Trials@uspto.gov
`571-272-7822
`
`Paper 13
`Entered: October 23, 2014
`
`
`UNITED STATES PATENT AND TRADEMARK OFFICE
`____________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`____________
`
`
`
`
`MERCEDES-BENZ USA, LLC,
`Petitioner,
`
`v.
`
`AMERICAN VEHICULAR SCIENCES LLC,
`Patent Owner.
`____________
`
`Case IPR2014-00646
`Patent 6,772,057 B2
`____________
`
`
`
`
`
`Before JAMESON LEE, TREVOR M. JEFFERSON, and
`LYNNE E. PETTIGREW, Administrative Patent Judges.
`
`
`PETTIGREW, Administrative Patent Judge.
`
`
`
`DECISION
`Institution of Inter Partes Review
`37 C.F.R. § 42.108
`
`
`
`
`
`
`571-272-7822
`
`Paper 13
`Entered: October 23, 2014
`
`
`UNITED STATES PATENT AND TRADEMARK OFFICE
`____________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`____________
`
`
`
`
`MERCEDES-BENZ USA, LLC,
`Petitioner,
`
`v.
`
`AMERICAN VEHICULAR SCIENCES LLC,
`Patent Owner.
`____________
`
`Case IPR2014-00646
`Patent 6,772,057 B2
`____________
`
`
`
`
`
`Before JAMESON LEE, TREVOR M. JEFFERSON, and
`LYNNE E. PETTIGREW, Administrative Patent Judges.
`
`
`PETTIGREW, Administrative Patent Judge.
`
`
`
`DECISION
`Institution of Inter Partes Review
`37 C.F.R. § 42.108
`
`
`
`
`
`
`
`Case IPR2014-00646
`Patent 6,772,057 B2
`
`
`I. INTRODUCTION
`
`Petitioner, Mercedes-Benz USA, LLC, filed a Petition for inter partes
`
`review of claims 1, 2, 4, 7, 16, 30, 31, 40, 41, 43, 46, 56, 59–62, 77, 78, and
`
`81–83 of U.S. Patent No. 6,772,057 B2 (Ex. 1001, “the ’057 patent”). Paper
`
`1 (“Pet.”). Patent Owner, American Vehicular Sciences LLC, filed a
`
`Preliminary Response. Paper 11 (“Prelim. Resp.”). We have jurisdiction
`
`under 35 U.S.C. § 314(a), which provides that an inter partes review may
`
`not be instituted “unless . . . the information presented in the petition . . . and
`
`any response . . . 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 the information presented shows there is a reasonable likelihood
`
`that Petitioner would prevail in establishing the unpatentability of claims
`
`1, 2, 4, 7, 16, 30, 31, 40, 41, 43, 46, 56, 59–62, 77, 78, and 81–83.
`
`Accordingly, we authorize an inter partes review to be instituted as to claims
`
`1, 2, 4, 7, 16, 30, 31, 40, 41, 43, 46, 56, 59–62, 77, 78, and 81–83 of the ’057
`
`patent.
`
`A. Related Matters
`
`The ’057 patent is the subject of another pending inter partes review:
`
`Toyota Motor Corp. v. American Vehicular Sciences LLC, IPR2013-00419
`
`(instituted Jan. 13, 2014). Pet. 1–2.
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`Patent 6,772,057 B2
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`B. The’057 Patent
`
`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 14:8–12, 14:32–37, 38:7–13, 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 38:10–12, 39:25–28. One or more receivers may be arranged on a rear
`
`view mirror of the vehicle. Id. at 14:58–60, 38:22–25. The system also may
`
`include radar or pulsed laser radar (lidar) for measuring distance between the
`
`vehicle and exterior objects. Id. at 14:38–40, 39:1–6.
