Ex Parte Fowler

Patent Trial and Appeal Board

Sep 11, 2014

12045056 (P.T.A.B. Sep. 11, 2014)

UNITED STATES PATENT AND TRADEMARK OFFICE
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BEFORE THE PATENT TRIAL AND APPEAL BOARD
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Ex parte JEFFREY M. FOWLER
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Appeal 2012-004397
Application 12/045,0561
Technology Center 3600
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Before BIBHU R. MOHANTY, NINA L. MEDLOCK, and
BRADLEY B. BAYAT, Administrative Patent Judges.

MEDLOCK, Administrative Patent Judge.

DECISION ON APPEAL

STATEMENT OF THE CASE
Appellant appeals under 35 U.S.C. § 134(a) from the Examiner’s final
rejection of claims 1, 3–6, 8–11, 13–16, and 18–24. We have jurisdiction
under 35 U.S.C. § 6(b).
We REVERSE.2

1 The real party in interest, identified by Appellant, is Xerox Corporation.
App. Br. 1.
2 Our decision references Appellant’s Appeal Brief (“App. Br.,” filed
May 9, 2011) and Reply Brief (“Reply Br.,” filed September 16, 2011), and
the Examiner’s Answer (“Ans.,” mailed July 22, 2011).
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CLAIMED INVENTION
Appellant’s claimed invention “generally relate[s] to design of
experiments type of experiment planning and more particularly to systems
and methods that optimize a cost function to minimize the costs between the
order in which runs occur and the factors (independent variables)”
(Spec. ¶ 1).
Claim 1, reproduced below, is illustrative of the subject matter on
appeal:
1. A computer-implemented method comprising:
using a computer, creating an experiment plan that
produces experimental results based on input variables, wherein
different runs of said experimental plan are distinguished from
one another by variations of tasks of said experiment plan and
by variations of said input variables, and said different runs of
said experimental plan produce different experimental results;
using said computer, creating a cost function for said
different runs of said experiment plan, wherein said cost
function considers an order in which said different runs occur;
using said computer, optimizing said cost function to
minimize correlations between said different experimental
results of said different runs of said experiment plan and said
order in which said different runs occur to produce an
optimized run order; and
using said computer, executing said experimental plan in
said optimized run order.

REJECTION
Claims 1, 3–6, 8–11, 13–16, and 18–24 are rejected under 35 U.S.C.
§ 103(a) as unpatentable over Gottlieb (US 2004/0107110 A1, pub. June 3,
2004), Lorenzen (US 5,253,331, iss. Oct. 12, 1993), Jain (US 5,155,679,
iss. Oct. 13, 1992), and Official Notice.
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ANALYSIS

Independent claim 1 and dependent claims 3–5 and 21
We are persuaded by Appellant’s argument that the Examiner erred in
rejecting claim 1 under 35 U.S.C. § 103(a) because neither Gottlieb nor Jain,
on which the Examiner relies, discloses or suggests
optimizing said cost function to minimize correlations between
said different experimental results of said different runs of said
experiment plan and said order in which said different runs
occur to produce an optimized run order; and using said
computer, executing said experimental plan in said optimized
run order,

as recited in claim 1 (App. Br. 17–23; see also Reply Br. 1–8).
Gottlieb is directed to a method and apparatus for optimizing the total
cost associated with transporting products on a pool of vehicles, and
describes that orders representing products are assigned to one or more
vehicles in the pool; the assignments define a sequence of pickup and
delivery activities for the vehicles (Gottlieb, Abstract). Gottlieb describes
that an initial trial assignment is generated and evaluated to determine a total
cost value. This trial assignment set is then varied (e.g., by deleting one or
more orders from one or more vehicles, inserting at least one order from the
deleted orders into a vehicle, changing the assignment for at least one
vehicle) to generate a variation set, which then is executed and its associated
cost determined. The cost of the variation set is compared to the cost of the
initial trial assignment to determine whether there is a cost improvement,
and, if so, the variation set is adopted as a new trial assignment set. The
process is then repeated until no further cost improvement is required
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(see Gottlieb ¶¶ 60–66 and 68), i.e., until a single, optimal transportation
plan is identified.
Jain similarly discloses an iterative method for identifying an optimal
sequencing of jobs in a manufacturing environment where the manufacturing
jobs have sequence-dependent set-up times (see, e.g., Jain Abstract).
Jain, thus, describes executing multiple sequences of job tasks to determine
an optimal sequence (which minimizes the number of components,
i.e., tools, switched for a given job sequence) by performing systematic
“perturbations” that involve switching a pair of jobs in the sequence
(see Jain, col. 3, l. 65 – col. 5, l. 21). “The results of the set-up optimization
exactly specify in what order each job should be performed . . . .” (Jain,
col. 6, ll. 29–32).
We agree with Appellant that neither Gottlieb nor Jain, whether
considered individually or in combination, discloses or suggests producing
an optimized order of different runs of an experimental plan and executing
the experimental plan in the optimized run order. Instead, Gottlieb and Jain
merely evaluate multiple different experimental transportation (Gottlieb) and
manufacturing job sequencing (Jain) plans, and select the optimal plan
(i.e., the single best transportation plan and the single best order of
manufacturing jobs) for execution (see App. Br. 18–20 and Reply Br. 4–5).
Claim 1 recites “optimizing said cost function . . . to produce an
optimized run order; and . . . executing said experimental plan in said
optimized run order.” Claim 1, thus, requires that multiple different runs of
the experimental plan must be executed in a specific order.
Both Gottlieb and Jain identify a single run only of the experimental
plan for execution, i.e., only the run that is determined to be optimal is
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executed. Neither reference, whether considered individually or collectively
with the other reference, discloses or suggests “optimizing said cost function
. . . to produce an optimized run order; and . . . executing said experimental
plan in said optimized run order,” as called for in claim 1.3
In view of the foregoing, we do not sustain the Examiner’s rejection
of claim 1 under 35 U.S.C. § 103(a). For the same reasons, we also do not
sustain the Examiner’s rejection of claims 3–5 and 21, which depend from
claim 1. Cf. In re Fritch, 972 F.2d 1260, 1266 (Fed. Cir. 1992)
(“[D]ependent claims are nonobvious if the independent claims from which
they depend are nonobvious.”).

Independent claims 6, 11, and 16 and dependent claims 8–10, 13–15, 18–20,
and 22–24

Independent claims 6, 11, and 16 include language substantially
similar to the language of claim 1. Therefore, we do not sustain the rejection
of claims 6, 11, and 16 under 35 U.S.C. § 103(a) for the same reasons as set
forth above with respect to claim 1. For the same reasons, we also do not
sustain the rejection of claims 8–10, 13–15, 18–20, and 22–24, each of
which depends from one of independent claims 6, 11, and 16.

3 The Examiner does not rely on Lorenzen or Official Notice as disclosing
these features.
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DECISION
The Examiner’s rejection of claims 1, 3–6, 8–11, 13–16, and 18–24
under 35 U.S.C. § 103(a) is reversed.

REVERSED

Klh