UNITED STATES PATENT AND TRADEMARK OFFICE
`______________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`______________
`
`LIBERTY MUTUAL INSURANCE CO.
`Petitioner
`
`v.
`
`PROGRESSIVE CASUALTY INSURANCE CO.
`Patent Owner
`______________
`
`Case CBM2012-00002
`Patent 6,064,970
`______________
`
`Before the Honorable JAMESON LEE, JONI Y. CHANG, and MICHAEL R.
`ZECHER, Administrative Patent Judges.
`
`REBUTTAL DECLARATION OF SCOTT ANDREWS ON BEHALF OF
`PETITIONER LIBERTY MUTUAL INSURANCE CO. REGARDING U.S.
`PATENT NO. 6,064,970
`
`I, Scott Andrews, hereby declare under penalty of perjury:
`
`I have previously been asked by Liberty Mutual Insurance (“Liberty”) to testify
`
`as an expert witness in this action. As part of my work in this action, I have been
`
`asked by Liberty to respond to certain assertions and opinions offered by Dr. Mark
`
`Ehsani and Progressive Casualty Insurance Co. (“Progressive”) in this proceeding
`
`concerning U.S. Patent No. 6,064,970 (“the ‘970 patent”).
`
`
`
`
`
`Liberty Mutual Exhibit 1019
`Liberty Mutual v. Progressive
`CBM2012-00002
`Page 00001
`
`
`______________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`______________
`
`LIBERTY MUTUAL INSURANCE CO.
`Petitioner
`
`v.
`
`PROGRESSIVE CASUALTY INSURANCE CO.
`Patent Owner
`______________
`
`Case CBM2012-00002
`Patent 6,064,970
`______________
`
`Before the Honorable JAMESON LEE, JONI Y. CHANG, and MICHAEL R.
`ZECHER, Administrative Patent Judges.
`
`REBUTTAL DECLARATION OF SCOTT ANDREWS ON BEHALF OF
`PETITIONER LIBERTY MUTUAL INSURANCE CO. REGARDING U.S.
`PATENT NO. 6,064,970
`
`I, Scott Andrews, hereby declare under penalty of perjury:
`
`I have previously been asked by Liberty Mutual Insurance (“Liberty”) to testify
`
`as an expert witness in this action. As part of my work in this action, I have been
`
`asked by Liberty to respond to certain assertions and opinions offered by Dr. Mark
`
`Ehsani and Progressive Casualty Insurance Co. (“Progressive”) in this proceeding
`
`concerning U.S. Patent No. 6,064,970 (“the ‘970 patent”).
`
`
`
`
`
`Liberty Mutual Exhibit 1019
`Liberty Mutual v. Progressive
`CBM2012-00002
`Page 00001
`
`
`
`
`
`
`Prior Testimony
`
`I.
`
`1.
`
`I am the same Scott Andrews who provided a Declaration in this matter
`
`executed on September 15, 2012 as Exhibit 1012.
`
`II. Experience, Qualifications, and Compensation
`
`2. My information regarding experience, qualifications, and compensation
`
`are provided along with my prior Declaration, Exhibit 1012, and CV, Exhibit 1013.
`
`III. Scope of Study and Rebuttal Opinions
`
`A. Questions Presented
`
`3.
`
`I have been asked to respond to certain assertions and opinions of Dr.
`
`Mark Ehsani expressed in his declaration of May 1, 2013 as Exhibit 2016, and certain
`
`assertions of Progressive in its Patent Owner’s Response of May 1, 2013.
`
`B. Materials Considered
`
`4.
`
`In developing my opinions below, and in addition to the materials
`
`identified in my prior declaration at paragraph 13, I have considered the following
`
`materials:
`
` Declaration of Dr. Mark Ehsani (Ex. 2016);
`
` CV of Dr. Mark Ehsani (Ex. 2017)
`
` Patent Owner’s Response Pursuant to 37 C.F.R. § 42.220 (Paper 27)
`(“Opposition” or “Opp.”);
`
` Board’s Decision on Institution of Covered Business Method Review
`(Paper 10);
`
`
`
`2
`
`Page 00002
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`
`
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` All other materials referenced as exhibits herein.
`IV. Analysis and Opinions
`
`A. Dr. Ehsani’s Opinions and Progressive’s Assertions Concerning
`Kosaka’s Disclosures Regarding Fuzzy Logic
`
`5.
