`
`UNITED STATES PATENT AND TRADEMARK OFFICE
`
`______________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`
`______________
`
`
`VOLKSWAGEN GROUP OF AMERICA, INC.,
`Petitioner,
`
`v.
`
`YECHEZKAL EVAN SPERO,
`Patent Owner.
`
`______________
`
`
`U.S. Patent No. 10,894,503 to Spero
`
`Case No.: IPR2023-00335
`
`______________
`
`
`
`
`
`DECLARATION OF MATTHEW A. TURK PhD
`
`
`
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`Case No.: IPR2023-00335
`Patent No.: 10,894,503
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`TABLE OF CONTENTS
`
`Atty. Dkt. No.: TRCH0117IPR
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`List of Exhibits ........................................................................................................... 4
`
`I.
`
`II.
`
`Qualifications and Professional Experience .................................................... 6
`
`Relevant Legal Standards ..............................................................................11
`
`III. Overview of the ‘503 patent ..........................................................................12
`
`A.
`B.
`C.
`
`Challenged Independent Claims..........................................................15
`Qualifications of one of ordinary skill in the art .................................18
`Proposed claim constructions ..............................................................19
`
`IV. Overview of the prior art ...............................................................................19
`
`Beam: U.S. Patent No. 6,144,158 .......................................................19
`A.
`Thominet: U.S. Application Pub. No. 2001/0019486 .........................26
`B.
`Stam: WIPO Publication No. 2001/1070538 ......................................29
`C.
`D. Harbers: WIPO Publication No. 2001/001038 ...................................30
`E.
`Karlsson: WIPO Publication No. 1998/054030 ..................................33
`
`V. Grounds for Challenge ...................................................................................39
`
`B.
`
`A. All Grounds: The Proffered Combinations Fail to Teach the
`Determining Subsections Limitation, Subsections Limitation,
`Determining Illumination for Subsections Limitation, the
`Determining Light Distribution Pattern Limitation or the
`Operating Light Cluster Limitations in Claims 1 and 37 ....................40
`All Grounds: A Person of Ordinary Skill in the Art Would Not
`Have Been Motivated to Combine Beam and Thominet or
`Karlsson and Harbers ..........................................................................52
`1.
`Petitioner’s Rationale for Combining References Renders
`the Primary Reference Inoperable or Unsatisfactory for
`Its Intended Purpose ..................................................................52
`a.
`Grounds 1B-1C and 2B-2C: The Proposed
`Combination Renders Beam Inoperable and
`Defeats Beam’s High-Intensity Illumination “At
`All Times” Purpose ........................................................52
`Petitioner’s Contorted Rationale Undermines the
`Crux of Beam’s Maximum Intensity “At All
`Times” Strategy ..............................................................53
`
`b.
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`2.
`
`3.
`
`4.
`
`b.
`
`Grounds 1B-1C and 2B-2C: Beam’s Maximum Intensity
`Field-Of-View Teaches Away from Thominet’s
`Illumination Functions Undermining Petitioner’s
`Rationale for Combining ...........................................................55
`Petitioner Does Not Explain How the Combination
`Would Operate for its Intended Purpose ...................................56
`a.
`Grounds 1B-1C and 2B-2C: Petitioner Does Not
`Explain How a POSITA Would Use Thominet’s
`Software or Hardware to Control Direction of
`Beam’s Maximum Intensity Microbeams ......................56
`Ground 7: Petitioner Does Not Explain How a
`POSITA Would Use Harber’s LEDs with
`Karlsson’s Spotlight Beams and Spotlight Sensors .......58
`Petitioner Fails to Explain Why the References Would
`Have Been Combined; the Rationale to Combine Suffers
`from Hindsight Bias ..................................................................59
`a.
`Grounds 1B-1C and 2B-2C: The Addition of
`Features Already Present in Beam Constitutes
`Impermissible Hindsight .................................................60
`Ground 7: The Addition of Features Already
`Present in Karlsson Constitutes Impermissible
`Hindsight .........................................................................61
`
`b.
`
`VI. Conclusion .....................................................................................................64
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`Case No.: IPR2023-00335
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`List of Exhibits
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`Atty. Dkt. No.: TRCH0117IPR
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`
`Exhibit
`Description
`No.
