Trials@uspto.gov
`Tel: 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 IPR2014-00647
`Patent 5,845,000
`
`
`
`
`Before JAMESON LEE, TREVOR M. JEFFERSON, and LYNNE E.
`PETTIGREW, Administrative Patent Judges.
`
`
`JEFFERSON, Administrative Patent Judge.
`
`
`
`DECISION
`Institution of Inter Partes Review
`37 C.F.R. § 42.108
`
`
`
`
`
`
`Tel: 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 IPR2014-00647
`Patent 5,845,000
`
`
`
`
`Before JAMESON LEE, TREVOR M. JEFFERSON, and LYNNE E.
`PETTIGREW, Administrative Patent Judges.
`
`
`JEFFERSON, Administrative Patent Judge.
`
`
`
`DECISION
`Institution of Inter Partes Review
`37 C.F.R. § 42.108
`
`
`
`
`
`
`
`IPR2014-00647
`Patent 5,845,000
`
`
`I.
`
`INTRODUCTION
`
`Mercedes-Benz USA, LLC (“Petitioner”), filed a Petition (“Pet.”)
`
`requesting inter partes review of claims 10, 11, 15, 16, 17, 19, 20, and 23 of
`
`U.S. Patent No. 5,745,000 (Ex. 1001, “the ’000 patent”) pursuant to 35
`
`U.S.C. §§ 311–319. Paper 1. American Vehicular Sciences LLC (“Patent
`
`Owner”) filed a Preliminary Response (“Prelim. Resp.”) substantively
`
`waiving its right to present a response on the merits. Paper 11, 2. 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, we determine that the information
`
`presented shows there is a reasonable likelihood that Petitioner would
`
`prevail in showing the unpatentability of the challenged claims.
`
`Accordingly, pursuant to 35 U.S.C. § 314, the Board institutes an inter
`
`partes review as to claims 10, 11, 15, 16, 17, 19, 20, and 23 of the ’000
`
`patent.
`
`A. Related Proceedings
`
`Petitioner states that the ’000 patent was the subject of a Petition filed
`
`in Toyota Co. v. American Vehicular Sciences LLC, Case IPR2013-00424
`
`(instituted January 14, 2014). Id. Petitioner also identifies American
`
`Vehicular Sciences LLC v. Toyota Motor Corp., Civil Action No. 6:12-CV-
`
`406 (E.D. Tex.); and (2) American Vehicular Sciences LLC v. BMW Grp.,
`2
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`IPR2014-00647
`Patent 5,845,000
`
`
`Civil Action No. 6:12-CV-413 (E.D. Tex.) as involving the ’000 patent. Pet.
`
`1; Paper 5, 2.
`
`B. The ’000 Patent
`
`The disclosed invention of the ’000 patent is directed to a vehicle
`
`interior monitoring system that monitors, identifies, and locates occupants
`
`and other objects in the passenger compartment of a vehicle and objects
`
`outside of the vehicle. Ex. 1001, Abstract. Objects are illuminated with
`
`electromagnetic radiation, and a lens is used to focus the illuminated images
`
`onto the arrays of a charge coupled device (CCD). Id. at Abstract; 7:26-40.
`
`Computational means using trained pattern recognition analyzes the signals
`
`received at the CCD to classify, identify, or locate the contents of external
`
`objects, which, in turn, are used to affect the operation of other vehicular
`
`systems. Id. at Abstract. The ’000 patent discloses that a vehicle
`
`computation system uses a “trainable or a trained pattern recognition
`
`system” which relies on pattern recognition to process signals and to
`
`“identify” an object exterior to the vehicle or an object within the vehicles
`
`interior. Id. at 3:21-44.
`
`Figures 7 and 7A, reproduced below, illustrate portions of the sensor
`
`system that use transmitters, receivers, circuitry, and processors to perform
`
`pattern recognition of external objects in anticipation of a side-impact
`
`collision:
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`3
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`IPR2014-00647
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`Figure 7, with Figure 7A inset, depicts vehicle 720 approaching the side of
`
`another vehicle 710 and shows transmitter 730 and receivers 734 and 736.
