Ex Parte Rossi

Patent Trial and Appeal Board

May 31, 2017

12981945 (P.T.A.B. May. 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.
12/981,945 12/30/2010 David ROSSI 94.0271-US-NP 7490
48879 7590 06/02/2017
SCHLUMBERGER INFORMATION SOLUTIONS
10001 Richmond Avenue
IP Administration Center of Excellence
HOUSTON, TX 77042
EXAMINER
JONES, HUGH M
ART UNIT PAPER NUMBER
2128
NOTIFICATION DATE DELIVERY MODE
06/02/2017 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):
U S Docketing @ sib. com
jalverson@slb.com
SMarckesoni@slb.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte DAVID ROSSI
Appeal 2015-003275
Application 12/981,9451
Technology Center 2100
Before JAMES R. HUGHES, TERRENCE W. McMILLIN, and
ALEX S. YAP, Administrative Patent Judges.
YAP, Administrative Patent Judge.
DECISION ON APPEAL
Appellant appeals under 35 U.S.C. § 134(a) from a Final Rejection of
claims 1, 2, 4, 5, 8, 9, 11—16, and 18—24, which are pending in this
application.2 We have jurisdiction under 35 U.S.C. § 6(b).
We affirm.
1 Appellant identifies Schlumberger Technology Corp. as the real party in
interest. App. Br. 4.
2 Claims 3, 6, 7, 10, and 17 are canceled. (App. Br. 21—23.)
Appeal 2015-003275
Application 12/981,945
STATEMENT OF THE CASE
Introduction
Appellant’s invention relates to “modeling a production system may
include providing a non-linear deterministic model representing the
production system, the model including one or more inputs and one or more
outputs.” (Dec. 30, 2010 Specification (“Spec.”) 1 8.) Claim 1 is
illustrative, and is reproduced below (with minor reformatting):
1. A method of modeling a production system, comprising:
providing a non-linear deterministic model representing
the production system, the model comprising one or more inputs
and one or more outputs;
associating a prior probability density function (PDF) with
a first input of the one or more inputs or a first output of the one
or more outputs, or both, wherein the first input or the first output
or both that have the prior PDF associated therewith and are not
measured and not deterministically known;
linearizing the non-linear deterministic model to generate
a linearized model;
obtaining a measurement of a second input of the one or
more inputs or a second output of the one or more outputs, or
both;
determining, by using a processor and using a joint mean
and covariance, a joint uncertainty related to the one or more
inputs or the one or more outputs, or both, by calculation using
the linearized model;
determining, using the joint mean and covariance and the
measurement and without using iterative optimization, a
conditional mean and covariance for the first input or the first
output or both;
updating the non-linear deterministic model using the
conditional mean and covariance; and
adjusting the production system based on the updating of
the non-linear deterministic model, wherein the adjusting
comprises at least one of calibrating a sensor based on the
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Appeal 2015-003275
Application 12/981,945
conditional mean and covariance and scheduling one or more
well tests.
Prior Art and Rejection on Appeal
US 5,539,704 July 23, 1996
WO 01/07755 A1 Feb. 1,2001
US 7,460,957 B2 Dec. 2, 2008
R. Johnson et al., Chapter 3. CTD and Related Measurements,
Bermuda Biological Station for Research, Inc., Bermuda Atlantic Time-
series Study (April 1997) (“Johnson”).
L. Jin et al., Joint Estimation of Porosity and Saturation by
Combining a Rock Physics Model and Constrained Pre-stack Seismic
Waveform Inversion, Jackson School of Geosciences, The University of
Texas at Austin (2007) (“Jin”).
Claims 1, 2, 4, 5, 8, 9, 11—16, and 18—20 stand rejected under 35
U.S.C. § 102(b) as anticipated by Doyen. (See June 5, 2014 Final Action
(“Final Act.”) 5-16,21.)
Claim 21 stands rejected under 35 U.S.C. § 103(a) as being
unpatentable in view of Doyen and Johnson. (See Final Act. 21—22.)
Claims 22—24 stand rejected under 35 U.S.C. § 103(a) as being
unpatentable in view of Doyen and Prange or Jin. (See Final Act. 16—21.)
Doyen et al.
(“Doyen”)
Malinvemo et al.
(“Malinvemo”)
Prange et al.
(“Prange”)
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Appeal 2015-003275
Application 12/981,945
ANALYSIS
We have reviewed the Examiner’s rejection in light of Appellant’s
arguments that the Examiner has erred. We are not persuaded that the
Examiner erred in rejecting claims 1, 2, 4, 5, 8, 9, 11—16, and 18—24. We
adopt as our own the findings and reasons set forth by the Examiner in the
action from which this appeal is taken and the reasons set forth by the
Examiner in the Examiner’s Answer in response to Appellant’s Appeal
Brief. (Ans. 3—13.) However, we highlight and address specific findings
and arguments for emphasis as follows.
“linearizing the non-linear deterministic model
to generate a linearized model ”
The Examiner finds that Doyen, on column 7, lines 1—24 discloses the
“linearizing the non-linear deterministic model to generate a linearized
model” limitation of claim 1. (Final Act. 11.) Appellant contends that
Doyen does not disclose linearizing because as depicted in Figure 6 of
Doyen, the Gaussian input is not preserved in the output model, therefore the
output model cannot be linear:
the Gaussian input is combined with a non-Gaussian probability
function, to yield a non-Gaussian posterior distribution (right
plot; the right side is clearly inconsistent with a Gaussian
distribution). That is, Gaussian in, non-Gaussian out. As such,
the model is non-linear, because linear systems preserve the
Gaussian characteristics-see Specification at paragraph 61. A
simple sequential process where a Gaussian posterior at one time
step can be used as the Gaussian prior for the next time step (such