`2001 Declaration of Matthew A. Turk, PhD
`2002 Curriculum Vitae of Matthew A. Turk, PhD
`2003 Best Practices and FAQs for Filing Requests for
`Reexamination Compliant with 37 CFR 1.510
`and 1.915, PTAB, May 2010
`Patent No.
`Petition
`challenging U.S.
`10,894,503, Volkswagen Group of America,
`Inc. v. Yechezkal Evan Spero, IPR2023-00197,
`Paper 1, November 30, 2022
`Patent No.
`Petition
`challenging U.S.
`10,894,503, Volkswagen Group of America,
`Inc. v. Yechezkal Evan Spero, IPR2023-00328,
`Paper 2, December 16, 2022
`
`2004
`
`2005
`
`Identifier
`Turk Decl.
`Turk CV
`Best Practices
`
`The ‘197 IPR Petition
`
`The ‘328 IPR Petition
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`
`
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`1.
`
`2.
`
`I, Matthew A. Turk, hereby declare as follows:
`
`I am making this declaration at the request of Yechezkal Evan Spero
`
`in the matter of Inter Partes Review of U.S. Patent No. 10,894,503 (“the ‘503
`
`Patent”) to Yechezkal Evan Spero.
`
`3.
`
`I am being compensated for my work in this matter at a rate of
`
`$490/hour for time spent reviewing materials and performing my analysis of the
`
`technical issues relevant to this matter. My compensation in no way depends on
`
`the outcome of this proceeding.
`
`4.
`
`In preparation of this declaration, I have studied the exhibits as listed
`
`in the Exhibit List shown above in my report.
`
`5.
`
`In forming the opinions expressed below, I have considered:
`
`a. The documents listed above as well as additional patents and
`
`documents referenced herein;
`
`b. The relevant legal standards, including the standard for obviousness
`
`provided in KSR International Co. v. Teleflex, Inc., 550 U.S. 398
`
`(2007), and any additional documents cited in the body of this
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`declaration; and
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`c. My knowledge and experience based upon my work and study in this
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`area as described below.
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`I.
`
`Qualifications and Professional Experience
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`Atty. Dkt. No.: TRCH0117IPR
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`6.
`
`I am a Professor and President of the Toyota Technological Institute
`
`at Chicago (TTIC) and an Emeritus Professor of the Department of Computer
`
`Science at the University of California, Santa Barbara. I have worked and studied
`
`in the field of computer vision since 1984, as well as related areas of image
`
`processing, pattern
`
`recognition, machine
`
`learning, and human-computer
`
`interaction. The field of computer vision is concerned with determining how
`
`computers can extract, analyze and understand information from images and video.
`
`7.
`
`I received my doctorate degree in Media Arts and Sciences from the
`
`Massachusetts Institute of Technology (MIT) in 1991. My doctoral work was in
`
`the area of computer vision, specifically concerning automatic face recognition. I
`
`received my Master of Science degree in Electrical and Computer Engineering
`
`from Carnegie Mellon University in 1984. My Master’s work was in the area of
`
`robot fine motion planning. I received my Bachelor of Science degree in Electrical
`
`Engineering from Virginia Tech (VPI&SU) in Blacksburg, Virginia in 1982, where
`
`my senior honors thesis was on image processing.
`
`8.
`
`In 1991, I published the paper “Eigenfaces for Recognition,” which I
`
`co-authored with my Ph.D. advisor, Dr. Alex Pentland. The research reported in
`
`this paper helped lead to practical automated face recognition systems that are used
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`in today’s security and surveillance systems, as well as in consumer applications.
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`My work on Eigenfaces has received several awards, including an IEEE Computer
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`Atty. Dkt. No.: TRCH0117IPR
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`Society Outstanding Paper award at the IEEE Conference on Computer Vision and
`
`Pattern Recognition (CVPR) in 1991 and a “Most Influential Paper of the Decade”
`
`award from the International Association for Pattern Recognition (IAPR)
`
`Workshop on Machine Vision Applications (MVA 2000).
`
`9.
