Trials@uspto.gov
`571-272-7822
`
`Paper 19
`Entered: January 13, 2014
`
`
`UNITED STATES PATENT AND TRADEMARK OFFICE
`____________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`____________
`
`
`
`
`TOYOTA MOTOR CORPORATION
`Petitioner
`
`v.
`
`AMERICAN VEHICULAR SCIENCES LLC
`Patent Owner
`____________
`
`Case IPR2013-00419
`Patent 6,772,057
`____________
`
`
`
`Before JAMESON LEE, MICHAEL W. KIM,
`and LYNNE E. PETTIGREW, Administrative Patent Judges.
`
`
`PETTIGREW, Administrative Patent Judge
`
`
`
`
`
`
`
`DECISION
`Institution of Inter Partes Review
`37 C.F.R. § 42.108
`
`
`
`1
`
`Mercedes-Benz USA, LLC, Petitioner - Ex. 1018
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`

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`Case IPR2013-00419
`Patent 6,772,057
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`I. INTRODUCTION
`
`Petitioner, Toyota Motor Corporation, filed a petition (Paper 3, “Pet.”)
`
`requesting inter partes review 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”). Patent Owner,
`
`American Vehicle Sciences LLC, filed a preliminary response (Paper 17, “Prelim.
`
`Resp.”). We have jurisdiction under 35 U.S.C. § 314.
`
`The standard for instituting an inter partes review is set forth in 35 U.S.C.
`
`§ 314(a), which provides:
`
`THRESHOLD—The Director may not authorize an inter partes
`review to be instituted unless the Director determines that 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.
`
`Upon consideration of the petition and the preliminary response, we
`
`conclude that there is a reasonable likelihood that Petitioner would prevail in
`
`challenging claims 1-4, 7-10, 30-34, 37-41, 43, 46, 48, 49, 56, 59-62, and 64 as
`
`unpatentable. Accordingly, we grant the petition and authorize an inter partes
`
`review to be instituted as to these claims of the ’057 patent.
`
`A. Related Proceedings
`
`Petitioner indicates that Patent Owner has asserted the ’057 patent against
`
`Petitioner in American Vehicular Sciences LLC v. Toyota Motor Corp., No. 6:12-
`
`cv-00410 (E.D. Tex.) (“the 410 litigation”), and also has asserted the ’057 patent in
`
`American Vehicular Sciences LLC v. BMW Group, No. 6:12-cv-00415 (E.D. Tex.);
`
`American Vehicular Sciences LLC v. Subaru of Am. Inc., No. 6:12-cv-004230
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`Case IPR2013-00419
`Patent 6,772,057
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`(E.D. Tex.); and American Vehicular Sciences LLC v. Mercedez-Benz U.S. Int’l,
`
`Inc., No. 6:13-cv-00309 (E.D. Tex.). Pet. 1.
`
`B. The’057 Patent (Ex. 1001)
`
`The ’057 patent, titled “Vehicular Monitoring Systems Using Image
`
`Processing,” generally relates to a vehicle monitoring arrangement for monitoring
`
`an environment exterior of a vehicle. Ex. 1001, Abstract. One embodiment of
`
`such an arrangement described in the ’057 patent includes a transmitter that
`
`transmits electromagnetic waves into the environment exterior of a vehicle and one
`
`or more receivers that receive reflections of the transmitted waves from exterior
`
`objects, such as approaching vehicles. Id. at col. 14, ll. 8-12, 32-37; col. 38, ll. 7-
`
`13; Fig. 7. In a preferred implementation, the transmitter is an infrared transmitter,
`
`and the receivers are CCD (charge coupled device) transducers that receive the
`
`reflected infrared waves. Id. at col. 38, ll. 10-12; col. 39, ll. 25-28. One or more
`
`receivers may be arranged on a rear view mirror of the vehicle. Id. at col. 14, ll.
`
`58-60; col. 38, ll. 22-25. The system also may include radar or pulsed laser radar
`
`(lidar) for measuring distance between the vehicle and exterior objects. Id. at col.
`
`14, ll. 38-40; col. 39, ll. 1-6.
`
`The waves received by the receivers contain information about exterior
`
`objects in the environment, and the receivers generate signals characteristic of the
`
`received waves. Id. at col. 14, ll. 12-14; col. 44-49. A trained pattern recognition
`
`means, such as a neural computer or neural network, processes the signals to
`
`provide a classification, identification, or location of an exterior object. Id. at col.
