Toyota Motor Corporation, Petitioner
`v.
`American Vehicular Sciences (“AVS”), Patent Owner
`
`
`
`
`
`Before Jameson Lee, Michael W. Kim, and Lynne E.
`Pettigrew, Administrative Patent Judges
`
`No. IPR2013-00419
`Patent No. 6,772,057
`
`Thomas J. Wimbiscus, Lead Counsel
`Scott P. McBride, Backup Counsel
`Christopher M. Scharff, Backup Counsel
`
`1
`
`AVS EXHIBIT 2008
`Toyota Inc. v. American Vehicular Sciences LLC
`IPR2013-00419
`
`
`v.
`American Vehicular Sciences (“AVS”), Patent Owner
`
`
`
`
`
`Before Jameson Lee, Michael W. Kim, and Lynne E.
`Pettigrew, Administrative Patent Judges
`
`No. IPR2013-00419
`Patent No. 6,772,057
`
`Thomas J. Wimbiscus, Lead Counsel
`Scott P. McBride, Backup Counsel
`Christopher M. Scharff, Backup Counsel
`
`1
`
`AVS EXHIBIT 2008
`Toyota Inc. v. American Vehicular Sciences LLC
`IPR2013-00419
`
`
`
`Grounds for Institution
`
`Trial Instituted on Claims:
` 1-4, 7-10, 30-34, 37-39, 40, 41, 43, 46, 48, 49, 56, 59-61, 62, 64
`
`• Claims 1-4, 7-10, 40, 41, 43, 46, 48, 49, 56, 59-61, 64 for anticipation by Lemelson
`
`• Claims 30-34, 37-39, 62 for obviousness by Lemelson and Borcherts
`
`• Claims 4, 43, and 59 for obviousness by Lemelson and Asayama
`
`• Claim 34 for obviousness by Lemelson, Borcherts, and Asayama
`
`• Claims 30-32, 37-39, 62 for obviousness by Yamamura and Borcherts
`
`2
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`AVS EXHIBIT 2008
`Toyota Inc. v. American Vehicular Sciences LLC
`IPR2013-00419
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`Grounds to Which AVS Responds
`
`
`• Claims 1-4, 7-10, 41, 56, 59-61, 64 for anticipation by Lemelson
`
`o Independent claims 1 and 56 and claim 41 require a “pattern recognition
`algorithm generated from data of possible exterior objects and patterns of
`received waves from the possible exterior objects”
`
`
`
`• Claims 30, 32-34, 37-39, 62 for obviousness by (a) Lemelson and
`Borcherts or (b) Yamamura and Borcherts
`
`o Independent claim 30 and claim 62 require “at least one receiver arranged on a
`rear view mirror of the vehicle”
`
`3
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`AVS EXHIBIT 2008
`Toyota Inc. v. American Vehicular Sciences LLC
`IPR2013-00419
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`
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`Independent Claim 1
`
`1. A monitoring arrangement for monitoring an 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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`AVS EXHIBIT 2008
`Toyota Inc. v. American Vehicular Sciences LLC
`IPR2013-00419
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`Claim 41
`
`41. The arrangement of claim 40, wherein said processor comprises
`trained pattern recognition means for processing the signal to provide
`the 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.
`
`5
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`AVS EXHIBIT 2008
`Toyota Inc. v. American Vehicular Sciences LLC
`IPR2013-00419
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`Independent Claim 56
`
`56. A vehicle including a monitoring arrangement for monitoring an environment exterior
`of the vehicle, the monitoring arrangement 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.
`
`
`
`6
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`Qualifications of Prof. Koutsougeras
`(AVS expert)
`
`4. I received my B.S. in 1983 from the National Technical University of
`Athens, my M.S. degree in 1984 from the University of Cincinnati, and my
`PhD in 1988 from Case Western Reserve University. My Ph.D. research
`and dissertation was on the topic of neural networks and more specifically
`on algorithms for training feed-forward types of neural networks.
`
`5. I also have experience in automotive technology involving external
`object detection and collision warning systems, and the use of pattern
`recognition technology in such systems, as I have participated in the
`DARPA 2005 Grand Challenge competition with a team that built an
`autonomous vehicle designed to drive completely unassisted in unknown
`and unrehearsed cross-country environments. The vehicle was a regular
`production SUV which was modified to be controlled by computers aided
`by sensors including Ladars and GPS.
`
`
`Ex. 2001, Koutsougeras Declaration at ¶ 4-5.
