Ex Parte Gustafsson et al

Patent Trial and Appeal Board

Oct 19, 2016

12979637 (P.T.A.B. Oct. 19, 2016)

UNITED STA TES p A TENT AND TRADEMARK OFFICE
APPLICATION NO. FILING DATE FIRST NAMED INVENTOR
12/979,637 12/28/2010
11185 7590 10/21/2016
Weaver Austin Villeneuve & Sampson LLP - CDXS
ATTN:CDXS
P.O. Box 70250
Oakland, CA 94612-0250
Claes Gustafsson
UNITED STATES DEPARTMENT OF COMMERCE
United States Patent and Trademark Office
Address: COMMISSIONER FOR PATENTS
P.O. Box 1450
Alexandria, Virginia 22313-1450
www .uspto.gov
ATTORNEY DOCKET NO. CONFIRMATION NO.
CDXSP004XlD1Cl 3371
EXAMINER
SKIBINSKY, ANNA
ART UNIT PAPER NUMBER
1631
NOTIFICATION DATE DELIVERY MODE
10/21/2016 ELECTRONIC
Please find below and/or attached an Office communication concerning this application or proceeding.
The time period for reply, if any, is set in the attached communication.
Notice of the Office communication was sent electronically on above-indicated "Notification Date" to the
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PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte CLAES GUSTAFSSON, SRIDHAR GOVINDARAJAN,
ROBIN A. EMIG, RICHARD JOHN FOX, AJOY K. ROY,
JEREMY S. MINSHULL, S. CHRISTOPHER DA VIS,
ANTHONY R. COX, PHILLIP A. PATTEN, LINDA A. CASTLE,
DANIELL. SIEHL, REBECCA LYNNE GORTON, and TEDDY CHEN
Appeal2014-008005
Application 12/979,637
Technology Center 1600
Before ERIC B. GRIMES, JEFFREY N. FREDMAN, and
JOHN E. SCHNEIDER, Administrative Patent Judges.
FREDMAN, Administrative Patent Judge.
DECISION ON APPEAL
This is an appeal 1 under 35 U.S.C. § 134 involving methods for
identifying members of a population of biopolymer sequence variants for
artificial evolution. The Examiner rejected the claims as obvious. We have
jurisdiction under 35 U.S.C. § 6(b). We reverse.
1 Appellants identify the Real Party in Interest as Codexis Mayflower
Holding, LLC (see App. Br. 1 ).
Appeal2014-008005
Application 12/979,637
Statement of the Case
Background
"The protein design problem was recently shown to belong to a class
of problems known as NP-hard ... indicating that there is no algorithm
known that can solve such problems in polynomial time" (Spec. 2:7-10).
"Because of this complexity, many approximate methods have been used to
design better proteins; chief among them is the method of directed
evolution" (Spec. 2:10-12). "'Directed evolution' or 'artificial evolution'
refers to a process of artificially changing a character string by artificial
selection, recombination, or other manipulation, i.e., which occurs in a
reproductive population" (Spec. 11: 1-3).
"The present invention provides methods that utilize Pareto front
optimization to select clones for carrying out future rounds of artificial
evolution ... in connection with the optimization of multiple polypeptide
properties (i.e., multiple objectives)" (Spec. 36:33 to 37:2). "Pareto front
optimization is a multi-objective evolutionary algorithm that simultaneously
improves two or more desired objectives" (Spec. 37:2-3).
The Claims
Claims 1---6, 8, 11, 12, 25, and 26 are on appeal. 2 Claim 1 is
representative and reads as follows:
1. A method of identifying members of a population of
biopolymer sequence variants for artificial evolution, the
method comprising:
2 Claims 13-24 were withdrawn (see App. Br. 1).
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Application 12/979,637
(a) selecting or screening the members of the population
of biopolymer sequence variants for two or more desired
objectives to produce a multi-objective fitness data set;
(b) identifying a Pareto front in the multi-objective
fitness data set;
( c) selecting one or more members proximal to the Pareto
front, thereby identifying the members of the population of
biopolymer sequence variants most suitable for artificial
evolution;
( d) evolving the members selected in operation ( c) using
artificial evolution procedures to produce one or more evolved
members, wherein the evolving is performed on a physical
population; and
(e) repeating operations (a), (b), and (c) using the evolved
members as at least some of the members of the population of
biopolymer sequence variants in step (a),
wherein (b) and ( c) are performed using one or more
computer systems.
