UNITED STATES PATENT AND TRADEMARK OFFICE
`__________________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`__________________________________________________________________
`
`TOYOTA MOTOR CORPORATION
`
`Petitioner
`
`
`
`
`
`Patent No. 6,772,057
`Issue Date: Aug. 3, 2004
`Title: VEHICULAR MONITORING SYSTEMS USING IMAGE PROCESSING
`__________________________________________________________________
`
`PETITION FOR INTER PARTES REVIEW
`OF U.S. PATENT NO. 6,772,057
`PURSUANT TO 35 U.S.C. § 312 and 37 C.F.R. § 42.104
`
`Case No. IPR2013-00419
`__________________________________________________________________
`
`
`
`
`
`

`

`TABLE OF CONTENTS
`
`I. Mandatory Notices (37 C.F.R. § 42.8) ........................................................ 1
`A.
`Real Party-In-Interest (37 C.F.R. § 42.8(b)(1)) ............................................ 1
`B.
`Related Matters (37 C.F.R. § 42.8(b)(2)) ....................................................... 1
`C.
`Counsel ............................................................................................................. 2
`Payment of Fees (37 C.F.R. § 42.103) ......................................................... 2
`II.
`III. Requirements for IPR (37 C.F.R. § 42.104) ................................................ 3
`A. Grounds for Standing (37 C.F.R. § 42.104(a)) ............................................. 3
`B.
`Identification of Challenge (37 C.F.R. § 42.104(b)) and Relief
`Requested (37 C.F.R. § 42.22(a)(1)) .............................................................. 3
`C.
`Claim Construction (37 C.F.R. § 42.104(b)(3)) ............................................ 6
`IV. Background of the ’057 Patent .................................................................... 9
`V. How Challenged Claims are Unpatentable (37 C.F.R. §
`42.104(b)(4)-(5)) ......................................................................................... 10
`A. Ground 1: Claims 1-4, 7-10, 40, 41, 43, 46, 48, 49, 56, 59-61
`and 64 are Anticipated Under 35 U.S.C. § 102(b) or (e) by
`Lemelson ........................................................................................................ 10
`Ground 2: Claims 30-34, 37-39 and 62 are Obvious Under
`35 U.S.C. § 103(a) Over Lemelson in View of Borcherts ........................ 22
`C. Ground 3: Claims 4, 43 and 59 are Obvious Under 35 U.S.C.
`§ 103(a) Over Lemelson in View of Asayama ........................................... 24
`D. Ground 4: Claim 34 is Obvious Under 35 U.S.C. § 103(a)
`Over Lemelson in View of Borcherts and in Further View of
`Asayama .......................................................................................................... 25
`E. Ground 5: Claims 30, 32, 34, 37-40, 43, 48 and 49 are
`Anticipated Under 35 U.S.C. § 102(a) by Watanabe ................................. 26
`Ground 6: Claims 33, 34, 43 and 46 are Obvious Under 35
`U.S.C. § 103(a) Over Watanabe in View of Asayama ............................... 32
`G. Ground 7: Claims 30 and 33 are Anticipated Under 35
`U.S.C. § 102(b) by Borcherts ....................................................................... 34
`
`B.
`
`F.
`
`
`
`i
`
`

`

`I.
`
`J.
`
`H. Ground 8: Claims 40, 43, 46, and 48 are Anticipated Under
`35 U.S.C. § 102(b) by Asayama .................................................................... 36
`Ground 9: Claims 1, 2, 4, 7, 9, 10, 40, 41, 46, 48, 49, 56, 59,
`61 and 64 are Anticipated Under 35 U.S.C. § 102(b) by
`Pomerleau ....................................................................................................... 40
`Ground 10: Claims 8, 30, 31, 37-39, 60 and 62 are Obvious
`Under 35 U.S.C. § 103(a) Over Pomerleau in view of
`Rombaut ......................................................................................................... 46
`K. Ground 11: Claims 3 and 43 are Obvious Under 35 U.S.C. §
`103(a) Over Pomerleau in View of Asayama ............................................. 49
`Ground 12: Claims 32, 33 and 34 are Obvious Under 35
`U.S.C. § 103(a) Over Pomerleau in View of Asayama and in
`Further View of Rombaut ........................................................................... 50
`M. Ground 13: Claims 30, 32, 37, and 38 are Anticipated Under
`35 U.S.C. § 102(b) by Suzuki ........................................................................ 51
`N. Ground 14: Claims 1, 2, 7-10, 56, 60, 61 and 64 are Obvious
`Under 35 U.S.C. § 103(a) Over Yamamura ................................................ 53
`O. Ground 15: Claims 3, 4 and 59 are Obvious Under 35 U.S.C.
`§ 103(a) Over Yamamura in View of Asayama ......................................... 59
`Ground 16: Claims 30-32, 37-39 and 62 are Obvious Under
`35 U.S.C. § 103(a) Over Yamamura in View of Borcherts ...................... 59
`VI. Conclusion ................................................................................................. 60
`
`L.
`
`P.
`
`
`
`ii
`
`