`
`Along with my co-authors, I have received several other awards at
`
`professional conferences and workshops for my work in computer vision, image
`
`processing, and augmented reality, including several best paper awards and
`
`winning the ACM Multimedia Open Source Competition in 2015.
`
`10. My industry experience includes working for Martin Marietta Denver
`
`Aerospace from 1984 to 1987, where I worked primarily on computer vision for
`
`autonomous vehicle navigation as part of the Defense Advanced Research Projects
`
`Agency’s Autonomous Land Vehicle program, the precursor to the later DARPA
`
`Grand Challenge events and subsequent advances in self-driving automobiles. In
`
`1992, I was a visiting researcher at LIFIA/ENSIMAG in Grenoble, France and
`
`from 1993 to 1994, I worked for Teleos Research in Palo Alto, CA; in both of
`
`those positions, I worked on computer vision methods for recognizing human
`
`faces, gestures, and activity. From 1994 to 2000, I was a researcher for Microsoft
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`Research, where I was a founding member of the Vision Technology Research
`
`Group and conducted research in vision-based human computer interaction and
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`other related areas.
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`Atty. Dkt. No.: TRCH0117IPR
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`11.
`
`In 2000, I joined the faculty of the University of California, Santa
`
`Barbara, as an Associate Professor. In 2005, I was promoted to Full Professor and
`
`served as the Chair of the Media Arts and Technology Graduate Program from
`
`2005 to 2010. In 2017, I was appointed as Chair of the Department of Computer
`
`Science. At UCSB, I taught graduate and undergraduate courses related to
`
`computer vision, computer imaging, human computer interaction, probabilistic
`
`models and methods, artificial intelligence, computer graphics, machine learning,
`
`and other areas. My graduate Computer Imaging course covered the fundamentals
`
`of imaging systems, including the capture, storage, display, retrieval, and
`
`processing of images, as well as the nature of light, color, optics, human vision,
`
`non-visible (UV and IR) and multispectral imaging, sensors, cameras, and
`
`illumination.
`
`12.
`
`I co-founded (in 2003) and co-directed the Four Eyes Lab at UCSB,
`
`where the research focus is on the “four I’s” of Imaging, Interaction, and
`
`Innovative Interfaces. The Four Eyes Lab researches a variety of topics, including
`
`computer vision, pattern recognition, virtual and augmented reality, and face
`
`processing technologies such as face recognition and facial expression analysis. I
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`worked on and supervised many research projects in these areas, including (1)
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`visual recognition of automobiles and (2) multi-flash imaging, which used multiple
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`image sources and special optics to infer 3D scene structure from images.
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`13.
`
`In 2014, I co-founded a startup company, Caugnate, to commercialize
`
`computer vision and augmented reality technology for remote collaboration.
`
`Caugnate was acquired by PTC Inc., in 2016, and now forms the core of the
`
`Vuforia Chalk augmented reality product.
`
`14.
`
`In 2019, I became President of the Toyota Technological Institute at
`
`Chicago, an independent graduate research institute, where the research and
`
`education focus of the faculty and Ph.D. students is on computer science theory,
`
`machine learning, artificial intelligence, and related areas such as computer vision,
`
`speech and language processing, robotics, and computational biology.
`
`15. As a result of my work and research, I am a named inventor of U.S.
`
`Patent Number 5,164,992 entitled “Face Recognition System,” U.S. Patent
`
`Number 6,674,877 entitled “System and Method for Visually Tracking Occluded
`
`Objects in Real-time,” and U.S. Patent Number 9,495,761 entitled “Environment
`
`mapping with automatic motion model selection.”
`
`16.
`
`I was a founding member of the Advisory Board for the International
`
`Conference on Multimodal Interfaces (ICMI), which provides a venue for
`
`disseminating recent advances in multimodal interaction research, systems, and
`
`methods, which includes combining visual modalities with audio, haptic, or other
`
`modalities. I served as the Chair of the ICMI Advisory Board from 2006 to 2009. I
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`was also a founding member of the Advisory Board for the IEEE International
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`Atty. Dkt. No.: TRCH0117IPR
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`Conference on Automatic Face and Gesture Recognition and served as the General
`
`Chair for the conference in 2011. I have been a primary organizer of several top
`
`research conferences, including serving as General Chair for the 2014 IEEE
`
`Conference on Computer Vision and Pattern Recognition, the primary annual
`
`conference in the field of computer vision, with over 2000 attendees, Program
`
`Chair for the 2017 IEEE Winter Conference on Applications of Computer Vision,
`
`and General Chair of the 2019 International Conference on Multimodal Interfaces
`
`(ICMI).
