Ex Parte Lou et al

Patent Trial and Appeal Board

Jan 31, 2017

12196544 (P.T.A.B. Jan. 31, 2017)

United States Patent and Trademark Office
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
APPLICATION NO. FILING DATE FIRST NAMED INVENTOR ATTORNEY DOCKET NO. CONFIRMATION NO.
12/196,544 08/22/2008 Xinsheng Lou W06/002-0(ALS-0019) 1585
117185 7590
ALSTOM
C/O Buchanan Ingersoll & Rooney PC
1737 King Street, Suite 500
Alexandria, VA 22314
EXAMINER
KIM, DAVID H
ART UNIT PAPER NUMBER
2124
NOTIFICATION DATE DELIVERY MODE
02/02/2017 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
following e-mail address(es):
ADIPDOCl@BIPC.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte XINSHENG LOU, CARL H. NEUSCHAEFER, PAUL J. PANOS,
and JOSEPH W. QUINN
Appeal 2016-000631
Application 12/196,5441
Technology Center 2100
Before JUSTIN BUSCH, CATHERINE SHIANG, and SCOTT E. BAIN,
Administrative Patent Judges.
BAIN, Administrative Patent Judge.
DECISION ON APPEAL
Appellants appeal under 35 U.S.C. § 134(a) from the Examiner’s
Final Rejection of claims 1—21 which constitute all claims pending in the
application. We have jurisdiction under 35 U.S.C. § 6(b).
We affirm.
1 Appellants identify the real party in interest as ALSTOM Technology Ltd.
App. Br. 1.
Appeal 2016-000631
Application 12/196,544
STATEMENT OF THE CASE
The Claimed Invention
The claimed invention relates to optimizing a fluidized bed
combustion (FBC) process in a power plant, through neural network
modeling techniques. Spec. Tflf 1—3. Claims 1 and 16 are independent.
Claim 1 is illustrative of the invention and the subject matter of the appeal,
and reads as follows:
1. A system for optimizing a modeled fluidized bed
combustion power plant, the system comprising:
a processor including[]
a model of a fluidized bed combustion system which
provides at least one simulated output of the fluidized bed
combustion power plant in response to a user selected input of
the fluidized bed combustion power plant; and
an optimizer which provides at least one optimized
simulated output of the fluidized bed combustion power plant in
response to at least one user selected optimization setting and the
at least one simulated output of the fluidized bed combustion
power plant.
Claims App. 1 (App. Br.) (emphases added).
The Rejections on Appeal
Claims 1—5, 7, 8, 10-12, 16—19, and 21 stand rejected under pre-AIA
35 U.S.C. § 102(b) as anticipated by Dong-Feng Wang et al., “Modeling the
Circulating Fluidized Bed Boiler Using RBF-NN Based on Immune Genetic
Algorithm” Proceedings of the First International Conference on
Machine Fearning and Cybernetics (2002) (“Wang”). Final Act. 2-6.
Claims 6, 9, 13, 14, and 20 stand rejected under pre-AIA 35
U.S.C. § 103(a) as unpatentable over Wang and Rahul B. Kasat & Santosh
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K. Gupta, “Multi-objective optimization of an industrial fluidized-bed
catalytic cracking unit (FCCU) using genetic algorithm (GA) with the
jumping genes operator” Computers and Chemical Engineering 27
(2003) (“Kasat”). Final Act. 7-10.
Claim 15 stands rejected under pre-AIA 35 U.S.C. § 103(a) as
unpatentable over Wang and Kofman et al. (US 7,529,722 B2; May 5, 2009)
(“Kofman”). Final Act. 10—11.
ANALYSIS
We have reviewed the Examiner’s rejections in light of Appellants’
arguments presented in this appeal. Arguments which Appellants could
have made but did not make in the Briefs are deemed to be waived. See
37 C.F.R. § 41.37(c)(l)(iv). On the record before us, we are not persuaded
the Examiner erred. We adopt as our own the findings and reasons set forth
in the rejections from which the appeal is taken and in the Examiner’s
Answer, and provide the following for highlighting and emphasis.
Rejection Under 35 U.S.C. § 102(b)
Regarding claim 1, Appellants argue the Examiner erred in finding
Wang discloses a “model of a fluidized bed combustion system,” as recited
in the claim. App. Br. 3—5. Specifically, Appellants contend the neural