`
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`Qualifications of Prof. Koutsougeras
`(AVS expert)
`
`Q. I’ll start by asking whether you can summarize for me your experience with
`neural networks?
`
`A. I first got familiar with the concept of neural networks while I was doing my
`Ph.D. dissertation. And I did my dissertation on neural networks, particularly
`methods for training neural networks. Subsequently I – as a faculty, I directed
`thesis of students and I taught classes that were either specifically on neural
`networks and substitute neural networks or for which neural networks was a
`substantial component. And then I can also say that I did various applications
`of neural networks, works with applications of neural networks, and that there
`were a substantial number of different domains in which I tried to apply them.
`
`Ex. 1022, Koutsougeras Dep. Tr. at 19:19-20:12
`
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`Qualifications of Prof. Koutsougeras
`(AVS expert)
`
`Q. So, prior to and including 1995, did you yourself have any experience working
`with vehicle exterior monitoring systems?
`
`A. I –yes, I was aware of some systems, and I had used them in the lab.
`
`Q. What systems had you used in the lab prior to and including 1995?
`
`A. Proximity sensors, ultrasound-based.
`
`*
`*
`*
`
`Q. Have you ever had any experience working with pattern recognition algorithms
`in vehicle exterior monitoring systems?
`
`A. Yes.
`
`Ex. 1022, Koutsougeras Dep. Tr. at 23:25-24:8
`
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`Claim Construction
`Prof. Koutsougeras (AVS expert)
`
`The ‘057 patent discloses and claims a specific method for generating the “training
`set.” The ‘057 patent discloses training the algorithm with “data of possible exterior
`objects and patterns of received waves from the possible exterior objects.” For
`example, if the vehicle uses a radar receiver, a neural network could be trained
`with examples of received radar waves from possible objects such as cars, plus
`labels to indicate the classification and possibly other information relating to the
`object. This “other information” might include geographic information (GIS) data
`relating to GPS information. The statement “received waves from possible exterior
`objects” is understood to describe actual readings of real waves from actual
`possible exterior objects.
`
`Ex. 2001, Koutsougeras Declaration at ¶ 19.
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`Claim Construction
`Prof. Koutsougeras (AVS expert)
`
`In the case of a radar system, the examples of received radar waves from possible
`objects used to train the system are real radar waves, so that the system knows how to
`recognize radar waves received from that same object or a similar one when the
`vehicle is later driving in actual conditions. This can be done, for example, by putting
`actual examples of a possible object in front of a vehicle radar system, subjecting the
`object with radar waves that are received back by the system, and then labeling the
`object for the system. This is different from other possible ways to train a pattern
`recognition system, such as through completely simulated data, which I discuss below.
`
`
`Ex. 2001, Koutsougeras Declaration at ¶ 20.
`
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`AVS EXHIBIT 2008
`Toyota Inc. v. American Vehicular Sciences LLC
`IPR2013-00419
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`
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`Lemelson
`
`“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.”
`
`Ex. 1002, Lemelson at 8:1-8
`
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`Prof. Koutsougeras (AVS’s Expert)
`
`43. Lemelson discloses a system for identifying objects exterior to a vehicle, and it does
`disclose using a type of pattern recognition algorithm, a neural network. (See Lemelson at
`Fig 3, 7:47, 8:1) The only pertinent discussion in Lemelson, however, relating to generating
`the neural network, is found at column 8, line 4, which states “[t]raining involves providing
`known inputs to the network resulting in desired output responses.” (Lemelson at 8:4-6.)
`
`44. I note that this is also the only reference to training in Lemelson found in the declaration
`of Dr. Papanikolopoulos. (See Exhibit 2002, Papanikolopoulos Decl. at ¶¶ 47-64.)
`Specifically, Dr. Papanikolopoulos only quotes the sentence from Lemelson that states
`“[t]raining involves providing known inputs to the network resulting in desired output
`responses.” (Id. at ¶55.) Nowhere else in his declaration does he use the words “training”,
`“trained,” or “generate” when discussing the Lemelson reference.
`
`
`Ex. 2001, Koutsougeras Declaration at ¶ 43-44.
`
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`Prof. Koutsougeras (AVS expert)
`
`55. First, in my opinion, the system in Lemelson could have been trained using
`completely simulated data, rather than data from possible exterior objects and patterns
`of received waves from the possible exterior objects.
