Cushing, Vincent J. et al.

Patent Trials and Appeals Board

Jan 15, 2020

13405153 - (D) (P.T.A.B. Jan. 15, 2020)

UNITED STATES PATENT AND TRADEMARK OFFICE
UNITED STATES DEPARTMENT OF COMMERCE
United States Patent and Trademark Office
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APPLICATION NO. FILING DATE FIRST NAMED INVENTOR ATTORNEY DOCKET NO. CONFIRMATION NO.
13/405,153 02/24/2012 Vincent J. Cushing A202884 6703
23373 7590 01/15/2020
SUGHRUE MION, PLLC
2000 PENNSYLVANIA AVENUE, N.W.
SUITE 900
WASHINGTON, DC 20006
EXAMINER
KONERU, SUJAY
ART UNIT PAPER NUMBER
3624
NOTIFICATION DATE DELIVERY MODE
01/15/2020 ELECTRONIC
Please find below and/or attached an Office communication concerning this application or proceeding.
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PTOL-90A (Rev. 04/07)

UNITED STATES PATENT AND TRADEMARK OFFICE
____________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________

Ex parte VINCENT J. CUSHING and GREGOR P. HENZE

Appeal 2019-001268
Application 13/405,153
Technology Center 3600

Before DONALD E. ADAMS, RICHARD M. LEBOVITZ, and
MICHAEL A. VALEK, Administrative Patent Judges.

LEBOVITZ, Administrative Patent Judge.

DECISION ON APPEAL
The Examiner rejected the claims under 35 U.S.C. § 103 as obvious
and under 35 U.S.C. § 112 as failing to comply with the written description
requirement. Pursuant to 35 U.S.C. § 134(a), Appellant1 appeals from the
Examiner’s decision to reject the claims. We have jurisdiction under
35 U.S.C. § 6(b).
We AFFIRM.

1 We use the word “Appellant” to refer to “applicant” as defined in 37
C.F.R. § 1.42. Appellant identifies the real party in interest as QCoefficient,
Inc. Appeal Br. 2.
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STATEMENT OF THE CASE
Claims 21–31 and 37–58 stand finally rejected by the Examiner as
follows:
Claims 21–31 and 37–58 under 35 U.S.C. § 103(a) as obvious in view
of Drees et al. (US 2011/0047418 A1, published Feb. 24, 2011) (“Drees”),
Brickfield et al. (US 2008/0177423 A1, published July 24, 2008)
(“Brickfield”), and Crabtree et al. (US 2010/0217550 A1, published Aug.
26, 2010) (“Crabtree”). Final Act. 7.
Claims 21–31 and 37–58 under 35 U.S.C. § 112(a) or 35 U.S.C. § 112
(pre-AIA), first paragraph, as failing to comply with the written description
requirement. Final Act. 6.
There are three independent claims on appeal, claims 21, 37, and 48.
Claim 21 is illustrative and is reproduced below (bracketed numbers have
been added for reference to the steps recited in the claim):
21. A method of managing energy consumption in a portfolio of
facilities, each facility having at least one controllable energy-
consuming component and at least one energy response
attribute, the method comprising the steps of:
[1] unbundling energy response attributes from each of
the facilities;
[2] rebundling the unbundled attributes into aggregations,
with at least one said aggregation including attributes from
plural facilities and representing a synthetic resource;
[3] predicting energy consumptive behavior of said
synthetic resource in response to possible future stimuli and
based on said attributes included in said synthetic resource; and
[4] controlling the components of each building, via a
processor-based control system, to support deployment of said
synthetic resource in at least one market;
[5] wherein at least one of said unbundling and
rebundling steps are optimized with respect to at least one
objective.
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OBVIOUSNESS REJECTION
The Examiner found that Drees describes all five steps of the claimed
method, but stated that although Drees shows bundling of attributes into
aggregations as recited in steps [1] and [2] of claim 21, it may not be
“explicit” that it is energy response attributes that are aggregated in Drees’s
method. Final Act 7–8. The Examiner found, however, that Brickfield
discloses energy response attributes that are rebundled as in claim 21. Id. at
8.
The Examiner determined that it would have been obvious to one of
ordinary skill in the art at the time of the invention to combine the teachings
of Drees and Brickfield “because using such attributes in energy
consumption decisions provides an opportunity to maximize energy
curtailment for buildings and optimize resources and efficiency.” Final Act.
9. The Examiner also stated it would have been obvious to use Brickfield’s
“method for automatic energy management and energy consumption
reduction, especially in commercial and multi-building systems” in Drees’s
system for its known and expected advantages. Id.
Crabtree was further cited by the Examiner for describing
“aggregation including attributes from plural facilities and representing a
synthetic resource” as recited in step [2] of claim 21 (“rebundling”) and
further managing the synthetic resources as in step [3] and [4] of claim 21.
Final Act. 9–10. The Examiner determined it would have been obvious to
one of ordinary skill in the art at the time of the invention to combine
Crabtree’s teachings with Drees and Brickfield “because integrating
aggregation data from a plural facilities and representing a synthetic
resource would enable better allocation of resources and thus lead to
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optimization of transmission and distribution of the grid system and improve
management of such markets.” Id. at 11.

