UNITED STATES PATENT AND TRADEMARK OFFICE
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`____________
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`BEFORE THE PATENT TRIAL AND APPEAL BOARD
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`____________
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`TOYOTA MOTOR CORPORATION,
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`Petitioner
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`v.
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`AMERICAN VEHICULAR SCIENCES,
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`Patent Owner
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`Patent No. 6,772,057
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`Issue Date: Aug. 3, 2004
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`Title: VEHICULAR MONITORING SYSTEMS USING IMAGE PROCESSING
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`____________
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`PETITIONER’S REPLY TO PATENT OWNER’S RESPONSE
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`Case No. IPR2013-00419
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`TABLE OF CONTENTS
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`Page
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`C.
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`I.
`II.
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`V.
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`INTRODUCTION ...................................................................................................... 1
`THE “GENERATED FROM” LANGUAGE IS NOT A
`LIMITATION FOR PURPOSES OF THE PATENTABILITY
`ANALYSIS .................................................................................................................... 3
`III. THE “GENERATED FROM” LANGUAGE IS NOT LIMITED TO
`TRAINING WITH REAL DATA ........................................................................... 4
`IV. LEMELSON EXPLICITLY DISCLOSES TRAINING WITH REAL
`DATA ............................................................................................................................. 5
`A.
`Lemelson Discloses Training With All Types of “Known Inputs,”
`Including “Real Data” ...................................................................................... 6
`B. One of Ordinary Skill Would Have Understood Lemelson’s
`Disclosure of “Known Inputs” to Refer to Training with Real
`Data ..................................................................................................................... 7
`Lemelson Separately Discloses “Adaptive Operation” and “On-
`Line Adjustment” of its Neural Network Which Constitutes
`Training with “Real Data” .............................................................................11
`D. Dr. Koutsougeras’s Declaration Should Be Given Little Weight
`Because He Lacks Expertise With Neural Networks in Vehicles ...........11
`SPECIFIC GROUNDS OF REVIEW ..................................................................12
`A. Ground of Review A: Claims 1-4, 7-10, 40, 41, 46, 48, 49, 56, 59-
`61, and 64 are Anticipated Under 35 U.S.C. § 102(e) by Lemelson ........12
`B. Ground of Review B: Claims 30-34, 37-39, and 62 are Obvious
`Under 35 U.S.C. § 103(a) Over Lemelson and Borcherts ........................12
`C. Ground of Review C: Claims 4, 43, and 59 are Obvious Under 35
`U.S.C. § 103(a) Over Lemelson and Asayama ............................................14
`D. Ground of Review D: Claim 34 is Obvious Under 35 U.S.C. §
`103(a) Over Borcherts, Lemelson and Asayama ........................................14
`E. Ground of Review E: Claims 30, 32, and 37-39 are Obvious
`Under 35 U.S.C. § 103(a) Over Yamamura and Borcherts ......................15
`VI. CONCLUSION..........................................................................................................15
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`TABLE OF AUTHORITIES
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`Cases
`Beckson Marine, Inc. v. NFM, Inc.,
` 292 F.3d 718 (Fed. Cir. 2002) ........................................................................................... 13
`Ex Parte Klasing et al.,
`App. No. 11/507,120, 2013 Pat. App. LEXIS 1619 (PTAB March 14, 2013) ............ 4
`Greenliant Sys.,
`Inc. v. Xicor LLC, 692 F.3d 1261 (Fed. Cir. 2012) ............................................................. 3
`In re Baxter Travelnol Labs.,
`952 F.2d 388 (Fed. Cir. 1991) .............................................................................................. 8
`In re Gleave,
`560 F.3d 1331 (Fed Cir. 2009) ............................................................................................. 7
`In re Graves,
`69 F.3d 1147 (Fed. Cir. 1996) .............................................................................................. 8
`In re Petering,
`301 F.2d 676 (C.C.P.A. 1962) .............................................................................................. 7
`In re Warmerdam,
`33 F.3d 1354 (Fed. Cir. 1994) ......................................................................................... 2, 3
`KSR Int’l v. Teleflex Inc.,
`550 U.S. 398 (2007) ............................................................................................................. 13
`SmithKline Beecham Corp. v. Apotex Corp.,
`439 F.3d 1312 (Fed. Cir. 2006) ....................................................................................... 1, 3
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`I.