`
`17.
`
`I am currently an Associate Editor of the ACM Transactions on
`
`Intelligent and Interactive Systems and the International Journal of Computer
`
`Vision and Signal Processing, and was formerly an Associate Editor of Image and
`
`Vision Computing. I have also served as a guest editor of several other journals
`
`related to computer vision, machine learning, and facial recognition.
`
`18.
`
`In 2020, I was elected a Fellow of the Association for Computing
`
`Machinery (ACM), the main professional society in the field of computer science,
`
`for “contributions
`
`to face recognition, computer vision, and multimodal
`
`interaction.” In 2013, I was elected as a Fellow of the IEEE (the Institute of
`
`Electrical and Electronics Engineers), an award conferred by the Board of
`
`Directors of the IEEE upon a person with an extraordinary record of
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`accomplishments in any of the IEEE fields of interest. In 2014, I was elected as a
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`Fellow of the International Association for Pattern Recognition (IAPR). In 2022, I
`
`was named as a Fellow of the Asia-Pacific Artificial Intelligence Association. I am
`
`also the recipient of the 2011-2012 Fulbright-Nokia Distinguished Chair in
`
`Information and Communications Technologies. I have given many keynote
`
`presentations at top conferences and workshops in my fields.
`
`19.
`
`I have provided my full background in the curriculum vitae that is
`
`attached as Exhibit 2002.
`
`II. Relevant Legal Standards
`
`20.
`
`I have been asked to provide opinions on the claims of the ‘503 Patent
`
`in light of the prior art.
`
`21.
`
`It is my understanding that a claimed invention is unpatentable under
`
`35 USC § 102 if a prior art reference teaches every element of the claim. Further, it
`
`is my understanding that a claimed invention is unpatentable under 35 U.S.C.
`
`§ 103 if the differences between the invention and the prior art are such that the
`
`subject matter as a whole would have been obvious at the time the alleged
`
`invention was made to a person having ordinary skill in the art to which the subject
`
`matter pertains. I also understand that an obviousness analysis takes into account
`
`factual inquiries including the level of ordinary skill in the art, the scope and
`
`content of the prior art, and the differences between the prior art and the claimed
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`subject matter.
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`22.
`
`It is my understanding that the Supreme Court has recognized several
`
`rationales for combining references or modifying a reference to show obviousness
`
`of the claimed subject matter. Some of these rationales include the following:
`
`combining prior art elements according to known methods to yield predictable
`
`results; simple substitution of one known element for another to obtain predictable
`
`results; a predictable use of prior art elements according to their established
`
`functions; applying a known technique to a known device to yield predictable
`
`results; choosing from a finite number of identified, predictable solutions, with a
`
`reasonable expectation of success; and some teaching, suggestion, or motivation in
`
`the prior art that would have led one of ordinary skill to modify the prior art
`
`reference or to combine prior art reference teachings to arrive at the claimed
`
`invention.
`
`III. Overview of the ‘503 patent
`
`23. The ‘503 Patent is directed to “a novel multi-light source approach to
`
`the design and construction of solid-state lighting fixtures (vs. solid state lamps),
`
`which is termed a ‘Digital Lighting Fixture’, due to the control of individual
`
`lighting element ‘digits’ to provide the ‘correct’ lighting solution for the situation
`
`at hand.” (Ex. 1001, 11:11-16.) The ‘503 Patent further explains that “[t]he
`
`terminology ‘digital’ as used herein refers to the discrete nature of the multiple
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`LED lamps provided in the luminaire, whereby, ‘digital’ control results from the
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`individual control of the discrete, i.e., ‘digital’ lighting elements, the LEDs, in the
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`luminaire.” (Id., 2:67-3:5.)
