Demand Energy Networks, Inc.

Patent Trials and Appeals Board

Aug 18, 2020

2020002155 (P.T.A.B. Aug. 18, 2020)

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.
15/414,543 01/24/2017 John Michael Fife 80734/1101 9334
32642 7590 08/18/2020
STOEL RIVES LLP - SLC
201 SOUTH MAIN STREET, SUITE 1100
ONE UTAH CENTER
SALT LAKE CITY, UT 84111
EXAMINER
HENRY, MATTHEW D
ART UNIT PAPER NUMBER
3683
NOTIFICATION DATE DELIVERY MODE
08/18/2020 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):
patlaw@stoel.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
____________________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________________

Ex parte JOHN MICHAEL FIFE
__________________

Appeal 2020-002155
Application 15/414,543
Technology Center 3600
____________________

Before JAMES P. CALVE, CYNTHIA L. MURPHY, and
ROBERT J. SILVERMAN, Administrative Patent Judges.

CALVE, Administrative Patent Judge.

DECISION ON APPEAL

STATEMENT OF THE CASE
Pursuant to 35 U.S.C. § 134(a), Appellant1 appeals from the decision
of the Examiner to reject claims 1–26, which are all of the pending claims.
See Appeal Br. 1, 11. We have jurisdiction under 35 U.S.C. § 6(b).
We AFFIRM.

1 “Appellant” refers to “applicant” as defined in 37 C.F.R. § 1.42. Appellant
identifies Enel X North America, Inc. as the real party in interest. Appeal
Br. 1.
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Application 15/414,543