`
`56. Simulated data is data that is not obtained from or based on actual real-time
`readings of real experiments or actual rehearsals in the domain of interest. Instead, it
`is generated by programs that simulate the target domain and produce estimates of
`what the sensors would be reading in a real instance. As such, it is essentially created
`by a programmer, who specifies parameters expected from an object (e.g., it would be
`expected to have a certain range of shapes and sizes, have varying positions and
`orientations, etc.). For example, data that represents scan readings of a road ahead of
`a vehicle can be obtained by actually driving the vehicle and recording the scans, or it
`can be estimated by computational geometric means on a computer-simulated road.
`Simulated data is therefore not data from objects or patterns of waves from objects—it
`is completely made-up data.
`
`
`Ex. 2001, Koutsougeras Declaration at ¶ 55-56.
`
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`Prof. Koutsougeras (AVS expert)
`
`57. As an analogy, simulated data is similar to a cartoon movie compared to a
`movie made with actors. The cartoon would provide a rough approximation for
`what a person is expected to look like, but would not be nearly as accurate as
`a video with a real actor. Or as another analogy, training with simulated data
`is analogous to training with only drawings of cars or other objects. One would
`not say that the cartoon or drawing is generated from “patterns of waves” from
`objects. They are made up.
`
`
`Ex. 2001, Koutsougeras Declaration at ¶ 57.
`
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`Professor Koutsougeras
`
`58. For example, Pomerleau’s 1992 Thesis describes two different such
`modes of training with simulated and with real sampled data (albeit with
`respect to roads, not objects). (See Exhibit 2004, Dean A. Pomerleau,
`Thesis: Neural Network Perception for Mobile Robot Guidance, School of
`Computer Science, Carnegie Mellon University (May 12, 1992).) Indeed, in
`Pomerleau, he devotes an entire section to “Training with Simulated Data”
`(Section 3.1) and a separate section to training “with Real Data” (Section
`3.2), in which he describes training with road (not “object”) data. (See id.)
`
`Ex. 2001, Koutsougeras Declaration at ¶ 58.
`
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`Pomerleau 1992 Thesis
`
`The problem, particularly with complex domains like perception based mobile
`robot guidance, is how to ensure that the training set is representative of the full
`range of situations the network might encounter during testing. My initial attempt
`at ensuring sufficient diversity in the training set involved generating synthetic
`images of situations the robot was likely to encounter and using them as training
`data. The use of synthetic training data was motivated by two factors. First,
`skepticism concerning the feasibility of using artificial neural networks for mobile
`robot guidance necessitated a "proof of concept” in simulation before
`implementing these ideas on a real robot. More importantly from a theoretical
`standpoint, I believed at the time that the only way to achieve variety in the
`training set sufficient to ensure that the network learns a general internal
`representation was to generate the training set synthetically.
`
`
`Exhibit 2004, Pomerleau Thesis at p. 38
`
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`Pomerleau 1992 Thesis, Cont.
`
`After training on artificial road images, the network was tested using three
`techniques. . . The best indication of the network’s performance came from
`driving tests on Navlab I, one of CMU’s robot vehicles. Specifically, the network
`could accurately drive Navlab I at a speed of 4 miles per hour along a 400 meter
`path through a wooded area of the CMU campus under sunny fall conditions.
`
`Despite its apparent success, this training paradigm had serious drawbacks.
`From a purely practical standpoint, generating the synthetic road scenes was
`quite time consuming . . .
`
`Exhibit 2004, Pomerleau Thesis at p. 40
`
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`Prof. Koutsougeras
`
`Because the research of Lemelson was conducted around the same time as
`that of Pomerleau’s thesis (Lemelson is a continuation of an application
`originally filed in 1993), it is very likely that Lemelson also came to the same
`conclusion as Pomerleau and initially thought that the “only way to achieve a
`variety in the training set” was to use simulated data. And Pomerleau’s thesis
`clearly demonstrates that simulated data was a “known input” at the time of
`Lemelson’s 1993 original patent application.
`
`Ex. 2001, Koutsougeras Declaration at ¶ 58
`
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`Simulated Data
`
`claim 4 “wherein said neural network training is
`accomplished by the use of simulated data”
`
`claim 6 “wherein said neural network training is
`accomplished by applying actual data”
`
`Ex. 2003, U.S. Pat. No. 5,537,327
`
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`Prof. Koutsougeras, Cont.
