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`UNITED STATES PATENT AND TRADEMARK OFFICE
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`____________________
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`BEFORE THE PATENT TRIAL AND APPEAL BOARD
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`____________________
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`LIBERTY MUTUAL INSURANCE CO.
`Petitioner
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`v.
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`PROGRESSIVE CASUALTY INSURANCE CO.
`Patent Owner
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`____________________
`
`Case CBM2013-00009 (JL)
`Patent 8,140,358
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`____________________
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`Declaration of Dr. Mark Ehsani
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`Declaration of Dr. Mark Ehsani
`I, Dr. Mark Ehsani, hereby declare under penalty of perjury:
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`I. Scope of Assignment
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`1)
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`I was retained by the law firm of Jones Day, on behalf of the Progressive
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`Casualty Insurance Company (“Progressive”), to render opinions regarding
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`fuzzy logic technology.
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`2) All of my statements and opinions herein are based on my training and
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`education as a Ph.D. in Electrical Engineering and as a Professor of
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`Electrical Engineering and Director of the Advanced Vehicle Systems
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`Research Program at Texas A&M, and on my work experience as a
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`research engineer and as an industry consultant to over 60 domestic and
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`international companies and agencies.
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`3) My retention agreement with Jones Day calls for me to be compensated at
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`my normal rate of $575 per hour, inclusive of any third party expert
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`service fees, plus out-of-pocket travel expenses.
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`II. Qualifications
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`4)
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`I am a Professor of electrical engineering and the founding Director of
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`Advanced Vehicle Systems Research Program and the Power Electronics
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`and Motor Drives Laboratory at Texas A&M University. I have worked as
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`a professor and lecturer at Texas A&M University for 32 years. Prior to
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`this, from 1974 to 1977, I worked as a research engineer at the Fusion
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`Research Center, University of Texas, and from 1977 to 1981, I worked as
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`a resident research associate at the Argonne National Laboratory, Argonne,
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`Illinois. During my time at the Argonne National Laboratory, I was also
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`performing doctoral work at the University of Wisconsin-Madison in
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`energy systems and control systems. My current research work is in power
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`electronics, motor drives, vehicle electronics, and hybrid vehicles and their
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`control systems. I have also performed research work in power electronics
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`and motor drives with regard to applications such as wind power, space
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`systems, military systems, power and energy storage, and consumer
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`products, among others. I have received grants of over $16,000,000 for
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`funded research since 1981. I am a consultant to over 60 domestic and
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`international companies and agencies.
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`5)
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`I received a Doctorate of Philosophy in electrical engineering from the
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`University of Wisconsin-Madison in 1981. Prior to this, I received B.S.
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`and M.S. degrees from the University of Texas at Austin in 1973 and 1974,
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`respectively. I was the recipient of the Prize Paper Awards in Static Power
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`Converters and Motor Drives at the IEEE Industry Applications Society
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`1985, 1987, and 1992 Annual Meetings. In 1984, I was named the
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`Outstanding Young Engineer of the Year by the Brazos chapter of the
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`Texas Society of Professional Engineers.
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`6)
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`In 1992, I was named the Halliburton Professor in the College of
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`Engineering at Texas A&M University. In 1994, I was also named the
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`Dresser Industries Professor at Texas A&M University. In 2001, I was
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`selected for the Ruth & William Neely / Dow Chemical Faculty Fellow of
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`the College of Engineering for 2001-2002, for “contributions to the
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`Engineering Program at Texas A&M, including classroom instruction,
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`scholarly activities, and professional service.” In 2003, I was selected for
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`the BP Amoco Faculty Award for Teaching Excellence in the College of
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`Engineering. I was also selected for the IEEE Vehicular Society 2001
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`Avant Garde Award for “Contributions to the theory and design of hybrid
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`electric vehicles.” In 2003, I was selected for the IEEE Undergraduate
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`Teaching Award for “outstanding contributions to advanced curriculum
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`development and teaching of power electronics and drives.” In 2004, I was
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`elected to the Robert M. Kennedy endowed Chair in Electrical Engineering
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`at Texas A&M University. In 2005, I was elected as a Fellow of the
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`Society of Automotive Engineers (SAE).
