UNITED STATES PATENT AND TRADEMARK OFFICE
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
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`Mercedes-Benz USA, LLC,
`Petitioner,
`
`v.
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`Yechezkal Evan Spero,
`Patent Owner.
`
`Case [To be assigned]
`Patent 11,208,029
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`DECLARATION OF DR. NIKOLAOS PAPANIKOLOPOULOS
`IN SUPPORT OF MERCEDES’ PETITION FOR INTER PARTES REVIEW
`OF CLAIMS 1-33OF U.S. PATENT NO. 11,208,029
`
`Filed on behalf of Petitioner:
`Celine Jimenez Crowson (Reg. No. 40,357)
`Joseph J. Raffetto (Reg. No. 66,218)
`Scott Hughes (Reg. No. 68,385)
`Ryan Stephenson (Reg. No. 76,608)
`HOGAN LOVELLS US LLP
`555 13th Street N.W.
`Washington, D.C. 20004
`Telephone: 202.637.5600
`Facsimile: 202.637.5710
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`Mercedes EX1003
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`TABLE OF CONTENTS
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`I.
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`V.
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`INTRODUCTION ......................................................................................... 1
`A.
`Engagement ........................................................................................... 1
`B.
`Background and Qualification .............................................................. 1
`C.
`Information Considered ....................................................................... 10
`II. BACKGROUND OF THE ’029 PATENT ................................................ 11
`A.
`The ’029 Patent ................................................................................... 11
`III. CLAIM CONSTRUCTION AND POSITA DEFINITION ..................... 14
`A.
`Claim Construction.............................................................................. 14
`B. Definition of a Person of Ordinary Skill in the Art ............................. 14
`IV. UNDERSTANDING OF LEGAL STANDARDS ..................................... 14
`A. Anticipation ......................................................................................... 14
`B. Obviousness ......................................................................................... 16
`THE ELEMENTS IN CLAIMS 1-33 OF THE ’029 patent are
`ANTICIPATED AND RENDERED OBVIOUS BY the prior art ......... 19
`A. Ground 1 – Alden in View of Kobayashi Renders Obvious Claims 1-
`8, 10-19, 21-30, and 32-33. ................................................................. 24
`1.
`Independent Claim 1 .................................................................24
`2.
`Independent Claim 12 ...............................................................48
`3.
`Independent Claim 23 ...............................................................49
`4.
`Dependent Claims 2, 13, 24 ......................................................51
`5.
`Dependent Claims 3, 14, 25 ......................................................54
`6.
`Dependent Claims 4, 15, 26 ......................................................59
`7.
`Dependent Claims 5, 16, 27 ......................................................62
`8.
`Dependent Claims 6, 17, 28 ......................................................64
`9.
`Dependent Claims 7, 18, 29 ......................................................71
`10. Dependent Claims 8, 19, 30 ......................................................74
`11. Dependent Claims 10-11, 21-22, 32-33 ....................................88
`B. Ground 2: Alden In View of Kobayashi, or, Alternatively,
`Alden/Kobayashi in view of Beam, Renders Claims 9, 20, and 31
`Obvious................................................................................................ 93
`VI. CERTIFICATION ......................................................................................... 1
`APPENDIX A ........................................................................................................... 1
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`I, Dr. Nikolaos Papanikolopoulos, declare:
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`I.
`
`INTRODUCTION
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`A. Engagement
`I have been retained on behalf of Mercedes-Benz USA, LLC
`1.
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`(“Mercedes”) to offer technical opinions relating to U.S. Patent No. 11,208,029
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`(EX1001) (the “’029 Patent”) and prior art references relating to its subject
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`matter.1
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`2.
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`I have no financial interest in either party or in the outcome of this
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`proceeding. I am being compensated for my work as an expert on an hourly basis
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`at my standard consulting rate of $450 per hour. My compensation is not
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`dependent on the outcome of these proceedings or the content of my opinions.
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`B.
`3.
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`Background and Qualification
`I am the McKnight Presidential Endowed Professor and Distinguished
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`McKnight University Professor in the Department of Computer Science and
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`Engineering of the University of Minnesota in Minneapolis, Minnesota. I am also
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`the Director for the Minnesota Robotics Institute.