`
`14, ll. 17-25; col. 39, ll. 49-54. Training of a neural network to provide
`
`classification, identification, or location of objects is accomplished by conducting a
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`large number of experiments in which the system is taught to differentiate among
`
`received signals corresponding to different objects. Id. at col. 36, ll. 22-39
`
`(describing a neural network training session in connection with an embodiment
`
`that monitors an interior of a vehicle, particularly the passenger seat). The
`
`classification, identification, or location of an exterior object may be used to affect
`
`operation of other systems in the vehicle, e.g., to show an image or icon on a
`
`display viewable by a driver or to deploy an airbag. Id. at col. 14, ll. 21-31; col.
`
`39, ll. 54-62.
`
`C. Illustrative Claims
`
`Of the challenged claims, claims 1, 30, 40, and 56 are independent. Claims
`
`1, 30, and 40 are illustrative:
`
`arrangement
`A monitoring
`1.
`environment exterior of a vehicle, comprising:
`
`for monitoring
`
`an
`
`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; 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, said
`trained pattern recognition means being structured and arranged to
`apply a trained pattern recognition algorithm generated from data of
`possible exterior objects and patterns of received waves from the
`possible exterior objects to provide the classification, identification or
`location of the exterior object;
`
`whereby a system in the vehicle is coupled to said processor
`such that the operation of the system is affected in response to the
`classification, identification or location of the exterior object.
`
`4
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`Case IPR2013-00419
`Patent 6,772,057
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`30. A vehicle including a monitoring arrangement for
`monitoring an environment exterior of the vehicle, the monitoring
`arrangement comprising:
`
`at least one receiver arranged on a rear view mirror of the
`vehicle 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; and
`
`a processor coupled to said at least one receiver and arranged to
`classify or identify the exterior object based on the signal and thereby
`provide the classification or identification of the exterior object;
`
`whereby a system in the vehicle is coupled to said processor
`such that the operation of the system is affected in response to the
`classification or identification of the exterior object.
`
`arrangement
`40. A monitoring
`environment exterior of a vehicle, comprising:
`
`for monitoring
`
`an
`
`a plurality of receivers arranged apart from one another and to
`receive waves from different parts of the environment exterior of the
`vehicle which contain information on any objects in the environment
`and generate a signal characteristic of the received waves; and
`
`a processor coupled to said receivers and arranged to classify,
`identify or locate the exterior object based on the signals generated by
`said receivers and thereby provide the classification identification or
`location of the exterior object,
`
`whereby a system in the vehicle is coupled to said processor
`such that the operation of the system is affected in response to the
`classification, identification or location of the exterior object.
`
`Ex. 1001, col. 54, ll. 13-32; col. 55, l. 58 – col. 56, l. 6; col. 56, ll. 37-52.
`
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`D. Asserted Grounds of Unpatentability
`
`Petitioner asserts that the challenged claims are unpatentable based on the
`
`following grounds:
`
`
`
`Reference[s]
`
`Basis
`
`Challenged Claims
`
`Lemelson1
`
`Lemelson and Borcherts2
`Lemelson and Asayama3
`Lemelson, Borcherts, and
`Asayama
`
`§ 102(e)
`
`§ 103(a)
`
`§ 103(a)
`
`1-4, 7-10, 40, 41, 43, 46, 48,
`49, 56, 59-61, and 64
`30-34, 37-39, and 62
`
`4, 43, and 59
`
`§ 103(a)
`
`34
`
`Watanabe4
`
`§ 102(a)
`
`Watanabe and Asayama
`
`§ 103(a)
`
`Borcherts
`
`Asayama
`
`Pomerleau5
`
`Pomerleau and Rombaut6
`Pomerleau and Asayama
`
`
`§ 102(b)
`
`§ 102(b)
`
`§ 102(b)
`
`§ 103(a)
`
`§ 103(a)
`
`30, 32, 34, 37-40, 43, 48, and
`49
`33, 34, 43, and 46
`
`30 and 33
`
`40, 43, 46, and 48
`1, 2, 4, 7, 9, 10, 40, 41, 46,
`48, 49, 56, 59, 61, and 64
`8, 30, 31, 37-39, 60, and 62
`
`3 and 43
`
`1 U.S. Patent No. 6,553,130, issued Apr. 22, 2003 (Ex. 1002) (“Lemelson”).
