Trials@uspto.gov
`571-272-7822
`
`
` Paper No. 16
`
`Entered: May 25, 2017
`
`UNITED STATES PATENT AND TRADEMARK OFFICE
`____________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`____________
`
`SAMSUNG ELECTRONICS CO., LTD. and
`SAMSUNG ELECTRONICS AMERICA, INC.,
`Petitioner,
`
`v.
`
`IMAGE PROCESSING TECHNOLOGIES LLC,
`Patent Owner.
`____________
`
`Case IPR2017-00357
`Patent 8,989,445 B2
`____________
`
`
`
`
`
`
`
`Before JONI Y. CHANG, MICHAEL R. ZECHER, and
`JESSICA C. KAISER, Administrative Patent Judges.
`
`ZECHER, Administrative Patent Judge.
`
`
`
`
`DECISION
`Granting Institution of Inter Partes Review
`35 U.S.C. § 314(a) and 37 C.F.R. § 42.108
`
`
`
`
`
`
`571-272-7822
`
`
` Paper No. 16
`
`Entered: May 25, 2017
`
`UNITED STATES PATENT AND TRADEMARK OFFICE
`____________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`____________
`
`SAMSUNG ELECTRONICS CO., LTD. and
`SAMSUNG ELECTRONICS AMERICA, INC.,
`Petitioner,
`
`v.
`
`IMAGE PROCESSING TECHNOLOGIES LLC,
`Patent Owner.
`____________
`
`Case IPR2017-00357
`Patent 8,989,445 B2
`____________
`
`
`
`
`
`
`
`Before JONI Y. CHANG, MICHAEL R. ZECHER, and
`JESSICA C. KAISER, Administrative Patent Judges.
`
`ZECHER, Administrative Patent Judge.
`
`
`
`
`DECISION
`Granting Institution of Inter Partes Review
`35 U.S.C. § 314(a) and 37 C.F.R. § 42.108
`
`
`
`
`
`
`
`IPR2017-00357
`Patent 8,989,445 B2
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`
`I. INTRODUCTION
`
`Petitioner, Samsung Electronics Co., Ltd. and Samsung Electronics
`America, Inc. (collectively “Samsung”), filed a Petition requesting an inter
`partes review of claims 1, 4, 6, 9, 18, 24, 25, and 27 of U.S. Patent No.
`8,989,445 B2 (Ex. 1001, “the ’445 patent”). Paper 2 (“Pet.”). Patent
`Owner, Image Processing Technologies LLC (“Image Processing”), filed a
`Preliminary Response. Paper 9 (“Prelim. Resp.”).
`Under 35 U.S.C. § 314(a), an inter partes review may not be instituted
`unless the information presented in the Petition shows “there is a reasonable
`likelihood that the petitioner would prevail with respect to at least 1 of the
`claims challenged in the petition.” Taking into account the arguments
`presented in Image Processing’s Preliminary Response, we conclude that the
`information presented in the Petition establishes that there is a reasonable
`likelihood that Samsung would prevail in challenging claims 1, 4, 6, 9, 18,
`24, 25, and 27 of the ’445 patent as unpatentable under 35 U.S.C. § 103(a).
`Pursuant to § 314, we hereby institute an inter partes review as to these
`claims of the ’445 patent.
`
`A. Related Matters
`
`The ’445 patent is involved in a district court case titled Imaging
`
`Processing Techs. LLC v. Samsung Elecs. Co., No. 2:16-cv-00505-JRG
`(E.D. Tex.). Pet. 1; Paper 7, 2. In addition to this Petition, Samsung filed
`other petitions challenging the patentability of certain subsets of claims in
`the following patents owned by Image Processing: (1) U.S. Patent No.
`6,959,293 B2 (Case IPR2017-00336); (2) U.S. Patent No. 8,805,001 B2
`(Case IPR2017-00347); (3) U.S. Patent No. 8,983,134 B2 (Case IPR2017-
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`00353); and (4) U.S. Patent No. 7,650,015 B2 (Case IPR2017-00355).
`Pet. 1; Paper 7, 2.