`
`(Ex. 1001, FIG 15, Annotated)
`
`
`
`24.
`
` “The added controllability offered by breaking the total light output
`
`up into discrete (‘digital’) specifically aimable and dimmable elements which
`
`can be addressed by control electronics to effect intensity, spectrum and spatial
`
`distribution of intensity and spectrum, yields a lighting fixture (vs. lamp) of
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`unparalleled performance.” (Id., 11:61-67.)
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`25. More specifically, the ‘503 Patent describes methods of using a multi-
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`LED light source to segment a field of view and deliver varied, optimized
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`illumination to the different segments within the field of view based on the
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`presence of a detected object therein, “covering a wide scope of applications”,
`
`including transportation. (Id., 11:18-21, 4:20-24, 50:40-41.)
`
`26.
`
`In the context of headlights,
`
`[a]dditional features can include an optional very narrow flashlight
`
`type of beam. . . Such a beam at the correct narrow aiming can
`
`continue to illuminate far ahead (in high beam) without blinding
`
`oncoming traffic. Analysis by a detector or an imaging system of the
`
`oncoming vehicles position (using its headlights for example) can be
`
`used to determine which exact aiming is the maximum allowable for
`
`any given traffic situation and road layout.
`
`(Ex.1001, 52:58-53:2.)
`
`27. Additionally, a spatially differentiated illumination pattern can be
`
`determined to differently illuminate segments of a field of view based on the
`
`detection of an object:
`
`Using color and pattern recognition techniques it is possible to
`
`determine what is a sign. It is then possible to provide the equivalent
`
`of “task lighting” and use the controller and properly aimed SLS to
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`follow and illuminate distant overhead and roadside signs for a longer
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`time, yet not blind oncoming traffic due to the narrow beam.
`
`(Ex.1001, 53:3-8.)
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`28. The claimed invention of the ‘503 Patent is thus directed to multi-
`
`LED headlight systems that precisely determine anti-glaring illumination output to
`
`optimize illumination for the vehicle operator under a myriad of driving situations,
`
`whilst reducing glaring illumination for oncoming vehicles.
`
`29. The dependent claims include additional innovative features that
`
`specify illumination strategies for a myriad of driving situations.
`
`A. Challenged Independent Claims
`
`30. The Challenged Claims include independent claims 1 and 37, which
`
`are reproduced below with claim identifiers:
`
`[1.P] A vehicle headlight system comprising:
`
`[1.1] one or more headlamps and a vehicle structure, arranged for affixing
`
`components of the system to the vehicle;
`
`[1.2] a plurality of light clusters, arranged in each of the one or more of the
`
`one or more headlamps,
`
`[1.3] the headlamps capable of projecting light so as to illuminate a plurality
`
`of sub-sections of a field-of-view with different illumination characteristics,
`
`[1.4] the subsections determined in response to sensor data, wherein the light
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`clusters operate to create a headlight beam;
`
`[1.5] electronic control circuitry configured to independently operate the
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`light clusters;
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`[1.6] one or more sensors configured to obtain and provide the sensor data
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`predefined as pertinent to illumination requirements for the field-of-view;
`
`and
`
`[1.7] a
`
`logical controller
`
`including a processor,
`
`the processor
`
`in
`
`communication with a memory storing one or more algorithms, with the
`
`sensors, and with the control circuitry,
`
`[1.8] wherein the processor is configured to process the one or more
`
`algorithms and the sensor data to determine an illumination for at least two
`
`of the sub-sections,
`
`[1.9] that reduces illumination to reduce glare in at least a first of the at least
`
`two subsections and
`
`[1.10] adapt the light emitted from one or more of the light-clusters so as to
`
`create the headlight beam that provides the determined illumination.