`2 U.S. Patent No. 5,245,422, issued Sept. 14, 1993 (Ex. 1004) (“Borcherts”).
`3 U.S. Patent No. 5,214,408, issued May 25, 1993 (Ex. 1005) (“Asayama”).
`4 Japanese Unexamined Patent Application Publication No. H07-125567, published
`May 16, 1995 (Ex. 1006) (“Watanabe”). Citations to Watanabe refer to its English
`translation (Ex. 1007).
`5 DEAN A. POMERLEAU, ALVINN: AN AUTONOMOUS LAND VEHICLE IN A NEURAL
`NETWORK (Jan. 1989) (Ex. 1008) (“Pomerleau”).
`6 M. Rombaut, ProLab 2: a driving assistance system, in 1993 IEEE/Tsukuba
`International Workshop on Advanced Robotics 97 (Nov. 8-9, 1993) (Ex. 1010)
`(“Rombaut”).
`
`6
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`Case IPR2013-00419
`Patent 6,772,057
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`Reference[s]
`
`Basis
`
`Challenged Claims
`
`Pomerleau, Asayama, and
`Rombaut
`Suzuki7
`Yamamura8
`Yamamura and Asayama
`
`§ 103(a)
`
`32-34
`
`§ 102(b)
`
`§ 103(a)
`
`§ 103(a)
`
`30, 32, 37, and 38
`
`1, 2, 7-10, 56, 60, 61, and 64
`
`3, 4, and 59
`
`Yamamura and Borcherts
`
`§ 103(a)
`
`30-32, 37-39, and 62
`
`
`
`II. ANALYSIS
`
`A. Claim Construction
`
`In an inter partes review, we construe claim terms in an unexpired patent
`
`according to their broadest reasonable interpretation in light of the specification of
`
`the patent in which they appear. 37 C.F.R. § 42.100(b); see also Office Patent
`
`Trial Practice Guide, 77 Fed. Reg. 48,756, 48,766 (Aug. 14, 2012). We give claim
`
`terms their ordinary and customary meaning, as understood by a person of ordinary
`
`skill in the art, in the context of the entire patent disclosure. In re Translogic
`
`Tech., Inc., 504 F.3d 1249, 1257 (Fed. Cir. 2007). Any special definition for a
`
`term must be set forth in the specification with reasonable clarity, deliberateness,
`
`and precision. In re Paulsen, 30 F.3d 1475, 1480 (Fed. Cir. 1994). We construe
`
`the following claim terms in accordance with these principles.
`
`
`
`7 Toshihiko Suzuki et al., Driving Environment Recognition for Active Safety,
`TOYOTA TECHNICAL REVIEW, Sept. 1993, at 44 (Ex. 1011) (“Suzuki”).
`8 Japanese Unexamined Patent Application Publication No. H06-124340, published
`May 6, 1994 (Ex. 1012) (“Yamamura”). Citations to Yamamura refer to its
`English translation (Ex. 1013).
`
`7
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`1. “trained pattern recognition algorithm”
`
`Independent claims 1 and 56 and dependent claims 31 and 41 recite a
`
`“trained pattern recognition algorithm.” Petitioner cites the following portion of
`
`the ’057 patent as setting forth a relevant definition for “pattern recognition” (Pet.
`
`7):
`
`“Pattern recognition” as used herein will generally mean 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, col. 4, ll. 18-26. Petitioner also cites examples of types of pattern
`
`recognition systems provided in the ’057 patent. Pet. 7 (citing Ex. 1001, col. 4, ll.
`
`43-46).
`
`Petitioner does not articulate a construction for “pattern recognition
`
`algorithm” or “trained pattern recognition algorithm,” but asserts that the ’057
`
`patent 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.” Pet. 8 (citing Ex. 1001, col. 4, ll. 32-35). Petitioner then
`
`asserts that a “neural network” is defined as a type of “trained pattern recognition”
`
`system in the ’057 patent. Pet. 8 (citing Ex. 1001, col. 4, ll. 35-36).
`
`Patent Owner contends that 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
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`that the object belongs to.” Prelim. Resp. 7-8 (citing Ex. 1001, col. 4, ll. 18-22).