`
`B. The ’445 Patent
`
`The ’445 patent, titled “Image Processing Apparatus and Method,”
`issued March 24, 2015, from U.S. Patent Application No. 14/449,809, filed
`on August 13, 2014. Ex. 1001, at [54], [45], [21], [22]. The ’445 patent has
`an extensive chain of priority that ultimately results in it claiming the benefit
`of Patent Cooperation Treaty (“PCT”) French Patent Application No.
`97/01354, filed on July 22, 1997. Id. at [60].
`The ’445 patent generally relates to an image process apparatus and,
`in particular, to a method and apparatus for identifying and localizing an
`area in relative movement in a scene, and determining the speed and
`direction of that area in real-time. Ex. 1001, 1:38–40. The ’445 patent
`discloses a number of known systems and methods for identifying and
`localizing an object in relative movement, but explains that each of those
`systems/methods are inadequate for various reasons (e.g., memory intensive,
`limited in terms of the information obtained about an object, did not provide
`information in real-time, used complex algorithms for computing object
`information, designed to detect only one type of object, etc.). See id. at
`1:44–3:17. The ’445 patent purportedly solves these problems by providing
`a method and apparatus for detecting the relative movement and
`non-movement of an area within an image. Id. at 9:17–19. According to the
`’445 patent, relative movement is any movement of an area, which may be
`an object (e.g., a person, a portion of a person, or any animals or inanimate
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`object), in a motionless environment or, alternatively, in an environment that
`is at least partially in movement. Id. at 9:19–24.
`Figure 11 of the ’445 patent, reproduced below, illustrates a block
`diagram showing the interrelationship between various histogram formation
`units that make up a histogram processor. Ex. 1001, 8:54–55.
`
`
`As shown in Figure 11 reproduced above, histogram processor 22(a) (not
`labeled) includes bus 23 that transmits signals between various components,
`including histogram formation and processing blocks 24–29. Id. at 16:57–
`63. The function of each histogram formation and processing block 24–29 is
`to form a histogram for the domain associated with that particular block. Id.
`at 16:63–65.
`According to the ’445 patent, each histogram formation and
`processing block 24–29 operates in the same manner. Ex. 1001, 17:47–50.
`As one example, Figure 13 of the ’445 patent, reproduced below, illustrates
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`a block diagram of histogram formation and processing block 25. Id. at
`8:58–59.
`
`
`As shown in Figure 13 reproduced above, histogram formation and
`processing block 25 includes histogram forming portion 25a, which forms
`the histogram for the block, and classifier 25b, which selects the criteria of
`pixels for which the histogram is to be formed. Id. at 17:50–53. Histogram
`forming portion 25a and classifier 25b operate under the control of computer
`software in integrated circuit 25c (not shown in Figure 13), which extracts
`certain limits of the histogram generated by the histogram formation block.
`Id. at 17:54–57. Classifier 25b includes register 106 that enables the
`classification criteria to be set by a user or, alternatively, by a separate
`computer program. Id. at 18:20–23.
`
`C. Illustrative Claim
`
`Of the challenged claims, claims 1 and 24 are independent.
`
`Independent claim 1 is directed to a process of tracking a target in an image
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`processing system, whereas independent claim 24 is directed to just an
`image processing system. Claims 4, 6, 9, and 18 directly or indirectly
`depend from independent claim 1, and claims 25 and 27 directly depend
`from independent claim 24. Independent claim 1 is illustrative of the
`challenged claims and is reproduced below:
`1.
`A process of tracking a target in an image
`processing system comprising:
`receiving an input signal including a plurality of frames,
`each frame including a plurality of pixels;
`generating a histogram based on classification values of a
`plurality of pixels in a first frame of the input signal;
`identifying a target from the histogram generated based on
`the first frame;
`determining a target location based on the histogram
`generated based on the first frame;
`generating a histogram based on classification values of a
`plurality of pixels in a second frame of the input signal
`subsequent to the first frame; and
`adjusting the target location based on the histogram
`generated based on the second frame.
`
`Ex. 1001, 26:38–52.
