`
`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 CBM2013-00009
`Patent 8,140,358
`______________
`
`Before the Honorable JAMESON LEE, JONI Y. CHANG, and MICHAEL R.
`ZECHER, Administrative Patent Judges.
`
`REBUTTAL DECLARATION OF MARY L. O’NEIL ON BEHALF OF
`PETITIONER LIBERTY MUTUAL INSURANCE CO. REGARDING U.S.
`PATENT NO. 8,140,358
`
`I, Mary L. O’Neil, hereby declare under penalty of perjury:
`
`I. Qualifications
`
`1.
`
`I am currently Principal of O’Neil Consulting Services, Inc. (OCS), an
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`independent actuarial consulting practice, which I established in 1986. I have over 30
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`years experience as a property casualty actuary in the insurance industry. My CV is
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`attached as Ex. 1032.
`
`2.
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`OCS provides actuarial consulting services to a variety of clients from
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`both the regulatory and private sectors. For example, the regulatory agencies in which
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`
`
`Liberty Mutual Exhibit 1031
`Liberty Mutual v. Progressive
`CBM2013-00009
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`I have provided consulting services include the North Carolina Department of
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`
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`Insurance (for which I have completed Private Passenger Automobile rate analyses
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`for more than twenty years), the New Jersey Department of Insurance, the New York
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`Department of Insurance, the Ontario Automobile Insurance Board, the Texas Office
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`of Public Insurance Counsel, the Georgia Department of Insurance, the Pennsylvania
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`Department of Insurance, and the Wyoming Department of Environmental Quality.
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`3.
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`Individual insurers or insurance pools for which I have provided
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`consulting services include Integrity Insurance Company in Liquidation (on behalf of
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`liquidator), Home State Holdings, Inc. in Liquidation (on behalf of liquidator),
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`Security Indemnity Insurance Company in Rehabilitation (on behalf of rehabilitator),
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`Pennsylvania Millers Mutual Insurance Company, several small insurers, and several
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`self-insurance pools.
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`4.
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`The services I have performed on behalf of OCS include analysis of
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`proposed rates by insurers, analysis of required insurer reserves in conjunction with
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`regulatory examinations of insurance companies, evaluation of loss reserves for
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`purposes of reinsurance commutation, preparation of required reserve opinions for
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`individual insurers and pools, evaluation of legislation, and other special projects.
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`5.
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`Rate analyses have included private passenger automobile, homeowners’,
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`dwelling fire, title, and workers’ compensation. These projects have been completed
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`for individual rate filings or full industry rate filings in selected states. I have also
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`estimated the required loss and loss adjustment expense reserves for a multibillion
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`dollar multi-line insurer group, a number of insurers in conjunction with financial
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`examinations, for purposes of commutations, several small insurers, and self-
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`insurance pools. These analyses have sometimes addressed the issues of mass torts or
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`catastrophes.
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`6. My previous work experience includes insurance actuary positions at the
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`New Department of Insurance (“NJDOI”), Prudential Property and Casualty
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`Insurance Company, and General Reinsurance Corporation.
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`7.
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`During my nearly two years at NJDOI, I served as the Department’s
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`Chief Actuary. My responsibilities included supervision of the actuarial aspects of
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`regulation for all lines of insurance: personal lines and commercial lines rates and
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`forms, life contracts and health rates. In addition, I supervised the life valuations and
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`supplied assistance to the Examinations Division in valuing property/casualty insurer
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`reserves. I also served as an advisor to the commissioner and other department staff
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`on all issues before the department.
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`8.
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`For the eleven years I worked at Prudential, I had a variety of
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`responsibilities, which included insurance pricing, marketing, reserving, financial
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`analysis, and various special projects. I started as an actuarial student and rose to the
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`level of Vice President and Assistant Actuary. Finally, at General Reinsurance
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`Corporation, I spent one year doing mostly statistical insurance work.
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`9.
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`I have also worked with several law firms in a consulting and/or expert
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`capacity. My attached CV lists all the matters in which I was involved, including my
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`testimonial experience. See Ex. 1032.
`
`10.