`
`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 14:12–14, 39:44–49. 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 14:17–25, 39:49–54. Training of a neural network
`
`to provide classification, identification, or location of objects is
`
`accomplished by conducting a large number of experiments in which the
`
`system is taught to differentiate among received signals corresponding to
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`different objects. Id. at 36:22–39 (describing a neural network training
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`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 14:21–31, 39:54–62.
`
`C. Illustrative Claims
`
`Of the challenged claims, claims 1, 16, 30, 40, 56, and 77 are
`
`independent. Claims 1, 16, 30, and 40 are illustrative of the claimed subject
`
`matter:
`
`A monitoring arrangement for monitoring an
`1.
`environment exterior of a vehicle, comprising:
`
`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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`16. A monitoring arrangement for monitoring an
`environment exterior of a vehicle, comprising:
`
`at least one CCD array positioned to obtain images of the
`environment exterior of the vehicle; and
`
`a processor coupled to said at least one CCD array and
`comprising trained pattern recognition means for processing the
`images obtained by said at least one CCD array to provide a
`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.
`
`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.
`
`40. A monitoring arrangement for monitoring an
`environment exterior of a vehicle, comprising:
`
`a plurality of receivers arranged apart from one another
`and to receive waves from different parts of the environment
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`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, 54:13–32, 55:5–17, 55:58–56:6, 56:37–52.
`
`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
`
`Lemelson and Nishio2
`
`Lemelson and Borcherts3
`Lemelson and Komoda4
`
`
`§ 102(e)
`
`§ 103(a)
`
`§ 103(a)
`
`§ 103(a)
`
`1, 2, 4, 7, 16, 40, 41, 43, 46, 56,
`59, 60, 61, 77, 78, and 81–83
`1, 2, 4, 7, 16, 40, 41, 43, 46, 56,
`59, 60, 61, 77, 78, and 81–83
`1, 2, 4, 7, 16, 40, 41, 43, 46, 56,
`59, 60, 61, 77, 78, and 81–83
`30, 31, and 62
`
`§ 103(a)
`
`30, 31, and 62
`
`1 U.S. Patent No. 6,553,130, issued Apr. 22, 2003 (Ex. 1002).
`2 European Patent Application Publication No. 0582236A1, published
`Feb. 9, 1994 (Ex. 1004).
`3 U.S. Patent No. 5,245,422, issued Sept. 14, 1993 (Ex. 1006).
`4 Norio Komoda et al., Automated Vehicle/Highway System, 13th Int’l
`Technical Conf. on Experimental Safety Vehicles, 1991, at 459 (Ex. 1007).
`6
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`Case IPR2014-00646
`Patent 6,772,057 B2
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`Reference[s]
`
`Basis
`
`Challenged Claims
`
`Lemelson and Kawai5
`Lemelson and Asayama6
`Lemelson and Suzuki7
`Lemelson and Ulke8
`Nishio
`
`Nishio and Asayama
`Nishio and Lemelson9
`Nishio and Borcherts
`
`§ 103(a)
`
`30, 31, and 62
`
`§ 103(a)
`
`4 and 59
`
`§ 103(a)
`
`43 and 81
`
`§ 103(a)
`
`60 and 82
`
`§ 102(b)
`
`1, 4, 16, 56, and 59
`
`§ 103(a)
`
`2, 4, 40, 41, 43, 59, 77, 78, and 81
`
`§ 103(a)
`
`7 and 61
`
`§ 103(a)
`
`30, 31, and 62
`
`Nishio and Komoda
`
`§ 103(a)
`
`30, 31, and 62
`
`Nishio and Kawai
`Yamamura10
`Yamamura and Lemelson
`
`§ 103(a)
`
`30, 31, and 62
`
`§ 102(b)
`
`40, 43, 77, and 81
`
`§ 103(a)
`
`46 and 83
`
`
`
`5 Mitsuo Kawai, Collision Avoidance Technologies, Leading Change: The
`Transportation Electronic Revolution, Proceedings of the 1994 Int’l
`Congress on Transp. Electronics, Oct. 1994, at 305 (Ex. 1008).