`
`Simulated data can be more desirable in some aspects compared to real-world
`data. Using simulated data has certain advantages in being able to generate a
`large training set easily and ensure balance. (See, e.g., Exhibit 2004,
`Pomerleau Thesis at 38 (“I believed at the time that the only way to achieve
`variety in the training set sufficient to ensure that the network learns a general
`internal representation was to generate the training set synthetically.”).)
`“Balance” means that the training set is not disproportionately weighted. For
`example, if I am training a system to detect roads, the system would not be
`balanced if it had mostly right-hand turns, and very few left-hand turns.
`
`Ex. 2001, Koutsougeras Declaration at ¶ 61
`
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`Prof. Papanikolopolous (Toyota’s Expert)
`
`A.
`
`Q. Could training a pattern recognition algorithm with simulated data involve
`less data that needs to be stored on the system. … As compared to
`training with images, for example, real life images?
`
`. . . [T]he answer is probably yes.
`
`Q. Could in 1995 simulated data involve smaller file sizes than digital image
`data?
`
`A. Potentially, yes.
`
`Exhibit 2002, Papanikolopoulos Dep. Tr. at 103:16-104:4, 123:19-22.
`
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`Ex. 1002, Lemelson at col. 8, lines 8-10
`
`“Adaptive operation is also possible with on-line adjustment of
`neural networks to meet imaging requirements.”
`
`Ex. 1002, Lemelson at 8:8-10
`
`“pattern recognition algorithm generated from data of
`possible exterior objects and patterns of received waves
`from the possible exterior objects”
`
`Ex. 1001, ‘057 patent at claims 1, 41, 56
`
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`Prof. Koutsougeras, Cont.
`
`“The fact that a computer was used to analyze images obtained
`when the system is in use (after it has been trained) tells
`nothing about the nature and extent of the training phase. An
`image analysis computer could be used to analyze received
`camera images when the vehicle is driving along the road, but
`the system could have been trained with something else
`entirely (such as simulated inputs as I discuss below).”
`
`Ex. 2001, Koutsougeras Declaration at ¶ 45
`
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`Prof. Koutsougeras, Cont.
`
`Accordingly, in my opinion, Lemelson does not inherently disclose
`generating a pattern recognition algorithm “from data of possible exterior
`objects and patterns of received waves from the possible exterior
`objects,” particularly given that it is possible that Lemelson’s pattern
`recognition algorithm was generated from simulated data. Lemelson’s
`neural network was not “necessarily” generated using data and patterns
`of received waves from possible objects
`
`Ex. 2001, Koutsougeras Declaration at ¶ 63
`
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`Papanikolopoulos Deposition Testimony
`
`Q. Is it technologically possible to use simulated data for a pattern
`recognition system for training that system for detecting
`automobiles?
`
`A. If you look at my declaration, I mention several systems in this
`domain. In this particular domain, you go to simulated data, or if
`you don’t have access to real data, to real images.
`
`Exhibit 2002, Papanikolopoulos Dep. Tr. at 102:5-14.
`
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`Papanikolopoulos 2nd Declaration
`
`In my opinion, one of ordinary skill in the art would have understood the phrase “known
`inputs” in Lemelson to refer to “real data” because Lemelson’s neural network was trained
`to identify exterior objects, and one of ordinary skill in 1995 would have known that training
`with “real data” would have yielded the best results for this purpose. One of ordinary skill in
`the art would not have understood that “known inputs” referred to simulated data or partial
`data in the context of Lemelson’s disclosure, since one of ordinary skill would not have had
`any reason to believe that those categories of data would have been effective for the
`purpose of object identification (e.g., for the purpose of identification of a pedestrian).
`
`Exhibit 1023, Papanikolopoulos Reply Decl. at 9.
`
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`Terms Construed in Toyota’s Petition
`
`“pattern recognition algorithm”
`
`“trained pattern recognition means” and “trained pattern recognition
`algorithm”
`
`“identify”/”identification”
`
`“measurement means for measuring a distance between the exterior
`object and the vehicle”
`
`“rear view mirror”
`
`
`Toyota’s Petition at pp. 6-8.
`
`
`
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`Term Construed in Toyota’s Reply
`
`“The ‘generated from’ language is not a limitation for purposes of the
`patentability analysis.”
`
`
`Toyota’s Reply at pp. 3.
`
`
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`Toyota’s Reply Brief
`
`“The claims merely specify that the algorithm is generated from (1) data of possible
`exterior objects, and (2) patterns of received waves from those possible exterior
`objects. The claimed patterns “of” received waves, as opposed to, “patterns from”
`received waves, merely require patterns representing what received waves would look
`like (which would include simulations).”