`

`Exhibit 1001
`
`Exhibit 1002
`
`Exhibit 1003
`
`Exhibit 1004
`
`Exhibit 1005
`
`Exhibit 1006
`
`
`Exhibit 1007
`
`
`Exhibit 1008
`
`
`Exhibit 1009
`
`
`Exhibit 1010
`
`
`Exhibit 1011
`
`
`Exhibit 1012
`
`
`Exhibit 1013
`
`
`
`
`
`LISTING OF EXHIBITS
`U.S. Patent No. 6,772,057 to Breed
`
`U.S. Patent No. 6,553,130 to Lemelson
`
`File History of U.S. App. No. 08/105,304 to Lemelson
`
`U.S. Patent No. 5,245,422 to Borcherts
`
`U.S. Patent No. 5,214,408 to Asayama
`
`Japanese Unexamined Patent Application Publication H07-
`125567 to Watanabe
`
`Certified English Translation of Japanese Unexamined
`Patent Application Publication H07-125567 to Watanabe
`
`Pomerleau, Dean, “ALVINN: An Autonomous Land
`Vehicle in a Neural Network,” Technical Report AIP-77,
`March 13, 1990
`
`Thorpe et al., “Vision and Navigation for the Carnegie-
`Mellon Navlab,” Annual Review of Computer Science,
`2:521-56, 1987
`
`Rombaut, M.., “PRO-LAB2: A Driving Assistance
`System,” Proceedings of the 1993 IEEE/Tsukuba
`International Workshop on Advanced Robotics, Tsukuba,
`Japan, Nov. 8-9, 1993
`
`Suzuki, et al., Driver Environment Recognition for Active
`Safety, Toyota Technical Review Vol. 43, No. 1 (Sept.
`1993)
`
`Japanese Unexamined Patent Application Publication H06-
`124340 to Yamamura
`
`Certified English Translation of Japanese Unexamined
`Patent Application Publication H06-124340 to Yamamura
`
`iii
`
`

`

`Exhibit 1014
`
`Exhibit 1015
`
`
`Exhibit 1016
`
`
`File History of U.S. App No. 08/247,760 to Breed
`
`Infringement Contentions of American Vehicular Sciences
`LLC with respect to U.S. Patent No. 6,772,057 in the
`litigation captioned American Vehicular Sciences LLC v.
`Toyota Motor Corporation et al., 12-cv-00410 (E.D. Tex.)
`
`Expert Declaration of Dr. Nikolaos Papanikolopoulos
`
`
`
`iv
`
`

`

`Pursuant to 35 U.S.C. §§ 311-319 and 37 C.F.R. § 42, real party in interest,
`
`Toyota Motor Corporation (“Toyota” or “Petitioner”) respectfully petitions for inter
`
`partes review (“IPR”) of claims 1-4, 7-10, 30-34, 37-41, 43, 46, 48, 49, 56, 59-62 and 64
`
`of U.S. Patent No. 6,772,057 (“the ’057 patent”), filed on Nov. 22, 2002, and issued
`
`on Aug. 3, 2004, to David S. BREED, and currently assigned to American Vehicular
`
`Sciences LLC (“AVS”) according to the U.S. Patent and Trademark Office (“the US
`
`PTO”) assignment records. There is a reasonable likelihood that Petitioner will
`
`prevail with respect to at least one claim challenged in this Petition.
`
`I. Mandatory Notices (37 C.F.R. § 42.8)
`A. Real Party-In-Interest (37 C.F.R. § 42.8(b)(1))
`Petitioner, Toyota, is the real party-in-interest.
`B. Related Matters (37 C.F.R. § 42.8(b)(2))
`The ’057 patent has been asserted by the Patent Owner in the following
`
`litigations: American Vehicular Sciences LLC v. Toyota Motor Corp. et al., 12-CV-410 (E.D.
`
`Tex.) (hereinafter, “410 Litigation”); America Vehicular Sciences LLC v. BMW Group et
`
`al., 12-CV-415 (E.D. Tex.); American Vehicular Sciences LLC v. Subaru of Am., Inc., 13-
`
`CV-230 (E.D. Tex.); and American Vehicular Sciences LLC v. Mercedes-Benz U.S.
`
`International, Inc. et al., 13-CV-309 (E.D. Tex.). Petitioner is a named defendant in the
`
`410 Litigation. The earliest that Petitioner or any of its subsidiaries was served was on
`
`July 26, 2012. This Petition is also being filed simultaneously with IPR2013-00420, -
`
`00421, -00422, -00423, and 00424, which address patents that were asserted against
`
`
`
`1
`
`