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`I have the professional designations of Fellow of the Casualty Actuarial
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`Society (FCAS), Member of the American Academy of Actuaries (MAAA), Chartered
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`Life Underwriter (CLU), and Chartered Financial Consultant (ChFC). I am also a
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`member of the Casualty Actuarial Society (CAS), American Academy of Actuaries
`
`(AAA), and the International Association of Insurance Receivers (IAIR).
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`11. My education includes a B.S. in Mathematics from Pennsylvania State
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`University, and an M.A. in Statistics, also from Pennsylvania State University.
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`12.
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`I have been retained on behalf of Petitioner and real party in interest,
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`Liberty Mutual Insurance Company (“Petitioner” or “Liberty Mutual”), I have been
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`asked by Liberty Mutual to respond to certain assertions and opinions offered by
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`Michael Miller and Progressive Casualty Insurance Co. (“Progressive”) concerning
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`U.S. Patent No. 8,140,358 (“the ‘358 patent”) in this matter.
`
`13.
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`I am being compensated at a rate of $500 per hour for my services, after
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`expert service fees. My compensation does not depend on the outcome of this
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`Business Method Review Petition or the pending litigation between Petitioner and
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`Progressive in the U.S. District Court for the Northern District of Ohio.
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`II. Scope of Rebuttal Declaration
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`
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`14.
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`I have been asked to respond to certain assertions and opinions of Mr.
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`Michael Miller expressed in his declaration of June 13, 2013 as Exhibit 2013, his
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`supplemental declaration of June 25, 2013 as Exhibit 2026, and certain assertions of
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`Progressive in its Patent Owner’s Response of June 13, 2013.
`
`15.
`
`In developing my opinions below, I have considered the following
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`materials:
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` Declaration of Michael Miller (Ex. 2013);
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` Supplemental Declaration of Michael Miller (Ex. 2026);
`
` Patent Owner’s Response (Paper 21) (“Opposition” or “Opp.”);
`
` Board’s Decision on Institution of Covered Business Method Review
`(Paper 10);
`
` Progressive’s U.S. Patent No. 8,140,358 (“the ‘358 Patent”) (Ex.
`1001);
`
` A certified English translation of Japanese Patent Publ’n H4-182868
`(“Kosaka”) (Ex. 1003);
`
` All other materials referenced as exhibits herein.
`III. Analysis and Opinions
`A. Mr. Miller’s Opinions and Progressive’s Assertions Regarding
`“Actuarial Classes” and Determining Auto Insurance Premiums
`
`16.
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`In providing a definition of “actuarial class,” Mr. Miller states:
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`In the field of motor vehicle insurance as of 1996, a person or ordinary
`skill in the art would have understood that “actuarial class” had the same
`meaning as risk class. . . . This definition is consistent with the definition
`in the Risk Classification Statement of Principles of the American
`Academy of Actuaries. A person of ordinary skill in the art in 1996
`would have adhered to this Statement of Principles.”
`
`Ex. 2013 ¶ 17 (Emphasis added).
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`17. Mr. Miller has presented the Risk Classification Statement of Principles
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`(Ex. 2012 in CBM2012-00002) as if it were a binding verbatim requirement to be
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`followed. That is incorrect. The guidance provided by the Statement of Principles
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`and its usage is explained by Interpretative Opinion 4: Actuarial Principles and
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`Practices (1982) of the American Academy of Actuaries (AAA, the umbrella
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`organization for all actuaries), which would have been in effect through 1996. In fact,
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`Interpretative Opinion 4: Actuarial Principles and Practices (Ex. 1033)1 states in
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`essence that the Statement of Principles cited by Mr. Miller is only a guideline—one
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`possible reference out of a large body of material that form the bases of Generally
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`Accepted Actuarial Principles and Practices (which are a broad overview of how
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`actuarial practice should be done):
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`(a) Generally Accepted Actuarial Principles and Practices.
`
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`1 Exhibit 1033 is a true and correct copy of “Interpretative Opinion 3: Professional
`Communications of Actuaries and Interpretive Opinion 4: Actuarial Principles and
`Practices,” adopted 1970-1982 by the American Academy of Actuaries and
`republished in 1992 by the Actuarial Standards Board, which I obtained online at
`http://www.actuarialstandardsboard.org/pdf/superseded/intopinion.PDF.