2
CLAIMED SUBJECT MATTER
Appellant’s disclosure relates to controllers to control an electrical
system. Spec. ¶ 2. Claims 1, 11, and 25 are independent. Claim 1 is
reproduced below.
1. A controller to optimize overall economics of
operation of an electrical system, the controller comprising:
a communication interface to connect to an electrical
system, the electrical system including one or more components
that are automatically adjustable based on a set of control
values for a set of control variables, the one or more
components comprising one or more devices selected from the
group consisting of a load, a generator, and an energy storage
system (ESS); and
one or more processors to:
determine the set of control values for the set of
control variables to effectuate a change to the one or more
components of the electrical system toward meeting a controller
objective for economical optimization of the electrical system,
the set of control values determined by the one or more
processors utilizing an optimization algorithm to identify the set
of control values in accordance with one or more constraints
and a plurality of cost elements associated with operation of the
one or more components of the electrical system, the
optimization algorithm comprising a cost function including a
sum of the plurality of cost elements, the plurality of cost
elements including at least time of use (ToU) supply charges
and demand charges;
wherein the ToU supply charges comprise charges
for electrical energy from an electrical grid based on one or
more supply rates of the electrical energy multiplied by a total
energy provided to the electrical system by the electrical grid
during one or more time windows corresponding to the one or
more supply rates,
wherein the demand charges comprise charges for
the electrical energy from the grid based on one or more
demand rates multiplied by a maximum rate of electrical energy
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provided to the electrical system by the electrical grid during
one or more corresponding demand time windows; and
automatically control operation of the one or more
components of the electrical system based on the optimization
algorithm by automatically performing, based on the
determined set of control values, at least one action selected
from the group consisting of increasing or decreasing an
amount of power consumed by the load, increasing or
decreasing generation by the generator, and charging or
discharging the ESS.
Appeal Br. 29–30 (Claims App.).
REJECTIONS
Claims 1–6, 8–12, 14–17, and 20–25 are rejected under 35 U.S.C.
§ 103 as unpatentable over Wang (US 2015/0355655 A1, pub. Dec. 10,
2015), Marhoefer (US 2009/0048716 A1, pub. Feb. 19, 2009), and Schaefer
(US 2010/0174643 A1, pub. July 8, 2010).
Claims 7, 18, and 19 are rejected under 35 U.S.C. § 103 as
unpatentable over Wang, Marhoefer, Schaefer, and Ghosh (US 2014/
0365022 A1, pub. Dec. 11, 2014).
Claims 13 and 26 are rejected under 35 U.S.C. § 103 as unpatentable
over Wang, Marhoefer, Schaefer, and Bozchalui (US 2013/0226637 A1,
pub. Aug. 29, 2013).
ANALYSIS
Claims 1–6, 8–12, 14–17, and 20–25
Rejected over Wang, Marhoefer, and Schaefer
Appellant presents the same arguments for each independent claim
and does not argue the dependent claims separately. See Appeal Br. 11–27.
We select claim 1 as the representative claim. 37 C.F.R. § 41.37(c)(1)(iv).
Claims 2–6, 8–12, 14–17, and 20–25 stand or fall with claim 1.
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Regarding claim 1, the Examiner finds that Wang teaches a method of
using a controller to optimize overall economics of operation of an electrical
system including receiving configuration elements that specify constraints of
the electrical system, defining plural cost elements for operating the system,
receiving a set of process (control) variables to be used to determine values
for the economical optimization of the system, determining control values
for the control variables by using an optimization algorithm to identify a set
of control values where the optimization algorithm comprises a cost function
including a sum of the plural cost elements, and controlling operation of the
system based on the optimization algorithm. Final Act. 5–9. The Examiner
finds that Wang lacks a communication interface to control operation of the
components automatically but Marhoefer teaches an interface and processor
that control energy optimization toward a controller objective for one or
more components based on control values that optimize energy production
and minimize energy costs. Id. at 9–11. The Examiner determines it would
have been obvious to modify Wang with Marhoefer’s automatic control to
improve efficiency of Wang’s system and better optimize electrical power
generation to ensure electrical energy is not wasted. Id. at 12; Ans. 8.
The Examiner finds that Wang, as modified, does not teach a time of
use supply charge or demand charge, but Schaefer optimizes energy usage
and minimizes costs by tracking a plurality of cost elements including time
of use supply charges and demand charges as claimed. Final Act. 12–14.
The Examiner determines it would have been obvious to modify Wang by
using Schaefer’s cost algorithm to control electrical systems to regulate
energy use by customers to ensure their needs are met while reducing the
total cost of power generation and energy use. Id. at 15; see Ans. 4–5.
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Appellant argues that Schaefer does not, in combination with the other
references, teach an optimization algorithm for a cost function that includes
time of use supply charges and demand charges. Appeal Br. 12. Appellant
also argues that Wang and Marhoefer do not teach cost elements including
time of use supply charges and demand charges, and Schaefer does not cure
this deficiency because it lacks an optimization algorithm comprising a cost
function that sums a plurality of cost elements including demand charges as
recited in claim 1. Id. at 13. Appellant further argues that Schaefer teaches
that utility companies may charge a demand charge but optimizing a demand
charge is considerably different and much harder than calculating a demand
charge as Schaefer does. Id. at 14. Appellant argues that Schaefer gathers
information to calculate a demand charge but does not include a demand
charge in a cost function of an optimization algorithm, which is much more
difficult than calculating a demand charge. Id. at 15. Appellant also asserts