`

`Toyota in the 410 litigation and in another related case: American Vehicular Sciences
`
`LLC v. Toyota Motor Corp. et al., No. 6:12-CV-406 (E.D. Tex.). Petitioner is not aware
`
`of any other pending administrative or judicial matters that would affect, or be
`
`affected by, a decision in this proceeding.
`
`C. Counsel
`Lead Counsel:
`Matt Berkowitz (Reg. No. 57,215)
`
`Back-up Counsel: Thomas R. Makin (pro hac to be requested upon authorization)
`
`Petitioner requests authorization to file a motion for Thomas R. Makin to
`
`appear pro hac vice as backup counsel. Mr. Makin is an experienced litigation attorney
`
`in patent cases, admitted to practice law in New York, and in several United States
`
`District Courts and Courts of Appeal. Mr. Makin has an established familiarity with
`
`the subject matter at issue and represents Petitioner as a defendant in the related 410
`
`Litigation, identified above.
`
`Electronic Service information: ptab@kenyon.com and mberkowitz@kenyon.com
`
`Post and Delivery: Kenyon & Kenyon LLP, One Broadway, New York, NY 10004
`
`Telephone: 212-425-7200 Facsimile: 212-425-5288
`
`II.
`
`Payment of Fees (37 C.F.R. § 42.103)
`Petitioner is requesting IPR of 28 claims. The US PTO is authorized to charge
`
`the filing fee, as well as any other fees that may be required in connection with this
`
`petition or these proceedings on behalf of Petitioner, to the deposit account of
`
`Kenyon & Kenyon LLP, Deposit Account 11-0600.
`
`
`
`2
`
`

`

`III. Requirements for IPR (37 C.F.R. § 42.104)
`A. Grounds for Standing (37 C.F.R. § 42.104(a))
`Petitioner certifies that the ’057 patent is available for IPR and that Petitioner is
`
`not barred or estopped from petitioning for an IPR challenging the patent claims on
`
`the grounds identified in this petition.
`
`B.
`
`Identification of Challenge (37 C.F.R. § 42.104(b)) and Relief
`Requested (37 C.F.R. § 42.22(a)(1))
`Petitioner requests IPR of and challenges claims 1-4, 7-10, 30-34, 37-41, 43, 46,
`
`48, 49, 56, 59-62 and 64 of the ’057 patent under 35 U.S.C. §§ 102 and 103.
`
`Cancellation of these claims is requested.
`
`The ’057 patent claims priority back through several applications, the earliest of
`
`which is App. No. 474,786, which was filed on June 7, 1995, and issued as U.S. Patent
`
`No. 5,845,000 (“the ’000 patent”).
`
`Petitioner relies upon the following references in support of its petition. None
`
`of these references were of record during prosecution of the ’057 patent.
`
`1) U.S. Patent No. 6,553,130 (“Lemelson,” Ex. 1002) issued from U.S. Appl. No.
`
`08/671,853 (“’853 app.”), filed on June 28, 1996. The ’853 application is a
`
`continuation of U.S. App. No. 08/105,304 (“’304 app.,” Ex. 1003), which was filed on
`
`Aug. 11, 1993, and, as noted where applicable in this petition, contains materially the
`
`same disclosure as the ’853 app. Accordingly, Lemelson constitutes prior art against
`
`the ’057 patent under 35 U.S.C. § 102(e).
`
`
`
`3
`
`