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`1 For brevity, when providing citations herein, I will generally refer to the name of
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`the reference, rather than the exhibit number. The exhibit numbers associated with
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`each reference can be found in Appendix A.
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`4.
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`I have been a professor at the University of Minnesota (originally as
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`an assistant professor, and then as an associate professor) since the Fall of 1992.
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`Between Fall 2001 and Spring 2004, and between Fall 2010 and Spring 2013, I
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`was the Director of Undergraduate Studies of the College of Science and
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`Engineering.
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`5.
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` In 1992, I received my Ph.D. in Electrical and Computer Engineering
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`from Carnegie Mellon University. My thesis was entitled “Controlled Active
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`Vision” and focused on using computer vision in a controlled fashion to detect,
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`track, and manipulate objects in the environment. In 1988, I received my M.S. in
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`Electrical and Computer Engineering from Carnegie Mellon University. My B.S.
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`in Electrical Engineering was received in 1987 from the National Technical
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`University in Athens, Greece.
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`6.
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`For over thirty years, my research and teaching work has focused on
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`image processing, computer vision, intelligent transportation systems, and robotics.
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`This research has included detection and tracking of humans and objects such as
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`vehicles using information from stationary or moving cameras. It has also included
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`object detection and recognition including work with artificial intelligence and
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`pattern recognition systems for surveillance applications.
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`7.
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` I currently teach three courses relating to object detection and
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`tracking: (i) CSci 5561 Computer Vision, (ii) CSci 5511 Artificial Intelligence, and
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`(iii) CSci 5551 Introduction to Intelligent Robotic Systems. I have been teaching
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`the class in Computer Vision since 1992. Many of the assignments in the class
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`include segmentation and tracking of objects in real-world settings.
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`8.
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`I am currently serving as a Senior Editor in the IEEE Transactions on
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`Intelligent Transportation Systems with responsibility in the area of imaging. I
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`was Associate Editor in the same journal for twelve years.
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`9.
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` I have received research grants for various projects involving
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`computer vision and sensing as applied to the transportation industry, including
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`vehicle tracking, assessing truck parking, detection of pedestrians, and obstacle
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`avoidance. My industry experience includes founding a robotics company in 2005
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`named ReconRobotics Inc. that develops different robotic platforms. I have also
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`been a consultant since 1997 for numerous companies and provided technical
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`expertise in developing computer vision algorithms for vehicle tracking, camera
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`calibration, inspection, precision agriculture, human tracking, object recognition,
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`and fingerprint recognition. In one of my consulting assignments, I had to develop
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`computer vision algorithms for inspection of industrial parts by using different
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`illumination sources (Banner Engineering). I have licensed my algorithms to
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`several companies, with the most recent one being Sentera Inc., which is using
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`computer vision for precision agriculture.
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`10. My research has produced more than 380 journal and conference
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`publications. More than 90 publications are in refereed journals. Many of my
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`publications and research grants relate to vehicle and human detection and tracking
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`systems. Some examples include:
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`Cook, D., Morris, T., Morellas, V., and Papanikolopoulos, N., “An Automated
`System for Persistent Real-Time Truck Parking Detection and Information
`Dissemination”, Proceedings of the 2014 IEEE Int. Conference on Robotics and
`Automation, Hong Kong, China, May 31 – June 7, 2014, PP. 3989-3994.
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`Somasundaram, G., Sivalingam, R., Morellas, V., and Papanikolopoulos, N.P.,
`“Classification and Counting of Composite Objects in Traffic Scenes Using Global
`and Local Image Analysis”, IEEE Trans. on Intelligent Transportation Systems,
`Volume 14, No. 1, March 2013, PP. 69-81.
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`Atev, S., Miller, G., and Papanikolopoulos, N.P., “Clustering of Vehicle
`Trajectories”, IEEE Trans. on Intelligent Transportation Systems, Volume 11, No.
`3, September 2010, PP. 647-657.