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`INTRODUCTION
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`Petitioner Reply Under 37 C.F.R. § 42.23-24
`IPR2013-00419
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`Petitioner Toyota Motor Corporation (“Petitioner”) submits the following
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`Reply under 37 C.F.R. § 42.23-24 to Patent Owner’s Response (Paper 33) in IPR2013-
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`00419 concerning U.S. Patent No. 6,772,057 (“the ’057 patent”). This filing is timely.
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`See Papers 20 and 30.
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`AVS argues that U.S. Patent No. 6,553,130 (“Lemelson”) does not disclose a
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`pattern recognition algorithm “generated from data of possible exterior objects and
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`patterns of received waves from the possible objects” (hereinafter, the “generated
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`from” language). AVS asserts that this language requires training with data and waves
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`from actual objects (hereinafter, “real data”), as opposed to simulated data and waves
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`(hereinafter, “simulated data”) or “data and waves not representing exterior objects to
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`be detected” (hereinafter, “partial data”). AVS also asserts that Lemelson’s disclosure
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`of training is too vague to discern which of the three categories of data (real,
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`simulated, or partial) is taught. AVS asserts that Petitioner and the Board must,
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`therefore, have implicitly been relying on the doctrine of inherency. AVS is wrong.
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`First, the “generated from” language is not a limitation, since it is a process step
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`within apparatus claims. See SmithKline Beecham Corp. v. Apotex Corp., 439 F.3d 1312,
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`1317, 1319 (Fed. Cir. 2006) (“one cannot avoid anticipation by an earlier product
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`disclosure by claiming the same product more narrowly, that is, by claiming the
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`product as produced by a particular process.”); In re Warmerdam, 33 F.3d 1354, 1360-
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`Petitioner Reply Under 37 C.F.R. § 42.23-24
`IPR2013-00419
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`61 & n. 6 (Fed. Cir. 1994) (noting that the claim language “data representing a bubble
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`hierarchy generated by the method of . . .” likely fit into the “conventional definition”
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`of a product-by-process claim). The claimed “pattern recognition algorithm”
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`constitutes computer code, regardless of how it was created. AVS makes no
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`argument that generating it with real data somehow structurally alters that code.
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`Second, even if the “generated from” language constitutes a limitation, it is not
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`limited to training with real data. The claims merely specify that the algorithm is
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`generated from (1) data of possible exterior objects, and (2) patterns of received waves
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`from those possible exterior objects. The claimed patterns “of” received waves, as
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`opposed to, “patterns from” received waves, merely require patterns representing
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`what received waves would look like (which would include simulations).
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`Third, Lemelson explicitly discloses the “generated from” language, even under
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`AVS’s construction. Lemelson discloses a neural network trained to identify roadway
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`hazards, such as automobiles and pedestrians, by providing “known inputs” until
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`desired output responses are obtained. Ex. 1002 at 8:1-10. AVS’s expert admits that
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`real data was one “known input” at the time of Lemelson, and that there were only
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`two other categories of data that he discussed in his declaration (simulated and partial
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`data). Ex. 1022 at 86:25-87:14, 163:18-164:7. The disclosure of “known inputs” is,
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`therefore, sufficient to connote to one of ordinary skill that any known category of
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`data could be used for training. In any event, as explained by Toyota’s expert, one of
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`Petitioner Reply Under 37 C.F.R. § 42.23-24
`IPR2013-00419
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`ordinary skill would have read Lemelson to refer to real data because neither
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`simulated data nor partial data could have reasonably been used to train a neural
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`network to identify exterior objects. Ex. 1023 at ¶¶10-12.
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`AVS’s remaining arguments directed to the obviousness grounds of review are
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`similarly meritless. AVS argues that the Borcherts reference discloses a receiver on a
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`“rear view mirror assembly,” and not necessary on the “rear view mirror,” as required
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`by claims 30-34, 37-39, and 62. But AVS does not seriously dispute that this was an
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`obvious difference, or otherwise rebut the specific motivations offered by Petitioner
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`and its expert.