`

`2) U.S. Patent No. 5,245,422 to Borcherts (“Borcherts,” Ex. 1004), which issued on
`
`Sept. 14, 1993, constitutes prior art against the ’057 patent under 35 U.S.C. § 102(b).
`
`3) U.S. Patent No. 5,214,408 to Asayama (“Asayama,” Ex. 1005), which issued on
`
`May 25, 1993, constitutes prior art against the ’057 patent under 35 U.S.C. § 102(b).
`
`4) Japanese Unexamined Patent Application Publication H07-125567 to Watanabe
`
`(“Watanabe,” Ex. 1006, certified English translation, Ex. 1007), which published on
`
`May 16, 1995, constitutes prior art against the ’057 patent under 35 U.S.C. § 102(a)
`
`regardless of priority date.
`
`5) Pomerleau, “ALVINN: An Autonomous Land Vehicle in a Neural Network,”
`
`Technical Report AIP-77, March 13, 1990 (“Pomerleau,” Ex. 1008). Pomerleau was
`
`unclassified and published by the Defense Technical Information Center by March
`
`13, 1990, (as evidenced by the date-stamp on p. 1). It constitutes prior art against the
`
`’057 patent under 35 U.S.C. § 102(b).
`
`6) Thorpe, et al., “Vision and Navigation for the Carnegie-Mellon NAVLAB,”
`
`American Review of Computer Science, 2:521-26 (“Thorpe” Ex. 1009), which
`
`published in 1987, constitutes prior art against the ’057 patent under 35 U.S.C. §
`
`102(b).
`
`7) Rombaut, M., “PRO-LAB2: A Driving Assistance System,” Proceedings of the
`
`1993 IEEE/Tsukuba International Workshop on Advanced Robotics, Tsukuba,
`
`Japan, Nov. 8-9, 1993 (“Rombaut,” Ex. 1010), which published in 1993, constitutes
`
`prior art under 35 U.S.C. § 102(b) against the ’057 patent under 35 U.S.C. § 102(b).
`4
`
`
`
`

`

`8) Suzuki, et al., “Driver Environment Recognition for Active Safety,” Toyota
`
`Technical Review Vol. 43, No. 1 (Sept. 1993) (“Suzuki,” Ex. 1011) published in Sept.
`
`1993. It constitutes prior art under 35 U.S.C. § 102(b) against the ’057 patent under
`
`35 U.S.C. § 102(b).
`
`9) Japanese Unexamined Patent Application Publication H06-124340 to Yamamura
`
`(“Yamamura,” Ex. 1012; certified English translation, Ex. 1013), which published on
`
`May 6, 1994, constitutes prior art against the ’057 patent under 35 U.S.C. § 102(b).
`
`Petitioner requests that claims 1-4, 7-10, 30-34, 37-41, 43, 46, 48, 49, 56, 59-62
`
`and 64 of the ’057 patent be cancelled on the following grounds:
`
`1: Claims 1-4, 7-10, 40, 41, 43, 46, 48, 49, 56, 59-61 and 64 are Anticipated Under 35
`
`U.S.C. § 102(b) or (e) by Lemelson.
`
`2: Claims 30-34, 37-39 and 62 are Obvious Under 35 U.S.C. § 103(a) Over Lemelson
`
`in View of Borcherts.
`
`3: Claims 4, 43 and 59 are Obvious Under 35 U.S.C. § 103(a) Over Lemelson in View
`
`of Asayama.
`
`4: Claim 34 is Obvious Under 35 U.S.C. § 103(a) Over Lemelson in View of
`
`Borcherts and in Further View of Asayama.
`
`5: Claims 30, 32, 34, 37-40, 43, 48 and 49 are Anticipated Under 35 U.S.C. § 102(a) by
`
`Watanabe.
`
`6: Claims 33, 34, 43 and 46 are Obvious Under 35 U.S.C. § 103(a) Over Watanabe in
`
`View of Asayama.
`
`
`
`5
`
`

`

`7: Claims 30 and 33 are Anticipated Under 35 U.S.C. § 102(b) by Borcherts.
`
`8: Claims 40, 43, 46 and 48 are Anticipated Under 35 U.S.C. § 102(b) by Asayama.
`
`9: Claims 1, 2, 4, 7, 9, 10, 40, 41, 46, 48, 49, 56, 59, 61 and 64 are Anticipated Under
`
`35 U.S.C. § 102(b) by Pomerleau.
`
`10: Claims 8, 30, 31, 37-39, 60 and 62 are Obvious Under 35 U.S.C. § 103(a) Over
`
`Pomerleau in View of Rombaut.
`
`11: Claims 3 and 43 are Obvious Under 35 U.S.C. § 103(a) Over Pomerleau in View of
`
`Asayama.
`
`12: Claims 32, 33, and 34 are Obvious Under 35 U.S.C. § 103(a) Over Pomerleau in
`
`View of Asayama and in Further View of Rombaut.
`
`13: Claims 30, 32, 37, and 38 are Anticipated under 35 U.S.C. § 102(b) by Suzuki.
`
`14: Claims 1, 2, 7-10, 56, 60, 61 and 64 are Obvious Under 35 U.S.C § 103(a) over
`
`Yamamura.
`
`15: Claims 3, 4 and 59 are Obvious Under 35 U.S.C. § 103(a) Over Yamamura in
`
`View of Asayama.
`
`16: Claims 30-32, 37-39 and 62 are Obvious Under 35 U.S.C. § 103(a) Over
`
`Yamamura in View of Borcherts.
`
`C.
`Claim Construction (37 C.F.R. § 42.104(b)(3))
`A claim subject to IPR is given its “broadest reasonable construction in light of
`
`the specification of the patent in which it appears.” (37 C.F.R. § 42.100(b).) The
`
`words of the claim are to be given their plain meaning unless it is inconsistent with
`
`
`
`6
`
`