Energy response attributes
Appellant argues that Drees does not describe “energy response
attributes.” Appeal Br. 15–17. Appellant also states that there is “no
discussion” in Brickfield “of energy response attributes, but in any event
applicant herein does not contest that energy response attributes of buildings
and of collections of buildings was a known concept in a demand response
system.” Appeal Br. 24.
We begin with determining the meaning of the term “energy response
attribute.” The term is not expressly defined in the Specification, but
Appellant contends that it means “‘an attribute of a facility that affects how
the energy consumption of the facility can be made to change in response to
a stimuli.’” Appeal Br. 13. Appellant states that the “response that the
invention is particularly interested in is the change in energy consumption.”
Id. at 12.
The Specification discloses examples of energy response attributes,
such as “the ability of an HVAC system to quickly change its operation and
so its electric load,” thermal inertia, and cold water. Spec. ¶¶ 53–54; see
also Appeal Br. 7. Appellant states that the latter “impact how the energy
consumption of facility can be made to respond” to a stimulus. Appeal Br.
9. As an example, Appellant describes how changing the set point of the
HVAC system trigger changes in the energy consumption of the building.
Id. at 12. Claim 29, which depends from claim 21, recites that at least one of
the “energy response attributes” comprises “an electric power response of an
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HVAC system or component thereof to a change of at least one of: an
HVAC system setpoint, HVAC system controller setting, or electric power
supply frequency.” Appellant further explains:
So in a system such as described above, changing the
thermostat setting is a stimulus that will provoke a response in
the form of a change in the energy consumptive behavior of the
building. And the response that is invoked will depend on
attributes of the building such as the heat retention properties of
the building (building construction), the efficiency of the
HVAC system technology, and others. In the context of a
single building, this would amount to “predicting energy
consumptive behavior of said building in response to possible
future stimuli and based on said attributes included in said
building.”
Appeal Br. 12.
We find that Appellant’s definition of “energy response attributes” is
the broadest reasonable interpretation of that term, as would be understood
in light of the Specification, and we adopt it here.
Appellant’s argument with respect to Drees is that Drees describes
energy consumption of a building, but not an energy response attribute as
required by claim 21. Appeal Br. 21.
We are not persuaded by Appellant’s argument.
Drees discloses that, in response to an employee “badging in” at
parking garage, the system controllers can “provide new setpoints to an
HVAC control algorithm of the HVAC subsystem.” Drees ¶ 38. This is the
same response attribute recited in claim 29.
Drees also discloses:
This configuration may advantageously provide much less
disruptive demand response behavior than conventional
systems. For example, the integrated control layer 116 may be
configured to assure that a demand response-driven upward
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adjustment to the setpoint for chilled water temperature (or
another component that directly or indirectly affects
temperature) does not result in an increase in fan energy (or
other energy used to cool a space) that would result in greater
total building energy use than was saved at the chiller. The
integrated control layer 116 may also be configured to provide
feedback to the demand response layer 112 so that the demand
response layer 112 may check that constraints (e.g.,
temperature, lighting levels, etc.) are properly maintained even
while demanded load shedding is in progress.
Drees ¶ 40 (emphasis added).
Thus, a set point for “chilled water” temperature is adjusted (“demand
response-drive upward adjustment”) and then the building response to the
adjustment (“increase in fan energy (or other energy used to cool a space”) is
then determined by the “total building energy use.” Id. As discussed above,
the Specification expressly discloses “cold water” as an energy response
attribute. Spec. ¶¶ 54, 55; Appeal Br. 7. Drees teaches adjusting the
temperature of cold water and determining how the “total building energy
use” is affected by it. Drees ¶ 40 (see Final Act. 14 referencing paragraph
40 of Drees). Drees also discloses managing “thermal energy storage” in a
collection of buildings (Drees ¶ 92), which is also an energy response
attribute recited in the Specification (Appeal Br. 7). Accordingly, we find
that Drees discloses “energy response attributes” as recited in claim 21.
Brickfield also discloses energy response attributes. In paragraph
101, Brickfield expressly teaches that its intelligent agents “monitor each
piece of equipment, forming a non-parametric model of its behavior,
allowing accurate predictions of the impact that specific energy control
actions will have on the building environment.” Brickfield ¶ 101. The
“control action” is therefore a “stimulus” that “affects how the energy
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consumption of the facility can be made to change,” meeting Appellant’s
definition of an energy response attribute (see definition on page 13 of the
Appeal Brief; supra at p. 4).
More specifically, Brickfield discloses:
When hundreds of energy-consuming devices such as air
handlers, chillers and lighting systems are covered by the
system, minor adjustments to each one can have a significant
impact on overall energy consumption. For example, the
system can meet energy reduction goals by raising the
temperature in unoccupied rooms from 70 degrees to 75
degrees.
Brickfield ¶ 120 (emphasis added).
Brickfield further explains:
At any point in the month when peak loads approach the preset
threshold, the intelligent agents in the invention can choose
from a wide variety of available strategies to prevent crossing
the line, such as raising (or lowering) thermostats throughout a
building(s); dimming lights; etc. However, if executing a
certain strategy would violate another parameter or other
parameters set by the customer (e.g., that temperature must
never go beyond a certain threshold or that lights cannot be
dimmed below defined lumens, etc.), then the intelligent agents
of the invention will either employ another strategy for
reducing peak load demand, or notify the customer that the goal
cannot be achieved.
Brickfield ¶ 121 (emphasis added).
Brickfield thus teaches setting a threshold (temperature in an
unoccupied room, thermostats, amount a light is dimmed) and then
determining how the response affects the “overall energy consumption.”
Brickfield ¶¶ 120, 121. Brickfield is therefore not only considering “energy
consumption” as asserted by Appellant (Appeal Br. 24), but is also
determining how the “overall energy consumption” changes in response to
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regulating the attribute (thermal mass of the room when temperature is
raised from 70 degrees to 75 degrees) (Brickfield ¶ 120). Appellant
specifically stated that the “response that the invention is particularly
interested in is the change in energy consumption.” Appeal Br. 12.
Brickfield also discloses that “the building managers can also
optionally simulate one or more ‘what if’ scenarios, using the intelligent
agents, to predict the effect of control actions on the energy bill and the
comfort in the building. Brickfield 109 (emphasis added). Brickfield
therefore is not only looking at energy consumption of a specific device, but
rather how the facility responds in energy consumption to a control action or
stimulus applied to the device. Brickfield is therefore also “predicting
energy consumptive behavior of said synthetic resource in response to
possible future stimuli” as recited in step [3] of claim 21 (Brickfield ¶ 109:
“predicting the effect of control actions”).
In sum, both Drees and Brickfield describe “an attribute of a facility”
(HVAC, cold water, thermal mass (temperature of a room)) as affecting “the
energy consumption of the facility . . . in response” to the “stimuli” of
changing a set point of the attribute, meeting the definition of “energy
response attribute” articulated by Appellant (Appeal Br. 13) and which we
have adopted.