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`7)
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`I have been a member of the IEEE Power Electronics Society (PELS)
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`AdCom, past Chairman of the PELS Educational Affairs Committee, past
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`Chairman of the IEEE-IAS Industrial Power Converter Committee, and
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`past chairman of the IEEE Myron Zucker Student-Faculty Grant program.
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`I was the General Chair of the IEEE Power Electronics Specialist
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`Conference for 1990. I am the founder of the IEEE Vehicle Power and
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`Propulsion Conference, the founding chairman of the IEEE Vehicular
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`Technology Society Vehicle Power and Propulsion Committee, and
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`chairman of Convergence Fellowship Committees. In 2002, I was elected
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`to the Board of Governors of the IEEE Vehicular Technology Society. I
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`also serve on the editorial board of several technical journals and am the
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`associate editor of IEEE Transactions on Industrial Electronics and IEEE
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`Transactions on Vehicular Technology.
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`8)
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`I am a Fellow of IEEE, an IEEE Industrial Electronics Society and
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`Vehicular Technology Society Distinguished Speaker, and an IEEE
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`Industry Applications Society and Power Engineering Society
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`Distinguished Lecturer. I am also a registered professional engineer in the
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`State of Texas.
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`9)
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`I am an author on over 350 publications in pulsed-power supplies, high-
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`voltage engineering, power electronics, motor drives, and advanced vehicle
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`systems. I am a co-author of 16 books on power electronics, motor drives,
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`and advanced vehicle systems, including “Vehicular Electric Power
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`Systems,” Marcel Dekker, Inc., 2003, and “Modern Electric Hybrid
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`Vehicles and Fuel Cell Vehicles – Fundamentals, Theory, and Design,”
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`CRC Press, 2004. I have over 30 granted or pending United States and
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`European Union patents.
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`10) In my work, I have used fuzzy logic-type algorithms for a variety of
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`functions (e.g., to perform real-time vehicle data acquisition, logging, and
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`analysis for driver-specific drive cycle analysis). Some of my work with
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`computer and fuzzy logic-type algorithms is reflected in my books and
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`research publications, including, for example, the following:
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` J. P. Johnson, K. M. Rahman, and M. Ehsani, “Application of a
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`Clustering Adaptive Fuzzy Logic Controller in a Brushless DC
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`Drive,” IEEE-IECON’97, New Orleans, LA, November 1997, pp.
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`1001-1005.
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` Zhiqiang Xu and Mehrdad Ehsani, “Reconstruction of Effective
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`Wind Speed for Fixed-Speed Wind Turbines Based On Frequency
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`Data Fusion,” Canadian Conference in Electrical and Computer
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`Engineering, Calgary, Canada, September, 2010.
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` Short Cycle Time Design of Advanced Motor Drives by the Real
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`Time Simulation and Hardware in the Loop technologies, a two
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`day short course offered to the automotive industry in Detroit,
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`Michigan, Nov. 3-4, 2003.
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` M. Ehsani, M. Masten, and I. Panahi, “Stiff System Control: A
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`New Concept in Real Time Control,” Invited Paper at American
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`Control Conference, San Diego, CA, May 1999.
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` Short Cycle Time Design of Advanced Motor Drives by the Real
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`Time Simulation and Hardware in the Loop Technologies, a two
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`day short course offered to the automotive industry in Detroit,
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`Michigan, Nov. 3-4, 2003.
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` E. Havaii, B. F. Yancey, and M. Ehsani “Computer Aided Design
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`Tool for Electric, Hybrid Electric and Plug-in Hybrid Electric
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`Vehicles,” IEEE-VPPC 2011, Chicago, Ill., Oct. 2011.
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` K. Butler, K. Stevens, and M. Ehsani, “A Versatile Computer
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`Simulation Tool for Design and Analysis of Electric and Hybrid
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`Drive Trains,” SAE Proceedings Electric and Hybrid Vehicle
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`Design Studies, Book # SP 1243, Paper # 970199, February 1997,
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`pp. 19-25, Detroit, MI.