`
`Ex. 1001, 9:48-52; 18:28-40. Figure 7A provides a detailed view of the
`
`electronics that drive transmitter 730 and circuitry 744 containing neural
`
`computer 745 to process signals reflected or received from the external
`
`object using pattern recognition. Id. at 18:33-40.
`
`Figure 8 also illustrates an exterior monitoring system and is
`
`reproduced below:
`
`4
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`IPR2014-00647
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`Figure 8 depicts a system for detecting the headlights or taillights of other
`
`vehicles used in conjunction with an automatic headlight dimming system.
`
`Ex. 1001, 9:54-58. CCD array in Figure 8 is designed to be sensitive to
`
`visible light and does not use a separate source of illumination as depicted in
`
`
`
`Figure 7. Id.
`
`The Summary of the Invention discusses an invention related to
`
`detection of objects in the interior of the vehicle and objects external to the
`
`vehicle. Id. at 7:25-30. Specifically, external objects are illuminated with
`
`“electromagnetic, and specifically infrared, radiation,” and lenses are used to
`
`focus images onto one or more CCDs arrays. Id. The disclosure further
`
`states that the invention provides (1) an “anticipatory sensor” located within
`
`the vehicle to “identify about-to-impact object[s] in the presence of snow
`
`and/or fog,” (2) “a smart headlight dimmer system” to sense and identify
`
`headlights and taillights and distinguish them from other reflective surfaces,
`
`and (3) blind spot detection. Id. at 8:37-53.
`
`5
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`IPR2014-00647
`Patent 5,845,000
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`C. Illustrative Claims
`
`Claims 10, 16 and 23 are illustrative of the claimed invention:
`
`In a motor vehicle having an interior and an
`10.
`exterior, a monitoring system for monitoring at least one
`object exterior to said vehicle comprising:
`a) transmitter means for transmitting
`electromagnetic waves to illuminate the at least one
`exterior object;
`b) reception means for receiving reflected
`electromagnetic illumination from the at least one
`exterior object;
`c) processor means coupled to said reception
`means for processing said received illumination and
`creating an electronic signal characteristic of said exterior
`object based thereon;
`d) categorization means coupled to said processor
`means for categorizing said electronic signal to identify
`said exterior object, said categorization means
`comprising trained pattern recognition means for
`processing said electronic signal based on said received
`illumination from said exterior object to provide an
`identification of said exterior object based thereon, said
`pattern recognition means being structured and arranged
`to apply a pattern recognition algorithm generated from
`data of possible exterior objects and patterns of received
`electromagnetic illumination from the possible exterior
`objects; and
`e) output means coupled to said categorization
`means for affecting another system in the vehicle in
`response to the identification of said exterior object.
`
`In a motor vehicle having an interior and an
`16.
`exterior, an automatic headlight dimming system
`comprising:
`
`6
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`IPR2014-00647
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`a) reception means for receiving electromagnetic
`radiation from the exterior of the vehicle;
`b) processor means coupled to said reception
`means for processing the received radiation and creating
`an electronic signal characteristic of the received
`radiation;
`c) categorization means coupled to said processor
`means for categorizing said electronic signal to identify a
`source of the radiation, said categorization means
`comprising trained pattern recognition means for
`processing said electronic signal based on said received
`radiation to provide an identification of the source of the
`radiation based thereon, said pattern recognition means
`being structured and arranged to apply a pattern
`recognition algorithm generated from data of possible
`sources of radiation including lights of vehicles and
`patterns of received radiation from the possible sources;
`and
`
`d) output means coupled to said categorization
`means for dimming the headlights in said vehicle in
`response to the identification of the source of the
`radiation.
`
`
`23. A method for affecting a system in a vehicle based
`on an object exterior of the vehicle, comprising the steps
`of:
`
`a) transmitting electromagnetic waves to
`illuminate the exterior object;
`b) receiving reflected electromagnetic illumination
`from the object on an array;
`c) processing the received illumination and
`creating an electronic signal characteristic of the exterior
`object based thereon;
`d) processing the electronic signal based on the
`received illumination from the exterior object to identify
`the exterior object, said processing step comprising the
`
`7
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`steps of generating a pattern recognition algorithm from
`data of possible exterior objects and patterns of received
`electromagnetic illumination from the possible exterior
`objects, storing the algorithm within a pattern recognition
`system and applying the pattern recognition algorithm
`using the electronic signal as input to obtain the
`identification of the exterior object; and
`e) affecting the system in the vehicle in response to
`the identification of the exterior object.
