper 17 at 30–32; Ex. 1011 at 125:4–129:2
`
`
`
`Ueno
`
`Col. 7, lines 17–25
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`
`22
`Ex. 1007 at 7:17–25; Paper 17 at 23
`
`
`
`Ueno
`
`.
`
`Col. 7, lines 17–25
`
`IPR2017-00353, Ex. 2010, Patent Owner Demonstrative Slides
`
`23
`Ex. 1007 at 7:17–25, Fig. 3; Paper 17 at 23
`
`
`
`Gilbert
`
`.
`
`Gilbert
`
`1With the learned densityr funetiens fur the baekgreund,
`plume, and target features {High}, HPILI],HT{x}},a Bayesian
`elassifier
`ll] ean be used
`
`Assuming equal
`a pried prebahilities and equal ntjselafiifieatien eests. the
`elassii'ieatien rule deeides that a given pixel feature a:
`is a
`haekgreund pixel if
`
`HFHWHHIII and Hfixiltflrixi.
`
`a target pixel if
`
`Hfixt rs Heats} and Hits) bHthL
`
`er a plume pixel if
`
`Hfix}}HF[x} and emu-Hm}.
`
`
`
`ANDREWSKURTH
`
`24
`Ex. 1005 at 50; Paper 17 at 43
`
`
`
`No Reason to Combine
`Gilbert and Hashima
`
`.
`
`25
`Ex. 2007 at ¶ 123
`
`
`
`No Reason to Combine
`Gilbert and Hashima
`
`.
`
`26
`Ex. 2007 at ¶ 137
`
`
`
`No Reason to Combine
`Ueno and Gilbert
`
`.
`
`27
`Ex. 2007 at ¶ 149
`
`
`
`Binarization in Hashima
`
`.
`
`Col. 8, lines 30–46
`
`Hashima discusses
`making a binary
`image at step S3
`then detecting the
`mark in steps S5-
`S11.
`
`28
`Ex. 1006 at 8:30–46; Paper 17 at 21–23, 39–41
`
`
`
`
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