UNITED STATES PATENT AND TRADEMARK OFFICE
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`
`Mercedes-Benz USA, LLC,
`Petitioner,
`
`v.
`
`Yechezkal Evan Spero,
`Patent Owner.
`
`Case [To be assigned]
`Patent 9,955,551
`
`DECLARATION OF DR. NIKOLAOS PAPANIKOLOPOULOS
`IN SUPPORT OF MERCEDES’ PETITION FOR INTER PARTES REVIEW
`OF CLAIMS 48-87 OF U.S. PATENT NO. 9,955,551
`
`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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`TABLE OF CONTENTS
`
`I.
`
`II.
`
`INTRODUCTION ......................................................................................... 1
`A.
`Engagement ........................................................................................... 1
`B.
`Background and Qualification .............................................................. 1
`C.
`Information Considered ....................................................................... 10
`BACKGROUND OF THE ’551 PATENT ................................................ 11
`D.
`The ’551 Patent ................................................................................... 11
`III. CLAIM CONSTRUCTION AND POSITA DEFINITION ..................... 14
`A.
`Claim Construction.............................................................................. 14
`B.
`Definition of a Person of Ordinary Skill in the Art ............................. 15
`IV. UNDERSTANDING OF LEGAL STANDARDS ..................................... 15
`A.
`Anticipation ......................................................................................... 15
`A.
`Obviousness ......................................................................................... 17
`THE ELEMENTS IN CLAIMS 48-87 OF THE ’551 Patent are
`ANTICIPATED AND RENDERED OBVIOUS BY the prior art ......... 20
`A.
`Ground 1 – Alden Anticipate and Renders Obvious Claims 48, 50-51,
`53-59, 61-67, 69-75, 77-83, 85-87 ...................................................... 23
`1.
`Independent Claim 48 ...............................................................23
`2.
`Independent Claim 56 ...............................................................57
`3.
`Dependent Claim 57 .................................................................67
`4.
`Independent Claim 64 ...............................................................72
`5.
`Dependent Claim 65 .................................................................77
`6.
`Independent Claim 72 ...............................................................80
`7.
`Dependent Claim 73 .................................................................83
`8.
`Independent Claim 80 ...............................................................85
`9.
`Dependent Claim 81 .................................................................86
`10. Dependent Claims 50/58/66/74/82 ...........................................89
`11. Dependent Claims 51/59/67/75/83 ...........................................94
`12. Dependent Claims 53/61/69/77/85 ...........................................96
`13. Dependent Claims 54/62/70/78/86 and Dependent Claims
`55/63/71/79/87 ..........................................................................97
`Ground 2 – Alden (Alone or as Modified by Stam) in View of Beam
`Renders Obvious Claim 49 ............................................................... 101
`1.
`Dependent Claim 49 ...............................................................101
`
`V.
`
`B.
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`C.
`
`Ground 3 – Alden in View of Kobayashi Renders Obvious Claims 52,
`60, 68, 76, 84 ..................................................................................... 111
`VI. CERTIFICATION ......................................................................................... 1
`APPENDIX C ........................................................................................................... 1
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`I, Dr. Nikolaos Papanikolopoulos, declare:
`
`I.
`
`INTRODUCTION
`
`A. Engagement
`I have been retained on behalf of Mercedes-Benz USA, LLC
`1.
`
`(“Mercedes”) to offer technical opinions relating to U.S. Patent No. 9,955,551
`
`(EX1001) (the “’551 Patent”) and prior art references relating to its subject matter.
`
`2.
`
`I have no financial interest in either party or in the outcome of this
`
`proceeding. I am being compensated for my work as an expert on an hourly basis
`
`at my standard consulting rate of $450 per hour. My compensation is not
`
`dependent on the outcome of these proceedings or the content of my opinions.
`
`B.
`3.
`
`Background and Qualification
`I am the McKnight Presidential Endowed Professor and Distinguished
`
`McKnight University Professor in the Department of Computer Science and
`
`Engineering of the University of Minnesota in Minneapolis, Minnesota. I am also
`
`the Director for the Minnesota Robotics Institute.
`
`4.
`
`I have been a professor at the University of Minnesota (originally as
`
`an assistant professor, and then as an associate professor) since the Fall of 1992.