`6 U.S. Patent No. 5,214,408, issued May 25, 1993 (Ex. 1009).
`7 Toshihiko Suzuki et al., Driving Environment Recognition for Active
`Safety, Toyota Technical Review, Sept. 1993, at 44 (Ex. 1010).
`8 Walter Ulke et al., Radar Based Automotive Obstacle Detection System,
`SAE Technical Paper Series, Feb. 28–Mar. 3, 1994, at 41 (Ex. 1011).
`9 Petitioner has asserted this ground based on Nishio and Lemelson as a
`different ground from that based on Lemelson and Nishio.
`10 Japanese Unexamined Patent Application Publication No. H06-124340,
`published May 6, 1994 (Ex. 1013). Citations to Yamamura refer to its
`English translation (Ex. 1012).
`
`7
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`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). 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).
`
`Petitioner proposes and applies the broadest reasonable constructions
`
`for claim terms in the ’057 patent that we determined in Toyota Motor Corp.
`
`v. American Vehicular Sciences LLC, IPR2013-00419, slip op. at 8–15
`
`(PTAB Jan. 13, 2014) (Paper 19). Pet. 5–6. For purposes of this decision,
`
`we adopt the constructions in Toyota Motor Corp. v. American Vehicular
`
`Sciences LLC, IPR2013-00419, slip op. at 8–15 (PTAB Jan. 13, 2014)
`
`(Paper 19), provided in the table below.
`
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`Case IPR2014-00646
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`
`Claim Term
`
`Board’s Construction
`
`“trained pattern
`recognition algorithm”
`
`“trained pattern
`recognition means”
`
`“identify”
`
`“exterior object”
`
`“rear view mirror”
`
`“transmitter”
`
`“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”
`“a neural computer or neural network
`trained for pattern recognition, and
`equivalents thereof”
`“determine that the object belongs to a
`particular set or class”
`“a material or physical thing outside the
`vehicle, not a part of the roadway on which
`the vehicle travels”
`“a mirror that faces to the rear”
`“device that transmits any type of
`electromagnetic waves, including visible
`light”
`
`Claims 1, 31, 41, and 56 further require the trained pattern recognition
`
`algorithm to be “generated from data of possible exterior objects and
`
`patterns of received waves from the possible exterior objects.” Petitioner in
`
`this case argues that the broadest reasonable construction of that claim
`
`language does not require that the training set used to train the pattern
`
`recognition algorithm be imaged directly from physical exterior objects.
`
`Pet. 10–11. In Petitioner’s view, the term “could mean any type of data so
`
`long as it relates to information about such objects, irrespective of whether it
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`9
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`is real image data or synthetically generated.” Id. at 11 (citing Ex. 1016 ¶ 33
`
`(Decl. of Dr. Larry S. Davis)).
`
`We are not persuaded that the broadest reasonable construction of
`
`“generated from data of possible exterior objects and patterns of received
`
`waves from the possible exterior objects” encompasses the training of a
`
`pattern recognition algorithm using simulated data, as Petitioner contends.
`
`According to the plain language of the limitation, the algorithm must be
`
`generated from “patterns of received waves from the possible exterior
`
`objects.” In describing the training of pattern recognition systems, such as
`
`neural networks, for use with the invention, the ’057 patent explains that a
`
`large number of experiments are conducted in which different objects are
`
`placed in numerous positions and orientations, and signals from a CCD array
`
`are returned from the objects and measured by sensors or transducers.
`
`Ex. 1001, 36:22–39. This is the only example of a pattern recognition
`
`training session provided in the ’057 patent. Although the described training
`
`session relates to objects inside a vehicle, the ’057 patent indicates that
`
`pattern recognition systems for identifying exterior objects are trained in a
`
`similar manner. See, e.g., id. at 40:1–9.