`D. Prior Art References Relied Upon
`
`Samsung relies upon the prior art references set forth in the table
`below:
`Inventor1 U.S. Patent No. Relevant Dates
`Hashima
`5,521,843
`issued May 28, 1996,
`PCT filed Jan. 29, 1993
`issued June 2, 1998,
`filed Apr. 27, 1995
`
`Brady
`
`5,761,326
`
`Exhibit No.
`1006
`
`1007
`
`
`1 For clarity and ease of reference, we only list the first named inventor.
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`
`
`Non-Patent Literature
`Alton L. Gilbert et. al., A Real-Time Video Tracking System,
`PAMI-2, No. 1 IEEE TRANSACTIONS ON PATTERN
`ANALYSIS AND MACHINE INTELLIGENCE 47–56 (1980)
`(“Gilbert”)
`O.D. Altan et. al., Computer Architecture and
`Implementation of Vision-Based Real-Time Lane Sensing,
`PROCEEDINGS OF THE INTELLIGENT VEHICLES ’92
`SYMPOSIUM 202–06 (1992) (“Altan”)
`
`Exhibit No.
`1005
`
`1008
`
`
`
`E. Asserted Grounds of Unpatentability
`Samsung challenges claims 1, 4, 6, 9, 18, 24, 25, and 27 of the
`
`’445 patent based on the asserted grounds of unpatentability (“grounds”) set
`forth in the table below. Pet. 3, 37–83.
`References
`Basis
`Gilbert and Brady
`§ 103(a)
`Gilbert, Brady, and Altan
`§ 103(a)
`Hashima and Gilbert
`§ 103(a)
`Hashima, Gilbert, and Altan § 103(a)
`Hashima and Brady
`§ 103(a)
`
`Challenged Claim(s)
`1, 4, 6, 9, 18, 24, 25, and 27
`18
`1, 4, 6, 9, 24, 25, and 27
`18
`1, 4, 6, 9, 18, 24, 25, and 27
`
`II. ANALYSIS
`
`A. Claim Construction
`
`As an initial matter, we determine the proper standard of construction
`to apply. The term of a patent grant begins on the date on which the patent
`issues and ends twenty (20) years from the date on which the application for
`the patent was filed in the United States, “or, if the application contains a
`specific reference to an earlier filed application or applications under
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`section 120, 121, 365(c), or 386(c), from the date on which the earliest such
`application was filed.” 35 U.S.C. § 154(a)(2) (2012 & Supp. III 2015).
`Samsung suggests that the earliest patent application referenced for the
`benefit of priority under 35 U.S.C. § 365(c) for the ’445 patent was filed on
`July 22, 1997, and the patent has no term extensions. See Pet. 3 (stating that
`“[t]he ’445 Patent will expire on July 22, 2017”). Image Processing does not
`dispute Samsung’s assertion in this regard. See generally Prelim. Resp. 7.
`The term of the ’445 patent, therefore, expires no later than July 22, 2017.
`On this record, because we agree with the parties that the term of the
`’445 patent will expire within eighteen (18) months from the entry of the
`Notice of Filing Date Accorded to the Petition, which, in this case is
`December 2, 2016 (Paper 3), we construe the claims of the ’445 patent under
`the standard applicable to expired patents. For claims of an expired patent,
`our claim interpretation is similar to that of a district court. See In re
`Rambus Inc., 694 F.3d 42, 46 (Fed. Cir. 2012). “In determining the meaning
`of the disputed claim limitation, we look principally to the intrinsic evidence
`of record, examining the claim language itself, the written description, and
`the prosecution history, if in evidence.” DePuy Spine, Inc. v. Medtronic
`Sofamor Danek, Inc., 469 F.3d 1005, 1014 (Fed. Cir. 2006) (citing Phillips
`v. AWH Corp., 415 F.3d 1303, 1312–17 (Fed. Cir. 2005) (en banc)). There
`is, however, a “heavy presumption” that a claim term carries its ordinary and
`customary meaning. CCS Fitness, Inc. v. Brunswick Corp., 288 F.3d 1359,
`1366 (Fed. Cir. 2002).