The Issues
1A... The Examiner rejected claims 1, 3---6, 11, 12, 25, and 26 under
35 U.S.C. § 103(a) as obvious over Fonseca3 and Wang4 (Final Act. 3---6).
B. The Examiner rejected claims 2 and 8 under 35 U.S.C. § 103(a) as
obvious over Fonseca, Wang, and Jin5 (Final Act. 6-8).
3 Carlos M. Fonseca and Peter J. Fleming, An Overview of
Evolutionary Algorithms in Multiobjective Optimization, 3
EVOLUTIONARY COMPUTATION 1-16 (1995) (Final Draft)
("Fonseca").
4 Wang et al., US 2002/0045175 Al, published Apr. 18, 2002
("Wang").
5 Jin et al., US 2002/0099929 Al, published July 25, 2002 ("Jin").
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A. 35 U.S.C. § 103(a) over Fonseca and Wang
The Examiner finds Fonseca teaches "Pareto-optimality (i.e.
suggesting an existence of a Pareto frontier) as applied to evolutionary
algorithms in multiobjective optimization (Abstract) wherein Fitness is
assigned from the best to the worst 'individuals' in a population" and
teaches "a distribution of a population along the 'front"' (Final Act. 3). The
Examiner finds Fonseca teaches "that the Pareto-set (i.e. individual on the
Pareto-front) are genetically similar (page 17, par. 3)) (i.e. selecting
members proximal to the Pareto front, thereby identifying the members most
suitable for artificial evolution)" (Final Act. 4).
The Examiner acknowledges that Fonseca does "not teach selecting or
screening the members of the population ofbiopolymer sequence variants
for two or more desired objectives to produce a multiobjective fitness data
set"; that Fonseca does "not teach evolving members of biopolymers in a
physical population using artificial evolution procedures to produce at least
one evolved member"; and that Fonseca does "not teach an iterative
process" (Final Act. 5).
The Examiner finds that Wang teaches "in vivo and in vitro (i.e.
physical population ofbiopolymers) evolution of polypeptides" and suggests
"an iterative process of screening for fitness and then performing another
round of evolving" (id.).
The Examiner finds it obvious
to study biopolymers as taught by Wang et al. using the method
of Fonseca et al. because Wang et al. teach that analytical
methods can improve determination of appropriate crossovers
which result in stable and functional biopolymers (Abstract)
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and Fonseca et al teach an analytical method for determining
fitness from an evolutionary algorithm.
(Final Act. 6).
The issue with respect to this rejection is: Does a preponderance of
the evidence of record support the Examiner's conclusion that Fonseca and
Wang render the method of claim 1 obvious?
Findings of Fact
1. Fonseca teaches the "application of evolutionary algorithms
(EAs) in multiobjective optimization is currently receiving growing interest
from researchers with various backgrounds" (Fonseca 1 ).
2. Fonseca teaches that "[e]volutionary algorithms (EAs),
however, have been recognized to be possibly well-suited to multiobjective
optimization since early in their development" (Fonseca 3).
3. Fonseca teaches an evolutionary process where "an individual's
rank corresponds to the number of individuals in the current population by
which it is dominated .... The algorithm proceeds by sorting the population
according to the multiobjective ranks previously determined" (Fonseca 9).
4. Fonseca teaches "[f]itness is assigned by interpolating, e.g.,
linearly, from the best to the worst individuals in the population, and then
averaging it between individuals with the same multiobjective rank"
(Fonseca 9).
5. Fonseca teaches that "[b ]y combining Pareto dominance with
partial preference information in the form of a goal vector, they have also
provided a means of evolving only a given region of the trade-off surface"
(Fonseca 9).
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6. Fonseca teaches that "Pareto-selection promotes improvement
by exerting a scale-independent selective pressure on the population in a
direction normal to the trade-off surface, sharing should attempt to balance
the distribution of the population along the front by applying a, possibly
scale-dependent, selective pressure tangentially to that surface" (Fonseca
12).
7. Fonseca teaches "[m]ating restriction in the objective domain,
or the absence of mating altogether, interprets the individuals populating the
Pareto-front as a continuum of species" (Fonseca 17).
8. Wang teaches "[s]ome of the advantages of directed evolution
methods are that they can be used with large polymers, for example proteins
with more than 500 amino acids; they produce[] unique and unexpected
results; and polymers can be evolved to achieve several goals
simultaneously" (Wang i-f 16).
9. Wang teaches "current computational methods have only been
used to improve a molecule's stability. The technique has not been used to
improve other properties of biopolymers, such as activity, selectivity,
efficiency, or other characteristics of biological fitness" (Wang i-f 21 ).