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`Guide 4(b) requires the actuary to “exercise due diligence to ensure . . .
`that the methods employed are consistent with the sound actuarial
`principles and practices established by precedents or common usage
`within the profession. . .” Such “sound actuarial principles and practices”
`constitute Generally Accepted Actuarial Principles and Practices.
`
`(b) Sources of Generally Accepted Actuarial Principles and Practices.
`
`Sources of Generally Accepted Actuarial Principles and Practices emerge
`from the utilization and adoption of concepts described in actuarial
`literature. Such literature includes, but is not limited to, the Actuarial standards
`and Actuarial Compliance Guidelines adopted by the Actuarial Standards Board,
`the Recommendations and Interpretations published under the auspices of the
`American Academy of Actuaries; the professional journals of recognized professional
`actuarial organizations (including the Statements of Principles promulgated by the
`Society of Actuaries and the Casualty Actuarial Society); recognized actuarial
`textbooks and study materials; and applicable provisions of law and regulations; and
`may include standard textbooks or other professional publications in related fields
`such as mathematics, statistics, accounting, economics and law.
`
`Ex. 1033 at 6 (emphasis added, footnote omitted).
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`18. Opinion 4 also states ultimately in subsection (d) that “In all cases the
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`professional judgment of the actuary should prevail” and provides Standards of
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`Practice and Compliance Guidelines:
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`(c) Standards of Practice and Compliance Guidelines.
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`An actuary working in a specialized field should take into consideration any
`published Standard of Practice or Compliance Guideline of the Actuarial
`Standards Board. An actuary who uses principles or practices which
`differ materially from any published Standard of Practice or Compliance
`Guideline must be prepared to support the particular use of such
`principles or practices and should include in an actuarial communication
`appropriate and explicit information with respect to such principles and
`practices. . . . When dealing with a specific situation not covered by a published
`Standard of Practice or Compliance Guideline, the actuary should be aware of
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`relevant precedent and generally available literature in deciding what constitutes
`Generally Accepted Actuarial Principles and Practices.
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`Ex. 1033 at 6-7 (emphasis added).
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`19.
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`Indeed, one such Standard of Practice—No. 12, attached to Mr. Miller’s
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`supplemental declaration Ex. 2020 to CBM2012-00002 and Ex. 1039 here—further
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`belies Mr. Miller’s strict adherence to the Statement of Principles and repeated
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`insistence on the use of actual claims data to generate actuarial classes (see Ex. 2013
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`¶¶ 17-19, 40-41, 43):
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`“5.1 Methods to Demonstrate Cost Differences—A risk classification
`system is equitable if material differences in costs for risk characteristics
`are appropriately reflected in the rate. Classification subsidies result
`when the price paid by an individual or class of individuals fails to reflect
`differences in costs among the risk classes.
`
`A relationship between a risk characteristic and cost is demonstrated if it
`can be shown that experience is different when the characteristic is
`present. In demonstrating the relationship, the actuary can rely on actual or
`reasonably anticipated experience; the actuary is not constrained to using only the
`experience of the actuary’s client or company. Relevant information from any reliable
`source, including statistical or other mathematical analysis of available data, may be
`used. Information gained from clinical experience, or from expert
`opinion regarding the effects of change on future experience (e.g.,
`medical or engineering) may be used.
`
`In the absence of actual experience, an actuary may rely on clear actuarial evidence
`that differences in costs are related to a particular risk characteristic. In demonstrating
`this, the actuary may rely on clinical experience or expert opinion. For example, an
`environmental that which has been demonstrated by clinical experience
`to be related to additional deaths may be used until further actual
`experience becomes available.
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`Sometimes it is appropriate for the actuary to make inferences without specific
`demonstration. For example, it would not be necessary to demonstrate that
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`persons with seriously impaired, uncorrected vision would represent a
`high risk as operators of motor vehicles.”
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`Ex. 1039 at Attachment pp.3-4 (emphasis added).
