TRW Automotive U.S. LLCv.Magna Electronics Inc.

Patent Trial and Appeal Board

Jun 26, 2014

12640425 (P.T.A.B. Jun. 26, 2014)

Trials@uspto.gov Paper 16
Tel: 571-272-7822 Entered: June 26, 2014

UNITED STATES PATENT AND TRADEMARK OFFICE
_______________
BEFORE THE PATENT TRIAL AND APPEAL BOARD
_______________
TRW AUTOMOTIVE US LLC,
Petitioner,
v.
MAGNA ELECTRONICS INC.,
Patent Owner.
_______________

Case IPR2014-00258
Patent 7,994,462 B2
_______________

Before JUSTIN T. ARBES, BENJAMIN D. M. WOOD, and
NEIL T. POWELL, Administrative Patent Judges.

WOOD, Administrative Patent Judge.

DECISION
Denying Institution of Inter Partes Review
37 C.F.R. § 42.108

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I. INTRODUCTION
A. Background
TRW Automotive US LLC (“TRW”) filed a Petition (Paper 1, “Pet.”)
requesting inter partes review of claims 1, 3, 5-13, 15-17, 19, 21, 23, 26, and
27 of U.S. Patent No. 7,994,462 B2 (Ex. 1002, “the ’462 patent”). Patent
Owner Magna Electronics Inc. (“Magna”) filed a Preliminary Response
(Paper 7, “Prelim. Resp.”).
Institution of an inter partes review is authorized by statute when “the
information presented in the petition filed under section 311 and any
response filed under section 313 shows that there is a reasonable likelihood
that the petitioner would prevail with respect to at least 1 of the claims
challenged in the petition.” 35 U.S.C. § 314(a); see also 37 C.F.R. § 42.108.
We determine that TRW has not shown a reasonable likelihood that it would
prevail with respect to at least one of the claims of the ’462 patent, as set
forth more fully below. Accordingly, we do not institute an inter partes
review.
B. Related Proceedings
TRW discloses that the ’462 patent has been asserted in Magna
Electronics, Inc. v. TRW Automotive Holdings Corp., Case 1:12-cv-00654-
PLM (W.D. Mich. 2012). The ’462 patent is also the subject of another
inter partes review, TRW Automotive US LLC v. Magna Electronics Inc.,
IPR2014-00266.
C. The ’462 Patent (Ex. 1002)
The ’462 patent, titled “Vehicular Image Sensing System,” describes a
system for controlling a vehicle—e.g., dimming the vehicle’s headlights—in
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response to detecting “objects of interest” in front of the vehicle—e.g., the
headlights of oncoming vehicles and the taillights of leading vehicles. Ex.
1002, 1:22-27. The system uses an image sensor that divides the scene in
front of the vehicle into “a plurality of spatially separated sensing regions.”
Id., 2:16-19. A control circuit with a processor receives image data from the
image sensor and determines if individual regions include light sources
having a particular characteristic, such as a “spectral characteristic” (color),
or intensity. Id., 1:65-2:9, 3:43-51. By comparing the lights’ characteristics
with the “distribution” of the lights across the regions, such as the lights’
proximity to each other and to the vehicle’s central axis, the system can
distinguish oncoming headlights and leading taillights from streetlights and
other lights that are not of interest. Id., 9:32-61, 10:53-56. The system also
may detect traffic signs and lane markers, and assist the driver in other ways,
such as alerting the driver to lane changes. Id., 11:60-12:13.
D. Illustrative Claims
Claims 1 and 23 are independent, and are drawn to an image sensing
system for a vehicle. Ex. 1002, 12:57-13:15, 14:35-61. The independent
claims share at least three common limitations: (1) an image sensor,
comprising a two-dimensional array of light-sensing photosensor elements;
(2) the image sensor being inside the vehicle on which it is mounted, having
a forward field of view through the vehicle’s windshield; and (3) a control
comprising a processor that processes the image data to identify objects of
interest. Id., 12:57-67, 14:35-46. The dependent claims further specify the
location of the image sensor inside the vehicle, the physical structure of the
image sensor, the specific processing techniques that the processor uses to
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identify objects of interest, and the actions taken in response to identifying
objects of interest. Id., 13:19-14:24, 15:1-10.
Claims 1 and 23 are illustrative and are reproduced below:
1. An image sensing system for a vehicle, said image sensing
system comprising:
an imaging sensor comprising a two-dimensional array of
light sensing photosensor elements;
said imaging sensor having a forward field of view to the
exterior of a vehicle equipped with said image sensing system
and through the windshield of the equipped vehicle;
wherein said imaging sensor is operable to capture frames
of image data;
a control comprising an image processor;
wherein said image sensing system identifies objects in said
forward field of view of said image sensor via processing of
said captured image data by said image processor;
wherein said image processing comprises pattern
recognition and wherein said pattern recognition comprises
detection of at least one of (a) a headlight, (b) a taillight and (c)
an object, and wherein said pattern recognition is based at least
in part on at least one of (i) shape, (ii) reflectivity, (iii)
luminance and (iv) spectral characteristic; and
wherein said pattern recognition is enhanced by comparing
image data of objects over successive frames of said captured
image data.