as in Eqs. 8a and 8b) does not result from Doyen, since the
posterior is not Gaussian, given the non-linear relationship.
(App. Br. 15, underline in original, italics added.) Figures 4 and 6 of Doyen
is reproduced below.
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Appeal 2015-003275
Application 12/981,945
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Figure 4 “is a graphic interpretation of the ratio between the
conditional probability distribution and the prior distribution of the primary
variable” and Figure 6 “shows the derivation of the local posterior
distribution attributable to pixel 10 of FIG. 1.” (Doyen, 4:8—16.) We agree
with the Examiner that Appellant is ignoring Figure 4, which shows that the
“Gaussians are preserved.” (Ans. 6.) Appellant also argues that the “lack of
preservation of a Gaussian distribution input to a model is evidence of
nonlinearity of the model, whereas a presence of preservation of a Gaussian
distribution input to a model is evidence of nothing.” (Reply 5.) Appellant
further argues that “preservation of a Gaussian distribution input to a model
has no relevance to these proceedings.” (Id.; see also id. 6.) Appellant has
not persuaded us of Examiner error. First, Appellant cannot have it both
ways by contending that a “Gaussian input [] combined with a non-Gaussian
probability function” will produce a “model [that] is non-linear” (App. Br.
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Appeal 2015-003275
Application 12/981,945
15, emphasis added) because the output is non-Gaussian (Figure 6) whereas
when the output is Gaussian (as shown in Figure 4), it “is evidence of
nothing” (Reply 5). Second, Appellant’s assertion in this regard is mere
attorney argument and a conclusory statement, which is unsupported by
factual evidence, and, thus is entitled to little probative value. In re Geisler,
116 F.3d 1465, 1470 (Fed. Cir. 1997); In re De Blauwe, 736 F.2d 699, 705
(Fed. Cir. 1984). In addition, the Declaration of David Rossi, the named
inventor, is also conclusory and neither explains nor elaborates on the many
conclusions made in the declaration. (May 19, 2014 Declaration of David
Rossi under 37 CFR § 1.132 (“Rossi Decl.”).) For example, paragraph
seven of the declaration states, without elaboration, that “[o]ne of ordinary
skill in the art would understand that solving Equations 8 a and 8b of the
Application does not require iterative optimization.” (Rossi Decl. 17.)
Similarly, paragraph eleven of the declaration merely states that “Doyen
does not disclose both” the linearizing and determining limitations. {Id. at
111-)
Appellant next contends that Figure 4 “simply explains one [special]
case where the non-linear model happens to land on a Gaussian output, [and]
given the Gaussian input as well as a Gaussian statistical relationship
between variables[, i]t does not reflect, inherently or expressly, that the
model is linear.” (App. Br. 15; Reply 6—7.) Appellant’s attorney argument,
which is unsupported by factual evidence, has similarly not persuaded us
that the Examiner has erred. In re Geisler, 116 F.3d at 1470; In re De
Blauwe, 736 F.2d at 705. Importantly, we agree with the Examiner’s finding
that “[n]on-linear models do not arbitrarily or happen to ‘land’ on Gaussians
[and i]t is unclear what [Appellant’s contention] even means [because]
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Appeal 2015-003275
Application 12/981,945
Applicant^ do[es] not explain how this can happen or provide evidence in
support thereof.” (Ans. 7—10, emphasis omitted.) Appellant does not offer
persuasive evidence in response nor does Appellant provide persuasive
evidence to support its contention that the output model shown in Figure 4 is
non-linear even though it is showing a Gaussian output.
“determining, using the joint mean and covariance and
the measurement and without using iterative optimization ...”
Appellant contends that “Doyen requires iterative techniques
(simulations) to solve for the posterior PDFs.” (App. Br. 15; see also Rossi
Decl. 17 (“One of ordinary skill in the art would understand that solving
Equations 8a and 8b of the Application does not require iterative
optimization.”).) We agree with the Examiner that the “claim requires
‘without iterative optimization’ not without ‘iterative techniques’
(simulations). . . .” (Ans. 10—11.) Moreover, we note that “the Board
reasonably interpreted Rule 41.37 to require more substantive arguments in
an appeal brief than a mere recitation of the claim elements and a naked
assertion that the corresponding elements were not found in the prior art.”
In re Lovin, 652 F.3d 1349, 1357 (Fed. Cir. 2011). Appellant has not
persuaded us of Examiner error.
For the foregoing reasons, we are not persuaded the Examiner erred in
rejecting claim 1 and sustain the 35 U.S.C. § 102 rejection of claim 1.
Independent claims 8 and 15 contain similar limitations at issue and
Appellant does not present separate arguments for claims 8 and 15 than
those regarding claim 1, therefore, we sustain the 35 U.S.C. § 102 rejection
of claims 8 and 15, and claims 2, 4, 5, 9, 11—14, 16, and 18—20, which
depend on either claim 1, 8, or 15, and are not argued separately. (App. Br.
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Appeal 2015-003275
Application 12/981,945
14—16.) We also sustain the 35 U.S.C. § 103 rejections of claims 21—24,
which are also not argued separately. (Id.)
DECISION
For the above reasons, we affirm the decision of the Examiner to
reject claims 1, 2, 4, 5, 8, 9, 11—16, and 18—24.
No time period for taking any subsequent action in connection with
this appeal maybe extended. 37 C.F.R. § 1.136(a)(l)(iv).
AFFIRMED
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