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`20. Mr. Miller also opines that it is not required that actuarial classes are
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`used to meet the statutory standard of charging premiums that are “not unfairly
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`discriminatory.” Ex. 2013 ¶¶ 28-30. While it is true that the statute does not
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`prescribe the means by which the law must be complied with, the utilization of
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`actuarial classes is and has been the accepted means for such compliance. And,
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`notably, Mr. Miller gives no alternative examples (or any examples of an approved
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`policy that did not employ actuarial classes).
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`21. At paragraph 19, Mr. Miller states:
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`The future insurance loss (i.e., risk of loss) being estimated is the product
`of the probability of an occurrence of an insured claim times the likely
`cost of the claim. Because the probability of an insurance claim
`occurring is a different value than the probability of an auto accident
`occurring, auto insurance rates are typically calculated based on the
`likelihood of claim occurrence, not the likelihood of accident
`occurrence.
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`22. Mr. Miller’s opinion here is muddled. Accurately stated, estimated future
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`insurance loss costs are based on the expected pure premium, which is equal to the
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`product of the expected claim frequency and the expected claim severity. The further
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`opinion by Mr. Miller that an accident does not necessarily generate a one-to-one
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`relationship to claims is not relevant to the concept of expected loss costs or expected
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`pure premium as long as a consistent measure is utilized.
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`23.
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`For example, accident statistics, like any other risk characteristic that is
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`or can be used to create an actuarial class (such as age, mileage, sudden braking
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`events, or excessive speeding), does not necessarily have a one-to-one relationship to
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`claims. Yet accident statistics can indeed in themselves be the basis for an actuarial
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`class—see, e.g., U.S. Patent No. 6,064,970 (‘970 Patent), parent of the ‘358 patent, at
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`1:28-43 (number of at-fault accidents)—just as, for example, the ‘970 patent points
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`out that “number of sudden braking situations” can be an actuarial class at 4:30-45. It
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`is the job of an actuary to determine how risk characteristics, such as number of
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`accidents or sudden braking events, correlate to predicted future insurance losses so
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`that an insurer can charge an individual the proper premium. This can be done—as
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`explained, for example, in Standard of Practice No. 12—using “actual experience”
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`(actual frequency and severity claims data) or “any reliable source, including statistical or
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`other mathematical analysis of available data” (Ex. 1039 at Attachment pp.3-4
`
`(emphasis added)).
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`24.
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`Importantly, I further note that Mr. Miller’s entire discussion of the
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`necessity of collecting and applying actual claims data (see Ex. 2013 ¶¶ 17-19, 40-41,
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`43) is wholly lacking from the ‘358 patent. Nowhere does the ‘358 patent explain how
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`or where to obtain claims data, much less how to use such data to calculate overall
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`claims losses, pure premiums, expected loss costs, etc. Rather, those techniques are
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`left up to the knowledge of a POSITA, such as myself. While Mr. Miller repeatedly
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`states that the prior art references involved are lacking such discussions, so is the ‘358
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`patent, but nevertheless, it does not mean that a POSITA would simply not possess
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`such knowledge.
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`25. Mr. Miller states that the unit of exposure for automobile insurance is
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`the insured vehicle and further states that “[p]remiums for auto insurance are typically
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`not calculated or quoted on a per person or per driver basis.” Ex. 2013 ¶ 22. Mr.
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`Miller’s statement is again muddled and incomplete. Premiums are generally quoted
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`on a policy basis. It is generally the risk characteristics of the insured driver that are
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`applied (using actuarial classes) to the base premiums by coverage or car to determine
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`the premium.2 For liability coverages, the specific car driven is not part of the rating
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`process because the premium is determined using actuarial classes based on only the
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`driver’s risk characteristics and the base premium for the selected coverage. See, e.g., ‘970
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`Patent at 1:28-2:12. For physical damage coverages, the premium is determined using
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`actuarial classes based on the driver’s risk characteristics and the base premium by
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`coverage for the specific insured vehicle. See id.
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`2 Even Mr. Miller appears to contradict himself on this argument when defining “pure
`premium”: “The numerical value is a ratio of the expected loss of one actuarial class
`to another. The expected loss for an insured is sometimes called the ‘pure premium.’”