`The parties do not propose constructions for any claim terms recited
`in the challenged claims of the ’445 patent. See generally Pet. 3–4; Prelim.
`Resp. 7. Because there is no dispute between the parties regarding claim
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`construction, we need not construe explicitly any claim term of the
`’445 patent at this time. See, e.g., Vivid Techs., Inc. v. Am. Sci. & Eng’g,
`Inc., 200 F.3d 795, 803 (Fed. Cir. 1999) (explaining that only those claim
`terms or phrases that are in controversy need to be construed, and only to the
`extent necessary to resolve the controversy).
`
`B. Obviousness Over the Combined Teachings of Gilbert and Brady
`
`Samsung contends that claims 1, 4, 6, 9, 18, 24, 25, and 27 of the
`
`’445 patent are unpatentable under § 103(a) over the combined teachings of
`Gilbert and Brady. Pet. 37–59. Samsung explains how this proffered
`combination teaches or suggests the subject matter of each challenged claim,
`and provides reasoning as to why one of ordinary skill in the art would have
`been prompted to modify or combine their respective teachings. Id.
`Samsung also relies upon the Declaration of Dr. John C. Hart to support its
`positions. Ex. 1002 ¶¶ 89–119, 122–133. At this stage of the proceeding,
`we are persuaded by Samsung’s explanations and supporting evidence.
`
`We begin our analysis with the principles of law that generally apply
`to a ground based on obviousness, followed by brief overviews of Gilbert
`and Brady, and then we address the parties’ contentions with respect to the
`challenged claims.
`
`1. Principles of Law
`
`A claim is unpatentable under § 103(a) if the differences between the
`claimed subject matter and the prior art are such that the subject matter, as a
`whole, would have been obvious at the time the invention was made to a
`person having ordinary skill in the art to which said subject matter pertains.
`KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). The question of
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`obviousness is resolved on the basis of underlying factual determinations,
`including (1) the scope and content of the prior art; (2) any differences
`between the claimed subject matter and the prior art; (3) the level of skill in
`the art;2 and (4) when in evidence, objective indicia of non-obviousness
`(i.e., secondary considerations). Graham v. John Deere Co., 383 U.S. 1, 17–
`18 (1966). We analyze this asserted ground based on obviousness with the
`principles identified above in mind.
`
`2. Gilbert Overview
`
`Gilbert, titled “A Real-Time Video Tracking System,” is dated
`January 1980. Ex. 1005, 47.3 Gilbert relates to an object identification and
`tracking system, which includes an image processing system that includes a
`video processor, a projection processor, a tracker processor, and a control
`processor. Id. at 47–48. Gilbert’s video processor receives a digitized video
`signal in which each field consists of pixels. Id. at 48. Gilbert discloses that
`“[e]very 96 ns, a pixel intensity is digitized and quantized into eight bits
`(256 gray levels), counted into one of six 256-level histogram memories, and
`then converted by a decision memory to a 2-bit code indicating its
`
`
`2 Relying upon the testimony of Dr. Hart, Samsung offers an assessment as
`to the level of skill in the art. Pet. 4 (citing Ex. 1002 ¶¶ 47–50). Image
`Processing offers a similar assessment of the level of skill in the art. Prelim.
`Resp. 6. To the extent necessary, we accept the assessment offered by
`Samsung as it is consistent with the ’445 patent and the asserted prior art,
`but note that our conclusions would be the same under Image Processing’s
`assessment.
`3 All references to the page numbers in Gilbert are to the original page
`numbers located at the top of each page in Exhibit 1005, rather than the page
`numbers inserted by Samsung in the bottom of each page.
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`classification (target, plume, or background).” Id. Gilbert’s projection
`processor then uses pixels identified as being part of the target to create
`x- and y-projections. Id. at 50. Figure 4 of Gilbert, reproduced below,
`illustrates a projection location technique.