`
`Therefore, according to Patent Owner, a “pattern recognition algorithm” is “an
`
`algorithm 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.” Prelim. Resp. 8 (emphasis added). Patent Owner does not
`
`articulate a construction for “trained pattern recognition algorithm,” but notes that
`
`the ’057 patent provides that “[a] trainable or a trained pattern recognition system
`
`as used herein means a pattern recognition system which is taught various patterns
`
`by subjecting the system to a variety of examples.” Prelim. Resp. 10 (citing Ex.
`
`1011, col. 4, ll. 32-35) (emphases added).
`
`Incorporating the definitions of the component parts of “trained pattern
`
`recognition algorithm” discussed above, the broadest reasonable construction of
`
`the term, consistent with its use in the ’057 patent, is an algorithm that processes a
`
`signal that is generated by an object, or is modified by interacting with an object, in
`
`order to determine to which one of a set of classes the object belongs, the
`
`algorithm having been taught, through a variety of examples, various patterns of
`
`received signals generated or modified by objects. The examples from the ’057
`
`patent cited by Petitioner are not part of the broadest reasonable construction of the
`
`term.
`
`2. “trained pattern recognition means”
`
`Independent claims 1 and 56 recite:
`
`trained pattern recognition means for processing the signal to provide
`a classification, identification or location of the exterior object, said
`trained pattern recognition means being structured and arranged to
`apply a trained pattern recognition algorithm generated from data of
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`possible exterior objects and patterns of received waves from the
`possible exterior objects to provide the classification, identification or
`location of the exterior object.
`
`Dependent claims 31 and 41 recite a similar limitation:
`
`trained pattern recognition means for processing the signal to provide
`the classification or identification of the exterior object, said trained
`pattern recognition means being structured and arranged to apply a
`trained pattern recognition algorithm9 generated from data of possible
`exterior objects and patterns of received waves from the possible
`exterior objects.
`
`Both Petitioner and Patent Owner contend that these limitations should be
`
`construed as means-plus-function limitations in accordance with 35 U.S.C. § 112,
`
`¶ 6.10 Pet. 7-8; Prelim. Resp. 10-11. Both parties agree that the recited functions
`
`of the trained pattern recognition means are as follows: (i) processing the signal to
`
`provide a classification or identification (or location for claims 1 and 56) of the
`
`exterior object, and (ii) applying a trained pattern recognition algorithm generated
`
`from data of possible exterior objects and patterns of received waves from the
`
`possible exterior object. Pet. 7-8; Prelim. Resp. 10-11. Petitioner identifies a
`
`neural computer as a corresponding structure disclosed in the ’057 patent for
`
`performing the recited functions. Pet. 8. Petitioner further identifies a disclosed
`
`processor as corresponding structure that applies a trained pattern recognition
`
`
`
`9 Claim 31 recites a “pattern recognition algorithm” rather than a “trained pattern
`recognition algorithm.”
`10 Section 4(c) of the Leahy-Smith America Invents Act, Pub. L. No. 112-29, 125
`Stat. 284, 329 (2011) (“AIA”), re-designated 35 U.S.C. § 112, ¶ 6, as 35 U.S.C.
`§ 112(f). Because the ’057 patent has a filing date before September 16, 2012, the
`effective date of the AIA, we refer to the pre-AIA version of § 112 in this decision.
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`algorithm. Id. Patent Owner asserts that the corresponding structure is not simply
`
`a neural computer or processor, but one that is trained for pattern recognition.
`
`Prelim. Resp. 11.
`
`The “trained pattern recognition means” limitations are presumed to invoke
`
`§ 112, ¶ 6, because they contain “means for” language.” See Personalized Media
`
`Commc’ns, LLC v. Int’l Trade Comm’n, 161 F.3d 696, 703 (Fed. Cir. 1998). The
`
`presumption is not overcome in this case because the limitations fail to recite
`
`sufficient structure for performing the specified functions. See id. at 704.
`
`Therefore, we agree with the parties that the “trained pattern recognition means”
`
`limitations should be construed under § 112, ¶ 6.
`
`The first step in interpreting a means-plus-function limitation is to determine
`
`the recited function. Omega Eng’g, Inc. v. Raytek Corp., 334 F.3d 1314, 1321
`
`(Fed. Cir. 2003). Here, we agree with the parties that the recited functions of the
`
`trained pattern recognition means are as follows: (i) processing the signal to
`
`provide a classification or identification (or location) of the exterior object, and (ii)
`
`applying a trained pattern recognition algorithm generated from data of possible
`
`exterior objects and patterns of received waves from the possible exterior object.