23. An image sensing system for a vehicle, said image sensing
system comprising:
an imaging sensor comprising a two-dimensional array of
light sensing photosensor elements;
wherein said imaging sensor is at or proximate to the in-
cabin surface of the windshield of a vehicle equipped with said
image sensing system, and wherein said imaging sensor is
operable to capture image data;
a control comprising an image processor;
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wherein said image sensing system identifies objects in said
forward field of view of said image sensor via processing of
said captured image data by said image processor;
wherein said image processing comprises pattern
recognition and wherein said pattern recognition comprises
detection of at least one of (a) a headlight, (b) a taillight and (c)
an object, and wherein said pattern recognition is based at least
in part on at least one of (i) shape, (ii) reflectivity, (iii)
luminance and (iv) spectral characteristic; and
wherein objects are at least one of (a) qualified and (b)
disqualified based, at least in part, on object motion in said field
of view of said imaging sensor.

E. Prior Art Relied Upon
TRW relies upon the following prior art references:
Kenue US 4,970,653 Nov. 13, 1990 Ex. 1004
Bottesch US 5,166,681 Nov. 24, 1992 Ex. 1006
Yanagawa et al.
(“Yanagawa”)
JP S62-121837 June 15, 1987 Ex. 1005
Tadashi
1
JP H04-127280 Apr. 28, 1992 Ex. 1011
Wilson-Jones et al.
(“Wilson-Jones”)
EP0640903A1 March 1, 1995 Ex. 1007

Oliver Vellacott, CMOS in Camera, IEE REVIEW (May 1994)
(hereinafter “Vellacott”) (Ex. 1008);
Yon-Jian Zheng, et al., An Adaptive System for Traffic Sign
Recognition, INTELLIGENT VEHICLES ’94 SYMPOSIUM (Oct. 24-26, 1994)
(hereinafter “Zheng”) (Ex. 1010);
Mai Chen, AURORA: A Vision-Based Roadway Departure Warning
System, 1995 IEEE/RSJ INT’L CONF. ON INTELLIGENT ROBOTS AND SYSTEMS
(Aug. 9, 1995) (hereinafter “Aurora”) (Ex. 1009).