`
`
`Figure 4 of Gilbert, reproduced above, illustrates Y-projections and
`X-projections of the target. Gilbert’s system uses these projections to
`determine the center of the upper and lower portions of the target, and those
`points are then used to determine the center of the target (XC, YC). Id. at 50–
`51.
`
`3. Brady Overview
`
`Brady, titled “Method and Apparatus for Machine Vision
`Classification and Tracking,” issued on June 2, 1998. Ex. 1007, at [54],
`[45]. As suggested by its title, Brady generally relates to systems used for
`traffic detection, monitoring, management, and vehicle classification and
`tracking. Id. at 1:12–14. In particular, the invention disclosed therein is
`directed to a method and apparatus for classifying and tracking objects in
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`images (i.e., vehicles) provided by real-time video feed. Id. at 1:14–16.
`According to Brady, the apparatus of the disclosed invention includes a
`plurality of video cameras situated over a plurality of roadways, thereby
`allowing the video cameras to film each site it covers in real-time. Id. at
`3:61–63. The video cameras are interconnected electrically to a switcher,
`which allows for manual or automatic switching between each of the video
`cameras. Id. at 3:63–66. The video filmed by the video camera is
`transmitted to a plurality of image processors that analyze the image from
`the video and create classification and tracking data. Id. at 3:66–4:1.
`Brady discloses that a video image of a scene may be a 512x512 pixel
`three color image having an integer number defining intensity with a
`definition range for each color of 0–255. Ex. 1007, 5:40–43. Brady further
`discloses that image processing entails using a regional selection module
`that defines potential regions of interest, or candidate regions, for
`classification. Id. at 7:35–36. The region selection module also defines
`candidate regions. Id. at 7:39–40. Each vehicle class is assigned a set of
`appropriately sized and shaped regions, which, in one embodiment, are
`trapezoidal in shape. Id. at 7:38–54. Image processing also uses an edgel
`definition module that evaluates each pixel of the image array output from
`the scene being evaluated for the magnitude of its edge element intensity.
`Id. at 6:23–25. According to Brady, edgel intensity indicates the likelihood
`that a given pixel is located on some edge having particular orientation and
`contrast. Id. at 6:25–27. The greater the contrast between a particular pixel
`and the pixels surrounding it in a particular orientation, the greater the edgel
`intensity. Id. at 6:27–30.
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`For each pixel’s edge intensity in a region of interest, Brady discloses
`applying tent functions. Ex. 1007, 9:5–6. For each tent function a histogram
`is produced that records the frequency a range of angles occurs, as weighted
`by the intensity of the edgels, within a particular tent function. Id. at 9:6–9.
`Once all the histograms have been produced, they are strung together to
`form a vector, which, in turn, is output from a vectorization module. Id. at
`9:25–28. The vector then is evaluated by a vehicle learning module, which
`classifies vehicles based on the vector and may generate a signal indicative
`of the classification of a vehicle in question. Id. at 9:50–57. Icon
`assignment module may assign a unique icon identifying the vehicle class of
`the vehicle in question, which, in turn, may be output to a tracking module to
`facilitate visual tracking of that vehicle. Id. at 9:65–66. This icon will move
`with progression of the vehicle as the track progresses over time through a
`series of frames until the vehicle is no longer identifiable in the scene. Id. at
`10:7–9. In one embodiment, once the tracking module initiates a tracking
`sequence for the vehicle, the centroid of the tracked vehicle may be
`identified to allow centering of the tracking point over the vehicle. Id. at
`10:63–66.
`In one embodiment, Brady discloses that the identification and
`tracking of vehicles may occur at the time when the immediately previous
`image is acquired (i.e., every frame). Ex. 1007, 12:15–17. In another
`embodiment, Brady discloses using intermittent tracking. Id. at 12:17–20.
`When employing intermittent tracking, the target location is adjusted only if
`the target has moved significantly. Id. at 14:50–53.
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`4. Claims 1 and 24
`In its Petition, Samsung contends that the combined teachings of
`Gilbert and Brady account for all the steps recited in independent claim 1.