Unbundling and Rebundling Energy Response Attributes
Step [1] of claim 21 recites “unbundling energy response attributes
from each of the facilities.” In step [2], the attributes are rebundled into
“aggregations.”
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The Specification discloses that a facility can include industrial plants
and commercial buildings. Spec. ¶ 31. The term “unbundling” is not
expressly defined in the Specification, but Appellant states it is described in
Figs 4–5 and paragraph 50 of the Specification. Appeal Br. 7. Figure 4 of
the Specification shows a number of facilities which are unbundled by an
“Unbundling Algorithm” into attributes. The Specification teaches that each
facility has numerous attributes which are unbundled from one another.
Spec. ¶ 50, Fig. 5. The Specification describes these attributes as “facility
features or capabilities that may affect energy usage over time periods of
seconds to weeks.” Spec. ¶ 52. The Specification teaches “attributes from
each facility may be unbundled and rebundled into synthetic resources for
deployment as an aggregation 204 or portfolio of facility parts.” Spec. ¶ 33.
“The unbundled parts from multiple facilities may be mathematically and
optimally rebundled into synthetic resources that match selected markets for
optimal deployment to those markets.” Id. Thus, the “unbundling” is
separating the attributes from one or more buildings; the rebundling” is
aggregating or grouping the separated attributes into a new aggregate or
grouping.
The Examiner found that “Drees shows bundling attributes into
aggregation,” but further relied on Brickfield as teaching energy response
attributes that are aggregated. Final Act. 8.
As explained above, Brickfield describes energy response attributes.
Brickfield teaches unbundling the attributes and then rebundling them. For
example, Brickfield considers “buildings 10, 10A, 10B,” which serve as the
“facilities” of claim 21. Brickfield ¶ 102. Brickfield teaches:
Discomfort or inconvenience to occupants or users of buildings
10, 10A, 10B is considered and included in the data processing
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or computer system 25 so that a particular energy-using device
in the plurality of devices 11, 11A, l1B will not be curtailed in
its energy use in a manner that would cause discomfort or
negative impact. Thus, certain energy-using devices (such as
computer equipment, hospital equipment, etc.) are treated
differentially and intelligently so as not to be subjected to
energy curtailment in the same manner as other energy-using
devices, while other energy-using devices that are otherwise
identical but in different buildings may be subjected to different
energy curtailment based on time of day and occupancy or the
like in the respective buildings. Thus, if building 10 and
building 10A are in different time zones but otherwise have a
similar set of respective devices 11, 11A, they may be
controlled appropriately and in a maximally energy-intelligent
manner.
Brickfield ¶ 102.
Further, Brickfield teaches that its intelligent agents “can leverage
multiple devices” and that “our agents control devices, such as HVAC or
lighting across a whole portfolio of buildings.” Brickfield ¶¶ 217, 228.
Thus, Brickfield describes “bundling” devices, as that term is used in the
claims, from a plurality of buildings and controlling them by an intelligent
agent.
While Brickfield doesn’t expressly disclose the devices are
unbundled, because each device is originally part of one building, it must be
unbundled from the facility in order for it to be rebundled into a different
aggregation as described by Brickfield. For these reasons, we are not
persuaded by Appellant’s argument that “Brickfield notably does not suggest
is the unbundling of attributes from the buildings themselves” (Appeal Br.
24), i.e., because it would be reasonably understood from Brickfield’s
teaching that the attributes are unbundled from the buildings in order for
them to be rebundled and controlled by an intelligent agent.
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Brickfield teaches that the energy response to controlling the
aggregated devices (the “stimulus” in Appellant’s definition of “energy
response attribute”) is measured as a goal, e.g., to save a certain amount of
energy:
For example, a building manager may use the system of FIG. 5
to monitor the current state of devices 11, 11A, 11B in the
buildings 10, 10A, 10B. . . . The building managers can also
optionally simulate one or more “what if” scenarios, using the
intelligent agents, to predict the effect of control actions on the
energy bill and the comfort in the building. Building managers
optionally may manipulate the parameters of the intelligent
agents, such as by constraining the temperature band used by a
“supply air rest” agent. The building manager no longer needs
to control individual devices (as he would conventionally do)
because the intelligent use of energy system of FIG. 5 is “goal
based”. The manager gives the system a goal (such as to save
40 KW in the next two hours) and the intelligent use of energy
system of FIG. 5 determines how to best achieve the goal.
Brickfield ¶ 109 (emphasis added).
While Brickfield does not explicitly state in the paragraph reproduced
above that the aggregated devices are monitored and controlled, Brickfield
in paragraphs 102, 217, and 228 refer to controlling multiple devices, so it
would be recognized by the ordinary skilled worker that the devices can be
controlled as in paragraph 109. Furthermore, the table in paragraph 217
expressly states that an advantage of “Control over multiple buildings” is
that “Agents can leverage multiple devices and therefore have more degrees
of freedom in finding the best compromise between savings and comfort.” It
is clear from this disclosure that Brickfield is aggregating multiple devices
from more than one building, controlling them, and determining the
response in terms of energy usage (“savings”). The device itself is not the
“energy response attribute,” but Brickfield teaches that the change in control
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of the device “affects how the energy consumption of the facility” is
changed (Appeal Br. 13), and therefore the device control and aggregation
meets the definition of “energy response attribute” as set forth by Appellant.
Thus, while Brickfield unbundles and rebundles devices, the devices have
attributes; the Specification refers to cold water and humidity as attributes
(Spec., Fig. 7), but how can humidity be unbundled from a building, if not
by unbundling a device which produces or contains humidity?
The disclosure that multiple devices are leveraged, in our opinion,
coupled with disclosure in Brickfield that decisions are made using artificial
intelligence and neural networks (Brickfield ¶ 105) reasonably suggests that
different devices and energy response attributes can be leveraged differently,
and in different aggregates, depending on the energy demands. Indeed, the
teaching that the system intelligently determines how best to achieve a goal
of reducing energy consumption by controlling devices (Brickfield ¶ 109),
indicates that the intelligent system may bundle and then rebundle different
devices to achieve the desired goal.
Consistently, Brickfield distinguishes its system from “traditional”
management systems by explaining that “our agents control devices, such as
HVAC or lighting across a whole portfolio of buildings” which “prevents
the agents from merely finding local maxima but allow them to globally
optimize” and “equips the agents with increased degrees of freedom in
balancing energy savings requirements with tenant comfort across
buildings.” Brickfield ¶ 228.
Appellant admits that both Drees and Brickfield “are directed to
control of energy-consuming devices in buildings” that implement “control
with at least some knowledge of how the energy consumption will respond
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to the control.” Appeal Br. 24. However, Appellant contends that “the only
‘aggregation’ mentioned in the prior art is aggregation of some category of
data (i.e., the frequency of a particular fault) for purposes of display,
analysis, monitoring, etc.” Id. This argument is not persuasive for the
reason discussed above that Brickfield describes aggregation of attributes.
Brickfield ¶¶ 102, 103, 109, 217, 228.