1
We refer to “Yanagawa” and “Tadashi” as the English translations of the
original references. TRW provided affidavits attesting to the accuracy of the
translations. See Exs. 1005, 1011; 37 C.F.R. § 42.63(b).
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F. Asserted Grounds of Unpatentability
TRW contends that the challenged claims are unpatentable under 35
U.S.C. §§ 102 and/or 103 based on the following specific grounds (Pet. 4-5):

Reference[s] Basis Claims Challenged
Kenue § 102
1, 3, 5-7, 10, 15, 19, 23, 26,
and 27
Yanagawa, Bottesch, and Wilson-
Jones
§ 103
1, 3, 5-8, 10, 15, 19, 21, 23,
26, and 27
Aurora with either (a) Kenue; or
(b) Yanagawa, Bottesch, and
Wilson-Jones
§ 103 11
Zheng with either (a) Kenue; or
(b) Yanagawa, Bottesch, and
Wilson-Jones
§ 103 12 and 13
Vellacott with either (a) Kenue; or
(b) Yanagawa, Bottesch, and
Wilson-Jones
§ 103 16 and 17
Yanagawa, Bottesch, Wilson-
Jones and Tadashi
§ 103 9

II. ANALYSIS
A. Claim Construction
Consistent with the statute and the legislative history of the AIA, the
Board will interpret claims using the broadest reasonable construction. 37
C.F.R. § 100(b). Claim terms are given their ordinary and customary
meaning as would be understood by one of ordinary skill in the art in the
context of the entire disclosure. In re Translogic Tech., Inc., 504 F.3d 1249,
1257 (Fed. Cir. 2007). Any special definition for a claim term must be set
forth in the specification with reasonable clarity, deliberateness, and
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precision. In re Paulsen, 30 F.3d 1475, 1480 (Fed. Cir. 1994). For purposes
of this decision, we construe the following terms: (1) pattern recognition;
and (2) wherein objects of interest are at least one of (a) qualified and (b)
disqualified, at least in part, based on object motion in said field of view of
said imaging sensor.