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` M. Ehsani, P. Le Polles, M. S. Arefeen, I. Pitel, and J. D. Van Wyk,
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`“Computer Aided Design and Application of Integrated LC Filters,”
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`IEEE Trans. on Power Electronics, Vol. 11, No. 1, January 1996,
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`pp. 182-190.
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` K. Butler, K. Stevens, and M. Ehsani, “A Versatile Computer
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`Simulation Tool for Design and Analysis of Electric and Hybrid
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`Drive Trains,” SAE Proceedings Electric and Hybrid Vehicle
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`Design Studies, Book # SP 1243, Paper # 970199, February 1997,
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`pp. 19-25, Detroit, MI.
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` K. Butler, M. Ehsani, and P. Kamath, “A Matlab-Based Modeling
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`and Simulation Package for Electric and Hybrid Electric Vehicle
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`Design,” Invited Paper for the Special Issue of IEEE Trans. on
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`Vehicular Technology, Vol. 48, No. 6, Nov. 1999, pp. 1770-1778.
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` Husain and M. Ehsani, “Error Analysis in Indirect Rotor Position
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`Sensing of Switched Reluctance Motors,” IEEE Trans. on
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`Industrial Electronics, Vol. 41, No. 3, June 1994, pp. 301-307.
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` M. S. Arefeen, M. Ehsani, and T. A. Lipo, “An Analysis of the
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`Accuracy of Indirect Shaft Sensor for Synchronous Reluctance
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`Motor,” IEEE Trans. on Industry Applications, Vol. 30, No. 5,
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`September/October 1994, pp. 1202-1209.
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` K. Butler, K. Stevens, and M. Ehsani, “A Versatile Computer
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`Simulation Tool for Design and Analysis of Electric and Hybrid
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`Drive Trains,” SAE Proceedings Electric and Hybrid Vehicle
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`Design Studies, Book # SP 1243, Paper # 970199, February 1997,
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`pp. 19-25, Detroit, MI.
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` S. Gay and M. Ehsani, “Parametric Analysis of Eddy-Current
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`Brake Performance with a 2D Analytical Model,” submitted to
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`IEEE Transactions on Magnetics.
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` M. Ehsani, I. Husain, and K. R. Ramani, “An Analysis of the Error
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`in Indirect Rotor Position Sensing of Switched Reluctance Motors,”
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`IEEE-IECON’91, Kobe, Japan, October 1991, pp. 295-300.
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` M. S. Arefeen, M. Ehsani, and T. A. Lipo, “An Analysis of the
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`Accuracy of Indirect Shaft Sensor for Synchronous Reluctance
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`Motor,” IEEE-IAS’93, 1993, pp. 695-700.
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` T. Kim and M. Ehsani, “An Error Analysis of the Sensorless
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`Position Estimation for BLDC Motors”, 2003 IEEE-IAS (Industry
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`Applications Society) Conference, vol. 1, pp. 611-617, Oct. 2003.
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`11) I have more than 20 years of experience in designing, researching and
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`developing vehicle telematics systems. Vehicle telematics includes the
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`acquisition of automotive onboard vehicle data and its processing. In my
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`vehicle-related work, I have extensively performed real-time vehicle data
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`acquisition, logging, and analysis for driver-specific drive cycle analysis.
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`Some of my work in this area is reflected in my books and research
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`publications, including, for example, the following:
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` Contributor of a chapter on “ More Electric Vehicles” to CRC
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`Handbook of Power Electronics, 2002.
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` Contributor to SAE book “Hybrid Electric Vehicles,” SAE SP-1633,
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`published in 2001.
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` Vehicular Electric Power Systems, my Co-Authors: A. Emadi & JM
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`Miller, Marcel Dekker, Inc. 2004.
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` “Modern Electric, Hybrid Electric, and Fuel Cell Vehicles –
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`Fundamentals, Theory, and Design”, M. Ehsani, Y. Gao, S. E. Gay,
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`A. Emadi, CRC Press, Second Edition, 2010.
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` M. Ehsani and M.A. Masrur, “Vehicle Electrical Power System
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`Modeling,” 1st Annual US Army Ground-Automotive Power &
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`Energy Symposium, July 22, 2005, Troy, Michigan.