`
`Between Fall 2001 and Spring 2004, and between Fall 2010 and Spring 2013, I
`
`was the Director of Undergraduate Studies of the College of Science and
`
`Engineering.
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`5.
`
` In 1992, I received my Ph.D. in Electrical and Computer Engineering
`
`from Carnegie Mellon University. My thesis was entitled “Controlled Active
`
`Vision” and focused on using computer vision in a controlled fashion to detect,
`
`track, and manipulate objects in the environment. In 1988, I received my M.S. in
`
`Electrical and Computer Engineering from Carnegie Mellon University. My B.S.
`
`in Electrical Engineering was received in 1987 from the National Technical
`
`University in Athens, Greece.
`
`6.
`
`For over thirty years, my research and teaching work has focused on
`
`image processing, computer vision, intelligent transportation systems, and robotics.
`
`This research has included detection and tracking of humans and objects such as
`
`vehicles using information from stationary or moving cameras. It has also included
`
`object detection and recognition including work with artificial intelligence and
`
`pattern recognition systems for surveillance applications.
`
`7.
`
` I currently teach three courses relating to object detection and
`
`tracking: (i) CSci 5561 Computer Vision, (ii) CSci 5511 Artificial Intelligence, and
`
`(iii) CSci 5551 Introduction to Intelligent Robotic Systems. I have been teaching
`
`the class in Computer Vision since 1992. Many of the assignments in the class
`
`include segmentation and tracking of objects in real-world settings.
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`8.
`
`I am currently serving as a Senior Editor in the IEEE Transactions on
`
`Intelligent Transportation Systems with responsibility in the area of imaging. I
`
`was Associate Editor in the same journal for twelve years.
`
`9.
`
` I have received research grants for various projects involving
`
`computer vision and sensing as applied to the transportation industry, including
`
`vehicle tracking, assessing truck parking, detection of pedestrians, and obstacle
`
`avoidance. My industry experience includes founding a robotics company in 2005
`
`named ReconRobotics Inc. that develops different robotic platforms. I have also
`
`been a consultant since 1997 for numerous companies and provided technical
`
`expertise in developing computer vision algorithms for vehicle tracking, camera
`
`calibration, inspection, precision agriculture, human tracking, object recognition,
`
`and fingerprint recognition. In one of my consulting assignments, I had to develop
`
`computer vision algorithms for inspection of industrial parts by using different
`
`illumination sources (Banner Engineering). I have licensed my algorithms to
`
`several companies, with the most recent one being Sentera Inc., which is using
`
`computer vision for precision agriculture.
`
`10. My research has produced more than 380 journal and conference
`
`publications. More than 90 publications are in refereed journals. Many of my
`
`publications and research grants relate to vehicle and human detection and tracking
`
`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.
`
`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.
`
`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.
`
`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.
`
`11. My work in vehicle detection and tracking has focused on using
`
`image and video processing techniques, including object detection, tracking, and
`
`recognition, to accurately track vehicles in real-time. These algorithms have been
`
`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
`
`methodologies.
`
`12.
`
`I have done extensive work in sensor fusion and data integration as
`
`part of intelligent transportation systems. In particular, my research includes
`
`contributions in integrating data from multiple sensors to enhance vehicle tracking
`
`accuracy. By combining information from cameras, GPS systems, radar, LiDAR,
`
`and other sensors, my research has aimed to create a comprehensive tracking
`
`system that can handle various environmental conditions and challenges.
`
`13. My work in intelligent transportation systems includes the use of
`
`control algorithms and sensing methodologies to control light systems in the
`
`context of transportation applications. For example, I have developed a system that
`
`detects and tracks pedestrians as they cross the street in order to eventually extend
`
`the “Walk” signal. The images below are from a system deployment in the Twin
`
`Cities (the “Flashing” Red signal indicates the presence of pedestrians in the
`
`crossing) in the late 1990s. The images below are from a system deployment in the
`
`Twin Cities (the “Flashing” Red signal indicates the presence of pedestrians in the
`
`crossing) in the late 1990s. The images are from a final project report #2000-28
`
`published by Minnesota Department of Transportation, titled “Pedestrian Control
`
`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
`
`interests include developing tracking algorithms and systems that can handle the
`
`complexities of autonomous vehicles, including predicting their future movements
`
`and understanding their intentions. The work has been extended to monitor the
`
`intentions of pedestrians.