`
`
`Ex. 1001, 21:35-61; 22:17-39; 23:19–24:2.
`
`
`
`D. The Asserted Grounds
`
`Petitioner asserts that the challenged claims of the ’000 patent are
`
`unpatentable under 35 U.S.C. §§ 102 and 103 for the following specific
`
`grounds (Pet. 5–6):
`
`Reference(s)
`
`Basis
`
`Claims Challenged
`
`Lemelson1
`
`Lemelson
`
`§ 102(e)
`
`10, 11, 15, 19, and 23
`
`§ 103(a)
`
`10, 11, 15, 19, and 23
`
`Lemelson and Nishio2
`
`§ 103(a)
`
`10, 11, 15, 19, and 23
`
`
`
`1 U.S. Patent No. 6,553,130, issued on April 22, 2003 (Ex. 1002,
`“Lemelson”) from an continuation application of U.S. Application No.
`08/105,304 filed on Aug. 11, 1993 (Ex. 1003 “the ’204 application”).
`2 U.S. Patent No. 5,541,590, issued on July 30, 1996 (Ex. 1004, “Nishio”)
`from U.S. Application No. 08/097,178 (Ex. 1005, “the ’178 application”).
`
`8
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`IPR2014-00647
`Patent 5,845,000
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`Reference(s)
`Lemelson and Asayama3
`
`Basis
`
`Claims Challenged
`
`§ 103(a)
`
`10, 11, 15, 19, and 23
`
`Lemelson and Yanagawa4
`
`§ 103(a)
`
`16, 17, and 20
`
`Nishio
`
`Nishio
`
`§ 102(e)
`
`10, 15, 19, and 23
`
`§ 103(a)
`
`10, 15, 19, and 23
`
`Nishio and Asayama
`
`§ 103(a)
`
`10, 15, 19, and 23
`
`Nishio and Yanagawa
`
`§ 103(a)
`
`10, 15, 16, 17, 19, 20, and 23
`
`Nishio and Lemelson5
`
`§ 103(a)
`
`10, 11, 15, 19, and 23
`
`Nishio and Mizukoshi6
`
`§ 103(a)
`
`10, 11, 15, 16, 17, 19, and 23
`
`II. DISCUSSION
`
`A. Claim Construction
`
`Petitioner proposes and applies the broadest reasonable claim
`
`constructions for the ’000 patent that we determined in Toyota Motor Corp.
`
`v. American Vehicular Sciences LLC, IPR2013-00424, slip op. at 9–26
`
`
`3 U.S. Patent No. 5,214,408, issued on May 25, 1993 (Ex. 1006,
`“Asayama”).
`4 Japanese Unexamined Patent Application Publication No. S62-131837 (Ex.
`1009). Citations herein are to the English translation of Ex. 1009 (Ex. 1007
`“Yanagawa”).
`5 Petitioner has asserted this ground based on Nishio and Lemelson as a
`different ground from that based on Lemelson and Nishio. See Pet. 23–26,
`55–57.
`6 Japanese Unexamined Patent Application Publication No. JP-H06-267303
`to Mizukoshi (Ex. 1010). Citations herein are to the English translation of
`Ex. 1010 (Ex. 1008, “Mizukoshi”).
`
`9
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`
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`IPR2014-00647
`Patent 5,845,000
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`(PTAB Jan. 14, 2014) (Paper 16). Pet. 4–7. For purposes of this decision,
`
`we adopt the constructions in Toyota Motor Corp. v. American Vehicular
`
`Sciences LLC, IPR2013-00424, slip op. at 9–26 (PTAB Jan. 14, 2014)
`
`(Paper 16) provided in the table below.