`
`Atev, S., Arumugam, H., Masoud, O., Janardan, R., and Papanikolopoulos, N.P., “A
`Vision-Based Approach to Collision Prediction at Traffic Intersections”, IEEE
`Trans. on Intelligent Transportation Systems, Volume 6, No. 4, December 2005,
`PP. 416-423.
`
`Masoud, O., and Papanikolopoulos, N.P., “A Novel Method for Tracking and
`Counting Pedestrians in Real-time Using a Single Camera”, IEEE Trans. on
`Vehicular Technology, Volume 50, No. 5, September 2001, PP. 1267-1278.
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`Du, Y., and Papanikolopoulos, N.P., "Real-time Vehicle Following Through a Novel
`Symmetry-Based Approach", Proceedings of the 1997 IEEE Int. Conf. on Robotics
`and Automation, PP. 3160-3165, Albuquerque, NM, April 20-25, 1997.
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`11. My work in vehicle detection and tracking has focused on using
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`image and video processing techniques, including object detection, tracking, and
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`recognition, to accurately track vehicles in real-time. These algorithms have been
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`applied to different scenarios, such as urban traffic monitoring and surveillance.
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`Moreover, machine learning techniques have helped in the improvement of these
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`methodologies.
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`12.
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`I have done extensive work in sensor fusion and data integration as
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`part of intelligent transportation systems. In particular, my research includes
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`contributions in integrating data from multiple sensors to enhance vehicle tracking
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`accuracy. By combining information from cameras, GPS systems, radar, LiDAR,
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`and other sensors, my research has aimed to create a comprehensive tracking
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`system that can handle various environmental conditions and challenges.
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`13. My work in intelligent transportation systems includes the use of
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`control algorithms and sensing methodologies to control light systems in the
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`context of transportation applications. For example, I have developed a system that
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`detects and tracks pedestrians as they cross the street in order to eventually extend
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`the “Walk” signal. The images below are from a system deployment in the Twin
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`Cities (the “Flashing” Red signal indicates the presence of pedestrians in the
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`crossing) in the late 1990s. The images below are from a system deployment in the
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`Twin Cities (the “Flashing” Red signal indicates the presence of pedestrians in the
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`crossing) in the late 1990s. The images are from a final project report #2000-28
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`published by Minnesota Department of Transportation, titled “Pedestrian Control
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`at Intersections: Phase IV” which was prepared by me and Osama Masoud.
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`14. My work has been extended to tracking autonomous vehicles. My
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`interests include developing tracking algorithms and systems that can handle the
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`complexities of autonomous vehicles, including predicting their future movements
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`and understanding their intentions. The work has been extended to monitor the
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`intentions of pedestrians.
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`15. These vehicle tracking systems enable continuous monitoring and
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`tracking of vehicles, providing valuable information for traffic management, law
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`enforcement, and security purposes.
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`16. My paper “Detection and Classification of Vehicles,” published in the
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`IEEE Transactions on Intelligent Transportation Systems in March 2002, is one of
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`the most cited papers in the history of this journal (more than a thousand citations).
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`The paper, among other contributions, shows how to use detected headlights and
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`taillights to extract vehicles from videos and images in low-light conditions. One
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`should note that the majority of my papers in the broader area of intelligent vehicle
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`systems dealt with the detection of vehicles, pedestrians, trucks, etc., by using a
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`variety of techniques which were mainly based on computer vision. I also have
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`nine patents in pertinent areas.
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`17. My research work has been funded by several government agencies.
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`For example, I have received funding from the Department of Homeland Security
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`to monitor threats at Mass Transit Sites, like the 30th Street Station in Philadelphia,
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`PA, and the Light Rail Station in the Minneapolis-Saint Paul International Airport.
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`Monitoring parked vehicles was an essential task for which we developed a variety
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`of computer vision algorithms and tools.
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`18.
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`18. For more than a decade, I have been developing computer
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`vision-based systems to assess truck parking availability at truck rest areas. The
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`funding came from Federal Highway Administration (FHWA), Minnesota Dept. of
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`Transportation (MnDOT), Wisconsin Dept. of Transportation (WiscDOT), and
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`Kansas Dept. of Transportation (KansasDOT). The approach uses multiple
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`cameras to detect trucks and other vehicles and determine empty spaces. The
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`system operates in all weather conditions around the clock.