1. pattern recognition (claims 1 and 23)
The Specification states that
Pattern recognition may be used to further assist in the
detection of headlights, taillights, and other objects of interest.
Pattern recognition identifies objects of interest based upon
their shape, reflectivity, luminance, and spectral characteristics.
For example, the fact that headlights and taillights usually occur
in pairs could be used to assist in qualifying or disqualifying
objects as headlights and taillights. By looking for a triad
pattern, including the center high-mounted stoplight required on
the rear of vehicles, stoplight recognition can be enhanced.
Ex. 1002, 11:1-10. TRW asserts that pattern recognition should be
construed as “the identification of objects based upon their shape,
reflectivity, luminance, and spectral characteristic.” Pet. 8. For purposes of
this decision, we agree, with one caveat: that the terms “shape, reflectivity,
luminance, and spectral characteristics” are to be understood as disjunctive
rather than conjunctive. In other words, “pattern recognition” must be based
on at least one of these characteristics, not necessarily all of them at the
same time. This is indicated by the claims themselves. For example, claim
1 requires the image processing to comprise “pattern recognition . . . wherein
said pattern recognition is based at least in part on at least one of (i) shape,
(ii) reflectivity, (iii) luminance and (iv) spectral characteristic.” Ex. 1002,
13:5-11 (emphasis added). Thus, we construe “pattern recognition” to mean
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detection of an object of interest based upon shape, reflectivity, luminance,
or spectral characteristic.
2. wherein objects are at least one of (a) qualified and (b)
disqualified based, at least in part, on object motion in
said field of view of said imaging sensor (claims 6, 7, and
23)
Claim 23 recites the limitation “wherein objects are at least one of (a)
qualified and (b) disqualified based, at least in part, on object motion in said
field of view of said imaging sensor.” Similarly, claim 6 recites “wherein
detected objects are qualified based, at least in part, on object motion in said
field of view of said imaging sensor,” and claim 7 recites “wherein detected
objects are disqualified based, at least in part, on object motion in said field
of view of said imaging sensor.” Neither TRW nor Magna proposes a
construction for these terms. The ’462 patent uses the word “qualifying” to
mean determining that an object is a certain type of object, and uses the
word “disqualifying” to mean determining that the object is not a certain
type of object. For example, the ’462 patent discloses that “the fact that
headlights and taillights usually occur in pairs could be used to assist in
qualifying or disqualifying objects as headlights and taillights.” Ex. 1002,
11:5-7; see also id., 12:14-21 (“For example, a high level of ‘non-qualified’
light sources; namely, light sources that are not headlights or taillights, as
well as ‘qualified’ light sources can be used to determine a measurement of
the activity level around the vehicle; namely, that the vehicle is in an urban
environment which may be a useful input for particular control
algorithms.”). In light of this use of the terms “qualifying” and
“disqualifying,” we construe the claim language “objects are at least one of
(a) qualified and (b) disqualified” to mean that objects are at least one of
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determined to be objects of interest and determined to not be objects of
interest.
Accordingly, we construe the language “wherein objects are at least
one of (a) qualified and (b) disqualified based, at least in part, on object
motion in said field of view of said imaging sensor” as meaning that objects
are at least one of determined to be objects of interest and determined to not
be objects of interest based, at least in part, on object motion in said field of
view of said imaging sensor. Similarly, we construe “wherein detected
objects are qualified based, at least in part, on object motion in said field of
view of said imaging sensor” in claim 6 as meaning that objects are
determined to be objects of interest based, at least in part, on object motion
in said field of view of said imaging sensor, and construe “wherein detected
objects are disqualified based, at least in part, on object motion in said field
of view of said imaging sensor” in claim 7 as meaning that objects are
determined to not be objects of interest based, at least in part, on object
motion in said field of view of said imaging sensor.
B. Claims 1, 3, 5-7, 10, 15, 19, 23, 26, and 27—Anticipation—
Kenue
TRW asserts that Kenue anticipates claims 1, 3, 5-7, 10, 15, 19, 23,
26, and 27. Pet. 12-20, 42-48. Kenue describes a “computer vision system”
that detects lane markers and obstacles in front of an automobile. Ex. 1004,
1:53-61. Kenue’s system comprises a black and white CCD [charge coupled
device] video camera mounted on a vehicle’s windshield to capture the
driver’s view of the road in front of the vehicle. Id., 2:28-32. A computer
receives the digitized image data from the camera and, using two main
algorithms, “dynamically define[s] the search area for lane markers based on
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the lane boundaries of the previous [image] frame, and provide[s] estimates
of the position of missing markers on the basis of current frame and previous
frame information.” Id., 2:32-48. The system also detects and alerts the
driver to obstacles in the lane within about 50 feet of the vehicle.
Id., 2:48-51.
“A claim is anticipated only if each and every element as set forth in
the claim is found, either expressly or inherently described, in a single prior
art reference.” Verdegaal Bros. v. Union Oil Co. of California, 814 F.2d
628, 631 (Fed. Cir. 1987). For the reasons set forth below, we are not
persuaded that TRW is reasonably likely to show that Kenue teaches all of
the claim limitations of claims 1, 3, 5-7, 10, 15, 19, 23, 26, and 27.
1. Claim 23
Magna asserts that TRW failed to show that Kenue anticipates claim
23. Claim 23 requires that “objects of interest are at least one of (a)
qualified and (b) disqualified based, at least in part, on object motion in said
field of view of said imaging sensor” (hereinafter, the “qualified-disqualified
limitation”). Ex. 1002, 14:35-61. According to TRW, the following
portions of Kenue teach this limitation:
Kenue discloses “[a]fter the centroid of each marker is
calculated . . ., if the markers are not found where expected . . .
based on the previously detected lane geometry, the marker
locations from the previous frame are used . . . . The
determination of expected marker position . . . involves
comparing the position of each marker centroid with that of the
previous frame. If the change is more than nine pixels, a flag is
set. (1004-011 at 5:14-22)