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`II. THE “GENERATED FROM” LANGUAGE IS NOT A LIMITATION
`FOR PURPOSES OF THE PATENTABILITY ANALYSIS
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`The challenged claims are directed to a vehicle or a monitoring arrangement,
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`including a “pattern recognition algorithm” embodied as computer code on a
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`processor. AVS asserts that Lemelson could have generated the algorithm using any
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`of three categories of data, each of which would have been sufficient to train the
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`neural network. Paper 33 at 17, 20. But, under this framework, the “generated from”
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`language is not a limitation for purposes of patentability because it merely specifies
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`the method of creating the algorithm. See SmithKline Beecham Corp., 439 F.3d at 1317.
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`The “generated from” language does not further limit the structural elements in any
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`way. See Greenliant Sys., Inc. v. Xicor LLC, 692 F.3d 1261, 1268 (Fed. Cir. 2012);
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`Warmerdam, 33 F.3d at 1360-61, fn. 6 (holding that a software claim including the
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`Petitioner Reply Under 37 C.F.R. § 42.23-24
`IPR2013-00419
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`language “generated by” was likely in product-by-process format); see also Ex Parte
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`Klasing et al., App. No. 11/507,120, 2013 Pat. App. LEXIS 1619, at *8-10 (PTAB
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`March 14, 2013) (holding that the term “one-piece metal casting” was in product-by-
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`process format and did not limit the claim for purposes of patentability.) As noted,
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`AVS makes no argument that a pattern recognition algorithm is different depending
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`upon how it was generated, and, in fact, argues the opposite. Paper 33 at 17, 20. The
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`“generated from” language is, therefore, not a limitation for purposes of patentability.
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`III. THE “GENERATED FROM” LANGUAGE IS NOT LIMITED TO
`TRAINING WITH REAL DATA
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`Even if it is considered to be a limitation, the “generated from” language does
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`not require training with real data, as AVS argues. See Paper 33 at 10-12; Ex. 2001 at
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`¶¶ 40-42, 51-52. Claim 1 is representative and requires an “algorithm generated from
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`data of possible exterior objects and patterns of received waves from the possible
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`exterior objects.” The parties’ dispute revolves around the term “of” and whether, as
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`AVS would have it, “data of” and “patterns of” require the claimed algorithm to be
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`generated from real data, as opposed to simulated or partial data. Petitioner submits
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`that the term “of” is not so restrictive.
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`The “of” in “data of possible exterior objects” and “patterns of received
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`waves” merely requires that the “data” and “patterns” used to generate the claimed
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`algorithm represent “possible exterior objects,” and “received waves,” respectively.
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`This language does not exclude simulated data or patterns. By analogy, an image “of”
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`Petitioner Reply Under 37 C.F.R. § 42.23-24
`IPR2013-00419
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`a car might be a digital image file, a printed analog photo, a CAD drawing, or a
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`painting. Notably, the claims do not use the more specific preposition “from,” as in
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`“data from possible exterior objects”—even though AVS’ expert’s testimony (as well
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`as AVS’s Response) is explicitly based on that incorrect substitution of claim language:
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`The phrase “known inputs” could mean numerous things other
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`than data from possible exterior objects and patterns of received
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`waves from the possible exterior objects.
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`Ex. 2001 at ¶ 54 (emphasis added); see id. at ¶ 55, 56; Paper 33 at 14-15.
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`AVS is also wrong that training with partial data (such as data of license plates,
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`taillights, or a rear window) falls outside the scope of the “generated from” language.
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`These items are all “exterior object(s)” in their own right. See Paper 19 at 12-14; Ex.
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`1022 at 166:14-170:14. And, there is no claim requirement that training be performed
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`on the same exterior object that is later identified, classified or located. In fact, the
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`claims require, or at least allow, that the objects be different. Training is performed
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`on “possible exterior objects,” while the classification, identification or location is of
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`an exterior object that presents a hazard to the vehicle.
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`IV. LEMELSON EXPLICITLY DISCLOSES TRAINING WITH REAL
`DATA
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`Even if AVS were correct on claim construction, Lemelson discloses training
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`with real data and therefore would still meet the “generated from” language.