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`19.
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`I have received numerous honors and awards for my research and
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`contributions. I have been a Distinguished McKnight University Professor at the
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`University of Minnesota since 2007 and have been a McKnight Presidential
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`Endowed Professor in Computer Science since 2016. Also in 2007, I was
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`nominated and became an IEEE Fellow. In 2016, I received the IEEE RAS George
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`Saridis Leadership Award in Robotics and Automation as well as the Center for
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`Transportation Studies Research Partnership Award. I have also received the IEEE
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`VTS 2001 Best Land Transportation Paper Award for the paper “A Novel Method
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`for Tracking and Counting Pedestrians in Real-Time Using a Single Camera” (with
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`Osama Masoud).
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`20. The figures below show the detection and tracking of a vehicle by a
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`camera mounted in a car that follows a target vehicle. These figures are from a
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`paper that I had published in 1997: Du, Y., and Papanikolopoulos, N.P., “Real-time
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`Vehicle Following Through a Novel Symmetry-Based Approach,” Proceedings of
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`the 1997 IEEE Int. Conf. on Robotics and Automation, PP 3160-3165,
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`Albuquerque, NM, April 20-25, 1997.
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`I have also worked in the use of sensors to monitor driver fatigue. I
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`21.
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`developed a computer vision-based system that analyzes facial features and eye
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`movements to detect signs of drowsiness and driver fatigue. By monitoring factors
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`such as eye closure, head pose, and blink patterns, the system can alert drivers
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`when they show signs of fatigue and suggest appropriate actions (this research took
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`place in the late 90s). The proposed algorithms and systems considered the
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`continuously evolving light conditions that vehicular systems operate within.
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`22. As a result of my work and research, I am familiar with the design,
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`operation, and functionality of systems described in the ’029 Patent.
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`23. Further details regarding my employment and academic history are
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`included in my curriculum vitae, attached as EX1004.
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`C.
`24.
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`Information Considered
`I have reviewed the ’029 Patent and its prosecution history, as well as
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`the other materials referenced in Appendix A. Counsel has informed me that I
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`should consider these materials through the lens of one of ordinary skill in the art
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`related to the ’029 Patent, at the time of the earliest purported priority date of the
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`’029 Patent, and I have done so during my review of these materials. I have been
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`asked to assume, for purposes of this Declaration, that the ’029 Patent has a
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`priority date of July 12, 2002 (the “Critical Date”).
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`25. My analyses are based on my years of education, research, and work
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`experience, as well as my investigation and study of relevant materials. In my
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`analyses, I have considered the materials that I identify in this Declaration and
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`those listed in Appendix A.
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`26.
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`I may rely on these and additional materials to respond to arguments
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`raised by the Patent Owner. I may also consider additional documents and
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`information in further analyses—including documents that may not yet have been
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`provided to me.
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`27. My review and assessment of the materials provided in this
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`proceeding is ongoing, and I will continue to consider any new material as it is
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`provided. I reserve the right to review, supplement, and amend my analyses based
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`on new information and on my continuing review of the materials already
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`provided.
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`II. BACKGROUND OF THE ’029 PATENT
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`A. The ’029 Patent
`I understand the ’029 Patent was filed on January 19, 2021, and issued
`28.
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`on December 28, 2021. I also understand that the ’029 Patent is part of the
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`following patent family:
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`29.
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`I have assumed, for purpose of this declaration, that the ’029 Patent is
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`entitled to this Critical Date of July 12, 2002.
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`30. The ’029 Patent alleges that in the “prior art,” lighting fixtures were
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`often single, large lights that were “generic” and “universal.” (’029 Patent, 5:35-
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`59.) These lights were allegedly not tailored to individual applications, were
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`“inexact and wasteful,” produced light in more places than needed, and required
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`reflectors and refractors to re-direct light. (Id.) The ’029 Patent claims it came up
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`with a new approach it called a “Digital Lighting Fixture” that can “provide the
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`‘correct’ lighting solution for the situation at hand.” (Id., 10:63-11:14.) The ’029
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`purports to accomplish this through “controllability offered by breaking the total
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`light output up into discrete (‘digital’) specifically aimable and dimmable elements
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`which can be addressed by control electronics.” (Id., 11:56-65.)