`
`Claim Term
`
`“pattern recognition
`algorithm”
`
`“trained pattern recognition
`means” (claims 10 and 16)
`
`“identify” (claims 10, 16,
`and 23)
`
`“transmitter means for
`transmitting” (claim 10)
`
`“reception means for
`receiving” (claims 10 and
`16)
`“processor means . . . for
`processing” (claims 10 and
`16)
`“categorization means
`. . . for categorizing”
`(claims 10 and 16)
`
`“output means” (claims 10
`and 16)
`
`“dimming the headlights”
`(claim 16)
`
`Construction
`“an algorithm which processes a signal that
`is generated by an object, or is modified by
`interacting with an object, for determining
`to which one of a set of classes the object
`belongs”
`“a neural computer or microprocessor
`trained for pattern recognition, and
`equivalents thereof”
`“determining that the object belongs to a
`particular set or class”
`“infrared, radar, and pulsed GaAs laser
`systems” and “transmitters which emit
`visible light”
`
`“a CCD array and CCD transducer”
`
`recited processor provides sufficient
`structure
`
`“a neural computer, a microprocessor, and
`their equivalents”
`
`“electronic circuit or circuits capable of
`outputting a signal to another vehicle
`system”
`“decreasing the intensity or output of the
`headlight to a lower level of illumination”
`
`10
`
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`Claim Term
`“measurement means for
`measuring” (claim 11)
`“wherein said categories
`further comprise radiation
`from taillights of a vehicle-
`in-front” (claim 17)
`
`
`Construction
`
`recited radar provides sufficient structure
`
`“categorizing radiation from taillights of a
`vehicle-in-front, which may include
`additional types of radiation”
`
`1. “a pattern recognition algorithm generated from . . .” (claims 10,
`16 and 23)
`
`The challenged claims recite “a pattern recognition algorithm
`
`generated from data of possible exterior objects and patterns of received
`
`electromagnetic illumination from the possible exterior objects” as recited in
`
`claim 10; or “generating a pattern recognition algorithm from data of
`
`possible exterior objects and patterns of received electromagnetic
`
`illumination from the possible exterior objects” as recited in claim 23. Claim
`
`16 contains a similar limitation. Petitioner contends that the “generating a
`
`pattern recognition algorithm” limitation as recited in independent claim 10
`
`and a similar limitation found in independent claim 23 do not require that
`
`the training used to generate the pattern recognition algorithm be directly
`
`imaged from physical exterior objects. Pet. 11 (citing Ex. 1013 ¶¶ 40–42).
`
`We disagree.
`
`The plain language of the limitation states that “data of possible
`
`exterior objects and patterns of received electromagnetic illumination from
`
`the possible exterior objects” is required. Petitioner has not provided
`
`persuasive evidence that “generated from data of possible exterior objects”
`
`11
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`IPR2014-00647
`Patent 5,845,000
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`broadly includes training using data that is simulated to represent exterior
`
`objects. In addition, the limited discussion of training in the ’000 patent
`
`specification supports that neural network training is performed using a large
`
`number of real possible objects. See Ex. 1001, 16:61–17:2 (discussing
`
`training on possible interior objects to train neural network). Petitioner’s
`
`construction would mean that any type of data could be used so long as it
`
`relates to the information about an object and the type of radiation it emits.
`
`Pet. 11. We do not find that the plain language of the claims is so broad.
`
`Accordingly, we find that the broadest reasonable construction of the
`
`claim term “generating a pattern recognition algorithm from data of possible
`
`exterior objects and patterns of received electromagnetic illumination from
`
`the possible exterior objects” requires training using patterns of waves
`
`actually received from possible exterior objects.
`
`B. Claims 10, 11, 15, 19, and 23—Anticipation by Lemelson
`
`Petitioner argues that claims 10, 11, 15, 19, and 23 are unpatentable as
`
`anticipated under 35 U.S.C. § 102(e) by Lemelson. Pet. 8–22. Petitioner
`
`relies on analysis, claim charts, and the testimony of Dr. Larry S. Davis (Ex.
`
`1013) to support its contention that Lemelson discloses each limitation of
`
`claims 10, 11, 15, 19, and 23. Id.