`
`The second step in interpreting a means-plus-function limitation is to
`
`determine the corresponding structures in the written description that perform the
`
`recited functions. Id. As asserted by both parties, the corresponding structure in
`
`the ’057 patent for a trained pattern recognition means is a neural computer, also
`
`referred to as a neural network. See Ex. 1001, col. 14, ll. 17-21; col. 38, ll. 17-19;
`
`col. 39, ll. 49-54. We agree with Patent Owner, however, that the neural computer
`
`must be trained for pattern recognition in order to perform the function of applying
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`a trained pattern recognition algorithm generated from data of possible exterior
`
`objects and patterns of received waves from the possible exterior object.
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`Accordingly, we construe “trained pattern recognition means” as a neural computer
`
`or neural network trained for pattern recognition, and equivalents thereof.
`
`3. “identify” / “identification”
`
`Each challenged independent claim recites “identification . . . of the exterior
`
`object” or a “processor . . . arranged to . . . identify . . . the exterior object.”
`
`Petitioner cites the following portion of the ’057 patent as setting forth a relevant
`
`definition for “identify” (Pet. 8):
`
`To “identify” as used herein will usually mean 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. 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.
`
`Ex. 1001, col. 4, ll. 47-55. Patent Owner cites only the first sentence of the quoted
`
`passage. Prelim. Resp. 12.
`
`Based on the above, we construe “identify” as “determine that the object
`
`belongs to a particular set or class” and “identification” as “determination that the
`
`object belongs to a particular set or class.” We agree with Patent Owner that the
`
`remainder of the passage cited by Petitioner provides examples that are not part of
`
`the broadest reasonable construction of the term.
`
`4. “exterior object”
`
`All of the independent claims require classification or identification of
`
`exterior objects. Petitioner does not articulate a construction for “exterior object,”
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`but contends that identification of road lines (i.e., lane markers) described in some
`
`prior art references is identification of “exterior objects.” See, e.g., Pet. 34-36.
`
`Patent Owner proposes that “exterior object” be construed as “[a] material thing
`
`capable of collision with the vehicle.” Prelim. Resp. 15.
`
`An ordinary meaning of “object” is “anything perceptible by one or more of
`
`the senses, especially something that can be seen and felt; a material thing.” THE
`
`AMERICAN HERITAGE DICTIONARY OF THE ENGLISH LANGUAGE 904 (1980). The
`
`’057 patent does not provide a definition for “exterior object,” but it describes a
`
`system for monitoring the environment exterior of a vehicle that may be used for
`
`detecting approaching objects, such as vehicles, or for detecting vehicles and other
`
`objects in the blind spot of the vehicle’s driver. Ex. 1001, col. 38, ll. 7-55.
`
`Whether used as an anticipatory sensor system for detecting approaching objects or
`
`as a blind spot detector, the system permits recognition of an object “in the vicinity
`
`of [the] vehicle . . . , whether the object is alongside the vehicle, in a blind spot of
`
`the driver, in front of the vehicle or behind the vehicle.” Id. at col. 38, ll. 65-66.
`
`Based on the ordinary meaning of “object,” we agree with Patent Owner that
`
`an exterior object is a material thing. Furthermore, because the system described
`
`in the ’057 patent for monitoring the environment exterior of a vehicle detects
`
`objects in front of, behind, or alongside the vehicle, rather than the roadway on
`
`which the vehicle travels, the broadest reasonable construction of “exterior object”
`
`in view of the ’057 patent disclosure excludes the roadway and any markings on it.
`
`There is no basis, however, for limiting exterior objects to those objects that are
`
`capable of collision with the vehicle, as proposed by Patent Owner. In view of the
`
`ordinary meaning, and consistent with the ’057 patent disclosure, we conclude that
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`the broadest reasonable construction of “exterior object” is a material or physical
`
`thing outside the vehicle, not a part of the roadway on which the vehicle travels.
`
`5. “rear view mirror”
`
`Independent claim 30 and dependent claim 62 recite a “rear view mirror.”
`
`Petitioner asserts that this term “includes both the rear-facing mirror located at the
`
`center of the windshield, as well as the non-rear-facing side mirrors.” Pet. 9.
