Ex Parte Cascia et al

Patent Trial and Appeal Board

Jul 11, 2013

11001397 (P.T.A.B. Jul. 11, 2013)

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.
11/001,397 12/01/2004 Mark Cascia 2004P03160US01 6831
28524 7590 07/11/2013
SIEMENS CORPORATION
INTELLECTUAL PROPERTY DEPARTMENT
170 WOOD AVENUE SOUTH
ISELIN, NJ 08830
EXAMINER
LUU, CUONG V
ART UNIT PAPER NUMBER
2128
MAIL DATE DELIVERY MODE
07/11/2013 PAPER
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.
PTOL-90A (Rev. 04/07)

UNITED STATES PATENT AND TRADEMARK OFFICE
____________________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________________

Ex parte MARK CASCIA and OSMAN AHMED
____________________

Appeal 2010-008795
Application 11/001,397
Technology Center 2100
____________________

Before ALLEN R. MacDONALD, ROBERT E. NAPPI and
MIRIAM L. QUINN, Administrative Patent Judges.

MacDONALD, Administrative Patent Judge.

DECISION ON APPEAL

Appeal 2010-008795
Application 11/001,397

2
STATEMENT OF CASE1
Appellants appeal under 35 U.S.C. § 134(a) from a final rejection of
claims 1-20 and 22-25. We have jurisdiction under 35 U.S.C. § 6(b).
Exemplary Claims
Exemplary claims 1, 6, 13, and 19 under appeal read as follows
(emphasis added):
1. A system for optimizing global set points for a building
environmental management system comprising a computing device
configured to execute:
a system model for modeling components of a thermal plant;
an objective function for modeling a parameter of the thermal
plant; and
an optimization engine for generating at least one set point for
a measurable quantity of the environmental management system by
optimizing the parameter modeled by the objective function.

1 We have decided the appeal before us. However, should there be further
prosecution with respect to claims 1-20 and 22-25; the Examiner’s attention
is directed to the following:
(1) The Director recently issued guidance on 35 U.S.C. § 112 at 76
Fed. Reg. 7162, 7170-71 (Feb. 9, 2011) at Part 2.I.; Suppl. Examination
Guidelines for Determining Compliance with 35 U.S.C. 112 and for
Treatment of Related Issues in Patent Applications. With respect to the
claimed embodiment disclosed at pages 24-28 of Appellants’ Specification,
particular attention should be paid to the Director’s guidance as to (a)
LizardTech, Inc. v. Earth Res. Mapping, Inc., 424 F.3d 1336, 1343-46 (Fed.
Cir. 2005); and (b) Ariad Pharms., Inc. v. Eli Lilly & Co, 598 F.3d 1336
(Fed. Cir. 2010)(en banc).
(2) The Appellants repeatedly state (Appellants’ Admissions infra)
that the claimed embodiment disclosed at pages 9-24 is a mathematical
model which produces a set point value. Particular attention should be paid
to Parker v. Flook, 437 U.S. 584 (1978) (The Court concluded that a method
for updating the value of at least one alarm limit was unpatentable subject
matter under § 101.).
Appeal 2010-008795
Application 11/001,397

3
Claim 6. The system of claim 1 wherein the system model
comprises a neural network to model the thermal plant;
the objective function being a cost function for operating the
thermal plant; and
the optimization engine is a genetic algorithm for generating
optimized set points for minimizing the operation of the thermal plant.
Claim 13. A method for optimizing global set points for a
building environmental management system comprising:
modeling components of a thermal plant;
modeling a parameter of the thermal plant; and
generating at least one set point for a measurable quantity of
the building environmental system by optimizing the parameter
modeled by the objective function.
Claim 19. The method of claim 13, the thermal plant modeling
including:
model the thermal plant with a fuzzy logic system;
selecting a cost function for thermal plant parameter; and
generating with a genetic algorithm the optimized set points
for minimizing the operation of the thermal plant.
Rejection
The Examiner rejected claims 1-7, 9, 13-19, and 25 under 35 U.S.C.
§ 103(a) as being unpatentable over the combination of Sakamoto (US
6,223,101 B1) and Turner (US 2002/0072828 Al).2
The Examiner rejected claims 8, 10-12, 20, and 22-24 under
35 U.S.C. § 103(a) as being unpatentable over the combination of Sakamoto,
Turner, and Singh (US 2004/0159113 A1).3