Does Crabtree teach or suggest bundling energy consuming components of
facilities?
The Examiner further cited Crabtree as describing a “synthetic
resource” as recited in claim 21. Final Act. 9–10. A “synthetic resource” is
described in the Specification as an aggregation of attributes (Spec. ¶ 33: “in
Figure 2 the system’s 200 various attributes from each facility may be
unbundled and rebundled into synthetic resources for deployment as an
aggregation 204 or portfolio of facility parts.”). As explained above,
Brickfield teaches aggregation of attributes and therefore alone meets the
claimed limited.
The Examiner cited paragraphs 27, 63, 73, 78, 88, 91, 94, 108, 117 of
Crabtree as disclosing aggregating attributes into a synthetic resource and
predicting energy consumption of the resource. Final Act. 9–10.
Crabtree describes response profiles which “reflects an amount of
load likely to be actually reduced (or generated) by a user, when requested.”
Crabtree ¶ 61. For example, a user could agree to turn off a device.
Crabtree ¶ 62. This constitutes an attribute because the “amount of load”
that is reduced is a response to a control action or stimulus (e.g., turning the
device off). The energy response profiles are aggregated in Crabtree: “For
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example, a response package made up of the collected response profiles of
10,000 consumers might be expected to yield 1.5 MWh (megawatt hours) of
load reduction during a particular 15-minute peak load period.” Crabtree
¶ 63. Crabtree also describes “neighborhood energy management system”
that “coordinate among the participating neighborhood residents’ premise
iNodes 710 to, for example, coordinate the starting of heat pumps and air
conditioning compressors during periods of high heat load (which are
usually also periods of high electricity demand).” Crabtree ¶ 78. Crabtree
further teaches that the energy consumption from all the facilities (i.e.,
houses in a neighborhood) is “coordinate[d] to ensure that the overall energy
usage of a particular neighborhood does not exceed some specified limit.”
Id.
Paragraph 88 of Crabtree (emphasis added) also discloses bundling of
attributes from users and predicting responses: “For example, statistics
server 1030 can calculate the expected load reduction to be delivered by a
single end user or a collection of end users on receipt of a request for a
reduction in load.”
Therefore, the evidence supports the Examiner’s finding that Crabtree
describes bundling and rebundling as in steps [1] and [2] of claim 21.
Appellant argues that “the aggregations discussed by Crabtree are
aggregations of complete buildings, not attributes that have been unbundled
from the buildings.” Appeal Br. 25. We do not agree with this statement
because Crabtree teaches aggregation of heat pumps and air conditioners
(Crabtree ¶ 78) which are not “buildings” as stated by Appellant, but
attributes of the buildings that have been aggregated together, e.g., as part of
a response package (Crabtree ¶ 63) and then “coordinate[d] to ensure that
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the overall energy usage of a particular neighborhood does not exceed some
specified limit” (Crabtree ¶ 78).
Crabtree is also cited at paragraphs 88 and 91 by the Examiner for
teaching step [3] of the claim of “predicting energy consumptive behavior.”
Final Act. 10. Appellant argues that the Examiner did not give “proper
accord” to this limitation. Appeal Br. 26. However, the Examiner expressly
cited Crabtree for this limitation and Appellant did not identify a defect in
Crabtree’s disclosure nor the Examiner’s reasoning.

Step [4] of claim 21: “controlling the components of each building,
via a processor-based control system”
Appellant contends that Drees does not teach step [4] of claim 21 of
“controlling the components of each building, via a processor-based control
system, to support deployment of said synthetic resource in at least one
market.” Appeal Br. 19.
We do not agree.
Paragraph 66 of Drees discloses:
In one embodiment business rules engine 312 is configured to
respond to a DR [demand response] event by adjusting a
control algorithm or selecting a different control algorithm to
use (e.g., for a lighting system, for an HVAC system, for a
combination of multiple building subsystems, etc.).
The paragraph therefore teaches using a “business rules engine” and
“control algorithm,” which correspond to the claimed step of “controlling”
and a “processor-based control system,” to control multiple components
(“subsystems”) of buildings, such as lighting or an HVAC systems. Drees
also teaches that the smart building manager deploys selected equipment to
the energy market:
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For example, in the event of a brownout, the smart building
manager 106 may prioritize branches of a building’s internal
power grid-tightly regulating and ensuring voltage to high
priority equipment (e.g., communications equipment, data
center equipment, cooling equipment for a clean room or
chemical factory, etc.) while allowing voltage to lower priority
equipment to dip or be cut off by the smart grid (e.g., the power
provider).
Drees ¶ 69.
Thus, the energy response attributes are “deployed” to a synthetic
resource because they are aggregated together and deployed to an electric
“smart grid” market. As explained in the Appeal Brief, deployment to a
synthetic resource includes having the aggregated attributes participate in a
desired electric grid market (Appeal Br. 6), i.e., a provider of electricity.
Drees further discloses that that energy units from this selective
regulation “may offer units of energy during that period for sale back to the
smart grid (e.g., directly to the utility, to another purchaser, in exchange for
carbon credits, etc.)” (Dreed 69), which corresponds to deployment to at
least one market as required by step [4] of the claim. In the context of
paragraph 69 of paragraph (cited by the Examiner in the Final Action at 8),
Drees does not describe deployment of a synthetic resource from aggregated
energy response attributes from multiple facilities as required by the claim.
However, paragraph 91 discloses deployment from “a collection of building
subsystems for multiple buildings in a campus.” Therefore, one of ordinary
skill in the art would understand that the teachings in paragraph 69 could be
applied to rebundled subsystems from multiple facilities.