`
`Contrary to Dr. Ehsani’s testimony, a person of ordinary skill in the
`
`telematics aspects of the ‘970 patent1 would understand how to implement the fuzzy
`
`logic processing in Kosaka’s Risk Evaluation Unit. Progressive’s expert, Dr. Ehsani,
`
`is wrong when he states otherwise. E.g., EX2016 ¶¶ 28-29. Although a person of
`
`ordinary skill in the art might not be an “expert” in the sub-specialty of fuzzy logic,
`
`such a person would know enough about fuzzy logic to understand Kosaka and the
`
`technology for implementing it. In fact, Dr. Ehsani himself acknowledges that the
`
`membership functions provided by Kosaka can be found in conceptual introductions
`
`to basic fuzzy logic. EX2016 ¶ 31. Thus, understanding the fuzzy logic aspects of
`
`Kosaka requires a minimal understanding of fuzzy logic.
`
`6.
`
`Fuzzy logic was well established and fairly common by 1996. Dr. Ehsani
`
`himself references a 1994 book on fuzzy logic in one of his papers cited in his CV.
`
`J.P. Johnson, K. M. Rahman, & M. Ehsani, “Application of a Clustering Adaptive Fuzzy
`
`Logic Controller in a Brushless DC Drive,” IEEE-IECON’97, New Orleans, LA,
`
`November 1997, pp. 1001-1005 (EX1020) (referencing Wang, L., Adaptive Fuzzy
`
`
`1 As before, for purposes of this Declaration, unless otherwise noted, my statements
`and opinions below reflect the knowledge that existed in the field as of January 1996.
`
`
`
`3
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`Page 00003
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`
`
`
`
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`System and Control – Design and Stability Analysis, Prentice Hall, Englewood Cliffs,
`
`N.J., 1994) (cited in EX2017 at 8). And during this period fuzzy logic was also the
`
`subject of general study, as reflected, e.g., by the publication shortly thereafter of a
`
`textbook by Reza Langari & John Yen, Fuzzy Logic: Intelligence, Control, and Information 3
`
`(1999) (EX1021) (“[a]fter being mostly viewed as a controversial technology for two
`
`decades, fuzzy logic has finally been accepted as an emerging technology since the late
`
`1980s”). By 1996, I already had studied several fuzzy logic systems and supervised
`
`many engineers with similar fuzzy logic experience. In 1994, I was leading a team of
`
`engineers doing a variety of automotive systems development. These engineers were
`
`generally of the same level of skill as I have set forth as a typical POSITA. On one
`
`project they used fuzzy logic to track radar targets for an automotive radar system. On
`
`another they used fuzzy logic to classify accelerometer data from crash sensors in
`
`order to determine if and when to fire the airbags in a car. As far as I know, Dr.
`
`Ehsani is correct in that none of these engineers had received any formal training in
`
`fuzzy logic per se, but the technical concepts are not difficult, and with only a modest
`
`amount of research in target acquisition and tracking these engineers decided to use
`
`fuzzy logic, and then implemented the system. Thus, in my opinion, such a POSITA,
`
`faced with a technical problem (in this case classifying the driving behavior of
`
`insureds) would easily identify fuzzy logic as an effective classification system, and
`
`then set about implementing the technical aspects of the system by consulting
`
`references for any needed details, and testing solutions. In fact, few engineers practice
`
`
`
`4
`
`Page 00004
`
`
`
`
`
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`exclusively in technology for which they have formal training, and a good measure of
`
`the capability of an engineer, as I have learned in interviewing and hiring many
`
`engineers throughout my career, is their ability to apply their core technical skills to
`
`finding solutions to problems; in the real world outside of a formal curriculum, these
`
`problems are seldom like those found in textbooks. An engineer of ordinary capability
`
`knows where to look for a solution, and has the skills to understand those solutions
`
`based on basic technical principles.
`
`7.
`
`Progressive cites to their deposition of me and argues I had trouble
`
`reading and understanding Kosaka. Opp. at 32. However, they misunderstand my
`
`testimony. Instead, I was expressing difficulty based on the way the question asked of
`
`me was phrased—in other words, I was initially unclear as to what I was being asked.
`
`Once the question was clarified, I was able to respond based on Kosaka’s disclosure.
`
`EX2018 at 142:11-145:14. Additionally, Progressive cites to two errors in my
`
`declaration (neither of which substantively altered my conclusions) as evidence of
`
`Kosaka being confusing. Opp. at 32. However, these were not based on difficulties
`
`with Kosaka. First, in my original declaration, as I raised during the deposition, I
`
`accidentally said “second fuzzy logic part,” when I actually meant “third fuzzy logic
`
`part.” EX2018 at 138:3-7. Second, I acknowledged during my deposition that I
`
`misspoke when I stated that “operator control density” is integrated; rather, it is the
`
`product of integration. This does not change my conclusion that Kosaka discloses
`
`
`
`5
`
`Page 00005
`
`
`
`
`
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`storage of values prior to integration, and these misstatements are not reflective of any
`
`difficulty in understanding Kosaka.