`

`the specification. (In re Zletz, 893 F.2d 319, 321 (Fed. Cir. 1989).) As indicated below,
`
`several terms of the ’057 patent are explicitly defined in the specification or otherwise
`
`require further explanation. Petitioner contends that all other claim terms should
`
`carry their plain meaning.
`
`1. “pattern recognition algorithm” (claim 31)
`
`
`The ’057 patent defines “pattern recognition” as “any system which processes a
`
`signal that is generated by an object, or is modified by interacting with an object, in
`
`order to determine which one of a set of classes that the object belongs to. Such a
`
`system might determine only that the object is or is not a member of one specified
`
`class, or it might attempt to assign the object to one of a larger set of specified classes,
`
`or find that it is not a member of any of the classes in the set.” (Ex. 1001, 4:18-26.)
`
`The Board applied this definition during prosecution of App. No. 08/247,760, which
`
`is the parent of the ’000 patent (to which the ’057 patent claims priority). (Ex. 1014,
`
`p. 189-190.) The ’057 patent also defines “a neural network, sensor fusion, fuzzy
`
`logic, etc.” as types of pattern recognition systems. (Ex. 1001, 4:43-46.)
`
`2. “trained pattern recognition means” (claims 1, 31, 41, 56);
`“trained pattern recognition algorithm” (claims 1, 41, 56)
`
`
`The term “trained pattern recognition means” is written in means plus function
`
`format. (35 U.S.C. § 112, ¶ 6.) The corresponding functions are (1) “processing the
`
`signal to provide a classification, identification or location of the exterior object” and
`
`(2) applying a pattern recognition algorithm (specifically applying a “trained” pattern
`
`
`
`7
`
`

`

`recognition algorithm in the case of claims 1, 41 and 56) generated from data of
`
`possible exterior objects and patterns of received waves from the possible exterior
`
`objects. A “neural computer” is defined as one structure for performing these
`
`functions. (See Ex. 1001, 38:18; 39:51.) A “processor” can also apply a trained
`
`pattern recognition algorithm, (see id. at 43:60), albeit to identify interior objects. Such
`
`a “processor” should be considered to be within the scope of the claim (under 37
`
`C.F.R. § 42.100(b)), particularly since AVS has applied it that way in the 410
`
`Litigation. (Ex. 1015, p. 39 (“AVS identifies the collision determining computer or
`
`other processor as the trained pattern recognition means . . . .”) (emphasis added).)
`
`The specification also defines a “trainable or trained pattern recognition
`
`system” as “a pattern recognition system which is taught various patterns by
`
`subjecting the system to a variety of examples.” (Ex. 1001, 4:32-35.) A “neural
`
`network” is defined as a type of “trained pattern recognition” system. (Id. at 4:35-36.)
`
` “identify” / “identification” (claims 1, 30, 31, 40, 41, 56)
`
`3.
`The ’057 patent defines “identify” as “to determine that the object belongs to a
`
`particular set or class. The class may be one containing, for example, all rear facing
`
`child seats, one containing all human occupants, or all human occupants not sitting in
`
`a rear facing child seat depending on the purpose of the system.” (Ex. 1001, 4:47-52.)
`
`“In the case where a particular person is to be recognized, the set or class will contain
`
`only a single element, i.e., the person to be recognized.” (Id. at 4:52-55.)
`
`4. “measurement means for measuring a distance between the
`
`
`
`8
`
`

`

`exterior object and the vehicle” (claims 9, 38, 48, 64)
`
`This claim term is written in means plus function format. (35 U.S.C. § 112, ¶
`
`6.) The specification indicates that the structure employed for measuring distance
`
`includes a laser radar system, a radar system and/or a pair of cameras. (Ex. 1001,
`
`39:1-20.) The corresponding function should carry its plain and ordinary meaning.
`
`5. “rear view mirror” (claims 30, 62)
`
`The term “rear view mirror” includes both the rear-facing mirror located at the
`
`center of the windshield, as well as the non-rear-facing side mirrors. The ’057 patent
`
`states (with respect to Figure 7), “[a]n alternate mounting location [for the receiver] is
`
`shown as 738 which is in the door window trim panel where the rear view mirror (not
`
`shown) is frequently attached.” (Ex. 1001, 38:22-25; see also id. at 38:47-48.)
`
`IV. Background of the ’057 Patent
`The ’057 patent generally relates to a vehicle monitoring system that utilizes
`
`various types of sensors such as cameras, radar or laser radar (lidar) in order to detect
`
`objects. (Ex. 1001, 17:53-23:9; 39:1-20.) In a preferred embodiment, a processor
`
`receives the signals obtained by the sensor(s) and identifies, classifies or locates an
`
`object using a trained pattern recognition algorithm. (Id. at 14:8-25; 8:15-19.) A
`
`system in the vehicle, such as a visual display, can then be affected depending on the
`
`classification, identification or location of the exterior object. (Id. at 14:26-28.)
`
`Petitioner challenges four independent claims, claims 1, 30, 40 and 56.
`
`Independent claims 1 and 56 are very similar, and require (i) a “at least one
`
`
`
`9
`
`