`Ex. 2010 ¶ 19. Here he has defined it in terms of the insured (driver) rather than the
`vehicle. Progressive also misstates my views on the definition of “pure premium”:
`“Ms. O’Neil referred to ‘pure premium,’ which has the same meaning as expected
`losses.” Opp. at 11 n.2. Rather, “pure premium” is the loss dollars per unit of
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`26. Again, I also note that Mr. Miller’s opinions about any alleged issues
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`arising regarding the “per driver” versus “per vehicle” distinction are completely
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`lacking from the ‘358 patent. Thus, even if Mr. Miller’s opinions were accurate, he
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`relies exclusively on the knowledge of a POSITA—the same knowledge that the
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`POSITA would have when reading any of the prior art references Mr. Miller criticizes.
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`27. Mr. Miller also attempts to utilize the muddled distinction he has created
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`between drivers and vehicles to conclude that insureds are not “assigned” to actuarial
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`classes, stating as follows:
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`In the case of auto insurance, it is not an insured person that is being
`“assigned” to an actuarial class of similar risks. It is the premium
`charged, and any future claim losses, associated with the insured car that
`are coded to an actuarial class for each of the risk characteristics used in
`determining the premium for the insured car.
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`Ex. 2013 ¶ 31.
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`28. This is incorrect and cannot be concluded from his former muddled
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`point about driver versus vehicle. In fact, insurance policies typically identify the
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`assigned classification of the driver for rating purposes. Similarly, distinct aspects of the
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`vehicle’s classification are identified. The final premium is determined by a rating
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`algorithm combining the driver, vehicle, and selected coverages. The resulting insurer
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`premium and claim experience for a particular classification consists of the premium
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`exposure.
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`and claim amounts for all drivers combined who have been assigned to that
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`classification.
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`29.
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`In an attempt to further his argument about driver versus vehicle, Mr.
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`Miller provides an incorrect example to illustrate his contention that insured cars, not
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`drivers, are the basis for classification analysis:
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`For example, assume the premium for an insured car is determined
`based on only three risk characteristics: the rated-driver of the insured
`car is an adult driver, the coverage is subject to a $500 deductible, and
`the insured is eligible for a claims-free discount. The insurer’s
`policyholder records for this insured car will reflect a separate code for
`each of the three risk characteristics (i.e., adult driver, $500 deductible,
`and claims-free). When analyzing the difference in risk between adult
`drivers and youthful drivers, the premium and claims data of our
`hypothetical insured car will be included with the adult risk class. When
`analyzing the difference in risk between a $500 deductible and a $250
`deductible, the premium and claims data of our hypothetical insured car
`will be included with the $500 deductible risk class. When analyzing the
`difference in risk between insureds that are claims-free and those that are
`not, the premium and claims data of our hypothetical insured car will be
`included with the claims-free risk class.
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`Ex. 2013 ¶ 32.
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`30. Mr. Miller’s example is incomplete and incorrect. The driver in Mr.
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`Miller’s example would have been assigned a classification of adult driver. The base
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`rate for the physical damage coverage for the vehicle would be modified for a $500
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`deductible (if some other deductible amount was reflected in the base rate). The final
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`rate would be modified by a claims free discount. The comparison of premium and
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`claim experience of adult and youthful drivers would be a comparison of data for the
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`adult and youthful classifications based on their risk characteristics. For the deductible
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`option, the comparison of premium and claim data would be a comparison of the
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`data for the $500 and $250 classification options. Finally, the claim-free discount
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`would compare the premium and claim data for classifications with the discount and
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`without the discount. In each of these rate making analyses, the claim data would be
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`collected for each resulting exposure, or the pure premium. This analysis remains
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`consistent with the initial assignment of drivers to rating classifications in order to
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`determine the policy premium.
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`31. Mr. Miller argues that obtaining “household” data is necessary to rate an
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`insurance policy:
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`The “rated-driver” is the specific driver among multiple drivers in the
`household, whose risk characteristics impact on the premium calculation
`for a specific insured auto. . . . The premium is calculated to reflect the
`total insured risk associated with the ownership and operation of a
`vehicle that potentially has multiple operators. The insureds under a
`personal auto insurance policy are the named insured(s) listed on the
`policy’s declaration page, as well as all relatives resident in the household.
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`Ex. 2010 ¶¶ 23-25.