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` Liang Chu, Jiayun Gu, Minghui Liu, Jun Li, Yimin Gao and M.
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`Ehsani, "Study on CAN communication of EBS and Braking
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`Performance Test for Commercial vehicles,” IEEE Vehicle Power
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`and Propulsion Conference, VPPC07, Arlington, Texas, September
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`10-12, 2007.
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` J. S. Won, R. Langari, and M. Ehsani, “An Energy Management and
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`Charge Sustaining Strategy for a Parallel Hybrid Vehicle with CVT,”
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`IEEE Trans. on Control Systems Technology, Vol. 13, No. 2, March
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`2005.
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` M.Ehsani, M.Falahi, S.Lotfifard, “Vehicle to Grid Services:
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`Potentials and Applications,” accepted for publication in the special
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`issue of Energies Journal for vehicle to grid technologies, 2012.
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` Lin Lai and Mehrdad Ehsani, “Dynamic Programming Optimized
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`Constrained Engine On and Off Control Strategy for Parallel HEV,”
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`IEEE-ENERGYCON 2012 - Sustainable Transportation Systems,
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`Florence, Italy, September 9-12.
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` “ Vehicle Power systems”, Short Course, January 2006, US Army
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`Tank Automotive
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` Chapters contributor, “The 42-Volt Electrical System,” Book,
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`Society of Automotive Engineers, Inc. PT-99, ISBN 0-7680-1297-X,
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`2003.
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` “Modern Electric, Hybrid Electric, and Fuel Cell Vehicles –
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`Fundamentals, Theory, and Design”, M. Ehsani, Y. Gao, S. E. Gay,
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`A. Emadi, CRC Press, 2005.
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` Contributor of chapter on “Hybrid Drive Trains” to “Handbook of
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`Automotive Power Electronics and Motor Drives” CRC Press, 2005.
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` K. Butler, K. Stevens, and M. Ehsani, “A Versatile Computer
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`Simulation Tool for Design and Analysis of Electric and Hybrid
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`Drive Trains,” SAE Proceedings Electric and Hybrid Vehicle Design
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`Studies, Book # SP 1243, Paper # 970199, February 1997, pp. 19-25,
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`Detroit, MI.
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` Z. Rahman, K. Butler, and M. Ehsani, “A Study of Design Issues on
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`Electrically Peaking Hybrid Electric Vehicles for Diverse Urban
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`Driving Patterns,” Advances in Electric Vehicle Technologies, SP-
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`1417, Paper #: 1999-01-1151, Society of Automotive Engineers,
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`March 1999, pp. 1-9.
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` K. Butler, M. Ehsani, and P. Kamath, “A Matlab-Based Modeling
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`and Simulation Package for Electric and Hybrid Electric Vehicle
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`Design,” Invited Paper for the Special Issue of IEEE Trans. on
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`Vehicular Technology, Vol. 48, No. 6, Nov. 1999, pp. 1770-1778.
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` M. Ehsani and M.A. Masrur, “Vehicle Electrical Power System
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`Modeling,” 1st Annual US Army Ground-Automotive Power &
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`Energy Symposium, July 22, 2005, Troy, Michigan.
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` Liang Chu, Lanli Hou, Minghui Liu, Jun Li, Yimin Gao and M.
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`Ehsani, "Study on the Dynamic Characteristics of Pneumatic ABS
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`Solenoid valve for Commercial Vehicles,” IEEE Vehicle Power and
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`Propulsion Conference, VPPC07, Arlington, Texas, September 10-
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`12, 2007.
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` Liang Chu, Lanli Hou, Minghui Liu, Jun Li, Yimin Gao and M.
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`Ehsani, "Development of Air-ABS-HIL-Simulation Test Bench,”
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`IEEE Vehicle Power and Propulsion Conference, VPPC07,
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`Arlington, Texas, September 10-12, 2007.
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` Yimin Gao and M. Ehsani, “Design and Control Methodology of
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`Plug-in Hybrid Electric vehicles” , IEEE Vehicle Power and
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`Propulsion Conference, VPPC08, Harbin, China, September 3-5,
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`2008.