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Step [5] of claim 21: “unbundling and rebundling steps are optimized with
respect to at least one objective”
Appellant argues that Drees does not disclose step [5] of the claim
“wherein at least one of said unbundling and rebundling steps are optimized
with respect to at least one objective.” Appeal Br. 25.
We do not agree with Appellant that the Examiner erred in finding
this limitation met by the cited prior art.
Drees teaches the “smart building manager described herein is
configured to achieve energy consumption and energy demand reductions by
integrating the management of the building subsystems.” Drees ¶ 29.
Appellant distinguishes this disclosure by arguing that, in the claim, it is the
“unbundling” and “rebundling” which achieves the objective. Appeal Br.
25.
Drees teaches “Multi-campus/Multi-building Energy Management.”
Drees ¶ 90. This section describes managing “thermal energy storage”
(Drees ¶ 91), one of the energy response attributes expressly disclosed in the
Specification. Drees further teaches that “the building subsystems shown,
e.g., in FIGS. 1A and 1B may be a collection of building subsystems for
multiple buildings in a campus.” Id. A subsystem can be a “building
electrical subsystem 134, an information communication technology (ICT)
subsystem 136, a security subsystem 138, a HVAC subsystem 140, a
lighting subsystem 142, a lift/escalators subsystem 132, and a fire safety
subsystem 130.” Drees ¶ 27. Because these subsystems from the buildings
in the campus are managed together, they necessarily were unbundled from
the building and then rebundled in a different grouping to control their
performance. As discussed above, based on paragraph 33 of the
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Specification, the “unbundling” can be separating the attributes from one
building and the “rebundling” can be aggregating the separated attributes
from more than one building. Thus, the disclosure in paragraph 29 of Drees
describing managing energy consumption is applicable to the multi-
campus/multi-building environment (Drees ¶ 91) where the subsystems are
unbundled from the building and rebundled as an aggregation. The claim
only requires one bundling and rebundling step. Therefore, one step of
rebundling subsystems to optimize energy consumption meets the claim
limitation.
Brickfield also teaches “unbundling and rebundling steps are
optimized with respect to at least one objective.” This is discussed supra at
p. 9–11 (“Brickfield teaches that the energy response to controlling the
aggregated devices (the ‘stimulus’ in Appellant’s definition of ‘energy
response attribute’) is measured as a goal, e.g., to save a certain amount of
energy,” citing Brickfield ¶¶ 109, 217, 228).

Dependent claims
Claims 22, 38, and 49
Appellant separately argues claims 22, 38 and 49 which recites that
“said synthetic resource included multiple different attributes.” Appellant
contends that Drees does not teach the disputed claim limitation (Appeal Br.
27), but failed to consider the disclosure in Crabtree expressly cited by the
Examiner (Final Act. 12).
In paragraph 73 cited by the Examiner, Crabtree describes software
that can,
automatically shed some or most electrical loads under its
control (that is, controlled by actuators or control elements in
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turn controlled by one of its child load iNodes 321a-c) by
sending signals to the appropriate load iNodes instructing them
to interrupt current to one or more of their controlled loads.
An iNode, as explained by Crabtree, is a data device that be connected to
different electrical devices or electrical loads. Crabtree ¶¶ 58, 68, 78.
There is no teaching in Crabtree that the iNodes are connected to the
same type of attribute; indeed, this would make no sense because the iNodes
represent all the types of devices in a home. Crabtree further teaches that the
system is
adapted for being a data collection element of a larger system
by managing the collection of operating data from all of its
child iNodes, processing that data as by aggregating it, and
passing the data ‘upstream’ via data network 301 to other
system elements that may for example aggregate data from a
large number of gateways 315.
Crabtree ¶ 73. Crabtree also teaches using its system to manage electrical
loads in neighborhoods and aggregating loads of homeowners in a range.
Crabtree ¶¶ 78, 114, 115. Crabtree thus teaches aggregating or bundling
multiple different attributes, namely multiple iNodes associated with
different types of electrical loads, and from more than one user facility (e.g.,
a plurality of homes in a neighborhood) which can be aggregated together in
a single resource, meeting the claim limitation.

Claims 23, 39, and 50
Appellant separately argues claims 23, 39 and 50 which recite “at
least one market is one of energy, capacity, spinning reserve, frequency
regulation, and load balancing.” Appellant contends that “the only control
taught in Drees is control to support a building or group of buildings to the
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grid, not control to support deployment of an aggregation of energy
consuming attributes from different buildings.” Appeal Br. 27–28.
We have already addressed Appellant’s argument about the failure of
Drees to teach an “energy response attribute” as required by all the rejected
claims. First, as already pointed out, Drees, in fact, teaches “thermal
energy” (Drees ¶ 91), the same attribute disclosed in the Specification as an
energy response attribute (Spec. ¶ 53). Second, as discussed above,
Brickfield teaches energy response attributes.
Drees teaches curtailing load based on energy providers (Drees ¶¶ 65,
69), meeting the limitation of “at least one market is one of energy.” While
these disclosures refer to a single building, Drees also teaches that
subsystems from multiple buildings may be managed (Drees ¶ 91), and
expressly refers to load shedding decisions and using the previously
described control algorithms to do so (id.). We further note that Crabtree
also describes energy markets (Crabtree, e.g., at ¶ 112). Thus, we are not
persuaded by Appellant’s argument that the Examiner erred in rejecting
claim 23, 39, and 50.