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`Petitioner Reply Under 37 C.F.R. § 42.23-24
`IPR2013-00419
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`A. Lemelson Discloses Training With All Types of “Known Inputs,”
`Including “Real Data”
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`AVS asserts that there are three categories of data that could have been used to
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`train the Lemelson neural network (real, simulated and partial), and that only training
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`with real data meets the “generated from” language. See Paper 33 at 14-21; Ex. 2001
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`at ¶¶ 55-71; Ex. 1022 at 89:15-25. But even if AVS is correct, Lemelson still discloses
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`the “generated from” language. AVS is incorrect in its assertion that, because
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`Lemelson teaches training with “known inputs,” as opposed to using the words “real
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`data” or “real objects,” it does not explicitly meet the claim limitation.
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` AVS’s argument essentially is that the question before the Board is an
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`inherency dispute, i.e., that because “known inputs” could allegedly mean several
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`things, the Board must consider whether training would have “necessarily” been
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`performed with “real data.” See Paper 33 at 12-14. But this is not a situation in which
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`a reference is wholly silent as to a particular limitation and the doctrine of inherency is
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`raised as a mechanism to fill the gap. Rather, one of ordinary skill in the art would
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`have understood that “known inputs” include real data. Ex. 1023 at ¶¶ 10-12. Even
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`AVS’s expert admits that: (i) “known inputs” would have had some meaning to one
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`of ordinary skill; (ii) it would have referred to some category of data; (iii) training with
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`real data was one of those known categories at the time of Lemelson; and (iv) it would
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`not have been unreasonable to think that a skilled artisan reading Lemelson’s
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`disclosure would have thought that “known inputs” referred to training with real data.
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`Petitioner Reply Under 37 C.F.R. § 42.23-24
`IPR2013-00419
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`See Ex. 1022 at 132:24-138:5, 157:12-159:14, 163:18-164:7.
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`There is no requirement for anticipation that Lemelson separately list the
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`alleged three categories of training data; the shorthand phrase “known inputs” was
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`sufficient. This situation is analogous to the genus/species context, where “known
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`inputs” is the genus and “real data” is the claimed species (assuming AVS’s claim
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`construction is correct). Even if AVS is correct that there potentially are three such
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`categories of data (i.e., three possible species) within the “known input” genus and
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`that any of those data categories could have been used for training, the “generated
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`from” language is still met by Lemelson because the size of the genus is small and one
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`of ordinary skill can at once envisage the claimed species. See In re Gleave, 560 F.3d
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`1331, 1337-38 (Fed Cir. 2009); In re Petering, 301 F.2d 676, 681-82 (C.C.P.A. 1962).
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`B. One of Ordinary Skill Would Have Understood Lemelson’s
`Disclosure of “Known Inputs” to Refer to Training with Real
`Data
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`AVS asserts that Lemelson’s “known inputs” disclosure could have been
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`referring to training using simulated or partial data, instead of real data. But, as Dr.
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`Papanikolopoulos explains, one of ordinary skill would not have understood
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`Lemelson’s disclosure in this way, since simulated and partial data would have had
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`little use in the context of training a neural network to identify objects outside of a
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`vehicle. AVS’s argument is based entirely on the declaration of Dr. Koutsougeras,
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`who has no experience with neural networks in vehicles. See Section IV.D below.
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`Petitioner Reply Under 37 C.F.R. § 42.23-24
`IPR2013-00419
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`Lemelson discloses a collision avoidance system, wherein a neural network is
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`used to identify different types of potential roadway hazards, including, for example,
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`road barriers, trucks, automobiles, pedestrians, signs and symbols. Ex. 1002 at 5:41-
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`43; 8:1-6. The Lemelson neural network is specifically trained “with data identifying
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`the potential roadway hazards,” (Paper 19 at 20) which include, for example,
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`“automobiles, trucks, and pedestrians.” Ex. 1002 at 8:1-8. One of ordinary skill
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`would have understood that training a neural network to identify exterior objects (as
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`indicated by Lemelson) would have been done with real data, and not with simulated
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`or partial data. Ex. 1023 at ¶¶ 10-26. Lemelson therefore anticipates the challenged
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`claims. In re Baxter Travelnol Labs., 952 F.2d 388, 390 (Fed. Cir. 1991); In re Graves, 69
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`F.3d 1147, 1152-53 (Fed. Cir. 1995).
