Samsung Electronics Co., Ltd. et al., Petitioner
`
`(cid:89)(cid:17)
`
`Image Processing Technologies, LLC, Patent Owner
`
`(cid:44)(cid:51)(cid:53)(cid:21)(cid:19)(cid:20)(cid:26)(cid:16)(cid:19)(cid:20)(cid:20)(cid:28)(cid:19)
`(cid:51)(cid:68)(cid:87)(cid:72)(cid:81)(cid:87)(cid:3)(cid:49)(cid:82)(cid:17)(cid:3)(cid:25)(cid:15)(cid:26)(cid:20)(cid:26)(cid:15)(cid:24)(cid:20)(cid:27)
`
`(cid:51)(cid:68)(cid:87)(cid:72)(cid:81)(cid:87)(cid:3)(cid:50)(cid:90)(cid:81)(cid:72)(cid:85)(cid:182)(cid:86)(cid:3)(cid:39)(cid:72)(cid:80)(cid:82)(cid:81)(cid:86)(cid:87)(cid:85)(cid:68)(cid:87)(cid:76)(cid:89)(cid:72)(cid:86)
`(cid:45)(cid:88)(cid:81)(cid:72)(cid:3)(cid:21)(cid:28)(cid:15)(cid:3)(cid:21)(cid:19)(cid:20)(cid:27)(cid:3)(cid:50)(cid:85)(cid:68)(cid:79)(cid:3)(cid:36)(cid:85)(cid:74)(cid:88)(cid:80)(cid:72)(cid:81)(cid:87)
`
`Exhibit 2016
`IPR2017-01190
`Petitioner - Samsung Electronics Co., Ltd., et al.
`Patent Owner - Image Processing Technologies LLC
`1
`
`
`
`(cid:89)(cid:17)
`
`Image Processing Technologies, LLC, Patent Owner
`
`(cid:44)(cid:51)(cid:53)(cid:21)(cid:19)(cid:20)(cid:26)(cid:16)(cid:19)(cid:20)(cid:20)(cid:28)(cid:19)
`(cid:51)(cid:68)(cid:87)(cid:72)(cid:81)(cid:87)(cid:3)(cid:49)(cid:82)(cid:17)(cid:3)(cid:25)(cid:15)(cid:26)(cid:20)(cid:26)(cid:15)(cid:24)(cid:20)(cid:27)
`
`(cid:51)(cid:68)(cid:87)(cid:72)(cid:81)(cid:87)(cid:3)(cid:50)(cid:90)(cid:81)(cid:72)(cid:85)(cid:182)(cid:86)(cid:3)(cid:39)(cid:72)(cid:80)(cid:82)(cid:81)(cid:86)(cid:87)(cid:85)(cid:68)(cid:87)(cid:76)(cid:89)(cid:72)(cid:86)
`(cid:45)(cid:88)(cid:81)(cid:72)(cid:3)(cid:21)(cid:28)(cid:15)(cid:3)(cid:21)(cid:19)(cid:20)(cid:27)(cid:3)(cid:50)(cid:85)(cid:68)(cid:79)(cid:3)(cid:36)(cid:85)(cid:74)(cid:88)(cid:80)(cid:72)(cid:81)(cid:87)
`
`Exhibit 2016
`IPR2017-01190
`Petitioner - Samsung Electronics Co., Ltd., et al.
`Patent Owner - Image Processing Technologies LLC
`1
`
`
`
`Grounds Asserted in Petition
`
`Paper 2 (Petition) at 3.
`
`2
`
`
`
`Anatomy of the Eye
`
`Paper 15 (PO Resp.) at 6.
`
`3
`
`
`
`’518 Patent: Two Alternative Embodiments
`
`Embodiment #1: Detection of eye based on
`head frame
`
`Embodiment #2: Detection of eye based on
`facial characteristics
`
`Paper 15 (PO Resp.) at 16–17.
`
`4
`
`
`
`’518 Patent: Detection of Eye Based on Head
`Frame
`
`Paper 15 (PO Resp.) at 17–20.
`
`5
`
`
`
`’518 Patent: Detection of Eye Based on Facial
`Characteristic
`
`Paper 15 (PO Resp.) at 21–25, 40.
`
`6
`
`
`
`’518 Patent: Eye as a Whole May be Analyzed
`
`Paper 15 (PO Resp.) at 25–26.
`
`7
`
`
`
`’518 Patent, Claim 39
`
`’518 Patent, Claim 39
`
`[pre] A process of detecting a feature of an eye,
`
`the
`
`[d] selecting pixels of the portion of the image having
`
`process comprising the steps of:
`
`characteristics
`
`corresponding to the
`
`feature
`
`to be
`
`[a] acquiring an image of the face of the person, the
`
`detected;
`
`image comprising pixels corresponding to the feature to
`
`[e] forming at least one histogram of the selected pixels;
`
`be detected;
`
`and
`
`[b] identifying a characteristic of the face other than the
`
`feature to be detected;
`
`identifying a portion of the image of the face
`
`comprising the
`
`feature
`
`to be
`
`detected using an
`
`anthropomorphic model based on the location of the
`
`identified facial characteristic;
`
`[t] analyzing the at
`
`least one histogram over time to
`
`identify characteristics of the feature to be detected;
`
`[g] said feature being the iris, pupil or cornea.