`

`receiver” arranged to receive waves from the vehicle exterior, (ii) a “processor
`
`comprising trained pattern recognition means” that applies a “trained pattern
`
`recognition algorithm” to provide the “classification, identification or location of the
`
`exterior object” and (iii) a system in the vehicle that is “affected in response” thereto.
`
`Independent claim 30 is similar to claims 1 and 56 except that it requires the at
`
`least one receiver to be “arranged on a rear view mirror vehicle” and does not require
`
`“trained pattern recognition means” or a “trained pattern recognition algorithm.”
`
`Independent claim 40 requires a “plurality of receivers” but does not require “trained
`
`pattern recognition means” or a “trained pattern recognition algorithm.”
`
`V. How Challenged Claims are Unpatentable (37 C.F.R. § 42.104(b)(4)-(5))
`
`
`A. Ground 1: Claims 1-4, 7-10, 40, 41, 43, 46, 48, 49, 56, 59-61 and 64 are
`Anticipated Under 35 U.S.C. § 102(b) or (e) by Lemelson
`Claims 1-4, 7-10, 40, 41, 43, 46, 48, 49, 56, 59-61 and 64 are anticipated by
`
`Lemelson under 35 U.S.C. § 102(e). Lemelson describes a vehicle exterior monitoring
`
`system that can identify hazardous exterior objects and either warn the driver or affect
`
`a vehicle system such as the brakes or steering to minimize the likelihood or effects of
`
`a collision. (Ex. 1002, Abstract; 2:53-63; 3:5-26; 5:15-18; 8:38-39; Fig. 1; Ex. 1003, pp.
`
`5; 7-10; 12; 17-18; Fig. 1.)1
`
`Figure 1, reproduced below, depicts a radar/lidar computer 14 for locating an
`
`exterior object based on received radar or lidar signals, camera receiver 16 to receive
`
`1 Cites are made to the priority ’304 app. (Ex. 1003) to show disclosure continuity.
`
`
`
`10
`
`

`

`waves from the exterior environment, an image analysis computer 19 (hereinafter
`
`“IAC”) for classifying and identifying exterior objects, brakes 33 and steering 36 that
`
`are affected depending on the identified exterior objects and a display 32 for warning
`
`the driver of a potential collision. (Ex. 1002, 5:31-56; 5:67-6:8; Ex. 1003, pp. 12-13.)
`
`
`
`Lemelson indicates that the signal output from the camera (or cameras) is
`
`digitized and passed to the IAC. (Ex. 1002, 5:36-39; Ex. 1003, pp. 12-13.) The IAC
`
`operates to “identify” the object(s) detected outside the vehicle using “neural
`
`networks” that have been “trained” using “known inputs.” (Ex. 1002, 5:39-45; 7:47-
`
`8:10; 8:21-23; Ex. 1003, pp. 13; 16-17.)
`
`Lemelson anticipates each of challenged independent claims 1, 40 and 56.
`
`First, Lemelson discloses the “receiver” limitations (“plurality of receivers” in
`
`
`
`11
`
`

`

`claim 40). Lemelson discloses radar and lidar2 receivers, (Ex. 1002, 5:67-6:8; Ex. 1003,
`
`p. 13), as well as “multiple cameras” that are used “for stereo imaging capabilities”
`
`(Ex. 1002, 6:37-38; Ex. 1003, p. 14).
`
`Second, Lemelson discloses the processing of received signals to provide a
`
`classification, identification or location of the exterior object. Lemelson further
`
`discloses that “trained pattern recognition means . . .” can be used for such
`
`classification, identification or location, as required by claims 1 and 56. Lemelson
`
`indicates that the signal output from the camera(s) is passed to the IAC. (Ex. 1002,
`
`5:36-39; Ex. 1003, pp. 12-13.) The IAC meets the “trained pattern recognition means
`
`. . .” limitation because it discloses a processor that implements a neural network. It is
`
`therefore a “neural computer.” (Ex. 1016, ¶¶ 52-55.)
`
`Moreover, the IAC classifies, identifies and locates exterior objects. It
`
`accomplishes this using a neural network program that has been trained on a data set.
`
`As set forth above, “neural networks” are defined by the ’057 patent to be a type of
`
`“trained pattern recognition algorithm.” (Ex. 1001, 4:35-36.) In particular, Lemelson
`
`states that the IAC is “provided, implemented and programmed using neural
`
`networks and artificial intelligence as well as fuzzy logic algorithms” to “(a) identify
`
`objects on the road ahead such as other vehicles, pedestrians, barriers and dividers,
`
`turns in the road, signs and symbols, etc., and generate identification codes, and (b)
`
`detect distances from such objects by their size (and shape) . . . .” (Ex. 1002, 5:39-45;
`
`Lidar is the same as laser radar. (Ex. 1016, ¶ 48.)
`2
`12
`
`
`
`