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`Simulated Data
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`1.
`AVS and Dr. Koutsougeras rely upon two references—Pomerleau’s 1992 thesis
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`and U.S. Patent No. 5,537,327—in support of the argument that Lemelson may have
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`been referring to training with simulated data. But neither reference demonstrates
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`that simulated data can be used to train a neural network to identify exterior objects.
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`Pomerleau investigated the use of simulated data for training a neural network
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`in a vehicle; but, Pomerleau only attempted to identify road surfaces with the neural
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`network, not the dozens (if not hundreds) of possible exterior objects that could
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`cause a collision. See Paper 19 at 35-36 (discussing similar Pomerleau reference, Ex.
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`Petitioner Reply Under 37 C.F.R. § 42.23-24
`IPR2013-00419
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`1008); Ex. 1023 at ¶¶ 18-25. Indeed, this fact is central to AVS’s argument about
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`Pomerleau in its Preliminary Response, and a reason that the Board did not institute
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`the IPR based on a Pomerleau reference. Paper 17 at 40; Paper 33 at 2; Paper 19 at
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`35. Therefore, even if Pomerleau teaches that simulations could be used to train a
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`neural network, one of ordinary skill would not have understood from Pomerleau that
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`such simulations could be generated and used to train a neural network to identify
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`different exterior objects. Ex. 1023 at ¶¶ 23-25.
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`Moreover, AVS and its expert fail to read and comprehend the entirety of
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`Pomerleau. Pomerleau actually concludes that training with simulated data “has
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`serious drawbacks.” Ex. 2004 at 40. Pomerleau explains that “differences between
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`the synthetic road images on which the network was trained and the real situations on
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`which the network was tested often resulted in poor performance in real driving
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`situations.” Id. “[I]t quickly became apparent that extending the synthetic training
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`paradigm to deal with more complex situations such as multi-lane and off-road
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`driving would require prohibitively complex training data generators.” Id.; see also id. at
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`56. So, contrary to AVS’s arguments, one of ordinary skill would not have
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`understood from Pomerleau that a neural network in a vehicle could be adequately
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`trained to identify exterior objects using simulated data. Ex. 1023 at ¶¶ 23-25.
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`AVS also relies on U.S. Patent No. 5,537,327; but, this patent relates to the use
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`of neural networks to identify fault impedances in electrical power systems; it involves
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`Petitioner Reply Under 37 C.F.R. § 42.23-24
`IPR2013-00419
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`none of the same complications that would be involved in identifying all possible
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`exterior objects that could collide with a vehicle. Ex. 1023 at ¶ 26.
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`Partial Data
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`2.
`One of ordinary skill would have understood that partial data would have been
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`similarly useless in the context of Lemelson’s collision avoidance system. Ex. 1023 at
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`¶¶ 12-17. Dr. Koutsougeras gives just three examples of partial data that Lemelson
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`could have been using: license plates, taillights or rear windows. See Ex. 2001 at ¶ 70;
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`Ex. 1022 at 175:23-176:3. But none of these items would have made sense in the
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`Lemelson system. Dr. Koutsougeras admits that a neural network could not be
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`trained to identify pedestrians (as disclosed by Lemelson) using data of license plates,
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`taillights or rear windows. Ex. 1022 at 176:4-21. Training using partial data may be
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`useful when there is only a single type of exterior object that is of interest, but not
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`when there are hundreds of potentially different hazardous objects, as in Lemelson.1
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`Ex. 1023 at ¶¶ 12-17.
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`In sum, one of ordinary skill in the art would have understood that “known
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`inputs” in Lemelson refers to real data, and not to simulated or partial data.
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`Nor would one of ordinary skill generally believe that partial data would be
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`useful to train a neural network. Ex. 1023 at ¶¶ 12-17.
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`Petitioner Reply Under 37 C.F.R. § 42.23-24
`IPR2013-00419
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`C. Lemelson Separately Discloses “Adaptive Operation” and “On-
`Line Adjustment” of its Neural Network Which Constitutes
`Training with “Real Data”
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`Lemelson also discloses real-world, post-laboratory training: “Adaptive
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`operation is also possible with on-line adjustment of network weights to meet imaging
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`requirements.” Ex. 1002 at 8:9-10; see also id. at Figs. 1 and 2 (showing the TV camera
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`as an input into the image analyzing computer, which stores the neural network).