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`12) At this time, I have nearly 40 years of continuous professional experience
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`in the fields of electronics, motor drives, power electronics, control
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`systems, vehicle electronics, and hybrid electric vehicles, among others. In
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`forming the opinions expressed in this report I have relied upon my
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`education and my nearly 40 years of professional experience. Attached as
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`Exhibit 2016 is a copy of my curriculum vitae, which sets forth my
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`experience, qualifications, and publications.
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`III. Materials Reviewed
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`13) In preparing this Declaration, I have considered the following materials
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`listed:
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`a. The ‘358 patent (Ex. 1001).
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`b. Kosaka (Ex. 1003).
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`c. RDSS (Ex. 1004).
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`d. GeoStar 10-K (Ex. 1005).
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`e. Liberty Mutual’s ‘358 Petition filed in the CBM2013-00009
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`proceeding (Paper 1).
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`f. Declaration of Mr. Scott Andrews (Ex. 1014).
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`g. The Board’s Institution Decision (Paper 10).
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`h. Other materials cited in this Declaration.
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`IV. Fuzzy Logic Background – Fuzzy Sets vs. Classical or Crisp Sets
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`14) Fuzzy logic provides an approach that emulates the way the human mind
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`works. A person does not typically view experiences in the world as
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`entirely black or white (1 or 0), but also different shades of gray. For
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`example, different people may view the temperature of a room as too high,
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`a little high, a little low, etc.
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`15) The classical approach would use a single value to characterize any
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`temperature value (e.g., any room temperature above 75 degrees
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`Fahrenheit could be assigned the single crisp value of “high”). In contrast,
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`fuzzy logic would use multiple different “fuzzy” values to characterize
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`these temperatures. In other words, fuzzy logic considers the relative
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`degree of trueness of each state of comfort, such as “comfortable”, “cold”,
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`or “hot”, for each given temperature in the entire applicable temperature
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`range, such as 0 to 120 degrees Fahrenheit. The exact degree to which a
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`temperature can be considered a “hot” temperature is determined by an
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`empirically derived function called its membership function. In this way,
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`fuzzy logic considers an infinite number of values of membership in the
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`“hot” membership function. For example, at 80 degrees the room is more
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`“hot” than “comfortable” and not much “cold”, and at 75 degrees more
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`“comfortable” than “hot” and not much “cold”. Similarly, at 65 degrees
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`the room is more “cold” than “comfortable” and not much “hot”. These
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`“more”, “less”, and “not much” assessments are arrived at from numerical
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`values of the infinite valued membership functions of “hot”,
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`“comfortable”, and “cold”. The “hot”, “comfortable”, and “cold”
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`functions are called linguistic variables. These functions tie the vagaries of
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`human language to the rigorous mathematical and logical methodology
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`(algorithm) that can be used in a computer.
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`16) Additional membership functions are typically constructed to describe
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`more completely the fuzzy variable of interest (e.g., room temperature). In
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`this example, a “healthy” fuzzy logic membership function could be
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`established which would also consider an infinite number of degrees of
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`membership. Other membership functions could include an “unhealthy”
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`membership function (with a continuum of infinite values), a “dangerous”
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`membership function (with a continuum of infinite values), etc.
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`17) How much a particular temperature is considered “hot,” “comfortable,” or
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`“cold” is determined by the membership amount the temperature has in
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`each membership function. For example, 70 degrees Fahrenheit might
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`have a larger membership value in the “hot” membership function than in
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`the “comfortable” membership function and “cold” membership function.
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`Multiple fuzzy values are generated for a given temperature that
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`correspond to the amount of membership in each membership function and
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`output variables and represent them with linguistic variables. This allows
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`the converting or mapping of crisp data to fuzzy linguistic values which
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`have varying degrees for the same data point or datum.