`

`Ex. 1003, p. 13.) Lemelson explains that the neural network in the IAC may be
`
`“trained” using “known inputs.” (Ex. 1002, 7:47-8:10; 8:21-23; Ex. 1003, pp. 16-17.)
`
`This disclosure meets the “trained pattern recognition means . . .” limitation of claims
`
`1 and 56, as well as the broader “classify, identify or locate” limitation of claim 40.
`
`Last, Lemelson discloses the limitations of claims 1, 40 and 56 requiring that a
`
`vehicle system be affected in response to the classification, identification or location
`
`of the exterior object. Based on the neural network object determination, the IAC
`
`can display “symbols representing the hazard objects.” (Ex. 1002, 6:43-55; Fig. 2;
`
`9:60-62; Ex. 1003, pp. 14-15, 20.) The IAC also provides codes to a decision
`
`computer 23, which “integrates the inputs from the image analysis computer 19” as
`
`well as a “radar or lidar computer 14.” (Ex. 1002, 8:30-33; 6:1-8; Ex. 1003, pp. 13;
`
`17.) The decision computer 23 then generates control signals to control a vehicle
`
`system such as the brakes or steering. (Ex. 1002, 5:46-51; 2:53-3:26; Ex. 1003, pp. 7-
`
`8; 13.) Thus, Lemelson anticipates each of independent claims 1, 40 and 56.
`
`Lemelson also anticipates claims 2-4, 7-10, 41, 43, 46, 48, 49, 59-61 and 64.
`
`Claim 2 (from 1) requires that the “at least one receiver comprises a pair of
`
`receivers spaced apart from one another.” Lemelson teaches that “[m]ultiple cameras
`
`may be used” for “stereo imaging capabilities.” (Ex. 1002, 6:37-38; Ex. 1003, p. 14.)
`
`Claim 3 (from 1) requires that the “at least one receiver is arranged to receive
`
`infrared waves.” Lemelson teaches that the camera may be implemented with known
`
`“infrared imaging methods.” (Ex. 1002, 6:34-37; Ex. 1003, p. 14.)
`13
`
`
`
`

`

`Dependent claims 4 (from 1), 43 (from 40), and 59 (from 56) require a
`
`“transmitter for transmitting waves into the environment exterior of the vehicle
`
`whereby the at least one receiver” (“plurality of receivers,” in the case of claim 43) is
`
`“arranged to receive waves transmitted by said transmitter and reflected by any
`
`exterior objects.” Lemelson meets this because it teaches a vehicle with “headlights,”
`
`(Ex. 1002, 3:29; 5:57; Ex. 1003, pp. 9; 13), which project light that is reflected off
`
`exterior objects (Ex. 1016, ¶ 59). Lemelson teaches that a camera (or cameras, for
`
`stereo imaging capabilities) may be used for “front” viewing. (Ex. 1002, 6:37-38; Ex.
`
`1003, p. 14.) Such a positioned camera (or cameras) would receive light transmitted
`
`by the vehicle headlights and reflected off exterior objects. (Ex. 1016, ¶ 59.)
`
`Claims 7 (from 1), 46 (from 40), and 61 (from 56) require that the affected
`
`vehicle system is a “display viewable to the driver and arranged to show an image or
`
`icon of the exterior object.” In Lemelson, the IAC can display “symbols representing
`
`the hazard objects.” (Ex. 1002, 6:43-55; Fig. 2; 9:60-62; Ex. 1003, pp. 14-15; 20.)
`
`Lemelson also anticipates claim 8 (from 1) because it discloses that the camera
`
`is preferably a “CCD array.” (Ex. 1002, 6:31-32; Ex. 1003, p. 14.)
`
`Claims 9 (from 1), 48 (from 40), and 64 (from 56) require “measurement means
`
`for measuring a distance between the exterior object and a vehicle.” Lemelson meets
`
`this limitation because it discloses “multiple cameras” for “stereo imaging
`
`capabilities.” (Ex. 1002, 6:37-38; Ex. 1003, p. 14.) It also uses radar and/or lidar for
`
`“distance measurements.” (Ex. 1002, 8:56; 5:67-6:8; Ex. 1003, p. 13; 18.) Lemelson
`14
`
`
`
`