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`15. These vehicle tracking systems enable continuous monitoring and
`
`tracking of vehicles, providing valuable information for traffic management, law
`
`enforcement, and security purposes.
`
`16. My paper “Detection and Classification of Vehicles,” published in the
`
`IEEE Transactions on Intelligent Transportation Systems in March 2002, is one of
`
`the most cited papers in the history of this journal (more than a thousand citations).
`
`The paper, among other contributions, shows how to use detected headlights and
`
`taillights to extract vehicles from videos and images in low-light conditions. One
`
`should note that the majority of my papers in the broader area of intelligent vehicle
`
`systems dealt with the detection of vehicles, pedestrians, trucks, etc., by using a
`
`variety of techniques which were mainly based on computer vision. I also have
`
`nine patents in pertinent areas.
`
`17. My research work has been funded by several government agencies.
`
`For example, I have received funding from the Department of Homeland Security
`
`to monitor threats at Mass Transit Sites, like the 30th Street Station in Philadelphia,
`
`PA, and the Light Rail Station in the Minneapolis-Saint Paul International Airport.
`
`Monitoring parked vehicles was an essential task for which we developed a variety
`
`of computer vision algorithms and tools.
`
`18. For more than a decade, I have been developing computer vision-
`
`based systems to assess truck parking availability at truck rest areas. The funding
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`came from Federal Highway Administration (FHWA), Minnesota Dept. of
`
`Transportation (MnDOT), Wisconsin Dept. of Transportation (WiscDOT), and
`
`Kansas Dept. of Transportation (KansasDOT). The approach uses multiple
`
`cameras to detect trucks and other vehicles and determine empty spaces. The
`
`system operates in all weather conditions around the clock.
`
`19.
`
`I have received numerous honors and awards for my research and
`
`contributions. I have been a Distinguished McKnight University Professor at the
`
`University of Minnesota since 2007 and have been a McKnight Presidential
`
`Endowed Professor in Computer Science since 2016. Also in 2007, I was
`
`nominated and became an IEEE Fellow. In 2016, I received the IEEE RAS George
`
`Saridis Leadership Award in Robotics and Automation as well as the Center for
`
`Transportation Studies Research Partnership Award. I have also received the IEEE
`
`VTS 2001 Best Land Transportation Paper Award for the paper “A Novel Method
`
`for Tracking and Counting Pedestrians in Real-Time Using a Single Camera” (with
`
`Osama Masoud).
`
`20. The figures below show the detection and tracking of a vehicle by a
`
`camera mounted in a car that follows a target vehicle. These figures are from a
`
`paper that I had published in 1997: Du, Y., and Papanikolopoulos, N.P., “Real-time
`
`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.
`
`
`I have also worked in the use of sensors to monitor driver fatigue. I
`
`21.
`
`developed a computer vision-based system that analyzes facial features and eye
`
`movements to detect signs of drowsiness and driver fatigue. By monitoring factors
`
`such as eye closure, head pose, and blink patterns, the system can alert drivers
`
`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
`
`continuously evolving light conditions that vehicular systems operate within.
`
`22. As a result of my work and research, I am familiar with the design,
`
`operation, and functionality of systems described in the ’551 Patent.
`
`23. Further details regarding my employment and academic history are
`
`included in my curriculum vitae, attached as EX1004.
`
`C.
`24.
`
`Information Considered
`I have reviewed the ’551 Patent and its prosecution history, as well as
`
`the other materials referenced in Appendix C. Counsel has informed me that I
`
`should consider these materials through the lens of one of ordinary skill in the art
`
`related to the ’551 Patent, at the time of the earliest purported priority date of the
`
`’551 Patent, and I have done so during my review of these materials. I have been
`
`asked to assume, for purposes of this Declaration, that the ’551 Patent has a
`
`priority date of July 12, 2002 (the “Critical Date”).
`
`25. My analyses are based on my years of education, research, and work
`
`experience, as well as my investigation and study of relevant materials. In my
`
`analyses, I have considered the materials that I identify in this Declaration and
`
`those listed in Appendix C.
`
`26.
`
`I may rely on these and additional materials to respond to arguments
`
`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
`
`provided to me.
`
`27. My review and assessment of the materials provided in this
`
`proceeding is ongoing, and I will continue to consider any new material as it is
`
`provided. I reserve the right to review, supplement, and amend my analyses based
`
`on new information and on my continuing review of the materials already
`
`provided.