Kenue further states “FIG. 3 illustrates the presence of a vehicle
30 or other obstacle in the roadway of FIG. 2. If the other
vehicle is close, say, within 50 feet of the trailing vehicle, it
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tends to obscure the lane markers 24 to such an extent that there
is insufficient information to determine the boundaries. In that
case an obstacle warning is given and no further image
processing is done on that frame. When the obstacle 30 is more
than 50 feet away the image is processed but the obstacle is
effectively erased from the image by removing horizontal and
vertical lines, thereby making the subsequent processing steps
simpler.” (1009-101 at 3:10-21)
Pet. 18-20.
TRW’s declarant, Jeffrey A. Miller, Ph.D., further explains how these
excerpts from Kenue correspond to the qualified-disqualified limitation.
Referring to the first excerpt that discusses marker positions, Dr. Miller
states, “[t]he above [is] qualifying an object of interest based on object
movement because the processing determines if a marker position changes
more than nine pixels from one frame and then sets a flag if the marker has
changed accordingly.” Ex. 1012 ¶ 15-16. Regarding the second excerpt, Dr.
Miller states, “[t]he above is disqualifying an object of interest because
where an obstacle moves to more than 50 feet away, the obstacle is erased
effectively from the image.” Id.
Magna disagrees that Kenue teaches this limitation. Magna asserts
that the quoted language is “significantly different from the claim language,
such that a mere quotation of the language does not show anticipation of the
claim.” Prelim. Resp. 15; see also Prelim. Resp. 16 (same). Magna also
asserts that Dr. Miller’s testimony is insufficient to explain the alleged
match. Id. at 16. We agree.
As Magna states, the quoted language from Kenue does not, on its
face, correspond to the qualified-disqualified limitation. For example, it is
unclear from the first excerpt how lane markers are “qualified,” i.e.,
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determined to be objects of interest. Rather, it would seem that Kenue’s
system predetermines lane markers to be of interest, as it is specifically
designed to detect them. Dr. Miller’s testimony in this regard is not helpful,
because it assumes without discussion that the first excerpt describes
“qualifying an object,” and merely explains why such qualification is based
on “object movement.” Ex. 1012 ¶ 15. As for the second excerpt from
Kenue on which TRW relies, it is unclear how it describes disqualifying an
object, i.e., determining it to be not of interest, based on object motion.
Instead, it appears to describe issuing a warning to the driver based on object
position (within 50 feet of the vehicle), rather than object motion. Again,
Dr. Miller’s testimony regarding this excerpt (id.) is not helpful, as it does
little more than repeat the claim language. See, e.g., Ex. 1012 ¶¶ 12-13.
For the above reasons, we determine that TRW is not reasonably
likely to show that Kenue teaches the qualified-disqualified limitation, and
therefore has not shown a reasonable likelihood of prevailing on its assertion
that Kenue anticipates claim 23, as well as claims 26 and 27, which depend
from claim 23.
2. Claim 1
Magna argues that TRW failed to show that Kenue anticipates
independent claim 1. Prelim. Resp. 12-17. Specifically, Magna asserts that
TRW failed to show that Kenue teaches the limitation in claim 1 “wherein
said pattern recognition is enhanced by comparing image data of objects