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`32. Mr. Miller is incorrect. There is no requirement that household data be
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`collected because, for example, the insurer’s rating algorithm can be applied even if
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`only one driver participates in the monitoring. Also, an individual driver can get a
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`policy on a single car for himself only, so there is no requirement that an insurer get
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`information on multiple drivers or “household” data.
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`33. This household “requirement” argued by Mr. Miller is yet another point
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`that is not discussed in the ‘358 patent. So, again, even if Mr. Miller’s opinions were
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`correct, his exclusive reliance on the knowledge of a POSITA (rather than any ‘358
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`disclosures) is the same knowledge the POSITA would have in analyzing the prior art
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`references.
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`B. Mr. Miller’s Opinions and Progressive’s Assertions Regarding
`Kosaka and Fuzzy Logic
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`34. Regarding a POSITA’s awareness of fuzzy logic, Mr. Miller states that
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`“[a] person of ordinary skill as of 1996 would not have had experience using or
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`applying fuzzy logic to the determination of insurance premiums, but would have
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`been relatively sophisticated in the use of multi-variant statistical analysis of risk
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`classification data.” Ex. 2013 ¶ 14.
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`35. Mr. Miller’s opinion improperly narrows the knowledge of a POSITA.
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`Although fuzzy logic was not the predominant mathematical process applied in rate
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`filings for determination of the overall premium in 1996, the literature confirms that
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`the methodology was well known and applications to classification rating and
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`underwriting were well-documented. See, e.g., Ex. 1034 (Shapiro Article) 3 (providing a
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`history of the application of fuzzy logic in insurance since 1982 and an extensive list
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`3 Arnold F. Shapiro, An Overview of Insurance Uses of Fuzzy Logic, in Paul P. Wang, et al.,
`eds., Computational Intelligence in Economics and Finance Volume II pp. 25-61 (Chapter
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`of references at pages 57 through 61, many predating 1996; this reference also
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`demonstrates the application of fuzzy logic to rating territories and classifications
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`based on age groupings); Ex. 1035 (Carreno Article)4 (describing “knowledge based
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`system . . . that combines fuzzy processing with [a] rule-based expert system” for
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`“improved decision aid for evaluating risk for life insurance” (p.536) ; the “output of
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`the system consists of a crisp value for Risk in the range [0, 1]” (p.538)); see also Ex.
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`1036 (Lemaire Article)5 ((summarizing the history of fuzzy logic), 54-55 (extensive
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`bibliography showing many papers applying fuzzy logic); Ex. 1037 (Derrig Article)6
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`(presenting an application of fuzzy techniques to derive Massachusetts automobile
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`rating territories); Ex. 1038 (Young Article)7 (extensive bibliography at 761-62
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`consisting primarily of papers written about fuzzy logic in insurance well before 1996).
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`The abundance of literature available in 1996 related to the application of fuzzy logic
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`to insurance further demonstrates that a POSITA would have had knowledge of the
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`One) (Springer Berlin Heidelberg, 2007).
`4 Luis A. Carreno, et al., A Fuzzy Expert System Approach to Insurance Risk Assessment
`Using FuzzyCLIPS, in WESCON Conference Record pp.536-541 (1993) (reference no. 13
`from Shapiro Article at 58).
`5 Jean Lemaire, Fuzzy Insurance, ASTIN Bulletin International Actuarial Association
`Vol. 20, No. 1, pp.33-56 (1990) (date of article shown on download page at end of
`exhibit).
`6 Richard A. Derrig, et al., Fuzzy Techniques of Pattern Recognition in Risk and Claim
`Classification, Journal of Risk and Insurance Vol. 62, No.3, Sept. 1995.
`7 Virginia R. Young, Adjusting Indicated Insurance Rates: Fuzzy Rules that Consider Both
`Experience and Auxiliary Data, in Proceedings of the Casualty Actuarial Society Casualty
`Actuarial Society - Arlington, Virginia, 1997, pp.734-765 (date of article shown on
`
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`topic and its applications to classification ratemaking, and would have known how to
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`address it (such as where to look for any further needed information). The rapid
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`growth in the topic of fuzzy logic took place well before 1996, including in the
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`insurance industry. Hence, Mr. Miller has inaccurately narrowed the knowledge of a
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`POSITA.