`
`Ex. 1001 at 39–40.
`
`8
`
`
`
`
`
`Claim Construction
`
`“histogram”
`
`“anthropomorphic model”
`
`“characteristic of the face” / “facial
`characteristic”
`
`“Selecting pixels of the portion of the
`image having characteristics
`corresponding to the feature to be
`detected; forming at least one
`histogram of the selected pixels”
`
`“a statistical representation of the frequency of
`occurrence with which values of a parameter fall
`within a series of intervals”
`
`“mathematical representation specifying the
`spatial relationship of human facial features”
`
`“a distinguishing element of a
`face, such as the nose, nostril, ears, eyebrows,
`mouth, etc.”
`
`requires selecting and forming a histogram of
`pixels that have characteristics corresponding to
`the feature to be detected
`
`Paper 15 (PO Resp.) at 28–34.
`
`9
`
`
`
`Interpretation of “Histogram”
`
`Samsung and Dr. Hart’s interpret “histogram” unreasonably.
`
`• A plot of intensity values for a line of pixels (as in Eriksson,
`Stringa) is not a histogram.
`
`• Dr. Hart resorts to characterizing intensity values as a
`“frequency of photons” to read a plot of intensity values
`as a histogram.
`
`Paper 15 (PO Resp.) at 28–34.
`
`10
`
`
`
`Dr. Hart’s Interpretation of “Histogram”
`
`Q. Does the plot of image intensities along the
`line going through the pupil from left to right of
`Eriksson, does that count anything?
`A. Yes.
`Q. What does it count?
`A. Anytime that you're looking at luminance – for
`example, when you're using a luminance
`histogram, an intensity histogram, you're looking
`at a region of the image. Intensity is basically a
`count of . . . Numbers of photons, so in this case
`you've got a histogram of the number of photons
`coming off of the eye in a single line. And this is a ‐
`‐ this is a histogram of those photons.
`
`Paper 15 (PO Resp.) at 47; Ex. 2003 (Hart Depo. Tr.) at 143:10–144:8 (emphasis added).
`
`11
`
`
`
`Dr. Hart’s Interpretation of “Histogram”
`
`Q. Does the curve generated by plotting the
`image intensity across the line going through
`the pupil from left to right of Eriksson show
`the frequency of occurrence of anything?
`A. Yes. It shows the frequency of occurrence
`of photons.
`Q. And how does it show the frequency of
`occurrence of photons?
`A. The intensity value is the number of
`photons that would be emitted or received or
`detected in a given amount of time.
`
`Paper 15 (PO Resp.) at 47; Ex. 2003 (Hart Depo. Tr.) at 143:10–144:8 (emphasis added).
`
`12
`
`
`
`Dr. Hart’s Interpretation of “Histogram”
`
`Q. If you look at the very ‐‐ let's look at Figure 5
`[of Eriksson]. The very left‐hand bar on the left‐
`hand side, are you with me on that?
`A. Yes. I see the left‐hand bar.
`Q. What does that bar represent?
`A. I believe that bar represents the intensity of a
`left most pixel in that line.
`Q. Do the bars on the graph of Figure 5 show how
`many pixels on the line going through the pupil
`have a certain intensity value?
`A. Yes. I believe each of these bars indicates the
`intensity value of a pixel on that line going through
`the pupil. They also indicate the frequency of
`photons or other radiometric energy, radiometric
`power specifically, from that line going through the
`eye.
`
`Paper 15 (PO Resp.) at 29, 47; Ex. 2003 (Hart Depo. Tr.) at 144:9–23.
`
`13
`
`
`
`Claim Elements [c], [d], [e]
`
`Paper 15 (PO Resp.) at 36–37.
`
`14
`
`
`
`Claim Elements [c], [d], [e]
`
`Paper 15 (PO Resp.) at 35.
`
`15
`
`
`
`Claim Elements [c], [d], [e]
`
`Paper 15 (PO Resp.) at 36.
`
`16
`
`
`
`Eriksson: No Histogram
`
`Eriksson uses “match
`values” to determine
`whether the eye is open or
`closed, not Figure 5.
`
`Ex. 1005 at 9
`
`Q So what Eriksson does is it determines a
`match value for each frame. For example,
`there's an average match value found for the
`first number of frames during initialization,
`right?
`A So there's a match value that ‐‐ when the
`match is significantly lower than the average,
`then it’s a closed frame, otherwise it's an open
`frame.
`Q And that match value is how Eriksson
`determines whether the eye is open or closed.
`That's what you mean by open frame, closed
`frame, right?
`A Yes. That's my understanding.
`
`Paper 15 (PO Resp.) at 45–46; Ex. 2003, 138:18–139:3.
`
`17
`
`
`
`Eriksson: Not a Histogram
`
`Figure 5 is merely a
`bar graph of intensity
`values.