network of Wang “can be a model of a fluidized bed combustion system
only after training,” and “[bjefore training, it is a neural network with no
specific use.” Id. at 3 (emphasis added). We disagree.
As the Examiner finds, Wang discloses a genetic algorithm that
incrementally improves the performance of a model output; in other words,
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Application 12/196,544
it optimizes the model. Ans. 12 (citing Wang § 2.2,11). The abstract of
Wang further supports the Examiner’s finding: “a modeling method using
parallel Radial Basis Function Neural Network (RBF-NN) is proposed based
on immune GA [Genetic Algorithm] optimization.’ '’ Wang Abs. (emphasis
added). We discern no error in the Examiner’s finding that “improved
accuracy [in the Wang model] doesn’t change the fact that the algorithm is a
model of the fluidized bed both before and after training.” Ans. 12
(emphasis added). The claim does not require any particular extent of
training or accuracy before an algorithm is considered a “model” of the
fluidized bed. Id.
With regard to optimization, as the Examiner finds, Wang teaches
“the genetic algorithm is incremental,” id., and “converges to a local optimal
solution,” Wang § 2.2,11. Giving the claim its broadest reasonable
construction in light of the Specification, as we must, In re Am. Acad, of Sci.
Tech. Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004), we discern no error in the
Examiner’s finding that Wang’s incremental improvement and “optimal
solution” discloses the “optimizer” recited in claim 1.
Appellants argue independent claim 16 separately, but the thrust of
the argument is the same as the argument with respect to claim 1.
Specifically, Appellants contend “there can be no simulated output from the
model of the fluidized bed combustion system,” as recited in claim 16,
“because there is no feature of the optimizer” disclosed in Wang. App. Br.
4. For the reasons explained above, this argument does not persuade us of
error.
The remaining claims subject to the rejection are dependent, and are
not argued separately. Accordingly, for the foregoing reasons, we sustain
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Application 12/196,544
the rejection of claims 1—5, 7, 8, 10-12, 16—19, and 21 under pre-AIA 35
U.S.C. § 102(b) as anticipated by Wang.
Rejections Under 35 U.S.C. § 103(a)
Appellants argue the Examiner erred in rejecting dependent claim 6 as
unpatentable over Wang and Kasat because, according to Appellants, Kasat
does not teach “user selected input” that “includes economic inputs,” as
required by the claim. App. Br. 5. Although Appellants concede Kasat
teaches “maximization of the yield of gasoline (economic reasons),”
Appellants contend “these are not user inputs.” Id. (emphasis added). This
argument, however, is unpersuasive of error.
The Examiner relies not on Kasat alone, but the combination of Kasat
with Wang in rejecting claim 6. Ans. 13—14. As the Examiner finds, Kasat
“teaches that there are economic considerations for fuel,” Ans. 13; see also
Kasat at 1790, while Wang teaches fuel is a user input, Ans. 13 (citing Wang
§ 4, |2). Because “one cannot show non-obviousness by attacking
references individually where . . . the rejections are based on combinations
of references,” In re Keller, 642 F.2d 413, 426 (CCPA 1981), we are not
persuaded the Examiner erred in rejecting claim 6.
The remaining claims rejected as obvious are not argued separately.
Accordingly, we sustain the rejection of claims 6, 9, 13, 14, and 20 under
pre-AIA 35 U.S.C. § 103(a) as unpatentable over Wang and Kasat, and the
rejection of claim 15 under pre-AIA 35 U.S.C. § 103(a) as unpatentable over
Wang and Kofman.
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DECISION
We affirm the Examiner’s rejections of claims 1—21.
No time period for taking any subsequent action in connection with
this appeal may be extended under 37 C.F.R. § 1.136(a)(1). See 37 C.F.R.
§ 41.50(f).
AFFIRMED
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