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`36. Mr. Miller states his understanding of Kosaka as follows:
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`Kosaka discloses a risk evaluation device and an insurance premium
`determination device. The risk evaluation device detects the speed
`relative to a preceding vehicle. I understand that the speed and wave
`length data are among the input values used to determine a range of
`fuzzy risk values via fuzzy logic.
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`Ex. 2013 ¶ 35.
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`37. This is a muddled and incomplete understanding of Kosaka. The risk
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`evaluation device measures certain operating characteristics of the vehicle, with
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`Kosaka providing examples such as speed and relative distance. These readings are
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`utilized via the process of fuzzy logic to determine risk evaluation values with a
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`defined range. The overall risk evaluation value is a single numerical value (based on
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`fuzzy inputs) that is indicative of risk and that Kosaka uses to adjust the premium. See
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`Ex. 1003 (Kosaka) at 1, 8, Fig. 11. The process of using fuzzy logic has been well
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`described in several of the references which I have listed. One example of the process
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`is for Massachusetts rating territories (Ex. 1037).
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`download pages at end of exhibit).
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`38. Mr. Miller also argues that Kosaka teaches nothing regarding premium
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`determination using actuarial classes. See Ex. 2013 ¶¶ 38-43. First, Mr. Miller states
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`that the POSITA would have had no experience with fuzzy logic, would not know
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`how to use fuzzy logic, or know how to apply fuzzy logic to establish actuarial classes;
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`nor, he says, would the POSITA know how to process the data in Kosaka using fuzzy
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`logic. Ex. 2013 ¶¶ 35-38, 42-43.
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`39. Mr. Miller is incorrect in his opinions about a POSITA’s understanding
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`of Kosaka’s fuzzy logic disclosures. Importantly, I note that Kosaka explicitly states
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`that fuzzy logic need not be used: “fuzzy logic was used as the means for determining
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`risk evaluation values in this example of embodiment, but determination may be
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`carried out without using fuzzy logic. Calculation may also be carried out using a
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`common insurance table.” Ex. 1003 at 6. In any event, as commented above, Mr.
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`Miller has unnecessarily limited the definition of POSITA given that there was
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`significant literature on the subject of fuzzy logic prior to 1996 regardless of whether
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`or not its application was widely utilized.
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`40.
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` Moreover, fuzzy logic is not the key point in the Kosaka reference. It is
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`merely a different way to process the vehicle operation data collected using the data
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`collection device. Fuzzy logic is applied at various steps in an example of the process
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`of deriving overall risk evaluation values in Kosaka, and Kosaka indicates (as noted
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`above) that these values are produced whether or not fuzzy logic is employed. The
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`POSITA would have recognized that the actual data derived from the operation of an
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`automobile in Kosaka could be translated into risk evaluation values (with or without
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`the application of fuzzy logic). And a POSITA would have understood that the
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`Kosaka overall output risk evaluation values are single numerical values.
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`41. As supposed support for Mr. Miller’s opinions about fuzzy logic, Mr.
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`Miller states:
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`I understand that fuzzy logic relies on a fuzzy-set mathematical theory
`that results in data sets that are not mutually exclusive. The person of
`ordinary skill could not be certain there was any true difference in risk
`between two Kosaka risk values produced via fuzzy logic, especially if
`those risk values were near the intersection of two sets. Two operators
`with different Kosaka risk values would not necessarily have objectively
`different driving patterns or objectively different insurance risks. The
`ordinary skilled artisan would not use such data for purposes of
`establishing actuarial classes.
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`Ex. 2013 ¶ 38.
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`42. Mr. Miller has confused the process with the result. Kosaka utilizes (as
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`one example) fuzzy logic on the data collected by the data collection device and
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`generates distinct risk evaluation values to assess insurance risk. Indeed, as I
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`mentioned above, fuzzy logic applications for insurance in general, and, in particular,
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`classification rate making, have been well demonstrated in the literature. See, e.g., Ex.
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`1037 (Derrig Article) (describing an application of fuzzy techniques to derive
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`Massachusetts automobile rating territories; the resulting rating territories conform to
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`the required criteria for rating classifications such as non-overlap between classes); Ex.
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