`
`In view of the description in the ’057 patent of a training session using
`
`patterns actually received from objects, we see no reasonable basis for
`
`interpreting the claim language “generated from data of possible exterior
`
`objects and patterns of received waves from the possible exterior objects” to
`
`encompass training of a pattern recognition algorithm using simulations.
`
`Petitioner has not presented any persuasive arguments to the contrary.
`
`10
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`Accordingly, for purposes of this decision, the broadest reasonable
`
`construction of the claim language at issue requires training of a pattern
`
`recognition algorithm using patterns of waves actually received from
`
`possible exterior objects.
`
`B. Anticipation by Lemelson
`
`Petitioner contends that claims 1, 2, 4, 7, 16, 40, 41, 43, 46, 56, 59,
`
`60, 61, 77, 78, and 81–83 are unpatentable under 35 U.S.C. § 102(e) as
`
`anticipated by Lemelson. Pet. 7–24. To support its assertion, Petitioner
`
`provides detailed analysis and claim charts and relies on the analysis of
`
`Dr. Larry S. Davis, as set forth in his Declaration (Ex. 1016).
`
`1. Lemelson
`
`Lemelson discloses a computerized system in a motor vehicle that
`
`identifies possible obstacles on a roadway and either warns the driver or
`
`controls the operation of vehicle systems, such as the brakes or steering
`
`mechanism, to avoid or lessen the effect of a collision. Ex. 1002, Abstract,
`
`5:15–29, 8:38–39. The system includes at least one video camera,
`
`preferably a CCD array, and may include multiple cameras for front, side,
`
`and rear viewing and for stereo imaging capabilities. Id. at 6:27–42. The
`
`video camera also may be implemented with other technologies, including
`
`infrared imaging methods. Id. at 6:34–37. In addition, the system may use
`
`radar or lidar for range detection. Id. at 5:67–6:4. “[V]ideo 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.” Id. at 6:5–8.
`11
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`The analog signal output from the video camera(s) is digitized in an
`
`analog-to-digital convertor and passed to an image analyzing computer
`
`(IAC), 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,
`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 for flashing external and/or
`internal warning lights; a horn control 43, etc.
`
`Id. at 5:39–59. The IAC also may display symbols representing hazard
`
`objects. Id. at 6:52–55, 9:60–62.
`
`Lemelson discloses further details regarding a neural network
`
`embodiment of the IAC for identifying objects:
`
`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
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`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 8:1–14.
`
`2. Independent Claims 1, 16, 40, 56, and 77
`
`Petitioner contends that Lemelson discloses all of the limitations of
`
`independent claims 1, 16, 40, 56, and 77 of the ’057 patent. Pet. 8–14; see
`
`also Ex. 1016 ¶¶ 22–41, 46–49 (Davis Decl.). For example, Petitioner
`
`asserts that Lemelson discloses the “receiver” limitations of these claims
`
`because it describes several devices (multiple cameras for stereo imaging
`
`capabilities and radar and lidar receivers) that receive waves from the
`
`exterior environment and generate signals characteristic of the received
`
`waves. Pet. 8–9 (citing Ex. 1002, 5:67–6:8, 6:37–38, Figs. 1 and 2).
`
`Petitioner notes that Lemelson’s camera preferably is a CCD array, which is
`
`recited in claim 16. Pet. 9 (citing Ex. 1002, 6:28–34). Claims 40 and 77
`
`recite “a plurality of receivers arranged apart from one another and to
`
`receive waves from different parts of the environment,” which Petitioner
`
`asserts is met by Lemelson’s multiple cameras that may be used for front,
`
`side, and rear viewing. Pet. 9 (citing Ex. 1002, 6:37–42).