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`D. Dr. Koutsougeras’s Declaration Should Be Given Little Weight
`Because He Lacks Expertise With Neural Networks in Vehicles
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`As discussed above, AVS relies on a declaration from Dr. Koutsougeras for the
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`propositions that Lemelson’s disclosure is too vague to meet the “generated from”
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`language, and that simulated or partial data could have been used for training.2
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`However, his testimony should be given little weight because his neural network
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`experience is almost exclusively related to handwriting recognition. See Ex. 2001 at
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`43-45 (unpaginated C.V.). He has never published a paper on pattern recognition in
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`vehicles, see Ex. 1022 at 39:10-22, and did not do any work with pattern recognition
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`Although AVS and Dr. Koutsougeras argue that Lemelson’s disclosure of
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`training is vague, the inventor of the ’057 patent used a nearly verbatim disclosure in
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`another one of his patents: “Neural networks used in the accident avoidance system
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`of this invention are trained to recognize roadway hazards including automobiles,
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`trucks, animals and pedestrians. Training involves providing known inputs to the
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`network resulting in desired output responses.” Ex. 1024 at 50:30-38.
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`Petitioner Reply Under 37 C.F.R. § 42.23-24
`IPR2013-00419
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`algorithms in vehicles until at least 2004. See Id. at 26:14-20. Dr. Koutsougeras’s only
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`purported experience with vehicle exterior monitoring systems was a 2004
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`autonomous vehicle competition with a team of students; but, this project—unlike
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`Lemelson and the ’057 patent—did not employ pattern recognition or a neural
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`network, or even identify exterior objects. Id. at 30:6-31:22.
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`V.
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`SPECIFIC GROUNDS OF REVIEW
`A. Ground of Review A: Claims 1-4, 7-10, 40, 41, 46, 48, 49, 56, 59-
`61, and 64 are Anticipated Under 35 U.S.C. § 102(e) by Lemelson
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`AVS concedes that claims 40, 43, 46, 48 and 49 are anticipated by Lemelson.
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`Paper 33 at 4 n.1. As for the remaining claims in Ground of Review A (claims 1-4, 7-
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`10, 41, 56, 59, 60-62, and 64), AVS argues only that Lemelson fails to disclose the
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`“generated from” language. For the reasons set forth above, this is wrong. The
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`Board should therefore hold the claims in Ground of Review A to be unpatentable.
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`B. Ground of Review B: Claims 30-34, 37-39, and 62 are Obvious
`Under 35 U.S.C. § 103(a) Over Lemelson and Borcherts
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`As Petitioner sets forth in its petition, it would have been obvious to one of
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`ordinary skill in the art to arrange the receiver of Lemelson on the rear view mirror, in
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`view of Borcherts. Lemelson nearly discloses this limitation itself, indicating that
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`“television camera(s) 16 having a wide angle lens 16L is mounted at the front of the
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`vehicle such as the front end of the roof . . . .” Ex. 1002 at 5:31-33. Like Lemelson,
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`Borcherts discloses an automatic vehicle control system. It contains a clear Figure
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`showing the receiver (number 12 in the figure below) at the precise location of the
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`rear view mirror. See Ex. 1004 at Fig. 1; Ex. 1016 at ¶¶ 109-11.
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`AVS argues that the receiver of Borcherts is not explicitly “on” the rear view
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`mirror, and is instead merely near it, or perhaps on the rear view mirror assembly.
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`But AVS’s expert admits that the camera 12 in Borcherts is “where the rear view
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`mirror” is “in most cars.” Ex. 1022 at 236:20-237:4. In any event, the question at
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`issue is whether the claimed invention as a whole would have been obvious to one of
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`ordinary skill, not whether each limitation is explicitly met by the references. See
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`Beckson Marine, Inc. v. NFM, Inc., 292 F.3d 718, 727 (Fed. Cir. 2002). Petitioner and its
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`expert offered several reasons why it would have been obvious to place the camera on
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`the rear view mirror. Ex. 1016 at ¶¶ 110-11. Neither AVS nor its expert rebutted
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`these reasons, or otherwise provide any arguments as to why the claim limitation
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`would not have been a simple substitution of one known element for another to
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`obtain predictable results. See, e.g., KSR Int’l v. Teleflex Inc., 550 U.S. 398, 416 (2007).