that Schaefer teaches away from optimization by recording and reporting
only demand charges and teaching that prospective consideration of demand
charges is done by the user or unskilled operator and not the system. Id.
These arguments are not persuasive. Wang optimizes flexible power
generation factors/costs by using an algorithm to identify control values for
constraints with a cost function that sums costs including a flexible index of
costs. Wang ¶¶ 9–22; see Final Act. 5–9. Schaefer tracks flexible costs for
energy systems as time of use supply charges and demand charges. Schaefer
¶¶ 40–44, 116; Final Act. 5–15. Schaefer teaches to track the flexible costs
in energy systems. The Examiner proposes to improve Wang’s optimization
model by using such flexible energy costs to establish control values thereby
providing more efficient, cost-effective energy usage. See Final Act. 15.
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We do not agree that Schaefer teaches away from optimization of its
cost functions using Wang’s optimization model. See Appeal Br. 15. Wang
optimizes flexible constraints of an electrical power system by using a multi-
dimensional flexible model for power generation costs. See Wang ¶¶ 17, 18,
20–24. Wang’s model uses a flexible index of power generation costs but
does not identify specific costs to optimize. See id. ¶¶ 8–18. Because Wang
does not identify particular flexible costs, the Examiner relies on Schaefer to
teach base demand and base time of use as two flexible energy costs that are
tracked for electrical energy systems. Id. ¶¶ 40–44. The Examiner proposes
to track these flexible costs in Wang’s flexible, multi-variable optimization
model to optimize energy usage and cost by reducing peak demand through
cost adjustments. Final Act. 15; see Ans. 3–5.
Wang does not discourage using such costs in Wang’s flexible cost
optimization model. Nor does Schaefer for that matter. See In re Gurley, 27
F.3d 551, 553 (Fed. Cir. 1994) (a reference teaches away if a skilled artisan
would be led in a direction divergent from the path taken by an applicant).
The Examiner is not modifying Schaefer. The issue is whether it
would have been obvious to modify Wang’s flexible optimization model for
power generation costs, which already considers flexible cost constraints, to
optimize operations using time of use and demand energy as flexible costs.
In this regard, Wang optimizes energy costs using a flexible index of power
generation costs. Wang ¶¶ 9–22. Schaefer calculates flexible energy costs
by considering time of use and demand energy usage. Schaefer ¶¶ 40–77. A
skilled artisan reasonably would consider flexible time of use and demand
charges of Schaefer when optimizing energy usage and reducing flexible
energy costs with Wang’s model to improve optimization of flexible costs.
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We disagree with Appellant’s position that adding Schaefer’s costs to
Wang would have been beyond the level of ordinary skill in the art, because
the prior art of record evidences otherwise. For example, Wang evidences
that it would be within the ken of one of ordinary skill to incorporate cost-
related parameters when developing a multi-dimensional, flexible power
generation cost optimization model. Wang ¶¶ 17–24. Wang adjusts models
and cost factors to account for different flexible factors, including cost, to
provide a comprehensive optimization of economic efficiency, safety, and
reliability while also considering flexible power generation costs of the
system to optimize cost. See id. ¶¶ 2, 7–24.
The Examiner proposes to modify Wang’s model to sum the demand
rate charges taught as BaseDemand in Schafer and the amount of energy
consumed at a specific rate during the day taught as BaseUse in Schaefer to
generate a total energy cost in Wang. Ans. 3–5. The Examiner explains that
Schaefer teaches summing time of use and demand energy rates to generate
energy costs. Ans. 4. The Examiner cites Schaefer’s teaching of using these
costs in a controller to control and optimize energy usage and device settings
through a site management application. Schaefer ¶ 123; Ans. 4. Schaefer’s
calculation of time of use and demand energy in a control system to optimize
energy costs provides a motivation for a skilled artisan to modify Wang’s
flexible power generation cost optimization model to consider time of use
and demand energy costs among the flexible index of power generation costs
of Wang’s model for better optimization and energy efficiency control. The
consideration of such flexible costs in Wang’s flexible optimization system
would produce similar benefits in Wang. See KSR Int’l Co. v. Teleflex Inc.,
550 U.S. 398, 417 (2007).
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Appellant’s argument that Schaefer lacks an optimization algorithm
with a cost function, even if accurate, does not undermine the Examiner’s
reason to combine the teachings of Wang and Schaefer to render obvious
this claimed subject matter. Wang teaches an optimization algorithm and
flexible cost function. Schaefer teaches that usage and demand energy costs
are flexible costs that utilities consider when calculating a cost function for
an energy production system. Schaefer ¶ 40.
Appellant also argues that Wang and Marhoefer are not combinable
because Wang relates to technology in front of the meter (i.e., on the grid to
generate electrical power to the grid) whereas Marhoefer optimizes the use
or consumption of energy behind the meter for consumers of power from the
grid. Appeal Br. 24–25. Appellant argues that Wang is directed to massive,
complex generation systems, but Marhoefer is directed to small consumer
systems so combining Marhoefer’s goal of optimizing power consumption
(decreasing reliance on the grid) with Wang would render Wang inoperable
for its intended purpose of optimizing a cost of generating power because
minimizing a flow of money from a consumer to the grid, as Marhoefer
teaches, is misaligned to Wang’s purpose of maximizing profits. Id. at 25.
“If references taken in combination would produce a ‘seemingly
inoperative device,’ . . . such references teach away from the combination
and thus cannot serve as predicates for a prima facie case of obviousness.”
In re Urbanski, 809 F.3d 1237, 1243 (Fed. Cir. 2016) (quoting McGinley v.
Franklin Sports, Inc., 262 F.3d 1339, 1354 (Fed. Cir. 2001)). But we see no
reason why Wang’s optimization model and system would be rendered
inoperable merely by modifying it to include Marhoefer’s communication