`
`Petitioner further asserts that Lemelson’s IAC corresponds to the
`
`recited processor that provides a classification, identification, or location of
`
`the exterior object based on the generated signals. Pet. 9–10. Because
`
`Lemelson’s IAC is implemented as a neural computing network, Petitioner
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`asserts it is a processor comprising “trained pattern recognition means,” as
`
`recited in claims 1, 16, and 56. Id. at 10. Also, Petitioner asserts that the
`
`operation of a system in Lemelson, such as the brakes or steering
`
`mechanism, is affected in response to the classification, identification, or
`
`location of the exterior object, as recited in claims 1, 16, 40, 56, and 77.
`
`Id. at 14.
`
`Claims 1 and 56 further require the trained pattern recognition means
`
`to be “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.” As construed for
`
`purposes of this decision, see Section II.A, this limitation requires the
`
`trained pattern recognition algorithm to be trained using patterns of waves
`
`(e.g., images) actually received from objects. Petitioner contends that
`
`Lemelson discloses this limitation because it “teaches that ‘[t]raining [of the
`
`neural network] involves providing known inputs to the network’ and that
`
`‘[a]daptive operation is also possible with on-line adjustment.’” Pet. 11
`
`(quoting Ex. 1002, 8:4–10). According to Petitioner, this disclosure
`
`necessarily would convey to one having ordinary skill in the art that
`
`Lemelson’s neural network was trained on images directly obtained from
`
`actual objects. Id. Relying on the declaration testimony of Dr. Davis,
`
`Petitioner further contends that at the time of the invention, one of ordinary
`
`skill in the art would have known that the statistical patterns provided by real
`
`imagery could not have been found in synthetic data. Id. at 11–12 (citing
`
`Ex. 1016 ¶¶ 33–36). Moreover, Petitioner contends, “directly imaged data
`
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`was by far the most realistic type of data that could be obtained to train a
`
`neural network to classify or identify the virtually limitless variety of
`
`complex 3-dimensional objects a vehicle would be expected to encounter in
`
`operation.” Id. at 12 (citing Ex. 1016 ¶¶ 34–36). In addition, Petitioner
`
`notes that Lemelson itself cites a reference that explicitly discloses training a
`
`neural network using real images as training data. Id. at 13 (citing Ex. 1016
`
`¶ 38).
`
`Based on our review of the record, we are not persuaded that
`
`Petitioner has shown sufficiently that Lemelson discloses a “trained pattern
`
`recognition algorithm generated from data of possible exterior objects and
`
`patterns of received waves from the possible exterior objects,” as recited in
`
`claims 1 and 56. Lemelson discloses that training involves “known inputs”
`
`(Ex. 1002, 8:5), but we do not find, nor does Petitioner cite, any additional
`
`language in Lemelson that explains what the known inputs are. Nor are we
`
`persuaded that “on-line adjustment of network weights” during operation
`
`(Ex. 1002, 8:9–10) necessarily implies that the known inputs provided
`
`during training of the neural network are actual images of exterior objects.
`
`For the foregoing reasons, Petitioner has made a sufficient showing,
`
`on the present record, that Lemelson discloses all of the limitations of
`
`independent claims 16, 40, and 77. Petitioner, however, has not shown
`
`sufficiently that Lemelson discloses all of the limitations in claims 1 and 56.
`
`Accordingly, the information presented shows a reasonable likelihood that
`
`Petitioner would prevail in demonstrating that independent claims 16, 40,
`
`and 77 are unpatentable as anticipated by Lemelson, but does not show a
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`reasonable likelihood that Petitioner would prevail in demonstrating that
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`independent claims 1 and 56 are unpatentable as anticipated by Lemelson.
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`3. Dependent Claims 2, 4, 7, 41, 59–61, and 78
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`Petitioner contends that Lemelson discloses all of the limitations of
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`claims 2, 4, and 7, which depend from claim 1, and claims 59–61, which
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`depend from claim 56. Pet. 15–17, 21–24. As discussed, the information
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`presented does not show a reasonable likelihood that Petitioner would
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`prevail in demonstrating that claims 1 and 56 are anticipated by Lemelson.