Claim 24
Appellant separately argues claim 24 which recites “wherein the at
least one market comprises an internal market among electric system
participants.” Appellant contends that Drees does not teach this limitation.
Appeal Br. 28.
Drees discloses that “the smart building manager may communicate
with smart meters associated with an energy utility and directly or indirectly
with independent systems operators (ISOs) which may be regional power
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providers.” Drees ¶ 91. The ISO is not limited to being a regional power
provider, and therefore it could be an internal market comprising the
participating buildings in Drees.
The claimed internal market is expressly described in Crabtree, which
discloses “a neighborhood cooperative energy management” system that can
comprise:
distributed battery storage systems, and possibly also in several
generating devices, and these may be operated under control of
local iNode 720b to manage overall load as viewed by regional
iNode 730. Additionally, in such an arrangement, when prices
are high due to high demand, local iNode 720b could direct
generators and storage systems to deliver power to the members
of the local community.
Crabtree ¶ 78. The delivery of power “to the members of the local
community” is “internal market among electric system participants” as
recited in claim 24.

Claim 25
Appellant separately argues claim 25 which recites “wherein at least
one of the unbundling and rebundling steps utilizes decision analysis.”
Appellant cites to paragraph 47 of the Specification which describes
“decision analysis” as follows:
Decision analysis techniques involve a simulation of
unbundling, rebundling and/or deployment strategies and
comparison of the predicted results of any or all of those
strategies to determine an optimal unbundling, rebundling or
deployment. Decision analysis techniques may include a wide
range of analytical methods, including, for example, real
options, general option theory, mean-variance portfolio theory,
linear programming, Monte Carlo simulation, sensitivity
analysis, regression analysis, linear and non-linear optimization
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models, time series forecasting, arbitrage pricing theory,
decision trees, and techniques for quantitatively analyzing
decisions under uncertainty, including the use of distributions to
model uncertainty.
Based on this disclosure, Appellant contends that “‘decision analysis’
is an optimizing technique wherein different scenarios are tried and the
results compared and a decision made as to what works best.” Appeal Br.
28–29. Appellant argues that there “is no discussion anywhere in Drees,
including in the cited paragraphs [0038], [0039] or [0091], of the use of
decision analysis to unbundle, rebundle or deploy anything, much less
energy response attributes.” Appeal Br. 28–29.
Appellant’s arguments are not persuasive. Drees teaches non-human
monitoring of energy consumption (the “load”), in which the “non-human
constant monitoring and/or adjustment preferably is by artificial intelligence;
and, preferably is to monitor and/or adjust at least one factor that influences
energy consumption monitoring and adjustment.” Drees ¶ 26. Because the
factor “influences energy consumption,” the concept described by Drees
meets Appellant’s definition of an “energy response attribute,” i.e., “an
attribute of a facility that affects how the energy consumption of the facility
can be made to change in response to a stimuli.” Appellant has not
established that the artificial intelligence described in Drees (at ¶¶ 26, 35,
38) is not “decision analysis” as required by the claim. Drees discloses that
the artificial intelligence can be used to learn about the needs of buildings or
buildings in a system. Drees ¶¶ 38, 47, 53. The learning is used to make
adjustments to energy consumption. Drees ¶ 51. Drees further teaches:
Where computerized reporting has been mentioned, the
aggregation level for the computerized reporting may be at an
individual device, at everything in a building, at a set of
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buildings, or everything commonly owned. The optimal energy-
saving command decision may comprise a rotation of energy
curtailment that minimizes impact over energy users in the
system.
Drees ¶ 59.
In our view, Drees’s reference to aggregation levels for the
computerized reporting (which would invoke the artificial intelligence
described in Drees), coupled with the later discussion of managing multiple
buildings and the building subsystems within them (Drees ¶ 91), would be
reasonably understood by the skilled artisan to mean, or suggest, that the
energy response attributes could be differently aggregated (rebundled)
depending on the energy demands at a specific time as determined by
artificial intelligence. In addition (and as addressed above under step [2]),
Brickfield also describes unbundling attributes from a building and
rebundling them using rules, logic and artificial intelligence. Brickfield ¶¶
102, 103, 105.
Accordingly, we are not persuaded by Appellant’s argument that the
Examiner erred in rejecting claim 25 as obvious.

Claim 26
Appellant separately argues claim 26 which recites “wherein at least
one of the unbundling and rebundling steps utilizes computer models of said
facilities.” Appellant argues that the “[E]xaminer refers to paragraphs
[0053], [0057], [0066] and [0074] of Drees, but these paragraphs do not
teach or suggest this claimed feature of the invention.” Appeal Br. 29.
Paragraph 57 describes using artificial intelligence. Appellant has not
distinguished artificial intelligence from the recited computer model. With
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respect to unbundling and rebundling features of the claim, we have
addressed these limitations in the discussions of step [2] of claim 21 and in
claim 25. Accordingly, for all the foregoing reasons, we are not persuaded
by Appellant’s argument that the Examiner erred in rejecting claim 26 as
obvious.

Claims 27, 40, and 51
Appellant argues claims 27, 40, and 51 separately, which recite
“wherein optimizing at least one of said unbundling and rebundling steps
comprises selecting which of the markets in which to deploy said synthetic
resource and further selecting when said synthetic resource is deployed.”
Appellant states that the Examiner “refers to paragraphs [0061]-
[0064] of Drees, but the examiner has not considered all of what is required
by these claims.” Appeal Br. 29. Appellant states that what is required by
the claim “is an optimization wherein the market in which to deploy the
aggregation, and when the aggregation is to be deployed in that market, have
an impact on how the unbundling or rebundling are done.” Id. We do not
agree with Appellant’s interpretation of the claim.
The claim requires optimizing at least one of the “unbundling” or
“rebundling” steps. The “optimizing” comprises “selecting which of the
markets in which to deploy said synthetic resource.” The claim does not
require that the market has an “impact” on how “unbundling” or
“rebundling” step is done as asserted by Appellant. The only requirement of
the claim is that optimizing comprises a step of selecting the market. It does
not state that that the market selection must “impact” or otherwise affect the
how the unbundling or rebundling is done.
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The only deficiency in the rejection argued by Appellant is the
Examiner’s interpretation of the claim. Because we conclude that the
Examiner properly interpreted claims 27, 40, and 51, the rejection is
affirmed.