`
`Toyota’s Reply at p. 2.
`
`“a trained pattern recognition algorithm generated from data of possible exterior
`objects and patterns of received waves from the possible exterior objects”
`
`Claim Language
`
`30
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`Patent Trial Practice Guide
`
`“While replies can help crystalize issues for decision, a reply that
`raises a new issue or belatedly presents evidence will not be
`considered and may be returned.”
`
`Patent Trial Practice Guide, Fed. Reg. Vol. 77, No. 157 at p. 48767.
`
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`Prof. Papanikolopoulos’s Deposition
`
`Q. So can you think of an example of a trained pattern recognition
`algorithm that is not generated from data of possible exterior objects
`and patterns of received waves from the possible exterior objects?
`
`A. With respect to the specific claim construction that I had to
`examine, no.
`
`
` Ex. 2002, Papanikolopoulos Dep. Tr. at 90:20-91:3.
`
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`Claims 30, 32-34, 37-39, 62
`
`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.
`
`
`62. The vehicle of claim 56, wherein said at least one receiver is mounted on a
`rear view mirror or in a rear window.
`
`33
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`Borcherts
`
`“Image input device 12 may be mounted in combination
`with the rear view mirror assembly or separate therefrom or
`at any other location which adequately monitors the road in
`front of the vehicle 10.”
`
`Ex. 1004, Borcherts at 2:56-60.
`
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`Borcherts, Cont.
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`35
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`Prof. Koutsougeras (“AVS expert”)
`
`In my opinion, the use of the term “in combination with” something is
`intended to be broader than the term “attached on something.” The term
`“in combination with,” is for example synonymous to “in coordination” or
`“in coordinated positions.” Borcherts explains in 2:50 that “[a]n image
`input device 12 is mounted to the front portion of the vehicle 10 at a
`location near the rear view mirror assembly.” Thus in my opinion, a
`receiver arranged “in combination” with the rear view mirror “assembly”
`is not intended to mean necessarily “on” the rearview mirror. A receiver
`could be set below the rearview mirror, next to it, etc., and still be in
`combination with the “assembly” as the rearview mirror.
`
`Ex. 2001, Koutsougeras Declaration at ¶ 83.
`
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`AVS EXHIBIT 2008
`Toyota Inc. v. American Vehicular Sciences LLC
`IPR2013-00419
`
`
`
`Prof. Koutsougeras (“AVS expert”)
`
`A receiver arranged “on” a rearview mirror could, for example, rotate
`along with the rearview mirror, while a receiver that is merely arranged
`“in combination with” the rearview mirror “assembly” could be stationary
`and fixed to the windshield.
`
`
`Ex. 2001, Koutsougeras Declaration at ¶ 84.
`
`37
`
`AVS EXHIBIT 2008
`Toyota Inc. v. American Vehicular Sciences LLC
`IPR2013-00419
`
`
`
`Papanikolopoulos’ Testimony
` (Toyota’s Expert)
`
`Q. If I put the receiver on the back bumper of the car, can you tell me if that’s arranged on
`the rearview mirror?
`
`
`A. What is the size in this case?
`
`
`Q. Any receiver. Any receiver that’s positioned on the back bumper of a car, is that
`positioned on the rearview mirror?
`
`
`A. Is it supposed to be a receiver that’s too big to fit on the rear end of the car?
`
`
`Q. I’m not asking anything like that. If I point to a car—I put a car in front of you. I point to
`the camera that’s on the rear bumper and I ask you: ‘Is that camera on the rear bumper on
`the rearview mirror,’ you can’t answer yes or no?
`
`
`A. Because there is no yes-or-no answer on this.
`
`
`Ex. 2002, Papanikolopoulos Dep. Tr. at 155:3-23
`
`38
`
`AVS EXHIBIT 2008
`Toyota Inc. v. American Vehicular Sciences LLC
`IPR2013-00419
`
`
`
`Papanikolopoulos’ Testimony
` (Toyota’s Expert)
`
`Q. So in your opinion, near the rearview mirror, that's good enough to count as on a
`rearview mirror?
`
`A. For someone with ordinary skill in the art, I think this will be sufficient.
`
`Ex. 2002, Papanikolopoulos Dep. Tr. at 143:19-25 (objection omitted)
`
`39
`
`AVS EXHIBIT 2008
`Toyota Inc. v. American Vehicular Sciences LLC
`IPR2013-00419
`
`
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