`
`Figure 5 plots all
`values in the line, not
`selected values.
`
`Paper 15 (PO Resp.) at 47.
`
`18
`
`
`
`Eriksson: Not a Histogram
`
`Q Let's look at the left‐hand bar, again, on Figure 5. Let's say
`that's a value of 255 for intensity, for assumption purposes. Are
`you with me?
`A I can assume that. That would be 255 sure.
`Q I say that because it's kind of at the top of the levels.
`A Sure. It's an assumption because we don't have a vertical
`scale.
`Q Does the curve generated by plotting the image intensities
`along the line going through the pupil from left to right shown
`in Figure 5 count how many pixels on that line have an intensity
`of 255?
`A No. This is not a histogram of intensity.
`
`Paper 15 (PO Resp.) at 29; Ex. 2003 at 144:24–145:11.
`
`19
`
`
`
`Eriksson: Not a Histogram
`
`Q So you're not offering the opinion that the intensity curve
`of Figure 5 is a projection histogram; is that right?
`A There could be a formulation where it becomes a
`projection histogram. I didn't need to make it a projection
`histogram in order to have it meet this claim element, so I
`didn't try to formulate it as a projection histogram. I'm
`treating it just as a histogram, as it’s provided here.
`Q So you're not offering an opinion that it’s a projection
`histogram?
`A That's right.
`
`Paper 15 (PO Resp.) at 29; Ex. 2003 at 146:1–12.
`
`20
`
`
`
`Stringa: “Horizontal Grey Level Distribution” Is
`Not a Histogram
`
`The “horizontal grey‐
`level distribution” is
`not a histogram.
`
`The distribution
`includes all pixels in a
`line, not selected
`pixels.
`
`Paper 15 (PO Resp.) at 48, 50–51.
`
`21
`
`
`
`Stringa: “Horizontal Grey Level Distribution” Is
`Not a Histogram
`
`Q …I understand the horizontal gray level
`distribution you're pointing to, it's the values ‐‐
`the smooth values of intensity at each X
`position on a particular line Y, right?
`A Right.
`Q So we –
`A That’s G super Y of X.
`Q We could represent a smooth value ‐‐ a
`smooth intensity value for each value of X on a
`line Y by a one‐dimensional array with Index X,
`right?
`A Right. X is the parameter there.
`
`Paper 15 (PO Resp.) at 48; Ex. 2003, 167:6–16.
`
`22
`
`
`
`Stringa: “Horizontal Grey Level Distribution” Is
`Not a Histogram
`
`Q Does the horizontal gray level distribution
`represent a frequency of occurrence of
`anything?
`A I think it's being treated as a characteristic.
`I don't know that it measures ‐‐ other than
`as in the previous example, it's a smooth
`version of a measure of the intensity which
`is a sub of, for example, photons of
`radiometric power.
`
`Paper 15 (PO Resp.) at 48; Ex. 2003, 167:17–23.
`
`23
`
`
`
`Ando
`
`Ando forms a
`histogram of all pixels
`in the area Sd, not
`selected pixels.
`
`Paper 15 (PO Resp.) at 58.
`
`24
`
`
`
`Suenaga
`
`Suenaga
`
`FIG.
`
`1
`
`EVALUATION FUNCTION
`CALCULATION MEANS
`
`OPENED-OR-CLOSED
`COhDITION
`
` Ex. 1007 (Suenaga), Figure 1 (excerpt, annotated in yellow).
`JUDGMENT MEANS
`
`Ex. 1007 (Suenaga) at Figure 61 (page 62)
`
`Paper 15 (PO Resp.) at 61.
`
`25
`
`
`
`
`
`Suenaga
`
`Suenaga analyzes all
`binarized pixels for its
`“shape function,” not just
`pixels of the iris, pupil, or
`cornea.
`
`Paper 15 (PO Resp.) at 64.
`
`26
`
`
`
`CERTIFICATE OF SERVICE
`
`Pursuant to 37 C.F.R. § 42.6(e), the undersigned certifies that on June 25, 2018, the
`foregoing PATENT OWNER IMAGE PROCESSING TECHNOLOGIES
`LLC’S DEMONSTRATIVES was served via electronic mail upon the following
`counsel of record for the Petitioner:
`
`
`John Kappos (Reg. No. 37,861)
`jkappos@omm.com
`
`Marc J. Pensabene (Reg. No. 37,416)
`mpensabene@omm.com
`
`Nicholas J. Whilt (Reg. No. 72,081)
`nwhilt@omm.com
`
`Brian M. Cook (Reg. No. 59,356)
`bcook@omm.com
`
`Clarence Rowland (Reg. No. 73,775)
`crowland@omm.com
`
`IPTSAMSUNGOMM@OMM.COM
`
`
`
`
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`
`
`/s/ Chris J. Coulson
`Chris J. Coulson (Reg. No. 61,771)
`BUNSOW DE MORY LLP
`101 Brambach Rd.
`Scarsdale, NY 10583
`Tel.: (646) 502-6973
`ccoulson@bdiplaw.com
`
`
`
`
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