`
`
`
`[37.P] A vehicle headlight system comprising:
`
`[37.1] one or more headlamps affixable to a first vehicle;
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`[37.2] a plurality of light clusters arranged in each of the one or more of the
`
`headlamps and
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`[37.3] capable of illuminating sub-sections of a field-ofview with different
`
`illumination characteristics,
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`[37.4] wherein at least two of the light clusters operate to create a headlight
`
`beam;
`
`[37.5] one or more sensors configured to obtain and provide sensor data,
`
`[37.6] at least a portion of the sensor data indicating a second vehicle within
`
`the field-of-view; and
`
`[37.7] at least one processor configured to: receive the sensor data; process
`
`the sensor data to determine a light distribution pattern for an adaptive
`
`headlight beam,
`
`[37.8] such that a headlight beam adapted to the pattern substantially
`
`illuminates a first subsection of the field-of-view including at least a portion
`
`of the second vehicle with an illuminance below a predefined threshold
`
`value,
`
`[37.9] while the beam substantially illuminates at least the one or more
`
`second subsections of the field-ofview to either side of the first subsection
`
`with an illuminance above the predefined threshold value; and
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`[37.10] instruct operation of at least two of the light clusters to create the
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`adapted headlight beam.
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`B. Qualifications of one of ordinary skill in the art
`
`31.
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`I have been asked to provide my opinion as to the level of skill of a
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`person having ordinary skill in the art (“POSITA”) of the ’503 Patent at the time of
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`the claimed invention. In determining the characteristics of a hypothetical person
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`of ordinary skill in the art of the ‘503 Patent at the time of the claimed invention, I
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`was told to consider several factors, including the type of problems encountered in
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`the art, the solutions to those problems, the rapidity with which innovations are
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`made in the field, the sophistication of the technology, and the education level of
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`active workers in the field. I also placed myself back in the time frame of the
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`claimed invention and considered the skill level of colleagues with whom I had
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`worked at that time.
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`32.
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`In my opinion, a POSITA of the ‘503 Patent at the time of its filing
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`would have been a person having a Master of Science Degree (or a similar
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`technical Master’s Degree, or higher degree) in an academic area emphasizing
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`electrical engineering, computer engineering, or computer science with experience
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`or education in optics and imaging systems or, alternatively, a Bachelor’s Degree
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`(or higher degree) in an academic area emphasizing electrical, computer
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`engineering or computer science and having two or more years of experience in the
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`field of optical and imaging systems.
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`33. At the time of the filing of the ‘503 Patent, a skilled artisan
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`developing a lighting system utilizing LEDs and corresponding controls would
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`Atty. Dkt. No.: TRCH0117IPR
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`have encountered new challenges with respect to both optics and LED controls.
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`Accordingly, it is my opinion that a POSITA would have had experience with
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`both, i.e., engineering and optics.
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`C.
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`Proposed claim constructions
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`34. To properly evaluate these claims, I understand that the terms of the
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`claims must first be construed. In my opinion, the claims of the ‘503 Patent do not
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`include any terms that require specific construction. Instead, each of the claims
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`includes terms that an ordinary artisan would understand according to their plain
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`and ordinary meaning.
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`IV. Overview of the prior art
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`A. Beam: U.S. Patent No. 6,144,158
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`35. Beam discloses a complete, but generalized, Adaptive Anti-Blinding
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`Headlight (“AABH”) system, where there is no need to differentiate among light
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`sources, to aim the LEDs, or to provide any form of lateral illumination. The
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`AABH system has a multiplicity of LED-produced microbeams that “operate at
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`maximum intensity at all times” allowing “the driver to have the equivalent of high
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`beam headlights or brighter on at all times, greatly enhancing the ability to see at
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`night.” (Ex.1005, 6:11-12, 6:40-44.) Beam provides significantly reduced
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`illumination only in those portions of the headlight beam that would otherwise
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`dazzle or annoy other drivers. (Ex.1005, 6:40-44, 3:8-14.) This is accomplished by
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`“darken[ing] those microbeams that would otherwise illuminate areas near to the
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`source of the impinging beam, thus eliminating the blinding of a driver in an
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`oncoming vehicle, while continuing to provide intense forward illumination.”
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`(Ex.1005, 1:14-18, Claims 4, 5, emphasis added.)