`Pet. 40–46. Beginning with the “receiving” step recited in independent
`claim 1, Samsung contends that Gilbert teaches this step because its tracking
`system uses a video signal (i.e., input signal) that includes digitized fields
`(i.e., frames) with a frame rate of 60 fields per second (i.e., a succession of
`frames), where each field further includes an n X m matrix of digitized
`points (i.e., a succession of pixels). Id. at 41 (citing Ex. 1005, 48; Ex. 1002
`¶ 97). Samsung also argues that Brady teaches the “receiving” step because
`it discloses using a digitized real-time video signal that includes a plurality
`of image frames, each of which includes a plurality of pixels. Id. (citing
`Ex. 1007, 5:38–45; Ex. 1002 ¶ 97).
`With respect to the first “generating” step recited in independent
`claim 1, Samsung contends that Gilbert teaches this step because its video
`processor generates intensity histograms for each of the target, plume, and
`background regions (i.e., a plurality of pixels) in a first frame of the video
`signal based on the 256-level grayscale value (i.e., classification value) for
`each pixel. Pet. 41–42 (citing Ex. 1005, 48, 49; Ex. 1002 ¶ 98). Samsung
`also argues that Brady teaches the first “generating” step because it discloses
`generating histograms of pixels in the region-of-interest in the frame being
`evaluated (i.e., a first frame) based on the pixels’ edgel intensity, as
`transformed by a tent function. Id. at 42 (citing Ex. 1007, 8:60–9:9;
`Ex. 1002 ¶ 98).
`With respect to the “identifying” step recited in independent claim 1,
`Samsung contends that Gilbert teaches this step because it discloses
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`identifying a target (i.e., a missile in flight) based on the previously
`generated intensity histogram for the pixels in a first frame of the video
`signal. Pet. 42 (citing Ex. 1005, 48; Ex. 1002 ¶¶ 97, 99). Samsung also
`argues that Brady teaches the “identifying” step because it discloses
`identifying the target vehicles based on the analysis of the previously
`generated histogram of pixels in the region-of-interest in the frame being
`evaluated. Id. at 42–43 (citing Ex. 1007, 9:50–10:23; Ex. 1002 ¶¶ 97, 99).
`In particular, Samsung argues that Brady creates a vector output by stringing
`together nine histograms covering a region of interest, and then evaluates the
`vector to determine whether it represents one of a predetermined class of
`vehicles. Id. at 42 (citing Ex. 1007, 9:25–27, 9:50–62; Ex. 1002 ¶ 99).
`With respect to the “determining” step recited in independent claim 1,
`Samsung contends that Gilbert teaches this step because it discloses
`determining a target location based on the X- and Y-projection histograms
`created after the pixels corresponding to the target are identified. Pet. 43–44
`(citing Ex. 1005, 50, Fig. 4; Ex. 1002 ¶ 100). Samsung asserts that Gilbert’s
`X- and Y-projection histograms are generated based on the first frame. Id. at
`44 (citing Ex. 1002 ¶ 100). Samsung also contends that Brady teaches the
`“determining” step because it discloses determining the target location based
`on the nine histograms representing the edgel intensities as transformed by
`the tent functions. Id. at 44 (citing Ex. 1007, 10:63–11:13; Ex. 1002 ¶ 101).
`With respect to the second “generating” step recited in independent
`claim 1, Samsung contends that Gilbert teaches this step because it discloses
`generating histograms on a frame-by-frame basis. Pet. 44 (citing Ex. 1005,
`49). In other words, Samsung argues that after Gilbert generates an intensity
`histogram based on one frame (i.e., the first frame), Gilbert repeats the same
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`process for the next frame (i.e., the second frame). Id. (citing Ex. 1005, 48–
`49; Ex. 1002 ¶ 102). Samsung also argues that Brady teaches the second
`“generating” step because it discloses generating histograms on a frame-by-
`frame basis. Id. at 45. Samsung argues that Brady does not identify and
`track vehicles in every frame, but rather discloses “intermittent tracking” in
`which the tracking system skips certain frames, particularly those frames
`where the target has not moved significantly. Id. (citing Ex. 1007, 12:15–
`20; Ex. 1002 ¶ 103). According to Samsung, in Brady’s “intermittent
`tracking” mode, the second frame would not be the frames skipped by the
`process, but rather would be the frame immediately following the last
`processed frame. Id.