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`18) Deriving membership functions is difficult and requires an intimate
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`knowledge of the application area as well as the different types of
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`membership functions for different linguistic variables in that application
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`area. These membership functions could be triangular trapezoidal,
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`Gaussian, bell-shaped, sigmoidal, S-curve, and many others. Which type is
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`to be used is dependent on the application area, the degree of accuracy
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`required, and the knowledge of the designer. For example, the
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`“comfortable”, “hot”, and “cold” membership functions may have to be
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`developed from a large amount of data, gathered from many people in
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`various geographic locations and then analyzed and converted to a
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`membership function. The required degree of accuracy of this membership
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`function will determine the type and amount of membership function data
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`needed and its mathematical form. Detailed knowledge is needed of the
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`application area, in terms of what data is relevant, how to gather and
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`correlate the data, what the end use of the fuzzy logic algorithm requires to
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`function properly, what mathematical methods are used to fit data into a
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`mathematical membership function. Although translation of expert
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`knowledge to the design of the membership functions can be a complex
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`task and labor intensive, this is done in the design phase of the fuzzy logic
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`controller. The implementation of the fuzzy logic controllers, including
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`their membership functions, in a product is inherently a simple
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`computational task. This is one of the advantages of using fuzzy logic
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`controllers, that is, a sophisticated expert knowledge based control system
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`is reduced to a simple computer program and small memory,
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`implementable in very low cost microprocessors, that can be used in cost
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`sensitive consumer products. For example, fuzzy logic controllers are used
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`in mass-produced, low cost rice cookers, as well as in elevator controllers
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`and vehicle control applications.
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`V. Liberty Mutual’s POSITA Standards
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`19) I understand that the Petitioner (Liberty Mutual) has engaged Mr. Scott
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`Andrews as an expert for this proceeding. In his declaration, Mr. Andrews
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`provided the following description of a POSITA in paragraph 17 of his
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`declaration: “In my opinion, a person of ordinary skill in the vehicle
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`telematics aspects pertinent to the ‘358 patent (apart from the insurance
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`cost aspects), as of January 1996, would have at least a B.S. degree in
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`electrical engineering, computer engineering, computer science or the
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`equivalent thereof and at least one to two years of experience with
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`telematics systems for vehicles, particularly, telematics systems including
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`communications and locations technologies.” (Ex. 1014 at ¶17.) I will
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`apply this standard in my opinions regarding a POSITA in this declaration.
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`VI. Teachings of the Kosaka Reference
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`20) Kosaka discloses applying fuzzy logic to evaluate risk for use in
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`determining a change in insurance premiums for moving bodies (vehicles).
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`With the use of fuzzy logic, Kosaka asserts that the “risk evaluation values
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`can be expected to be more accurate, because they are not susceptible to
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`external noise and the like.” (Kosaka at 25:1:11-13.)
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`21) Figure 1 in Kosaka shows an insurance premium determination system.
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`The system includes a fuzzy logic unit (3) which generates comprehensive
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`risk evaluation output based on sensor data. Kosaka’s specification for
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`Figure 1 does not reveal details of how the comprehensive risk evaluation
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`output is generated. Kosaka, however, does discuss this with respect to
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`Figure 9.
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`22) Figure 9 of Kosaka shows three fuzzy logic units (FIU-I, FIU-II, and FIU-
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`III). FIU-I generates fuzzy risk evaluation values (S, M, B) for evaluating
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`the risk “related to the frontward moving body” (id. at 24:2:11), and FIU-II
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`generates fuzzy risk evaluation values (S, M, B) for evaluating the risk
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`“related to ‘self’ internal states” (id. at 24:2:9). Both outputs from FIU-I
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`and FIU-II are disclosed as fuzzy values: “The output of the first fuzzy
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`logic part 62 and the second fuzzy logic part 64 are conducted to a third
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`fuzzy logic part 65 as fuzzy input values.” (Id. at 24:2:12-14, emphasis
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`added.) More specifically, the fuzzy S, M, and B values respectively show
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`the partial membership assignment degrees for the S, M, and B
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`memberships. There are three fuzzy S, M, and B output values from FIU-
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`I, and three fuzzy S, M, and B output values from FIU-II.