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`31. While the ’029 Patent gives various example applications, its claims
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`are directed to the alleged invention as used in a “headlight system for vehicles.”
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`(Id., Claims 1, 12, 23; see also Abstract.) The claims recite how light clusters or
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`sources (e.g., LEDs) can be included in a headlamp on a vehicle:
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`(Id., Fig. 15 (annotated).) “Sensors” obtain relevant situational data, including
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`detecting things like an approaching road curvature. (See, e.g., id., Claim 1,
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`Abstract.) A “processor” can then determine, based on this, how to adjust the
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`output from the individual lights to increase light in the direction of the curvature.
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`(See, e.g., id.)
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`III. CLAIM CONSTRUCTION AND POSITA DEFINITION
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`A. Claim Construction
`I understand that in an inter partes review, claims must be given their
`32.
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`ordinary and customary meaning, as understood by one of ordinary skill in the art
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`in light of the specification and prosecution history. I apply that standard in my
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`analysis below to the words and phrases in the claims of the ’029 Patent.
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`B. Definition of a Person of Ordinary Skill in the Art
`33. A person of ordinary skill in the art (“POSITA”) in the field of the
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`’029 Patent would have had an undergraduate degree in mechanical engineering,
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`electrical engineering, automotive engineering, optical engineering, applied
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`physics, computer science, or similar disciplines, along with two years of
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`experience working with intelligent vehicle systems, automotive control systems,
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`or lighting control systems. The more education one has, the less experience
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`needed to attain an ordinary level of skill. Similarly, more experience in the field
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`may serve as a substitute for formal education.
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`IV. UNDERSTANDING OF LEGAL STANDARDS
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`A. Anticipation
`I understand that a patent claim is invalid when the invention that it
`34.
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`claims is not new. To establish that a claimed invention is not new (i.e., “not
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`novel”), I understand that one may establish that a single publication, or other
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`reference in the prior art discloses (explicitly or inherently) every element required
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`in a patent claim (i.e., all features or “limitations” recited in the patent claim). I
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`understand that a reference in the prior art “anticipates” a claimed invention if that
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`reference discloses, either explicitly or inherently, every element of the claim.
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`35.
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`I understand that “prior art” and “prior art reference” are legal terms of
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`art referring to, for example, devices, methods, and publications that predate the
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`earliest effective filing date of the claimed invention.
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`36.
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`I understand that a prior art reference that anticipates a claim may
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`disclose an element or limitation of a patent claim expressly or inherently. A prior
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`art reference discloses an element or limitation of a patent claim inherently when the
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`prior art’s disclosure necessarily requires or implies that the claimed element or
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`limitation be present in the process, machine, manufacture, or composition
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`disclosed. I understand there is still considered to be an inherent disclosure even if
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`the author of the reference did not describe or understand the underlying inherent
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`principle. I understand that one may establish that a prior art reference inherently
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`discloses a claimed element or limitation through the use of other reference material
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`or through testing.
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`37.
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`I understand that the description in a written reference does not have to
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`be in the same words as the claim, but all of the requirements of the claim must be
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`there, either stated or necessarily implied, so that a POSITA looking at that one
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`reference would be able to make and use the claimed invention.
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`38.
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`I understand that any prior art reference that can be considered for
`
`purposes of determining whether it anticipates a patent claim may also be used to
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`determine whether the reference renders that claim obvious, as discussed below.
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`B. Obviousness
`I understand that, even if a claim is not fully disclosed in a single prior
`39.
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`art reference, the patent claim is invalid if the invention would have been obvious
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`to a POSITA at the time of the invention. In particular, I understand that a patent
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`claim is normally invalid as obvious if it would have been an “ordinary
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`innovation” within the relevant field to create the claimed product or method at the
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`time of the invention.