`
`1. Lemelson (Ex. 1002)
`
`Lemelson discloses a vehicle computer system to monitor and analyze
`
`image information for external objects by identifying objects and the
`
`distance between a vehicle and an external object or objects. Ex. 1002,
`
`12
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`Abstract, 1:10–16; 2:14–23; 2:39–3:39. Figure 1, reproduced below, shows
`
`a block diagram of the vehicle image analysis computer:
`
`
`
`Figure 1 shows computer control system 10 including microprocessor
`
`11 and image analyzing computer 19. Image analyzing computer 19
`
`employs neural networks and artificial intelligence along with fuzzy logic
`
`algorithms to identify objects exterior to the vehicle. Id. at 5:15–24, 5:30–
`
`45. The system employs camera 16 and laser scanners to generate image
`
`data which is analyzed by computer 19 to control various vehicle systems,
`
`including warning and display systems, braking systems, and headlight
`
`systems. Id. at 5:45–59; Fig. 1 (items 31, 32, 33, 41, and 42).
`
`Lemelson discloses using image analysis computer 19 in a hazard or
`
`external object avoidance system. Id. at 4:40–43. The imaging system
`
`detects objects and the distance between the vehicle and exterior object, and
`
`affects the operation of other vehicle systems. Id. at 6:9–20.
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`13
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`Figure 2, showing image analysis computer 19 in further detail, is
`
`reproduced below:
`
`
`
`Figure 2 shows a computer architecture based on neural networks that use a
`
`parallel processing system with dedicated imaging proicessing hardware. Id.
`
`at 6:21–27. The imaging system uses video camera 16, described as a CCD
`
`array, but also may use image intensifying electron gun and infrared imaging
`
`methods on the front, side, and rear of the vehicle to capture image data. Id.
`
`at 6:31–42.
`
`Lemelson further discloses that image analyzing computer 19 uses
`
`neural network processing that is trained to recognize roadway hazzards. Id.
`
`at 8:1–4; 7:47–50. The neural network training in Lemelson “involves
`
`providing known inputs to the network resulting in desired output
`
`responses” and uses various learning algorithms. Id. at 8:5–8.
`
`14
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`2. Analysis
`
`Petitioner contends Lemelson discloses training using data “imaged
`
`directly from actual exterior objects” as required in the “pattern recognition
`
`algorithm” limitation of claim 10. Pet. 12. Specifically, Petitioner argues
`
`that Lemelson’s “teach[ing] that ‘training involves providing known inputs
`
`to the network’ and that ‘adaptive operation is also possible with on-line
`
`adjustment . . .’ (Ex. 1002, 8:4-10; Ex. 1003, p. 13)” conveys to a person of
`
`ordinary skill in the art that the neural network of Lemelson was trained on
`
`natural image data obtained from actual objects. Id. (citing Ex. 1013 ¶¶ 43–
`
`46).
`
`We are not persuaded by Petitioner’s argument and evidence that
`
`Lemelson’s lack of references to simulated data, or the superiority of real
`
`image data to synthetic data in training neural networks indicates that
`
`Lemelson discloses the use of actual data for such training. Pet. 13–14. The
`
`Lemelson reference to a publication that discusses real data training of a
`
`neural network is not sufficient to show that the cited portions of Lemelson
`
`discloses training using data imaged directly from actual exterior objects.
`
`Id.
`
`Petitioner has not shown persuasively that the references to training
`
`via “known inputs” excludes the use of simulated or synthetic data or
`
`discloses to a person of ordinary skill in the art that training uses natural
`
`image data obtained from actual objects as required in independent claims
`
`10 and 23 and dependent claims 11, 15, and 19. Pet. 12–13. We are also
`
`not persuaded that “on-line adjustment of network weights” during operation
`
`15
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`(Ex. 1002, 8:9–10) necessarily implies that the known inputs provided
`
`during training of the neural network are actual images of exterior objects.
`
`Based on the foregoing, we find that Petitioner has not demonstrated a
`
`reasonable likelihood that Petitioner would prevail in showing that claims
`
`10, 11, 15, 19, and 23 are unpatentable as anticipated by Lemelson under 35
`
`U.S.C. § 102(e).