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`To the extent that cameras in the prior art were large and were placed
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`somewhere other than on the rear view mirror (such as the front roof, as in Lemelson
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`or Pomerleau), this was a function of available camera size and technology at the time,
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`and not a nonobvious feature of the claimed invention. See Ex. 1016 at ¶ 110.
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`AVS’s arguments largely avoid the merits of this obviousness issue, and instead
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`focus on attacking Petitioner’s expert. AVS’s attacks are unfair. For one, the
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`hypothetical questions quoted involve fictional prior art and were irrelevant to any
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`IPR issue. Second, when his deposition is read as a whole, it is clear that Dr.
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`Papanikolopoulos was forthcoming and did his best to answer reasonable and relevant
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`questions. Ex. 1025. AVS’s attacks should not detract from the ultimate point that it
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`would have been obvious to arrange the receiver of Lemelson on the rear view mirror.
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`C. Ground of Review C: Claims 4, 43, and 59 are Obvious Under 35
`U.S.C. § 103(A) Over Lemelson and Asayama
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`AVS’s only argument with respect to Ground of Review C is that Lemelson
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`does not disclose the “generated from” language. As set forth above, this is wrong.
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`The Board should therefore hold claims 4, 43 and 59 to be unpatentable.
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`D. Ground of Review D: Claim 34 is Obvious Under 35 U.S.C. §
`103(a) Over Borcherts, Lemelson and Asayama
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`AVS makes no independent argument with respect to claim 34 and Ground of
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`Review D. AVS merely asserts that it would not have been obvious to arrange the
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`receiver of Lemelson on the rear view mirror. However, for the reasons set forth
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`above (see, e.g., Section V.A., supra), this is incorrect.
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`IPR2013-00419
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`E. Ground of Review E: Claims 30, 32, and 37-39 are Obvious Under
`35 U.S.C. § 103(a) Over Yamamura and Borcherts
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`AVS’s arguments with respect to Ground of Review E are exactly the same as
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`they are for Ground of Review B. They should be rejected for the same reasons.
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`VI. CONCLUSION
`For the reasons in Toyota’s Petition for Inter Partes Review of U.S. Patent No.
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`6,772,057, for the reasons in the Board’s decision to institute an inter partes review, and
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`for the reasons set forth above, the Board should maintain its decision of
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`unpatentability of claims 1-4, 7-10, 30-34, 37-41, 43, 46, 48, 49, 56, 59-62, and 64.
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`May 27, 2014
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` /Matt Berkowitz/
`Matt Berkowitz (Reg. No. 57,215)
`A. Antony Pfeffer (Reg. No. 43,857)
`Thomas R. Makin
`Kenyon & Kenyon LLP
`One Broadway
`New York, NY 10004
`Tel: 212-425-7200
`Attorney for Petitioner,
`Toyota Motor Corporation
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`Petitioner Reply Under 37 C.F.R. § 42.23-24
`IPR2013-00419
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`CERTIFICATE OF SERVICE
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`The undersigned hereby confirms that Petitioner’s Reply to Patent Owner’s
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`Response and Exhibits 1022-1026 were served on May 27, 2014 via e-mail upon the
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`following counsel of record for Patent Owner:
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`Thomas J. Wimbiscus
`Scott P. McBride
`Christopher M. Scharff
`twimbiscus@mcandrews-ip.com
`smcbride@mcandrews-ip.com
`cscharff@mcandrews-ip.com
`AVS-IPR@mcandrews-ip.com
`MCANDREWS HELD & MALLOY, LTD.
`500 W. Madison St., 34th Floor
`Chicago, IL 60661
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`/Matt Berkowitz/
`Matt Berkowitz (Reg. No. 57,215)
`Kenyon & Kenyon LLP
`One Broadway
`New York, NY 10004
`Tel: 212-425-7200
`Fax: 212-425-5288
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