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`40.
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`I understand that the relevant portion of pre-AIA Section 103,
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`subsection (a) of the Patent Act states:
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`“A patent may not be obtained through the invention is not identically
`disclosed or described as set forth in section 102 of this title, if the
`differences between the subject matter sought to be patented and the
`prior art are such that the subject matter as a whole would have been
`obvious at the time the invention was made to a person having
`ordinary skill in the art to which said subject matter pertains.”
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`41.
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`I understand that, by way of example only, a claimed invention is
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`obvious if:
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`it combines prior art elements according to known methods to
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`yield predictable results;
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`it simply substitutes one known element for another to obtain
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`predictable results;
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`it uses a known technique to improve similar devices (methods,
`
`or products) in the same way;
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`it applies a known technique to a known device (method, or
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`product) ready for improvement to yield predictable results;
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`it was “obvious to try” in that the inventor chose from a finite
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`number of identified, predictable solutions, with a reasonable
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`expectation of success;
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`known work in one field of endeavor prompted variations of it
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`for use in either the same field or a different one based on design
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`incentives or other market forces and those variations were
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`predictable to one of ordinary skill in the art; or
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`some teaching, suggestion, or motivation in the prior art led one
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`of ordinary skill to modify the prior art reference or to combine prior
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`art reference teachings to arrive at the claimed invention.
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`42. When considering obviousness, I understand that I am to: (i) decide
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`the level of ordinary skill in the field that someone would have had at the time the
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`alleged invention was made; (ii) determine the scope and content of the prior art;
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`(iii) determine what differences, if any, existed between the prior art and the
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`Asserted Claims; and (iv) consider objective evidence of non-obviousness (also
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`known as secondary considerations). Further, when considering obviousness, I
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`understand that it is not necessary to seek out precise teachings, and it is
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`permissible to consider the inferences, common sense, and creative steps that a
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`POSITA (who is considered to have an ordinary level of creativity and is not an
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`automaton) would employ.
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`43.
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`I understand that objective evidence relevant to the issue of
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`obviousness may also be considered. This type of evidence is sometimes referred
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`to as “secondary considerations,” and may include evidence of commercial
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`success, long-felt but unsolved needs, failure of others, and unexpected results. I
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`understand that any secondary evidence must have a nexus to the relevant claims.
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`For example, I understand that commercial success must have a nexus to features
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`of the alleged invention not disclosed in the prior art, and in particular the prior art
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`references which support an obviousness theory. In other words, I understand that
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`commercial success is material only if it comes from the merits of the claimed
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`invention beyond what the prior art disclosed. With respect to secondary
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`considerations, I understand that Patent Owner bears the burden of proof, and I
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`have seen no evidence to date that any secondary considerations would establish
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`non-obviousness.
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`V. THE ELEMENTS IN CLAIMS 1-33 OF THE ’029 PATENT ARE
`ANTICIPATED AND RENDERED OBVIOUS BY THE PRIOR ART
`I have been asked to provide an analysis as to whether the elements of
`44.
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`Claims 1-33 of the ’029 Patent (the “Challenged Claims”) are disclosed in the
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`primary prior art reference: U.S. Patent Application Publication No. 2003/0137849
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`(“Alden”) (EX1005). I was also asked to consider this reference in view of certain
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`additional prior art for some of the limitations of the Challenged Claims, including
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`U.S. Patent No. 6,049,749 to Kobayashi (“Kobayashi”) (EX1009) and U.S. Patent
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`No. 6,144,158 to Beam (“Beam”) (EX1008).
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`45. My analysis of these prior art references relative to the elements of the
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`Challenged Claims—specifically, how and where the prior art references disclose
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`the limitations of the challenged claims—is provided below. The citations that I
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`have included are not intended to provide an exhaustive list, but rather provide
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`examples of how the references disclose or teach the elements of these claims.
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`46.