`
`II. BACKGROUND OF THE ’551 PATENT
`
`D. The ’551 Patent
`I understand the ’551 Patent was filed on January 24, 2012, and issued
`28.
`
`on April 24, 2018. I also understand that the ’551 Patent is part of the following
`
`patent family:
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`29.
`
`I have assumed, for purpose of this declaration, that the ’551 Patent is
`
`entitled to this Critical Date of July 12, 2002.
`
`30. The ’551 Patent alleges that in the “prior art,” lighting fixtures were
`
`often single, large lights that were “generic” and “universal.” (’551 Patent, 5:36-
`
`46, 11:1-5, 11:45-49.) The ’551 Patent further alleges that these lights were not
`
`tailored to individual applications, produced light in more places than needed, and
`
`required reflectors and refractors to re-direct light. (Id.) For example, the ’551
`
`Patent states that many prior art light sources were “universal” sources that emitted
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`light in many directions up to 360 degrees light distribution, even where only a
`
`more limited angular distribution of light was needed. (Id.) The ’551 Patent claims
`
`it came up with a new approach it called a “Digital Lighting Fixture” that can
`
`“provide the ‘correct’ lighting solution for the situation at hand.” (Id., 11:8-13.)
`
`The ’551 Patent alleges that it can accomplish this through “controllability offered
`
`by breaking the total light output up into discrete (‘digital’) specifically aimable
`
`and dimmable elements which can be addressed by control electronics.” (Id.,
`
`11:56-62.)
`
`31. The ’551 Patent gives various example applications of this Digital
`
`Lighting Fixture, but its claims are directed to use of the alleged invention in a
`
`“headlight system” for vehicles. (Id., Claims 48, 56, 64, 72, 80; see also Abstract.)
`
`The claims recite how light clusters or light sources (e.g., LEDs) can be included in
`
`a headlamp on a vehicle, such as shown in Figure 15 of the ’551:
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`(Id., Fig. 15 (annotated).) As can be seen in the figure above, the headlights of the
`
`vehicle are made up of a plurality of light sources, which may be LEDs. The
`
`claims recite that “sensors,” like sensor 280 shown in Figure 15 above, obtain
`
`relevant situational data, including detecting things like headlights of oncoming
`
`vehicles. (See, e.g., id., Claim 48, 50:38-52:54; 53:13-55:42.) A “processor” can
`
`then determine, based on this, how to adjust the output from the individual lights
`
`(or LEDs), e.g., “to reduce glare” in a particular area in the field-of-view, such as
`
`where other vehicles are detected whose drivers or passengers could experience
`
`glare. (See, e.g., id.)
`
`III. CLAIM CONSTRUCTION AND POSITA DEFINITION
`
`A. Claim Construction
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`32.
`
`I understand that in an inter partes review, claims must be given their
`
`ordinary and customary meaning, as understood by one of ordinary skill in the art
`
`in light of the specification and prosecution history. I apply that standard in my
`
`analysis below to the words and phrases in the claims of the ’551 Patent.
`
`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
`
`’551 Patent would have had an undergraduate degree in mechanical engineering,
`
`electrical engineering, automotive engineering, optical engineering, applied
`
`physics, computer science, or similar disciplines, along with two years of
`
`experience working with intelligent vehicle systems, automotive control systems,
`
`or lighting control systems. The more education one has, the less experience
`
`needed to attain an ordinary level of skill. Similarly, more experience in the field
`
`may serve as a substitute for formal education.
`
`IV. UNDERSTANDING OF LEGAL STANDARDS
`
`A. Anticipation
`I understand that a patent claim is invalid when the invention that it
`34.
`
`claims is not new. To establish that a claimed invention is not new (a.k.a., “not
`
`novel”), I understand that one may establish that a single publication, or other
`
`reference in the prior art discloses (explicitly or inherently) every element required
`
`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.
`
`35.
`
`I understand that “prior art” and “prior art reference” are legal terms of
`
`art referring to, for example, devices, methods, and publications that predate the
`
`earliest effective filing date of the claimed invention.
`
`36.