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`Thus, the information presented does not show a reasonable likelihood that
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`Petitioner would prevail in demonstrating that claims 2, 4, 7, and 59–61 are
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`unpatentable as anticipated by Lemelson.
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`Claim 41, which depends from claim 40, and claim 78, which depends
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`from claim 77, further recite a trained pattern recognition means that is
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`“structured and arranged to apply a trained pattern recognition algorithm
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`generated from data of possible exterior objects and patterns of received
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`waves from the possible exterior objects.” As discussed with respect to
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`claims 1 and 56, Petitioner has not shown sufficiently that Lemelson
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`discloses this limitation. Thus, the information presented does not show a
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`reasonable likelihood that Petitioner would prevail in demonstrating that
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`claims 41 and 77 are unpatentable as anticipated by Lemelson.
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`4. Dependent Claims 43, 46, and 81–83
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`Petitioner asserts that Lemelson discloses all of the limitations in
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`claims 43 and 46, which depend from claim 40, and claims 81–83, which
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`depend from claim 77. Pet. 15–17, 23–24. For example, claims 43 and 81
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`require a transmitter for transmitting waves into the exterior environment
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`and receivers arranged to receive the waves transmitted by the transmitter
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`and reflected by exterior objects. By virtue of their dependency from claims
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`40 and 77, respectively, claims 43 and 81 also require the plurality of
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`receivers to be arranged apart from one another and to receive waves from
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`different parts of the exterior environment. Petitioner contends that
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`Lemelson’s disclosure of the use of radar or lidar scanning “to identify and
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`indicate distances between the controlled vehicle and objects ahead of, to the
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`side(s) of, and to the rear of” the vehicle meets these claim limitations.
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`Id. at 16 (emphasis omitted) (quoting Ex. 1002, 6:5–9). Relying on
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`Dr. Davis’s declaration testimony, Petitioner argues that radar or lidar
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`scanning as described in Lemelson necessarily would use a radar or lidar
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`transmitter (i.e., a radar antenna or a laser) as well as a radar or lidar
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`receiver. Id. (citing Ex. 1016 ¶ 52).
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`Claims 46 and 83 require the system affected in response to
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`classification, identification, or location of an exterior object to be “a display
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`viewable by the driver and arranged to show an image or icon of the exterior
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`object,” which Petitioner contends is met when Lemelson’s IAC displays
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`symbols representing hazard objects. Pet. 15. Petitioner also contends that
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`Lemelson’s cameras that may be used for side and rear viewing are receivers
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`arranged to receive waves from a blind spot of the vehicle, as recited in
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`claim 82. Id. at 17.
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`We are not persuaded by Patent Owner’s argument that the ground of
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`anticipation by Lemelson should be rejected under 35 U.S.C. § 325(d)
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`17
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`because it was presented in Toyota Motor Corp. v. American Vehicular
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`Sciences LLC, IPR2013-00419. See Prelim. Resp. 3. We decline to exercise
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`our discretion under that provision in this case.
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`On the present record, Petitioner has made a sufficient showing that
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`Lemelson discloses all the limitations of claims 43, 46, and 81–83.
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`Accordingly, the information presented shows a reasonable likelihood that
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`Petitioner would prevail in demonstrating that claims 43, 46, and 81–83 are
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`unpatentable as anticipated by Lemelson.
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`C. Obviousness over Lemelson
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`Petitioner contends that claims 1, 2, 4, 7, 16, 40, 41, 43, 46, 56, 59,
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`60, 61, 77, 78, and 81–83 are unpatentable under 35 U.S.C. § 103(a) for
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`obviousness over Lemelson. Pet. 24–25. Claims 1, 41, 56, and 78 recite a
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`“trained pattern recognition algorithm generated from data of possible
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`exterior objects and patterns of received waves from the possible exterior
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`objects.” Petitioner argues that even if Lemelson does not disclose this
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`limitation, which we have construed to require training using patterns of
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`waves actually received from objects, it would have been obvious to a
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`person of ordinary skill in the art to generate an algorithm using such data.