2 Separate patentability is not argued for claims 2-4, 7, 9, 13-19, and 25.
Except for our ultimate decision, these claims are not discussed further
herein.
Appeal 2010-008795
Application 11/001,397

4
Appellants’ Admissions
1. Appellants state:
The system model 14 includes models for thermal plant
system components that may be implemented using classical
models or artificial intelligence models. Classical models are
those models that are implemented using linear programming,
unconstrained non-linear programming, or constrained non-
linear programming methodologies. Classical models are
computer programs that implement a set of mathematical
equations for modeling the components of the thermal plant.
The objective function is a computer program that implements a
polynomial equation for plant power as a function of the
change in the temperature of chilled water, although other
thermal plant parameters may be selected. The optimization
engine in a classical embodiment is a computer program that
computes a minimum using the first derivative of the objective
function with respect to the selected thermal plant parameter as
the criterion.
(Spec. 10:10-20)(emphasis added).
2. Appellants state:
In a classical implementation of the system 10, the
mathematical relationships between a key thermal plant input,
such as the chilled water differential temperature, and the
thermal plant outputs, such as chiller power, chilled water
pumps, and air handlers, are used to determine optimal set
points. These mathematical relationships and thermal plant
characterization factors, which may be determined through
regression analysis of building data collected from a remote
site, are used to define an optimization function that is
optimized by the optimization engine to determine optimal set
points for the thermal plant.

3 Appellants do not argue this rejection. Because this separate rejection is
not substantively argued, we reach the same result for these claims as we do
for claim 1. Except for our ultimate decision, these claims are not discussed
further herein.
Appeal 2010-008795
Application 11/001,397

5
(Spec. 12:12-19)(emphasis added).
3. Appellants state:
Classical optimization methods may be used to calculate
the optimal chilled water or hot water delta T (ΔTchw or ΔThw),
condenser water delta T (ΔTcw), and the air-side delta T (ΔTair)
from mathematical models of the chillers, boilers, pumps,
cooling towers, and air handler fans.
(Spec. 15:6-9)(emphasis added).
4. Appellants state:
As is well known, a control system typically operates with
three types of variables: set points, process values, and control
outputs. Set points are "desired values" for a variable quantity,
such as air temperature, humidity, pressure, etc. Process values
represent an actual condition of the system, and may be, for
example, a temperature, humidity or pressure measurement.
Control outputs are values calculated based on the difference
between the set points (desired conditions) and the process
values (actual conditions).
(App. Br. 7:16-22)(emphasis added).
Appellants’ Contention
1. Appellants contend that the Examiner erred in rejecting claim 1
under 35 U.S.C. § 103(a) because “the Examiner has provided no
motivation, suggestion or reason, and certainly no clear articulation of any
reason, to modify the specific operations of Sakamoto to identify set points
as mentioned in paragraph 0016 of Turner.” (App. Br. 9). Appellants further
contend that “it is not clear why one would modify the methods of Sakamoto
to generate set points.” (App. Br. 10).
Appeal 2010-008795
Application 11/001,397