`
`Patent Owner asserts that the term includes the rear-facing mirror located at the
`
`center of the windshield as well as a mirror attached to “the door window trim
`
`panel,” which faces to the side and rear, but does not include “non-rear-facing side
`
`mirrors.” Prelim. Resp. 14 (citing Ex. 1001, col. 38, ll. 22-25). Based on the
`
`ordinary meaning of the term, we agree with Patent Owner that “rear view mirror”
`
`is a mirror that faces to the rear, which necessarily excludes non-rear-facing
`
`mirrors.
`
`6. “transmitter”
`
`Dependent claims 4, 34, 43, and 59 recite a “transmitter for transmitting
`
`waves into the environment exterior of the vehicle.” Petitioner does not articulate
`
`a construction for “transmitter,” but contends that the term encompasses vehicle
`
`headlights. See, e.g., Pet. 14. Patent Owner proposes that “transmitter” be
`
`construed as a “device for transmitting primarily non-visible waves.” Prelim.
`
`Resp. 16-17. As support, Patent Owner points to examples of transmitters in the
`
`’057 patent, including infrared, radar, laser, and acoustical transmitters. Prelim.
`
`Resp. 17 (citing Ex. 1001, col. 38, ll. 6-11; col. 39, ll. 7-31). Patent Owner further
`
`argues that the ’057 patent distinguishes between detecting oncoming vehicles in
`
`the dark through recognition of headlights or taillights, and detecting other objects
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`during the day via a “transmitter.” Id. (citing Ex. 1001, col. 39, l. 63 – col. 40, l.
`
`9).
`
`We are not persuaded by Patent Owner’s arguments. The ’057 patent
`
`describes an embodiment of a system for monitoring an environment exterior of a
`
`vehicle that includes “a transmitter . . . transmitting electromagnetic, such as
`
`infrared, waves toward [an approaching vehicle].” Ex. 1001, col. 38, ll. 10-12.
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`The ’057 patent further discloses an infrared transmitter as a preferred
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`implementation to be used with receivers that receive reflected infrared waves
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`from the approaching vehicle. Id. at col. 39, ll. 25-28. The disclosure, however,
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`also indicates that electromagnetic waves “can be either visible light, infrared,
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`ultraviolet or radar or low frequency radiation.” Id. at col. 4, ll. 28-31. Thus, the
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`’057 patent broadly describes waves transmitted by a transmitter as including
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`visible light in addition to non-visible waves, such as infrared. Accordingly, we
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`decline to limit the construction of “transmitter” to a device for transmitting
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`primarily non-visible waves. Consistent with the disclosure of the ’057 patent, we
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`conclude that the broadest reasonable construction of a “transmitter for
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`transmitting waves into the environment” encompasses devices that transmit any
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`type of electromagnetic waves, including visible light.
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`B. Anticipation by Lemelson
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`Petitioner contends that claims 1-4, 7-10, 40, 41, 43, 46, 48, 49, 56, 59-61,
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`and 64 are unpatentable under 35 U.S.C. § 102(e) as anticipated by Lemelson. Pet.
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`10-22. To support its assertion, Petitioner provides detailed claim charts and relies
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`on the analysis of Dr. Nikolaos Papanikolopoulos, as set forth in his Declaration
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`(Ex. 1016).
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`15
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`

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`Case IPR2013-00419
`Patent 6,772,057
`
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`1. Lemelson (Ex. 1002)
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`Lemelson discloses a computerized system in a motor vehicle that identifies
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`possible obstacles on a roadway and either warns the driver or controls the
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`operation of vehicle systems, such as the brakes or steering mechanism, to avoid or
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`lessen the effect of a collision. Ex. 1002, Abstract; col. 5, ll. 15-29; col. 8, ll. 38-
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`39. The system includes at least one video camera, preferably a CCD array, and
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`may include multiple cameras for front, side, and rear viewing and for stereo
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`imaging capabilities. Id. at col. 6, ll. 27-42. The video camera also may be
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`implemented with other technologies, including infrared imaging methods. Id. at
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`col. 6, ll. 34-37. In addition, the system may use radar or lidar for range detection.
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`Id. at col. 5, l. 67 – col. 6, l. 4. “[V]ideo scanning and radar or lidar scanning may
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`be jointly employed to identify and indicate distances between the controlled
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`vehicle and objects ahead of, to the side(s) of, and to the rear of the controlled
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`vehicle.” Id. at col. 6, ll. 5-8.