10. Wang teaches that "[d]irected evolution methods, by contrast,
have the benefit of improving any property in a molecule that can be
detected and/or captured by a screen, for example catalytic activity of an
enzyme" (Wang i-f 22).
11. Wang teaches:
In a typical in vitro protein evolution experiment, a naturally
occurring or wild-type protein is identified, and its sequence is
altered to produce diversity, for example by mutation or
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recombination. This results in large numbers of mutant
proteins, which are screened according to appropriate fitness
criteria, for example, the most active mutants that are
reasonably stable may be selected. One or more of these
mutants may then be selected as a parent for another round of
evolution. This process may be repeated as desired, for example
until no further improvements in fitness are observed.
(Wang ii 14).
Principles of Law
A prima facie case for obviousness requires "a reason that would have
prompted a person of ordinary skill in the relevant field to combine the
elements in the way the claimed new invention does." KSR Int 'l Co. v.
Teleflex Inc., 550 U.S. 398, 418 (2007).
Analysis
Appellants contend that
Fonseca deals only with the algorithmic or mathematical issues
of multi-objective modeling in an evolutionary algorithm, while
Wang deals with the biological processes of artificial evolution
without touching on computational techniques related to multi-
objective models or data. Evolutionary algorithms and artificial
evolution are distinct concepts. One skilled in the art would not
find it obvious to combine references in evolutionary algorithm
and artificial evolution simply because the two fields share the
term "evolution."
(App. Br. 9). Appellants contend that "neither Wang nor Fonseca suggests
that the described Pareto mathematical technique should be applied to a
biological system, or how such application should be implemented" (App.
Br. 11).
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The Examiner responds that "though Fonseca does not directly teach
using Pareto fronts as applied to biological sequence data, Fonseca teaches a
technique that is obvious to apply and can be successfully applied to the
biological fitness data taught by Wang" (Ans. 10). The Examiner finds that
Fonseca teaches "using Pareto fronts in evolutionary algorithms in multi-
objective optimization while Wang et al. teach biopolymer fitness
characteristics (par. 0016, 0021) used in recombination of biopolymer
sequences" and that Wang "teaches evolving biopolymers through
recombination and Fonseca also teaches recombination" (id.).
While a close case, we find that Appellants have the better position.
In particular, we agree with Appellants that merely because both references
utilize the word "evolution" and deal with "fitness" in optimizing parameters
does not provide a reason, absent hindsight, to apply the specific algorithms
disclosed in Fonseca, drawn generically to any multiobjective optimization
whatsoever to the directed evolution of biopolymer process disclosed by
Wang. The rejection fails to persuasively provide a reason to graft
Fonseca' s specific type of mathematical algorithm into the directed
evolution methods of Wang, nor does the rejection identify a problem in
Wang that would have been solved by use of Fonseca's multiobjective
optimization algorithms. We agree with Appellants that hindsight
arguments are overused (App. Br. 11 ), but in this instance we also agree that
the "rejection based on combining Wang with Fonseca is based on
impermissible hindsight" (App. Br. 12).
We recognize, but find unpersuasive, the Examiner's finding that a
"cursory search in Google using the terms 'Pareto' and 'biological' produces
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thousands of hits which supports this Examiner's position that the use of
Pareto fronts in the biological field is well known" (Ans. 11 ). This finding
is not supported by evidence in the record, and even if properly evidenced,
Appellants respond that "filtering out results published after March 1, 2002
brings the results down to only three entries, none of which is relevant to
applying Pareto front techniques to directed evolution" (Reply Br. 4).
Whatever the relevant number of prior art search results, the mere notation
of two words in a Google search does not meet the requirements necessary
for demonstrating a prima facie case of obviousness, rather, the Examiner
must identify specific reasons that would support the combination of the
prior art.
Conclusion of Law
The preponderance of the evidence of record does not support the
Examiner's conclusion that Fonseca and Wang render the method of claim 1
obvious.
B. 35 U.S.C. § 103(a) over Fonseca, Wang, and Jin
This rejection relies upon the underlying obviousness rejection over
Fonseca and Wang. Having reversed that rejection, we also necessarily
reverse the further obviousness rejection including Jin because Jin is not
relied upon to provide a reason to combine the teachings of Fonseca and
Wang.
SUMMARY
In summary, we reverse the rejection of claims 1, 3---6, 11, 12, 25, and
26 under 35 U.S.C. § 103(a) as obvious over Fonseca and Wang.
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We reverse the rejection of claims 2 and 8 under 35 U.S.C. § 103(a)
as obvious over Fonseca, Wang, and Jin.
REVERSED
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