`
`C. Claims 10, 11, 15, 19, and 23—Obviousness over Lemelson
`
`Petitioner argues that claims 10, 11, 15, 19, and 23 are unpatentable as
`
`obvious under 35 U.S.C. § 103(a) by Lemelson. Pet. 23. Petitioner relies
`
`on analysis, claim charts presented with respect to anticipation of the claims
`
`by Lemelson under 35 U.S.C. § 102(e), and the declaration testimony of Dr.
`
`Davis (Ex. 1013) to support its contention that Lemelson discloses each
`
`limitation of claims 10, 11, 15, 19, and 23. Id.
`
`Petitioner provides charts and testimony that Lemelson discloses the
`
`transmitting, receiving, processing and categorizing limitations of
`
`independent claims 10 and 23. Pet. 6–22. Petitioner provides testimony and
`
`argument that Lemelson “conveys to one of ordinary skill in the art that the
`
`neural network of Lemelson was trained on images directly obtained from
`
`actual objects (i.e. natural image data).” Pet. 12. In addition, Petitioner
`
`provides testimony to support that it would have been obvious for a person
`
`of ordinary skill in the art to try to generate an algorithm using data of real
`
`objects. Pet. 23 (citing Ex. 1013 ¶ 53).
`
`16
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`Based on the present record, Petitioner has made a sufficient showing
`
`that Lemelson teaches the limitations of 10, 11, 15, 19, and 23. Petitioner
`
`also has provided support for combining the knowledge of a person of
`
`ordinary skill in the art with the Lemelson disclosure. Accordingly, the
`
`information presented shows a reasonable likelihood that Petitioner would
`
`prevail in showing that claims of 10, 11, 15, 19, and 23 are unpatentable as
`
`obvious over Lemelson.
`
`D. Claims 10, 11, 15, 19, and 23—Obviousness over Lemelson and
`Nishio
`
`1. Nishio (Ex. 1004)
`
`Nishio teaches a “vehicle crash predictive and evasive operation
`
`system by neural networks.” Ex. 1004, Abstract. Specifically, Nishio
`
`teaches a “neural network which is previously trained with training data to
`
`predict the possibility of a crash, the training data representing ever-
`
`changing views previously picked-up from the image picking-up device
`
`during driving of the vehicle.” Id. at 2:28–30. Figure 4 of Nishio,
`
`reproduced below, shows an embodiment of the crash-predicting system of
`
`Nishio.
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`17
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`Figure 4 depicts a block diagram of a crash predicting and evading system
`
`using neural networks. Id. at 3:13–15. Nishio discloses that “image pick-up
`
`device 21 picks up ever-changing images as analog image data as described
`
`above in conjunction with the conventional system. This image pick-up
`
`device 21 is also any one of suitable devices such as a CCD camera. The
`
`image pick-up operation is carried out during running of a vehicle . . . .
`
`These ever-changing images are collected as the training data for the neural
`
`network.” Id. at 7:42–58. Nishio also teaches that “a unique algorithm is
`
`established on completion of network training.” Id. at 8:20–21.
`
`2. Analysis
`
`In addition to the teaching and suggestions of Lemelson discussed
`
`above with respect to claims 10, 11, 15, 19, and 23, Petitioner provides
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`argument and evidence that Nishio expressly discloses using actual object
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`images obtained during vehicle operation to generate an algorithm for
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`training. Pet. 24–25 (citing Ex. 1013 ¶¶ 42–46, 54–60). Petitioner also
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`contends that training using such inputs is well within the level of ordinary
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`skill in the art at the time of invention. Pet. 24–25 (citing Ex. 1013 ¶¶ 42–
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`46, 54–60). Petitioner relies on the teachings of Lemelson discussed above
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`for claims 10, 11, 15, 19, and 23, in combination with the neural network
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`training using the image-pick-up device disclosed in Nishio, to teach the
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`“algorithm generated from possible exterior objects” limitations of claims
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`10, 11, 15, 19, and 23. Pet. 24. Finally, Petitioner provides a rationale for
`
`combining the teachings of Nishio with Lemelson. Pet. 25.
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`Based on Petitioner’s analysis and supporting evidence at this stage of
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`the proceeding, we determine that Petitioner has demonstrated that there is a
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`reasonable likelihood that Petitioner would prevail with respect to claims 10,
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`11, 15, 19, and 23 on the ground that these claims are unpatentable as
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`obvious over Lemelson and Nishio.