`

`meets dependent claims 10 (from 9) and claim 49 (from 48), which limit the
`
`measurement means to laser or laser radar, based on the same disclosure.
`
`Claim 41 (from 40) includes the same “trained pattern recognition means”
`
`limitation found in claims 1 and 56 and is therefore similarly anticipated.
`
`Claim 60 (from 56) requires that the “at least one receiver is arranged to receive
`
`waves from a blind spot of the vehicle.” Lemelson meets this limitation because it
`
`discloses that the camera(s) may be used for “side and rear viewing,” i.e., the locations
`
`that are blind to the driver while driving. (Ex. 1002, 6:37-38; Ex. 1003, p. 14.)
`
`Accordingly, and as set forth in the claim charts provided below, Lemelson
`
`anticipates challenged claims 1-4, 7-10, 40, 41, 43, 46, 48, 49, 56, 59-61 and 64 of the
`
`’057 patent. 3 Charts for claims 46, 48 and 49 are not included because they are
`
`substantively the same as claims 7, 9 and 10, respectively, and the relevant disclosure
`
`in Lemelson is the same. Similarly, claims 56, 59, 61 and 64 are not charted because
`
`they are substantively the same as claims 1, 4, 7 and 9, respectively.
`
`Lemelson (Ex. 1002); ’304 app. (Ex. 1003)
`
`’057 Patent,
`Claim 1
`1 (preamble). A
`monitoring
`arrangement
`for monitoring
`an environment
`exterior of a
`
`3
`Limitation indicators such as “preamble,” “a,” “b,” etc. have been added to the
`
`E.g., Fig. 1 (see also Ex. 1003, Fig. 1).
`E.g., 2:14-16 (“a video scanning system, such as a television
`camera and/or one or more laser scanners mounted on the
`vehicle scan the road in front of the vehicle . . . .”) (see also Ex.
`1003, p. 7).
`
`claim charts to facilitate cross-referencing.
`
`
`
`15
`
`

`

`vehicle,
`comprising:
`(a) at least one
`receiver
`arranged to
`receive waves
`from the
`environment
`exterior of the
`vehicle which
`contain
`information on
`any objects in
`the
`environment
`and generate a
`signal
`characteristic of
`the received
`waves;
`
`(b) and a
`processor
`coupled to said
`at least one
`receiver and
`comprising
`trained pattern
`recognition
`means for
`processing the
`signal to
`provide a
`classification,
`identification or
`location of the
`exterior object,
`
`(c) said trained
`
`E.g., 6:37-38 (“Multiple cameras may be used for front, side and
`rear viewing”) (see also Ex. 1003, p. 14).
`E.g., Figs. 1-2 (see also Ex. 1003, Fig. 1).
`E.g., 5:31-39 (“A television camera(s) 16 having a wide angle lens
`16L is mounted at the front of the vehicle such as the front end
`of the roof, bumper or end of the hood to scan the road ahead of
`the vehicle . . . . The analog signal output of camera 16 is digitized
`in an A/D convertor 18 and passed directly to or through a video
`preprocessor 51 to microprocessor 11, to an image field analyzing
`computer 19 . . . .”) (see also Ex. 1003, pp. 12-13).
`E.g., 5:67-6:8 (“An auxiliary range detection means comprises a
`range computer 21 which accepts digital code signals from a radar
`or lidar computer 14 which interprets radar and/or laser range
`signals from respective reflected radiation receiving means on the
`vehicle. In a modified form, video scanning and radar or lidar
`scanning may be jointly employed to identify and indicate
`distances between the controlled vehicle and objects ahead of, to
`the side(s) of, and to the rear of the controlled vehicle.” (see also
`Ex. 1003, p. 13).
`E.g., 6:37-38 (“Multiple cameras may be used for front, side and
`rear viewing”) (see also Ex. 1003, p. 14).
`E.g., Figs. 1-4 (see also Ex. 1003, Figs. 1-4).
`E.g., 5:36-45 (“The analog signal output of camera 16 is digitized
`in an A/D convertor 18 and passed directly to or through a video
`preprocessor 51 to microprocessor 11, to an image field analyzing
`computer 19, which is provided, implemented and programmed
`using neural networks and artificial intelligence as well as fuzzy
`logic algorithms to (a) identify objects on the road ahead such as
`other vehicles, pe