over successive frames of said captured image data.” Prelim. Resp. 16.
According to Magna, “TRW cited to a portion of Kenue with different
language than in the claim and does not explain why the cited portion
allegedly anticipates the claim limitation, specifically how ‘pattern
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recognition is enhanced.’” Id. Magna also contends that Dr. Miller’s
testimony does not provide any additional explanation because “it appears to
be an exact copy of what is in the petition and contains no other analysis or
discussion.” Id. (citing Ex. 1012 ¶¶ 12-13, 67-68).
TRW relies on the following passages of Kenue as teaching this
limitation:
[The] algorithms . . . dynamically define the search area for lane
markers based on the lane boundaries of the previous frame,
and provide estimates of the position of missing markers on the
basis of current frame and previous frame information. . . .
Moreover, the search area changes with marker position MP,
and increases in size if the marker was not found in the search
of the previous frame. . . . After the centroid of each marker is
calculated . . . if the markers are not found where expected . . .
based on the previously detected lane geometry, the marker
locations from the previous frame are used.
Pet. 14-16 (quoting Ex. 1004, 2:44-48, 4:37-39, 5:15-18). We agree with
Magna that neither TRW nor Dr. Miller explains sufficiently how these
passages correspond to the limitation at issue. Kenue’s teaching regarding
comparing a “previous frame” with a “current frame” is suggestive of
“comparing image data of objects over successive frames of said captured
image data,” but it is not self-evident how the quoted passages relate to
“enhanc[ing] pattern recognition,” based on “at least one of shape and
luminance.” Pet. 14. Thus, given TRW’s lack of explanation on this point,
we are not persuaded that TRW has shown a reasonable likelihood of
prevailing on this ground with respect to claim 1, and claims 3, 5-7, 10, 15,
and 19, which depend from claim 1.
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C. Obviousness Grounds
1. Claims 1, 3, 5-8, 10, 15, 19, 21, 23, 26, and 27—
Obviousness—Yanagawa, Bottesch, and Wilson-Jones
TRW asserts that claims 1, 3, 5-8, 10, 15, 19, 21, 23, 26, and 27 are
unpatentable under 35 U.S.C. § 103 as obvious over Yanagawa, Bottesch,
and Wilson-Jones. Yanagawa describes a vehicle-mounted imaging
apparatus that detects the headlights of oncoming vehicles and taillights of
leading vehicles based on the “color features” of the lights and whether the
lights are at the same height. Ex. 1005, 008-009. Yanagawa’s system dims
the vehicle’s headlights, if necessary, in response to such detection. Id. at
007. Bottesch describes a “vehicle presence detecting system” that uses a
“Passive Optical Sensor” (POS) comprised of two small hollow tubes having
an oval cross-section. Ex. 1006, 2:34-43, 3:67-4:3. The rear interior end of
each tube supports a photosensitive device comprised of a plurality of
identical photocells arranged in an array. Id., 4:3-10. Wilson-Jones
describes a video-based vehicle support system that detects other vehicles
and lane markings. Ex. 1007, 005:40-41.
TRW contends that Yanagawa discloses all of the limitations of the
above claims except that it does not expressly teach a two-dimensional array
of light sensing photosensor elements. Pet. 23-36.
2
TRW asserts that this
limitation is found in Bottesch. Id. at 23 (citing Ex. 1006, 2:40-45, 4:10-13,
6:57-60). Further, TRW asserts that a person of ordinary skill would have
had a reason to combine Bottesch with Yanagawa because:

2
TRW also contends that Wilson-Jones teaches pattern recognition based in
part on shape, but that this teaching is “not required” to support this
proposed ground of unpatentability. Pet. 27.
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To include in the camera 11 of Yanagawa (’837) at the time of
the alleged invention a two-dimensional array of photosensor
elements as shown in Bottesch (’681) is, under KSR, merely a
simple substitution of one known element for another to obtain
predictable results. Further, to the extent [held] that the two
dimensional array is not present in Yanagawa (’837), Petitioner
submits that those skilled in the art would consider it obvious to
try to include such a two-dimensional array in the camera 11 of
Yanagawa (’837), as this selection involves choosing from a
finite number of identified, predictable solutions, with a
reasonable expectation of success.
Pet. 24 (citing MPEP § 2141; Ex. 1012 ¶ 38).
TRW’s analysis falls short, as it is based on “mere conclusory
statements” that cannot support an obviousness rejection. KSR Int’l Co. v.
Teleflex, Inc., 550 U.S. 398, 418 (2007) (citing In re Kahn, 441 F.3d 977,
988 (2006)). TRW does not support its “simple substitution” or “obvious to
try” rationales with explanation or evidence. TRW does not explain, for
instance, why the alleged combination would be a “simple” substitution
achieving “predictable results,” or why selecting Bottesch’s array, as
opposed to any other, would be a choice from a “finite number” of solutions
with a “reasonable expectation of success.” See Pet. 24. Dr. Miller’s
testimony (Ex. 1012 ¶ 38) likewise is conclusory, as it simply repeats the
above statements verbatim, and thus does not provide any support for
TRW’s position. TRW’s reasons to combine Bottesch with Yanagawa thus
lack a supporting “articulated reasoning with some rational underpinning”
(Kahn, 441 F.3d at 988), and are, therefore, unpersuasive. Accordingly, we
are not persuaded that TRW is reasonably likely to prevail in showing that
the above claims would have been obvious over Yanagawa, Bottesch, and
Wilson-Jones. For the same reasons, we are not persuaded that TRW is
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reasonably likely to prevail in showing that claim 9, which depends from
claim 1, would have been obvious over Yanagawa, Bottesch, Wilson-Jones,
and Tadashi.
2. Obviousness Grounds Based on Aurora and Zheng
TRW proposes combining each of Aurora and Zheng with Kenue (or
Yanagawa, Bottesch, and Wilson-Jones) to demonstrate the unpatentability
of claim 11 (Aurora) and claims 12 and 13 (Zheng). Pet. 37-38. We have
reviewed TRW’s analyses and conclude, as above, that TRW’s proposed
reasons to combine each of Aurora and Zheng are based on conclusory
statements without adequate supporting explanation or evidence. For
example, TRW proposes combining Aurora with either Kenue or the
Yanagawa-Bottesch-Wilson-Jones combination because “doing so would be
nothing more than [the] use of a known technique to improve similar devices
in the same way,” and “such a combination would also be tantamount to
combining prior art elements according to known methods to yield
predictable results.” Pet. 37. TRW made exactly the same assertions for
combining Zheng with these references. Pet. 38. For the reasons discussed
above, we are not persuaded that TRW is reasonably likely to prevail with
respect to these grounds of unpatentability.
3. Claims 16 and 17—Vellacott with either (a) Kenue or (b)
Yanagawa, Bottesch, and Wilson-Jones
TRW asserts that the combination of Vellacott with either (a) Kenue
or (b) Yanagawa, Bottesch, and Wilson-Jones renders claims 16 and 17
unpatentable under 35 U.S.C. § 103. Pet. 39-42. We determined above
(§ II.D.2) that TRW has not adequately supported the combination of
Yanagawa and Bottesch, and TRW relies on its previous arguments
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regarding that combination. See Pet. 40-41. The combination of Vellacott
with Yanagawa, Bottesch and Wilson-Jones, therefore, is likewise
unsupported. Moreover, we have previously (§ II.C) determined that TRW
is not reasonably likely to show that Kenue teaches all of the limitations of
claim 1, from which claims 16 and 17 depend. TRW does not rely on
Vellacott as teaching the missing limitation. Pet. 39-42. Accordingly, we
determine that TRW is not reasonably likely to show that the combination of
Kenue and Vellacott renders claims 16 and 17 unpatentable.
III. CONCLUSION
For the foregoing reasons, we determine that TRW is not reasonably
likely to prevail on any of the challenged claims, and, accordingly, we deny
the Petition and do not institute an inter partes review of claims 1, 3, 5-13,
15-17, 19, 21, 23, 26, and 27 of the ’462 patent.

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PETITIONER:

Josh Snider
Timothy Sendek
A. Justin Poplin
LATHROP & GAGE LLP
patent@lathropgage.com
tsendek@lathropgage.com
jpoplin@lathropgage.com

PATENT OWNER:

Timothy A. Flory
Terence J. Linn
GARDNER, LINN, BURKHART & FLORY, LLP
Flory@glbf.com
linn@glbf.com

David K.S. Cornwell
STERNE, KESSLER, GOLDSTEIN & FOX PLLC
Davidc-PTAB@skgf.com