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`E. Claims 10, 11, 15, 19, and 23—Obviousness over Lemelson and
`Asayama
`
`Petitioner contends that claims 10, 11, 15, 19, and 23 are unpatentable
`
`under 35 U.S.C. § 103(a) over Lemelson (Ex. 1002) and Asayama (Ex.
`
`1006). Pet. 26–27.
`
`1. Asayama (Ex. 1006)
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`Asayama discloses a “distance detecting apparatus [that] enables the
`
`driver of a vehicle to readily and concurrently recognize the location and
`
`direction of each of a plurality of objects present in the driver’s field of view
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`[and] determine whether each of the objects is an obstacle” using image
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`sensors. Ex. 1006, Abstract. Figure 7, reproduced below, illustrates an
`
`embodiment of Asayama’s invention.
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`Figure 7 illustrates an infrared light source generated from generator
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`30 and filter 31 that removes almost all of the visible light to illuminate
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`object 5 that is sensed by image sensors 3 and 4 for processing and display
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`on screen 30. Id. at 7:4–40. Figure 7 also shows microcomputer 10 that
`
`processes the images from sensors 3 and 4. Id.
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`2. Analysis
`
`Petitioner contends that Asayama discloses the use of infrared light as
`
`transmitters of electromagnetic waves to be received by a set of sensors to
`
`measure the distance from a vehicle to exterior objects. Pet. 26. Petitioner
`
`further asserts that a person of ordinary skill in the art would have been
`
`motivated to combine the infrared light system of Asayama with the warning
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`system of Lemelson. Id. (citing Ex. 1013 ¶ 63). Petitioner relies on the
`
`claim charts and disclosure of Lemelson with respect to claims 10, 11, 15,
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`19, and 23 in combination with the infrared transmission system of
`
`Asayama. Pet. 26–27.
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`We are not persuaded by Patent Owner’s argument that the ground of
`
`Lemelson in combination with Asayama should be rejected under 35 U.S.C.
`
`§ 325(d) because it was presented in Toyota Motor Corp. v. American
`
`Vehicular Sciences LLC, IPR2013-00424. Prelim. Resp. 3. We decline to
`
`exercise our discretion under that provision in this case.
`
`Based on the present record, the information presented in the Petition
`
`shows a reasonable likelihood that Petitioner would prevail in showing that
`
`claims 10, 11, 15, 19, and 23 are unpatentable as obvious over Lemelson and
`
`Asayama.
`
`F. Claims 16, 17, and 20—Obviousness over Lemelson and Yanagawa
`
`Petitioner contends that claims 16, 17, and 20 are unpatentable under
`
`35 U.S.C. § 103(a) over Lemelson (Ex. 1002) and Yanagawa (Ex. 1007).
`
`Pet. 27–29.
`
`1. Yanagawa (Ex. 1007)
`
`Yanagawa discloses an onboard vehicular system that distinguishes
`
`and recognizes external taillights and headlights, calculates and determines
`
`the distance from vehicle and the external light sources, and automatically
`
`controls the vehicles headlights based on this recognition. Ex. 1007, 1
`
`(Section 3). Figure 1, reproduced below, depicts the system for recognizing
`
`the vehicle and automatically controlling the low and high headlight beams.
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`Figure 1 shows TV camera 11 and image signal processing 14 that extracts
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`the information from the headlight and taillights for recognition. Id. at 2:2.
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`Image signal processor 14 is depicted in Figure 4 and reproduced below.
`
`
`
`
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`Figure 4 illustrates that a determination is made between headlights and
`
`taillights in the recognition unit 143 and that a distance is computed between
`
`vehicles in computation unit 144. Id. at 4:1. Television camera 11, shown
`
`in Figure 1, provides the RGB signal input via decoder 13 into the image
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`signal processor 14 depicted in Figure 4. Yanagawa also teaches that
`
`headlights are adjusted from high to low beams, and vice versa, based on the
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`external vehicle recognition system. Id. at 3-5.