`With respect to the “adjusting” step recited in independent claim 1,
`Samsung contends that Gilbert teaches this step because it discloses
`calculating the target location on a frame-by-frame basis. Pet. 45 (citing
`Ex. 1005, 50; Ex. 1002 ¶ 104). According to Samsung, Gilbert’s method of
`calculating the target location allows one to re-calculate and adjust the center
`point of the target based on the updated projection histograms based on the
`second frame. Id. at 45–46 (citing Ex. 1005, 48, 49, 52; Ex. 1002 ¶ 104).
`Samsung also argues that Brady teaches the “adjusting” step because it
`discloses calculating the target location on a frame-by-frame basis. Id. at 46
`(citing Ex. 1007, 10:7–10; Ex. 1002 ¶ 104). Similar to Gilbert, Samsung
`argues that Brady’s method of calculating the target location allows one to
`re-calculate and adjust the center point of the target based on the updated
`projection histograms based on the second frame. Id. (citing Ex. 1007,
`10:63–67; Ex. 1002 ¶ 104).
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`Samsung also contends that the combined teachings of Gilbert and
`Brady account for all the limitations recited in independent claim 24.
`Pet. 54–57. In particular, with the exception of the “camera” and
`“processing system” features recited in independent claims 24, Samsung
`relies upon the same explanation and supporting evidence discussed above
`with respect to independent claim 1 to account for all the limitations recited
`in independent claim 24. Compare id. at 54–57, with id. at 40–46. Samsung
`turns to Gilbert’s television camera and image processor to teach the claimed
`“camera” and “processing system,” respectively. Id. at 54–55 (citing
`Ex. 1005, 47, 48, Fig. 1; Ex. 1002 ¶ 123). Samsung also argues that Brady’s
`video camera and tracking system that receives real-time video image from
`the video camera teaches the claimed “camera” and “processing system,”
`respectively. Id. at 55–56 (citing Ex. 1007, 5:35–44; Ex. 1002 ¶¶ 124, 125).
`Turning to Samsung’s rationale to combine the teachings of Gilbert
`and Brady, Samsung relies upon the testimony of Dr. Hart to explain why
`one of ordinary skill in the art would have had a sufficient reason to combine
`their respective teachings. Pet. 37–40; Ex. 1002 ¶¶ 90–95. For instance,
`apart from the exemplary rationales articulated in KSR, Samsung contends
`that one of ordinary skill in the art would have recognized that applying
`Brady’s “intermittent processing” to Gilbert’s tracking system would be
`beneficial because it would reduce the computational burden imposed on the
`system and improve efficiency, especially in situations where the object
`being tracked is slow moving or not moving (e.g., a missile before launch).
`Id. at 40 (Ex. 1007, 12:15–20, 14:44–67; Ex. 1002 ¶ 95). In addition,
`Samsung argues that one of ordinary skill in the art would have had a
`sufficient reason to apply Brady’s simultaneous, independent tracking of
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`multiple targets, as well as its ability to classify multiple targets by type in
`real-time, to Gilbert’s tracking system. Id. According to Samsung,
`combining the teachings of Gilbert and Brady in this particular manner
`would benefit Gilbert’s tracking system by allowing it to simultaneously
`identify, distinguish, and track (1) multiple missiles; or (2) two dissimilar
`objects, such as a missile and an aircraft. Id. (citing Ex. 1007, 3:49–60,
`9:50–52; Ex. 1002 ¶ 95).
`In its Preliminary Response, Image Processing presents two
`arguments, both of which focus on whether Samsung has demonstrated that
`a person of ordinary skill in the art would have combined the teachings of
`Gilbert and Brady. Prelim. Resp. 12–14. We address each argument in turn.