`
`8.
`
`Dr. Ehsani states that a person of ordinary skill could not understand the
`
`teachings of Kosaka because it does not explain what the actual membership
`
`functions are. EX2016 ¶ 31. I disagree. In Figure 10, Kosaka describes conceptually
`
`how the various measured parameters would be classified into their membership sets,
`
`and in Figure 11, shows how the fuzzy logic parts would generate secondary
`
`membership sets from the initial membership sets. The particular values associated
`
`with these membership functions would be based on the particular purpose of the
`
`classification. In Kosaka, the particular parameter values associated with this
`
`classification would be a question of insurance underwriting, which is something that
`
`neither Dr. Ehsani nor myself are qualified to determine – rather, this would be
`
`determined by a person of ordinary skill in the insurance aspects of the ‘970 patent.
`
`However, that does not stop a person of ordinary skill in the telematics aspects of the
`
`‘970 patent from understanding how fuzzy logic would be applied to this problem and
`
`implemented. Kosaka Figures 10 and 11 clearly illustrate how fuzzy logic would be
`
`used to classify these input values in a way that could be used by an insurance expert
`
`to determine proper risk assignments.
`
`9.
`
`Furthermore, Kosaka discloses using fuzzy logic to process the data and
`
`arrive at a risk evaluation value. Based on the Kosaka disclosures, a person of
`
`ordinary skill in the art would understand the risk evaluation value to be a single,
`
`
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`6
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`Page 00006
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`
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`“crisp” risk value. E.g., EX1004 at 1, 6. After the data is processed through fuzzy
`
`logic, a POSITA would understand it must be converted into a crisp value through a
`
`process called defuzzification – a standard part of basic fuzzy logic implementation.
`
`Langari at 38, 44. In fact, Kosaka explicitly describes using defuzzification. EX1004
`
`at 8. This step allows fuzzy logic systems to provide a usable, actionable output.
`
`10.
`
`In addition, Kosaka explicitly discloses that fuzzy logic need not be used
`
`at all. EX1004 at 6, 9. A person of ordinary skill in the art would understand that
`
`Kosaka teaches implementing its system using either fuzzy logic or standard crisp logic
`
`to generate single (crisp) risk evaluation values, and those same risk evaluation values,
`
`however they are generated, are thereafter used by the rest of the system. In
`
`implementing the system using crisp logic, many of the same components as
`
`described for the fuzzy logic system would be required. All of the measured
`
`parameters on which Kosaka bases its insurance determination would still be used.
`
`Only the signal processing to generate the risk evaluation value would be different. In
`
`either case, fuzzy or crisp, the output of the risk evaluation unit would be still a crisp
`
`risk evaluation value, and an ultimate output of the system would still be insurance
`
`premiums based on measured parameters reflective of driving behavior.
`
`B.
`
`11.
`
`Progressive’s Assertions Concerning Black Magic’s Disclosures
`
`I have read Black Magic and, in my opinion—contrary to Progressive’s
`
`assertions—it would have been understood by a person of ordinary skill in the art as a
`
`“black box” system that records and associates time, speed, and location.
`
`
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`7
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`Page 00007
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`12. Black Magic’s system uses “black box recorders” to monitor vehicle
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`fleets and “record[] . . . driving speed, time and distance traveled and fuel
`
`consumption.” EX1008 at 1. From these values, a fleet manager can derive
`
`additional information, such as “maximum speeds, engine idling time and harsh
`
`deceleration” in order to “analyze the performance of drivers.” Id. Black Magic
`
`further explicitly discloses that GPS technology can “produce all the data a black box
`
`can and record the vehicle’s location.” Id. at 2.
`
`13.
`
`Focusing on one sentence, Progressive asserts that a POSITA would
`
`understand that Black Magic does not disclose recording time (Opp. at 44), but this
`
`misstates the disclosure of Black Magic. A POSITA would understand Black Magic
`
`to teach that the black box needs to monitor speed linked to time (as well as location)
`
`in order to derive “deceleration” or determine whether a car was “idling” based on
`
`recorded “speed” values, to determine whether a car was exceeding a particular
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`location’s posted speed limit, and “to accurately rate premiums according to styles of
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`driving and locality of use.” EX1008 at 1-2.
`
`
`Executed this 6th day of August, 2013
`
`
`
` Scott Andrews
`
`
`
`
`At: Petaluma, CA
`
`
`
`
`
`8
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`Page 00008
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