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`36. Beam’s AABH system is non-mechanical and non-moveable, i.e. a
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`system with no moving parts or mechanisms to change the direction of the light
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`beam (“non-moveable”). Beam recognized that existing headlight systems operated
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`“either as a ‘high beam’ or as a ‘low beam,’” often under manual control, and
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`various unsatisfactory attempts to solve this problem included “systems which
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`attempt to control the intensity profile of the beam by using a reflector or lens
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`shaped to control the output beam in some predetermined way; systems to move
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`the headlamps in response to changes that occur to the automobile on which they
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`are mounted; . . . movement of headlight beams in response to changes in the
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`vehicle’s steering mechanism, etc.” (Ex.1005, 1:21-23, 1:63-2:5.) Beam also
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`recognized the problem of headlamps being misaligned and illuminating
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`unintended areas that could result in blinding of other drivers.
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`37. Beam solves the identified problems by providing a streamlined, non-
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`moveable AABH system having a multiplicity of microbeams that “operate at
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`maximum intensity at all times” thereby eliminating the complexity and potential
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`failures of various prior art systems that attempt to change the beam direction or
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`Atty. Dkt. No.: TRCH0117IPR
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`reduce overall intensity while still allowing “the driver to have the equivalent of
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`high beam headlights or brighter on at all times, greatly enhancing the ability to see
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`at night.” (Ex.1005, 6:11-12, 6:40-44, 6:54-56, 3:8-14.) Other than controlling
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`intensity of individual microbeams in response to detected incoming light, Beam
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`does not disclose or suggest light sources aimed at different angles, processing
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`incoming light to differentiate a vehicle from other light sources, or changing
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`direction of any microbeams, nor would the AABH system benefit from such
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`increased control complexity, as its normal mode is to “operate at maximum
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`intensity” to illuminate the field-of-view of the headlights, obviating a need to
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`“add” or “increase” light anywhere. (Ex.1005, 1:15-19, 6:11-12.) Thus, Beam
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`begins with a premise that a light, “operat[ing] at maximum intensity at all times,”
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`is already maximally placing light everywhere it can, leaving nothing left to place,
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`move, or add. (Ex.1005, 6:11-12.). Against such a fully and maximally illuminated
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`field-of-view, Beam then concerns itself with determining where to remove light.
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`(Ex.1005, FIGs. 1, 1C, 1D, Annotated)
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`
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`38.
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`In the AABH system, illuminator 109 projects individually controlled
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`narrow-angle microbeams to provide high-intensity illumination of illuminated
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`area 101. Incoming light—which could be associated with headlights of an
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`oncoming vehicle 102 and/or taillights of a preceding vehicle 103, streetlights,
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`motorcycles or other light sources—is transmitted through optical system 106 and
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`detected by a sensor (camera) 107. Sensor (camera) 107 is boresighted with the
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`headlight so that the field-of-view (FOV) of the camera is coincident with the FOV
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`of the headlight beam. Data from sensor 107 is used by controller 108 to map the
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`angular position of incoming light to a corresponding position/size of microbeams
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`that must be significantly reduced in intensity corresponding to the shaded areas
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`shown in Figs. 1C, and 1D. (Ex.1005, 4:9-24, 53-59, 62-64.)
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`39. Beam describes
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`two distinct embodiments using alternative
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`components
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`that significantly reduce
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`illumination
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`intensity of
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`individual
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`microbeams in response to detecting incoming light. (Ex.1005, Figs. 3, 4.)
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`However, neither embodiment discloses changing the direction of any of the
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`microbeams. (Id.) Likewise, neither embodiment discloses processing signals from
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`the incoming light sensor/camera to differentiate a vehicle from any other light
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`sources. (Id.) Rather, Beam’s AABH system significantly reduces intensity of
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`microbeams associated with detected incoming light, regardless of whether the
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`light source is a vehicle, streetlight, or any other source.
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`(Ex.1005, FIGs. 2, 5, Annotated)
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`40.
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`In the first embodiment illustrated in Fig. 2 (perspective view) and
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`Fig. 5 (top view), light from lamp 202 falls on spatial light modulator (SLM) 203,
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`which is controlled so that the associated individual microbeams are either 1)
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`reflected by SLM 203 at full intensity through beam splitter 201 and out through
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`headlamp optical system 106, or 2) “blanked (attenuated by being either absorbed
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`or deflected [by SLM 203] to an absorbing surface” so that they do not exit the
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`headlamp optical system 106. (Ex.1005, 4:3