`
`I understand that a prior art reference that anticipates a claim may
`
`disclose an element or limitation of a patent claim expressly or inherently. A prior
`
`art reference discloses an element or limitation of a patent claim inherently when the
`
`prior art’s disclosure necessarily requires or implies that the claimed element or
`
`limitation be present in the process, machine, manufacture, or composition
`
`disclosed. I understand there is still considered to be an inherent disclosure even if
`
`the author of the reference did not describe or understand the underlying inherent
`
`principle. I understand that one may establish that a prior art reference inherently
`
`discloses a claimed element or limitation through the use of other reference material
`
`or through testing.
`
`37.
`
`I understand that the description in a written reference does not have to
`
`be in the same words as the claim, but all of the requirements of the claim must be
`
`there, either stated or necessarily implied, so that a POSITA looking at that one
`
`reference would be able to make and use the claimed invention.
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`38.
`
`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
`
`determine whether the reference renders that claim obvious, as discussed below.
`
`A. Obviousness
`I understand that, even if a claim is not fully disclosed in a single prior
`39.
`
`art reference, the patent claim is invalid if the invention would have been obvious
`
`to a POSITA at the time of the invention. In particular, I understand that a patent
`
`claim is normally invalid as obvious if it would have been an “ordinary
`
`innovation” within the relevant field to create the claimed product or method at the
`
`time of the invention.
`
`40.
`
`I understand that the relevant portion of pre-AIA Section 103,
`
`subsection (a) of the Patent Act states:
`
`“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.”
`
`41.
`
`I understand that, by way of example only, a claimed invention is
`
`obvious if:
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`it combines prior art elements according to known methods to
`
`yield predictable results;
`
`it simply substitutes one known element for another to obtain
`
`predictable results;
`
`it uses a known technique to improve similar devices (methods,
`
`or products) in the same way;
`
`it applies a known technique to a known device (method, or
`
`product) ready for improvement to yield predictable results;
`
`it was “obvious to try” in that the inventor chose from a finite
`
`number of identified, predictable solutions, with a reasonable
`
`expectation of success;
`
`known work in one field of endeavor prompted variations of it
`
`for use in either the same field or a different one based on design
`
`incentives or other market forces and those variations were
`
`predictable to one of ordinary skill in the art; or
`
`some teaching, suggestion, or motivation in the prior art led one
`
`of ordinary skill to modify the prior art reference or to combine prior
`
`art reference teachings to arrive at the claimed invention.
`
`42. When considering obviousness, I understand that I am to: (i) decide
`
`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;
`
`(iii) determine what differences, if any, existed between the prior art and the
`
`Asserted Claims; and (iv) consider objective evidence of non-obviousness (also
`
`known as secondary considerations). Further, when considering obviousness, I
`
`understand that it is not necessary to seek out precise teachings, and it is
`
`permissible to consider the inferences, common sense, and creative steps that a
`
`POSITA (who is considered to have an ordinary level of creativity and is not an
`
`automaton) would employ.
`
`43.
`
`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 48-87 OF THE ’551 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 48-87 of the ’551 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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`PCT Patent Publication WO2001070538 (“Stam”) (EX1008), U.S. Patent No.
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`6,144,158 to Beam (“Beam”) (EX1007), and U.S. Patent No. 6,049,749 to
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`Kobayashi (“Kobayashi”) (EX1006).
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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 ’551 Patent file history shows that Alden was not
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`considered during prosecution of the ’551 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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`
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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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`headlight 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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`A. Ground 1 – Alden Anticipate and Renders Obvious Claims 48, 50-
`51, 53-59, 61-67, 69-75, 77-83, 85-87
`Independent Claim 48
`1.
`50. As viewed by a POSITA, Alden anticipates and renders obvious
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`Claim 48.
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`48[pre]: An illuminating device having automatic control of light provided to an
`illuminated area comprising:
`51. Alden discloses Claim 48[pre] to the extent the preamble limits the
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`scope of the claim. Alden’s headlight system is “for automatically controlling the
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`distribution of a headlight in response to environmental conditions.” (Alden,
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`[0006]; see also Abstract.) In Alden, a “first vehicle 31” includes an “automatic
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`segmented illumination” headlight system, allowing the vehicle to emit different
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`light illuminations in different areas in its field-of-view. (Id., [0025], Fig. 1
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`(annotated); see also [0026], [0029].) That is, the segmented headlight system can
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`provide different lighting illumination to different sectors of the headlight
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