`First, Image Processing contends that Samsung has not demonstrated that a
`person of ordinary skill in the art would have combined the teachings of
`Gilbert and Brady because these references are directed to different
`objectives. Id. at 12. According to Image Processing, Gilbert discloses
`tracking a single target, or possibly two targets in separate tracking
`windows, as well as using a moveable optical mount to follow a target. Id.
`(citing Ex. 1005, 47, 48). In contrast, Image Processing argues that Brady
`discloses monitoring vehicle traffic by switching between a plurality of
`stationary cameras situated in a plurality of roadways, and automatically
`tracking all vehicles in an image, which Brady criticizes the prior art for
`being unable to do. Id. at 12–13 (citing Ex. 1007, 2:23–27, 11:14–19).
`We are not persuaded by Image Processing’s argument that a person
`of ordinary skill in the art would not have had a sufficient reason to combine
`the teachings of Gilbert and Brady because, purportedly, these references are
`directed to different objectives. See Prelim. Resp. 12–13. It is well-settled
`
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`IPR2017-00357
`Patent 8,989,445 B2
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`that simply because two references have different objectives does not
`preclude a person of ordinary skill in the art from combining their respective
`teachings. In re Heck, 699 F.2d 1331, 1333 (Fed. Cir. 1983) (“The use of
`patents as references is not limited to what the patentees describe as their
`own inventions or to the problems with which they are concerned.” (quoting
`In re Lemelson, 397 F.2d 1006, 1009 (CCPA 1968))); see also EWP Corp. v.
`Reliance Universal Inc., 755 F.2d 898, 907 (Fed. Cir. 1985) (“A reference
`must be considered for everything that it teaches by way of technology and
`is not limited to the particular invention it is describing and attempting to
`protect.”).
`Second, Image Processing contends that Samsung has not
`demonstrated that a person of ordinary skill in the art would have combined
`the teachings of Gilbert and Brady because these references operate in
`different ways that are incompatible. Prelim. Resp. 13. For instance, Image
`Processing argues that Gilbert’s objective is to identify and track objects in
`complex and changing backgrounds; however, Gilbert does not seek to
`identify different types (i.e., classes) of targets. Id. (citing Ex. 1005,
`Abstract). In contrast, Image Processing argues that Brady’s tracking is
`performed against a static background (i.e., roadway), and the tracking
`algorithms are tailored specifically to roadway backgrounds by, for example,
`assuming that regions of interest are sized according to vehicle class (e.g.,
`car-sized or truck-sized trapezoids.) Id. at 13–14 (citing Ex. 1007, 7:46–54).
`According to Image Processing, defining candidate regions of appropriate
`size would not be possible in Gilbert’s tracking system because there are not
`static points of references, such as lane boundaries, and not known or
`predetermined vehicle shapes. Id. at 14. Image Processing further asserts
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`IPR2017-00357
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`that, without an appropriate candidate region, edgel values and fuzzy set
`theory would have limited utility, if any, in Gilbert’s tracking system. Id.
`Image Processing also asserts that Brady’s disclosure of dynamically
`generating candidate regions by “prior calibration of the scene” would not be
`possible in Gilbert’s complex and changing background. Id. (citing
`Ex. 1007, 7:42–45).
`At this stage of the proceeding, we are not persuaded by Image
`Processing’s argument that a person of ordinary skill in the art would not
`have had a sufficient reason to combine the teachings of Gilbert and Brady
`because, purportedly, these references operate in different ways that are
`incompatible. See Prelim. Resp. 13–14. Apart from mere attorney argument
`that includes directing us to disparate portions of Gilbert and Brady, the
`record before us does not include sufficient or credible evidence that
`Gilbert’s tracking system would become inoperable if modified to include
`the teachings of Brady, particularly its (1) “intermittent processing”;
`(2) simultaneous, independent tracking of multiple targets; and (3) ability to
`classify multiple targets by type in real-time. Cf. In re Geisler, 116 F.3d
`1465, 1470 (Fed. Cir. 1997) (explaining that attorney arguments and
`conclusory statements that are unsupported by factual evidence are entitled
`to little probative value). Instead, on the current record, we are persuaded
`that Samsung has presented sufficient evidence that
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