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`23) FIU-III receives the fuzzy input values from FIU-I and FIU-II to generate
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`an overall fuzzy risk evaluation output. This output is likewise fuzzy. For
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`example, Figure 10(E) shows the membership function which operates as
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`the “FIU-III output function.” (Id. at 29, Caption for Figure 10(E).) The
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`output of Figure 10(E)’s membership function comprises three fuzzy
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`values to indicate the degree of membership in each of the fuzzy logic
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`categories (“S” which presumably means small comprehensive risk, “M”
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`which presumably means medium comprehensive risk, and “B” which
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`presumably means big comprehensive risk). Because of the continuum in
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`each of the membership functions, there is an infinite number of degrees of
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`membership in each of these fuzzy categories. For example, one situation
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`might yield: multiple fuzzy values of 0.2 S, 0.4 M, and 0.1 B; and another
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`situation might yield: multiple fuzzy values of 0.6 S, 0.1 M, and 0.1 B.
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`Further, different situations yield an infinite possible set of fuzzy values
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`and permutations.
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`CLI-2116419v4
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`24) Figure 11 provides rules for mapping input fuzzy values to output fuzzy
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`values. For example, Figure 11(C) shows the rules for mapping the fuzzy
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`values from FIU-I and FIU-II to generate a consequent fuzzy value for
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`FUI-III. According to the rules table, an “S” fuzzy value from FIU-I and a
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`“B” fuzzy value from FIU-II would map to an “M” fuzzy value for FIU-
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`III.
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`25) Kosaka’s specification discloses that the fuzzy risk evaluation values (S,
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`M, B) are directly used as fuzzy values by the insurance premium
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`determination unit and does not provide any further processing details
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`involving these fuzzy values such as how they are used within the
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`insurance premium determination unit.
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`
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`VII. A fuzzy logic approach is beyond the POSITA put forth by Mr. Andrews
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`26) Kosaka’s approach would have been beyond the level of a POSITA as
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`established by Mr. Andrews. A POSITA under Mr. Andrews’ description
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`would not likely have training or understanding of fuzzy logic, let alone
`
`the interrelationships between the fuzzy membership functions of Figure
`
`10 and the fuzzy rule evaluation charts of Figure 11 in Kosaka. The
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`inconsistencies, omissions, and defects of Kosaka further make it very
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`difficult to understand Kosaka. For example, there is no disclosure in
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`20
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`Kosaka about how the multiple fuzzy risk values from Figure 10(E) would
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`be generated from the fuzzy rule evaluation charts of Figure 11(C).
`
`27) The fuzzy logic approach was obscure in 1996 and would not have been
`
`known by Mr. Andrews’ POSITA. This is shown in the course work and
`
`work experience of Mr. Andrews’ POSITA. The course work for Mr.
`
`Andrews’ POSITA would not include fuzzy logic. Fuzzy logic was not
`
`(and still is not) a required course or part of a required course for Mr.
`
`Andrews’ POSITA. Mr. Andrews’ POSITA would not know how to use
`
`fuzzy logic let alone how to apply it to the insurance industry because Mr.
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`Andrews’ POSITA would not have encountered such subject matter in the
`
`POSITA’s course work or work experience.
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`
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`VIII. The Kosaka reference is so defective that a POSITA would not know how to use
`Kosaka
`
`28) Due to the deficiencies in Kosaka’s disclosure, not only would a POSITA
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`(as described in Mr. Andrews’ declarations) not understand how the fuzzy
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`risk evaluation values are generated, but a fuzzy logic expert would also
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`have great difficulty, if not experience a complete failure, in trying to
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`understand how these values are generated based on the disclosure of
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`Kosaka.
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`21
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`29) Kosaka is missing significant details in how the fuzzy logic approach used
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`in Kosaka actually works. For example, Figures 10(A)-10(E) are
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`membership functions. The corresponding disclosure in Kosaka’s
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`specification is sparse and provides no additional details other than what is
`
`provided in these figures with respect to the mathematical specifications of
`
`the membership functions. The membership functions provided by Kosaka
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`are more symbolic than specific and can be found in conceptual
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`introductions to basic fuzzy logic and hence they do not provide any
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`description as to what the actual membership functions are.
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`30) As another example, Figures 11(A)-11(C) are rule charts for each of the
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`three fuzzy logic units. The corresponding disclosure in Kosaka’s
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`specification is limited and provides no additional details other than what
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`is provided in the figures. For example, there is no description as to how
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`the rules in Figure 11(C) for FIU-III are used with respect to the
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`membership functions of Figure 10.