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`The analog signal output from the video camera(s) is digitized in an analog-
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`to-digital convertor and passed to an image analyzing computer, which is
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`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) and provide codes indicating same for use by a
`decision computer, 23, which generates coded control signals which
`are applied through the computer 11 or are directly passed to various
`warning and vehicle operating devices such as a braking computer or
`drive, 35, which operates a brake servo 33, a steering computer or
`drive(s) 39 and 40 which operate steering servos 36; . . . a headlight
`controller 41 for flashing the head lights, a warning light control 42
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`16
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`

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`Case IPR2013-00419
`Patent 6,772,057
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`for flashing external and/or internal warning lights; a horn control 43,
`etc.
`
`Id. at col. 5, ll. 39-59. Lemelson discloses further details regarding a neural
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`network embodiment of the image analyzing computer for identifying objects:
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`Neural networks used in the vehicle . . . warning system are trained to
`recognize roadway hazards which
`the vehicle
`is approaching
`including automobiles, trucks, and pedestrians. Training involves
`providing known inputs to the network resulting in desired output
`responses. The weights are automatically adjusted based on error
`signal measurements until the desired outputs are generated. Various
`learning algorithms may be applied. Adaptive operation is also
`possible with on-line adjustment of network weights to meet imaging
`requirements. The neural network embodiment of the image analysis
`computer 19 provides a highly parallel image processing structure
`with rapid, real-time image recognition necessary for the Motor
`Vehicle Warning and Control System.
`
`Id. at col. 8, ll. 1-14.
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`2. Analysis
`
`Petitioner contends that Lemelson discloses all of the limitations of claims 1-
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`4, 7-10, 40, 41, 43, 46, 48, 49, 56, 59-61, and 64. Pet. 10-22; see also Ex. 1016
`
`¶¶ 47-64 (Papanikolopoulos Decl.). For example, with respect to independent
`
`claim 1, Petitioner asserts that Lemelson’s disclosed system is a monitoring
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`arrangement for monitoring an environment exterior of a vehicle. Petitioner also
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`asserts that the video camera in Lemelson’s system corresponds to the at least one
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`receiver arranged to receive waves from the environment exterior of the vehicle
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`that contain information on objects in the environment (i.e., images received by
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`Lemelson’s video camera) and generate a signal characteristic of the received
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`17
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`Case IPR2013-00419
`Patent 6,772,057
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`waves (digitized output from camera), as required by claim 1. Pet. 11-12, 15-16.
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`Petitioner further asserts that Lemelson’s image analyzing computer, implemented
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`as a neural computing network, corresponds to the processor in claim 1 comprising
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`trained pattern recognition means for processing the signal to provide a
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`classification, identification, or location of the exterior object. Pet. 12, 16. Also,
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`Petitioner asserts that the operation of a system in Lemelson, such as the brakes or
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`steering mechanism, is affected in response to the classification, identification, or
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`location of the exterior object, as required by claim 1. Pet. 13, 17-18.
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`Petitioner applies the same analysis to independent claim 56, which is
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`directed to a vehicle containing a monitoring arrangement and otherwise contains
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`the same limitations as claim 1. Pet. 11-13, 15. Petitioner applies a similar
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`analysis to independent claim 40, which includes some of the same limitations as
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`claim 1. Pet. 11-13, 20-21. Claim 40 further recites “a plurality of receivers
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`arranged apart from one another and to receive waves from different parts of the
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`environment,” which Petitioner asserts is met by Lemelson’s multiple cameras that
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`may be used for front, side, and rear viewing and for stereo imaging capabilities.
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`Pet. 11-12, 20 (citing Ex. 1002, col. 6, ll. 37-38). Claim 41 depends from claim 40
`
`and includes trained pattern recognition means, which Petitioner asserts is
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`disclosed in Lemelson, as discussed with respect to claim 1. Pet. 15, 21.
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`Petitioner also asserts that Lemelson discloses all of the limitations in the
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`remaining dependent claims against which this ground is asserted. For example,
`
`with respect to claim 2, which recites that the “at least one receiver comprises a
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`pair of receivers spaced apart from one another,” Petitioner cites Lemelson’s
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`multiple cameras that may be used for stereo imaging capabilities. Pet. 13, 18.
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`18
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`Case IPR2013-00419
`Patent 6,772,057
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`With respect to claim 3, Petitioner asserts that Lemelson discloses “wherein said at
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`least one receiver is arranged to receive infrared waves” because Lemelson’s video
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`camera may b