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`I have reviewed Alden. Alden is directed to a headlight system with
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`individually-controllable LEDs such that different areas in the field-of-view of the
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`vehicle can be illuminated in different ways, e.g., high beam and low beam areas
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`can be created concurrently. Alden was filed on January 22, 2002, and published
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`on July 24, 2003. I understand Alden is prior art under at least pre-AIA 35 U.S.C. §
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`102(e).
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`47. My review of the ’029 Patent file history shows that Alden was not
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`considered during prosecution of the ’029 Patent.
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`48. Alden discloses a headlight system where the headlights can
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`“segment” the illumination “into sectors of the light distribution area.” (Alden,
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`[0008].) Alden discloses that lighting elements which correspond to the individual
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`illumination sectors can be individually and automatically controlled as to intensity
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`and direction. (Id.) For example, in Alden, the individual lighting elements (119)
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`may be LEDs that are positioned along a curve inside of a headlight:
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`(Id., Fig. 5, [0029].) Alongside this, the Alden system includes (a) a “sensor unit”
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`for detecting objects, such as other vehicles along the road in the area of the
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`vehicle equipped with Alden’s headlight system and (b) a “light control circuit”
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`with a “CPU” to adjust the individual LEDs based on sensor data. (Id., Figs. 2-3,
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`[0026]-[0027].)
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`49. Alden teaches that, with its system of individually-controllable LEDs,
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`a vehicle can “emit[] a low beam illumination 35 in a first headlight distribution
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`sector while concurrently emitting a high beam, illumination 37 in a second
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`highlight distribution sector,” with “[t]he low beam illumination being emitted in
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`response to the detection of an oncoming vehicle 33” to reduce “glare”:
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`(Id., Fig. 1, [0004], [0025].) For example, looking at the figure above, Alden’s
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`system directs dimmed low beam light in the segments of the light distribution area
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`that contain detected vehicle 33. Other sectors where no other vehicles are detected
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`would have high beam light. Alden discloses that this is achieved by providing
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`sensor data to a processor, determining the location of other vehicles from the
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`sensor data, and then determining which headlight sectors or segments should be
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`switched to dim, low beam light and which should be switched to high beam light.
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`(Id., [0027]-0029], Fig. 4.) Alden’s system can also be used to “concentrate light”
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`to “look around corners,” “look up a hill,” and “look down a hill,” all “in response
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`to road conditions.” (Id., Figs. 11-13, [0036]-[0038].)
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`(Id., Fig. 11.)
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`50.
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`I have also reviewed Kobayashi. Kobayashi is titled “Lighting device
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`for a vehicle” and, like Alden, is directed to improvements in vehicle headlamp
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`systems including detection of positioning along a road. (Kobayashi, Abstract.)
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`Kobayashi was filed on December 12, 1997 and issued on April 11, 2000. I
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`understand that Kobayashi is prior art under § 102(b).
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`51. My review of the ’029 Patent file history shows that Kobayashi was
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`not considered during prosecution of the ’029 Patent.
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`52. Similar to Alden, Kobayashi’s headlamp system includes various
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`inputs, a controller, and a headlamp:
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`(Kobayashi, Fig. 1, 3:9-5:28; see also Fig. 12, 17:3-67.) Kobayashi discloses that
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`its controller (“irradiation control means 4”) receives “road map information” from
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`“road profile calculating means 2,” along with other sensor data, and use these
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`inputs to automatically adjust the illumination of headlamp 5. (Id.; see also 2:4-39.)
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`53. Kobayashi discloses that a “GPS navigation device 29 sends
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`information about the road profile and the present vehicle position to ECU 10.”
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`(Id., 17:39-40.) Kobayashi further discloses that “when the vehicle advances along
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`a scheduled course, irradiation control of the lamp is conducted in accordance with
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`the course concerned.” (Id.,17:59-61.)
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`A. Ground 1 – Alden in View of Kobayashi Renders Obvious Claims
`1-8, 10-19, 21-30, and 32-33.
`Independent Claim 1
`1.
`54. As viewed by a POSITA, Alden in view of Kobayashi renders
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`obvious Claim 1.
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`1[Pre]: A system, for a motor vehicle, comprising:
`55. Alden discloses Claim 1[Pre] to the extent the preamble limits the
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`scop