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`IX. There Are No “Operations Requiring Extensive Processing” In Kosaka
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`31) Given the Board’s decision to institute review of the ’358 patent based in
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`part on Kosaka, I will address it below.
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`CLI-2116419v4
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`22
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`32) I have reviewed the Board’s Institution Decision, including the following
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`passage directed to whether operations requiring extensive processing
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`should be performed at a central location:
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`Central to Liberty’s argument is the specially articulated
`recognition and reliance on this disclosure of RDSS, quoted
`with emphasis in Liberty’s petition (Pet. 20:3-6): “[o]perations
`requiring extensive processing are performed at GEOSTAR
`Central, reducing the sophistication and cost of the terminal.”
`Indeed, according to RDSS, while certain processing may be
`performed onboard the vehicle, operations “requiring extensive
`processing” are instead performed at the central location, thus
`“reducing the sophistication and cost of the terminal.” (Ex.
`1004, 52:1:1-9.) Liberty also clearly explains that one with
`ordinary skill in the art would have known to relocate the risk
`evaluation components of Kosaka, components beginning from
`fuzzy logic FLU 3, remote from the vehicle and to transmit the
`vehicle data thereto. (Pet. 26:13-19.)
`(Institution Decision at 22.)
`
`The Board continues at page 24 of the Institution Decision:
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`Because there will be a plurality of such vehicles in
`communication with the central station, the communication is
`over a distributed network. On that basis, all risk evaluation
`components of Kosaka, commencing with fuzzy logic 3 (FLU
`3) and fuzzy memory 4 (FLM 4) and including all components
`downstream therefrom as shown in Kosaka’s Figure 1, would
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`CLI-2116419v4
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`23
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`be implemented at a central station remote from the vehicle,
`and monitored vehicle data would be transmitted wirelessly to
`the central station for processing.
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`
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`33) I disagree with the statements in the previous paragraph regarding whether
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`a POSITA would have considered it advantageous to “relocate the analysis
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`components of Kosaka’s onboard insurance risk determination system to a
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`central location remote from the vehicle and wirelessly transmit the
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`monitored vehicle data from each vehicle to the central station for
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`analysis.” (Institution Decision at 24.) I disagree with such statements
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`because the premise for relocating Kosaka’s analysis components has no
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`application to Kosaka’s system. In other words, the analysis components
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`of Kosaka do not require extensive processing capability. (See ¶¶ 34-38
`
`infra.) In addition, a POSITA would also not want to relocate Kosaka’s
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`analysis components for other reasons, including a significant increase in
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`cost and complexity that would accompany that modification, the
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`concomitant need to overcome significant technical hurdles, and the
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`adverse impact the modifications would have on aspects of Kosaka’s
`
`functionality (such as Kosaka’s risk warning feature, as discussed in
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`Section XI below.)
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`CLI-2116419v4
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`24
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`34) A POSITA would recognize that the computer operations that are
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`performed in Kosaka do not require extensive processing capability. To
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`illustrate this, a POSITA would understand that the evaluation of the fuzzy
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`logic membership functions disclosed in Kosaka does not require extensive
`
`processing. Figures 10(A)-(E) “show the respective language value
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`membership functions of the fuzzy logic parts 62, 64, and 65.” (Kosaka at
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`24:2:21-23.) The figures symbolically depict the membership functions as
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`they do not, inter alia, provide values on the graphs. However given these
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`disclosed membership functions, a POSITA would know that the
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`membership functions can be stored on the vehicle using only a few
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`values. For example, Figure 10(A) of Kosaka discloses membership
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`functions used onboard a vehicle:
`
`
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`“S” Membership
`Function
`
`
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`25
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`CLI-2116419v4
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`Figure 10(A) shows three membership functions for the input function of
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`the first fuzzy logic part 62: (1) an “S” membership function; (2) an “M”
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`membership function; and (3) a “B” membership function.1 The “S”
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`membership is circled in the figure above. The membership function
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`disclosed in Figure 10(A) and all of the other membership functions
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`disclosed in Kosaka can be indicated by simple mathematical expressions
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`and stored onboard for use by the fuzzy logic ri
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