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`2. Analysis
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`Petitioner contends that Lemelson teaches each of the limitations of
`
`claim 16 but fails to teach “dimming the headlights.” Pet. 27; see Pet. 9–10.
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`Petitioner provides evidence that Lemelson discloses the limitations of claim
`
`16 via the similar limitations of claim 10. See Pet. 9–10, 29. Petitioner
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`provides testimony and argument that “Yanagawa teaches a system that
`
`recognizes taillights and headlights, calculates the distance between the
`
`traveling vehicle and the oncoming or preceding vehicle, and dims vehicle’s
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`headlights automatically to avoid blinding other drivers.” Pet. 27 (citing Ex.
`
`1007 at 1).
`
`We are not persuaded by Patent Owner’s argument that the ground of
`
`Lemelson in combination with Yanagawa should be rejected under 35
`
`U.S.C. § 325(d) because they were presented in Toyota Motor Corp. v.
`
`American Vehicular Sciences LLC, IPR2013-00424. Prelim. Resp. 3. We
`
`decline to exercise our discretion under that provision in this case.
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`Based on the present record, Petitioner has made a sufficient showing
`
`that the combination of Lemelson and Yanagawa teaches the limitations of
`
`claims 16, 17, and 20 and provided a reasonable basis for combining the
`
`references. Pet. 28–29. Accordingly, at this stage, we find that Petitioner
`
`shows a reasonable likelihood that Petitioner would prevail in showing that
`
`claims 16, 17, and 20 are unpatentable as obvious over Lemelson and
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`Yanagawa.
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`G. Claims 10, 15, 19, and 23—Anticipation by Nishio
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`Petitioner contends that claims 10, 15, 19, and 23 are unpatentable
`
`under 35 U.S.C. § 102(e) as anticipated by Nishio (Ex. 1004). Pet. 29–44.
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`Petitioner relies on argument, claim charts, and the testimony of Dr. Davis
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`(Ex. 1013) to support its contention that that Nishio discloses each limitation
`
`of claims 10, 15, 19, and 23.
`
`1. Analysis
`
`Petitioner contends that Nishio discloses the limitations of
`
`independent claims 10 and 23. Pet. 29–30. Specifically, Petitioner provides
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`testimony and argument that “image pick up device” 21, crash predicting
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`circuit and neural network trained with real object data during operation of
`
`the vehicle to create an algorithm (Ex. 1004, 2:42–49) discloses the
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`limitations of independent claims 10 and 23. Id.
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`Petitioner asserts that Nishio does not expressly disclose the
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`transmitter limitation of claim 10(a), but instead discloses inherently the use
`
`of headlights which act as transmitters. Pet. 30–31. Although Nishio
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`discloses elements for receiving images obtained during driving a vehicle,
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`Petitioner’s argument and evidence does not show that headlights of the
`
`automobile serve as transmitters for use with the reception means. See Pet.
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`31.
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`Because independent claims 10 and 23 and dependent claims 15 and
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`19 require a transmitting means, we find that Petitioner has not demonstrated
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`a reasonable likelihood that it would prevail in showing that claims 10, 15,
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`19, and 23 are unpatentable as anticipated by Nishio under 35 U.S.C.
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`§ 102(e).
`
`H. Claims 10, 15, 19, and 23—Obviousness over Nishio
`
`Petitioner argues that claims 10, 15, 19, and 23 are unpatentable under
`
`35 U.S.C. § 103(a) over Nishio (Ex. 1004). Pet. 44–45. Petitioner provides
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`analysis, claim charts, and testimony (Ex. 1013) in support of its
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`contentions. Id.
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`1. Analysis
`
`Petitioner provides evidence and argument that a person of ordinary
`
`skill in the art would have been motivated to modify the crash-protection
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`system of Nishio to better operate at night by the use of electromagnetic
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`transmitters, such as headlights. Pet. 44–45. Petitioner provides supporting
`
`testimony that it would have been obvious to include headlights in the crash-
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`prediction system of Nishio. Pet. 45. In addition, Petitioner relies on the
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`claim charts and analysis presented in support of Nishio anticipating the
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`limitations of claims 10, 15, 19, and 23 to show that Nishio teaches the
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