Claims 28, 41, and 52
Appellant argues claims 28, 41, and 52 separately, which recite
“wherein at least one of said unbundling and rebundling is optimized over
multiple time horizons.”
Appellant states that “the [E]xaminer refers to paragraph [0057] of
Drees, but no such concept is found there.” Appeal Br. 30. Appellant states
that paragraph 57
may describe displaying data over different time horizons, but
there is no suggestion of optimizing over different time
horizons (in addition to the fact that fault occurrence data is not
an energy response attribute, and is not part of any aggregation
of attributes deployed to a grid market, for all of the reasons
given earlier).
Id.
To begin, we note that the Specification as filed does not use the term
“multiple time horizons” and the Appeal Brief did not indicate where the
term is supported by the original Specification. Appellant also did not
explain what is meant by “multiple time horizons.” Absent such guidance,
we interpret the claim to mean that that the optimization of the bundling and
subsequent rebundling into aggregate from different facilities is done over
different time periods, e.g., analyzing the data over multiple times.
Appellant admits that paragraph 57 of Drees performs this function:
“Paragraph [0057] may describe displaying data over different time
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horizons.” However, Appellant argues that response energy attributes are
not being studied, instead “faults.”
While paragraph 57 indeed describes faults, the paragraph is more
general in describing a user interface that “may summarize relative energy
use and intensity across different buildings (real or modeled), different
campuses, or the like.” The same paragraph also describes that the “GUI
elements may include charts or histograms that allow the user to visually
analyze the magnitude of occurrence of specific faults or equipment for a
building, time frame, or other grouping.” Drees ¶ 57. Thus, although it
describes analyzing faults, it also describes equipment for buildings.
Appellant has read the paragraph too narrowly and ignored the explicit
mention of analyzing energy use across buildings and for equipment in
buildings over a time horizon. Drees also describes controlling set points of
HVAC, cold water temperature, “thermal energy” (Drees ¶¶ 38, 40, 92),
each which is an energy response attribute. We have already addressed
under claim 21 the step of rebundling as disclosed in Brickfield, Crabtree,
and Drees. Thus, we find these references describe a the “rebundling” step
that is optimized over multiple time horizons, as claimed.

Claims 29, 42, and 43
Appellant separately argues claims 29, 42, and 43, which recite
“wherein at least one of said attributes comprises an electric power response
of an HVAC system or component thereof to a change of at least one of: an
HVAC system setpoint, HVAC system controller setting, or electric power
supply frequency.”
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Appellant states that the Examiner “refers to paragraphs [0030]-
[0040], [0091] and [0097] of Drees, but no teaching of the claimed feature is
found there or anywhere else in Drees.” Appeal Br. 30. Appellant
acknowledges “that these attributes were known per se. Id. However,
Appellant argues that “No one prior to the present inventors has unbundled
these attributes from the buildings themselves and then rebundled the
attributes into aggregations of attributes for deployment to the grid.” Id.
This argument does not persuade us that the Examiner erred.
Appellant does not contest that Drees teaches providing a new setpoint to an
HVAC subsystem. Drees ¶ 38. Drees further teaches that the “control
algorithms of the smart building manager (e.g., control algorithms of the
integrated control layer) may be configured to use weather forecast
information to make setpoint or load shedding decisions (e.g., so that
comfort of buildings in the campus is not compromised).” Drees ¶ 92. This
same paragraph describes the smart building manager as configured to
manage the building and refers to “a collection of building subsystems for
multiple buildings on campus.” Id. This disclosure would be understood to
mean that a collection of subsystems, such as HVAC (see Fig. 1B listing
HVAC a subsystem) could be managed as a group by the smart manager,
thus representing attributes that have been unbundled from the building and
then rebundled together. Brickfield also discloses unbundling devices from
multiple buildings, including HVAC system, and leveraging them to achieve
energy benefits. Brickfield ¶¶ 217, 228. Crabtree also describes aggregating
air conditioners from a neighborhood. Crabtree ¶ 78.
For the foregoing reasons, the evidence does not support Appellant’s
argument and we therefore affirm the rejection of claims 29, 42, and 43.
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Claims 30, 43, and 54
Appellant separately argues claims 30, 34, and 54 which recites
“wherein said HVAC system setpoint is one of zone temperature, chilled
water supply, supply air temperature, duct static pressure, or hot water
supply.”
Appellant acknowledges that “Drees does suggest zone temperature
control.” Appeal Br. 30. Appellant argues limitations that appear in claims
21 and 29 which already been addressed. We therefore affirm the rejection
of claims 30, 43, and 54 for reasons discussed above.