interface and processor to control components of the system automatically.
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Both references optimize the mix of energy from available sources to
meet energy demands while optimizing cost. Final Act. 10–12. Therefore,
both references are concerned with the optimization of energy usage. Ans.
7–8. Marhoefer teaches a user interface that allows a user to select values
for constants, variables, and profiles used in various energy optimization
formulas and to display a system’s performance and progress towards
achieving optimization objectives. Marhoefer ¶ 96.
The Examiner reasons that incorporating Marhoefer’s interface and
automatic control into Wang’s electrical power optimization system would
better optimize Wang thereby ensuring electricity is not wasted. Ans. 8–9.
Marhoefer teaches advantages of implementing an energy cost optimization
model similar Wang’s model on a user interface, processor, and controller to
allow a user to select control values of control variables to optimize energy
usage costs. Marhoefer ¶¶ 95–99. These teachings would provide similar
benefits in Wang to optimize energy costs. See KSR, 550 U.S. at 417.
In view of these teachings, we disagree with the argument that “grid
side equipment of Wang simply cannot be modified to be controlled by
behind-the-meter equipment and teachings of Marhoefer [because] [t]he
Wang and Marhoefer systems have totally different factors and optimization
schemes and objectives.” Appeal Br. 26. Essentially, the Examiner relies
on Marhoefer’s teachings to automate Wang’s model for similar results of
controlling values of control variables to optimize energy costs.
“A reference must be considered for everything that it teaches, not
simply the described invention or a preferred embodiment.” In re Applied
Materials, Inc., 692 F.3d 1289, 1298 (Fed. Cir. 2012) (citing EWP Corp. v.
Reliance Universal Inc., 755 F.2d 898, 907 (Fed. Cir. 1985)).
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Often, it will be necessary for a court to look to interrelated
teachings of multiple patents; the effects of demands known to
the design community or present in the marketplace; and the
background knowledge possessed by a person having ordinary
skill in the art, all in order to determine whether there was an
apparent reason to combine the known elements in the fashion
claimed by the patent at issue.
KSR, 550 U.S. at 418. As a result, “[o]n the issue of obviousness, the
combined teachings of the prior art as a whole must be considered.” EWP,
755 F.2d at 907; see also KSR, 550 U.S. at 421 (“A person of ordinary skill
is also a person of ordinary creativity, not an automaton.”).
We are not persuaded by Appellant’s arguments that a skilled artisan
would not appreciate that Wang’s model to optimize energy costs could be
implemented on an interface and processor, as Marhoefer teaches, in order to
automate control. To the extent it would not have been apparent, from
Wang itself, to implement Wang’s flexible optimization model with such
generic computer components for the standard incentives of automation,
Marhoefer teaches to use such components with such multi-variable energy
optimization models as a way to automate and operate more efficiently to
minimize total energy cost. Marhoefer ¶¶ 2, 20–23, 95–99, Fig. 3.
Appellant argues that Schaefer teaches an equation not a cost function
that establishes a relationship between multiple variables. Reply Br. 4–6.
Inasmuch as there is even a prior-art-distinguishing difference between an
equation and a function, this argument is not persuasive because the alleged
equation in paragraph 40 of Schaefer is in the format VBase (period), which
Schaefer describes as a baseline primary energy billed value for a period of
time in dollars. Schaefer ¶¶ 40, 41. Thus, it establishes a relationship
between two variables, namely, a billed value and a period of time. Id.
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Appellant’s Specification provides no example of a cost function to
guide our interpretation of the claim language. A multivariate optimization
algorithm is disclosed at paragraph 89, but no cost function is identified.
Paragraph 91 indicates that costs and benefits can be summed into a net cost
function, which may be referred to as the “cost function” but no exemplary
cost function is described. Paragraph 93 indicates that a cost fc(X) may
consider control parameter values in X. Appellant’s Figure 10 illustrates
Cost Function fc(X) 102 with no further description. Thus, the Examiner’s
finding that Schaefer teaches a cost function is consistent with the broadest
reasonable interpretation of the term in light of the Specification.
Nor are we persuaded that Schaefer does not control electrical systems
and costs. See Reply Br. 6; Appeal Br. 13–15. Schaefer uses data from web
based server software to alter device settings and control devices to avoid
unacceptable energy costs. Schaefer ¶ 123 (“data from the web based server
software can be sent through the site gateway to the same devices to alter
their settings”). Schaefer predicts threshold levels and has means to control
energy devices and settings to reduce energy costs similar to Wang’s energy
cost optimization model. See id.; Ans. 4–5.
Thus, we sustain the rejection of claim 1 and claims 2–6, 8–12, 14–17,
and 20–25, which fall with claim 1. See Appeal Br. 27.
Claims 7, 13, 18, 19, and 26
The Examiner rejects claims 7, 13, 18, 19, and 26 over Wang,
Marhoefer, Schaefer, and either Ghosh or Bozchalui. See Final Act. 27–34.
Appellant argues that Ghosh and Bozchalui do not cure the deficiencies of
Wang, Marhoefer, and Schaefer as to claims 1, 11, and 25 from which
claims 7, 13, 18, 19, and 26 depend respectively. Appeal Br. 16, 20, 23, 27.
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Because we sustain the rejection of independent claims 1, 11, and 25,
these arguments are not persuasive. Thus, we also sustain the rejection of
dependent claims 7, 13, 18, 19, and 26.
CONCLUSION
In summary:
Claims
Rejected
35
U.S.C. §
Reference(s)/
Basis
Affirmed Reversed
1–6, 8–12,
14–17, 20–
25
103 Wang, Marhoefer,
Schaefer
1–6, 8–12,
14–17, 20–
25

7, 18, 19 103 Wang, Marhoefer,
Schaefer, Ghosh
7, 18, 19
13, 26 103 Wang, Marhoefer,
Schaefer, Bozchalui
13, 26
Overall
Outcome
1–26
No time period for taking any subsequent action in connection with
this appeal may be extended under 37 C.F.R. § 1.136(a). See 37 C.F.R.
§ 1.136(a)(1)(iv).
AFFIRMED