6
2. Appellants contend that the Examiner erred in rejecting claim 1
under 35 U.S.C. § 103(a):
Because Sakamoto does not teach or suggest use of its
genetic algorithm to develop set points, and because Turner
teaches a different method for generating set points, it would
appear that one of skill in the art would use the Turner process
to generate optimized set points for Sakamoto. As discussed
above, such a modification would not arrive at the invention.
(App. Br. 10-11).
3. Appellants contend that the Examiner erred in rejecting claim 1
under 35 U.S.C. § 103(a) because “the Final Office Action provides no
explanation as to what set point in Sakamoto would be optimized.” (App.
Br. 11).
4. Appellants contend that the Examiner erred in rejecting claim 1
under 35 U.S.C. § 103(a):
In any event, even if there is an inherent motivation to
have optimized set points, the Examiner simply has not clearly
articulated how Sakamoto would be modified to arrive at the
claimed invention, and whether there [is] any reason to make
such a modification. Optimized set points may be obtained in
Sakamoto in various ways that do not arrive at the invention, as
clearly exemplified by Turner.
(App. Br. 11).
5. Appellants contend that the Examiner erred in rejecting claim 5
under 35 U.S.C. § 103(a) “because the Examiner has not provided any
reason, much less clearly articulated reasons, for modifying Sakamoto to
incorporate an unconstrained non-linear program to model a thermal plant as
allegedly taught by Turner.” (App. Br. 12)(emphasis added).
Appeal 2010-008795
Application 11/001,397

7
6. Appellants contend that the Examiner erred in rejecting claim 6
under 35 U.S.C. § 103(a) “because the Examiner has not provided any
reason, much less clearly articulated reasons, for modifying Sakamoto to
incorporate a neural network to model a thermal plant as allegedly taught by
Turner.” (App. Br. 13)(emphasis added).

Issues on Appeal
Did the Examiner err in rejecting claim 1 as being obvious?
Did the Examiner err in rejecting claim 5 as being obvious?
Did the Examiner err in rejecting claim 6 as being obvious?

ANALYSIS
We have reviewed the Examiner’s rejections in light of Appellants’
arguments (Appeal Brief and Reply Brief) that the Examiner has erred.
We disagree with Appellants’ conclusions. We adopt as our own
(1) the findings and reasons set forth by the Examiner in the action from
which this appeal is taken and (2) the reasons set forth by the Examiner in
the Examiner’s Answer in response to Appellants’ Appeal Brief. We concur
with the conclusions reached by the Examiner. We highlight the following
for emphasis.
As to Appellants’ above contentions 1, 2, and 4, we disagree that the
Examiner has erred. Appellants’ argument is essentially that Sakamoto’s
silence as to the details of its “operation plan” means that set points are not
used. We disagree. Rather, we conclude that Sakamoto’s silence would
motivate an artisan to turn to the teachings of the art such as Turner which,
as pointed out by the Examiner (Ans. 4), teaches generating set points in
Appeal 2010-008795
Application 11/001,397

8
controlled systems such that of Sakamoto. We further, agree with the
Examiner that an artisan would know that the parameter optimization
techniques of Sakamoto could be used to optimize such set points.
Appellants’ argument that the Turner optimization technique would not
arrive at the claimed invention does not show error because, while it could
alternatively be used to generate optimum set points (as could numerous
other techniques), it was not the basis of the Examiner’s rejection of claim 1.
As to Appellants’ above contention 3, we disagree that the Examiner
has erred. Appellants’ claim 1 recites “generating at least one set point”
without specifying any particular set point. While the Examiner’s rejection
is required to articulate why it is obvious to optimize set points generally
(and has done so), the Examiner is not required to additionally articulate
why it is obvious to optimize particular set points which are not claimed.
As to Appellants’ above contentions 5 and 6, we disagree that the
Examiner has erred. Appellants’ argument is essentially that using a known
alternative is not a sufficient reason under 35 U.S.C. § 103. We disagree.
Use of a known alternative that performs the same function is indicative of
obviousness. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007)
(“[W]hen a patent claims a structure already known in the prior art that is
altered by the mere substitution of one element for another known in the
field, the combination must do more than yield a predictable result.”).

CONCLUSIONS
(1) The Examiner has not erred in rejecting claims 1-20 and 22-25 as
being unpatentable under 35 U.S.C. § 103(a).
(2) Claims 1-20 and 22-25 are not patentable.
Appeal 2010-008795
Application 11/001,397

9

DECISION
The Examiner’s rejections of claims 1-20 and 22-25 are affirmed.

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)(iv).

AFFIRMED

msc