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`Id. (citing Ex. 1016 ¶¶ 34–37). Relying on the declaration testimony of
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`Dr. Davis, Petitioner contends that an ordinarily skilled artisan would have
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`been motivated to generate an algorithm from such data “because it was
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`more plentiful, and less costly and time-consuming to produce than synthetic
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`data and was vastly more representative of the myriad complex objects a
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`vehicle safety system would be expected to encounter in operation.”
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`Id. at 25. Also, Petitioner asserts that such data was the only data available
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`at the time that could have been used to train a neural network to identify
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`accurately three-dimensional objects, such as pedestrians and vehicles, as
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`would be required of a vehicle safety system. Id. On this record, we
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`determine that the information presented shows a reasonable likelihood that
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`Petitioner would prevail in demonstrating that claims 1, 41, 56, and 78 are
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`unpatentable for obviousness over Lemelson.
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`Petitioner asserts that Lemelson discloses the additional limitations
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`recited in dependent claims 2, 4, 7, and 59–61. Pet. 14–17. We determine
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`that Petitioner has made a sufficient showing that Lemelson discloses these
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`limitations, which also appear in other claims discussed above. Thus, the
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`information presented shows a reasonable likelihood that Petitioner would
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`prevail in demonstrating that claims 2, 4, 7, and 59–61 are unpatentable for
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`obviousness over Lemelson.
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`With respect to claims 16, 40, 43, 46, 77, and 81–83, which do not
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`include the same “trained pattern recognition algorithm” limitation recited in
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`claims 1 and 56, the Petition does not identify any differences between the
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`claimed subject matter and Lemelson, as required for a proper obviousness
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`analysis. See KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 406 (2007);
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`Graham v. John Deere Co., 383 U.S. 1, 17–18 (1966). Without any specific
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`analysis regarding the alleged obviousness of these claims over Lemelson,
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`we are unable to conclude that the information presented shows a reasonable
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`likelihood that Petitioner would prevail in demonstrating that claims 16, 40,
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`43, 46, 77, and 81–83 are unpatentable for obviousness over Lemelson.
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`19
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`D. Obviousness over Lemelson and Nishio
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`Petitioner contends that claims 1, 2, 4, 7, 16, 40, 41, 43, 46, 56, 59,
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`60, 61, 77, 78, and 81–83 are unpatentable under 35 U.S.C. § 103(a) for
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`obviousness over Lemelson and Nishio. Pet. 25–28.
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`1. Nishio
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`Nishio describes a system for predicting and evading a vehicle crash
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`that is implemented using a neural network. Ex. 1004, Abstract. The neural
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`network has been trained previously with training data to predict the
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`possibility of a crash. Id. The training data are “ever-changing images”
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`collected during driving of a vehicle by an image pick-up device, such as a
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`CCD camera, mounted on the vehicle. Id. at 3:2–9, 3:20–25, 9:47–10:9.
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`Examples of collected images are a vehicle coming across the center line
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`and a vehicle suddenly appearing from a blind corner of a cross-street.
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`Id. at 10:2–7, Fig. 6. Training of the neural network using the image data
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`results in a “unique algorithm.” Id. at 10:42–45. After completion of
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`training, the neural network is used in a “crash predicting circuit,” which
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`predicts crashes between a vehicle and potentially dangerous objects based
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`on images provided by the vehicle’s image pick-up device. Id. at 3:8–12,
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`6:24–28. A “safety driving ensuring device” is connected to the crash
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`predicting circuit for actuating, in response to a signal indicating the
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`possibility of a crash, an “occupant protecting mechanism.” Id. at 3:14–19.
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`For example, the crash predicting circuit may evade a predicted crash using
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`an automatic steering system or a brake system. Id. at 6:29–31.
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`2. Anal
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