Claims 31, 44 and 45
Appellant separately argues claims 31, 44, and 45 which recite,
wherein said unbundling and rebundling steps comprise
determining optimal aggregations for a set of advocate
facilities, wherein the set of advocate facilities is a subset of
said portfolio, and said controlling step comprises controlling
components in all of the facilities of said portfolio in
accordance with a control plan determined for said subset.
Appellant argues that the “first flaw” in the Examiner’s rejection of
these claims is the “assertion . . . that building subsystems can be considered
a subset of a campus.” Appeal Br. 31. Appellant states that the claims
“refers to a subset of the portfolio, and the portfolio is a set of buildings. So
the claim refers to a subset of buildings, and subsystems in Drees are not a
subset of buildings.” Id. Appellant further argues “there is simply no
suggestion in Drees, including the paragraphs cited by the examiner, of
creating a control plan for a subset of anything and then controlling
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components in all of the facilities based on the control plan that was
developed for the subset.” Id.
Drees discloses controlling buildings. Drees ¶¶ 90, 91. For example,
Drees discloses:
where the smart building manager 106 is controlling an entire
campus or set of campuses, one or more smart building
managers may be layered to effect hierarchical control
activities. For example, an enterprise level smart building
manager may provide overall DR [demand response] strategy
decisions to a plurality of lower level smart building managers
that process the strategy decisions (e.g., using the framework
shown in FIG. 3) to effect change at an individual campus or
building. . . . Such a DR layer can execute a campus-wide DR
strategy by passing appropriate DR events to the separate lower
level smart building mangers having integrated control layers
and building system integration layers.
Drees ¶ 92. In other words, the building manager can manage individual
buildings separately or multiple buildings on a campus or set of campuses.
Brickfield specifically discloses managing “portfolios of buildings”
(Brickfield ¶ 95), the same language in the claim. Brickfield also discloses
distinguishing a subset of the buildings: “if building 10 and building 10A are
in different time zones but otherwise have a similar set of respective devices
11, 11A they may be controlled appropriately and in a maximally energy-
intelligent manner.” Brickfield ¶ 102. Therefore, Brickfield teaches
controlling all the components of the buildings in accordance with a control
plan determined for the subset as recited in the claim.
The claim further requires applying this plan to other
facilities/buildings in the portfolio. Brickfield does not require that the
determination of how to control building 10 be made independently of
building 10A, which has the same devices. In view of the similarities
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between buildings, one of ordinary skill in the art would have found it
obvious to apply the same optimization performed for building 10 to
building 10A.
Accordingly, we are not persuaded by Appellant’s argument that the
Examiner erred in rejecting claims 31, 44, and 45 obvious.

Claim 46
Appellant separately argues claim 46, which recites “wherein said
controller simultaneously optimizes deployment of plural aggregations each
with respect to a different grid market.”
Appellant states that the Examiner “cites to paragraphs [0033],
[0038], [0061] and [0063] [of Drees] as allegedly teaching this feature. But
these paragraphs do not describe multiple different aggregations of
attributes, nor deployment to different markets, much less simultaneously to
different markets.” Appeal Br. 31. Appellant acknowledges that “Paragraph
[0038] describes aggregating subsystems to control the aggregate as a single
system,”2 but argues that “these are all subsystems of a single building.” Id.
Appellant also acknowledges that “Paragraph [0061] talks about responding
to grid signaling, but does not talk about simultaneously deployment to
different markets.” Id. Appellant further argues that “what is important to
this claim is the simultaneous deployment to different markets of different
aggregations of energy response attributes of multiple buildings. There is no
way to get to that point simply from the teaching of” Drees. Id.

2 This argument is inconsistent with the argument with the argument made
on page 24 of the Appeal Brief that only aggregation of data is disclosed by
Drees.
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We have already found for claim 22 that Crabtree discloses bundling
different aggregates. Crabtree ¶¶ 73, 78, 114, 115. Crabtree also describes
participating in different grid markets. For example, Crabtree teaches “the
exchange activates additional response packages until the required supply
level is achieved, or alternatively the exchange buys power on the open
market (presumably at higher prices . . .).” Crabtree ¶ 129. Crabtree also
describes using an exchange market and a sub-exchange market, depending
on energy demands and consumption. Id. See also Crabtree ¶¶ 152–154,
158 describing making decisions about energy sources based on demands.
Thus, we do not agree that the claimed limitation of optimizing aggregations
to different grid markets is new. According the rejection of claim 46 is
affirmed.

Claim 47
Appellant separately argues claim 47, which recites “wherein said
objective is selected from the group of: increased revenue, reduced capital
expense, reduced operating expense, energy efficiency, peak demand
reduction, financial risk management, and reliability.”
Appellant states
it is agreed that some of these objectives are known, and with
the claim using a Markush group, the objectives themselves as
recited in claim 47 are not patentably distinctive, but as applied
to the subject matter of parent claim 37 the subject matter of
claim 47 is new and non-obvious.
Appeal Br. 31.
This argument is not persuasive. Only one member of a Markush
needs to be met to satisfy the claim limitation. The claim only requires one
objective from the group to be selected. Appellant admits that the objectives
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are known. Appellant did not explain how this known objective makes the
claim non-obvious. Accordingly, the obviousness rejection of claim 47 is
affirmed for the reasons set forth by the Examiner.

WRITTEN DESCRIPTION
The Examiner stated that is no support in the Specification for step [4]
of claim 21 of “predicting energy consumptive behavior of said synthetic
resource in response to possible future stimuli and based on said attributes
included in said synthetic resource.” Final Act. 6. Claim 37 and 48 recite a
similar limitation.
The inventor must “convey with reasonable clarity to those skilled in
the art that, as of the filing date sought, he or she was in possession of the
invention.” Vas-Cath Inc. v. Mahurkar, 935 F.2d 1555, 1563–64 (Fed. Cir.
1991). “One shows that one is ‘in possession’ of the invention by describing
the invention, with all its claimed limitations.” Lockwood v. Am. Airlines,
Inc., 107 F.3d 1565, 1572 (Fed. Cir. 1997) (citation omitted). In describing
the claimed invention, there is no requirement that the wording be identical
to that used in the specification as long as there is sufficient disclosure to
show one of skill in the art that the inventor “invented what is claimed.”
Union Oil Co. v. Atlantic Richfield Co., 208 F.3d 989, 997 (Fed. Cir. 2000)
(citation omitted). The written description “need not recite the claimed
invention in haec verba but [it] must do more than merely disclose that
which would render the claimed invention obvious.” ICU Med., Inc. v.
Alaris Med. Sys., Inc., 558 F.3d 1368, 1377 (Fed. Cir. 2009). Thus, as long
as a person “of ordinary skill in the art would have understood the inventor
to have been in possession of the claimed invention at the time of filing,
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even if every nuance of the claims is not explicitly described in the
specification, then the adequate written description requirement is met.” In
re Alton, 76 F.3d 1168, 1175 (Fed. Cir. 1996).
Paragraph 69 discloses “Advanced building control and fault detection
methods may utilize building energy models to predict or estimate expected
building performance.” It is further disclosed that “building models may
predict cooling loads and energy consumption for optimal control strategy
evaluation, as well as online next-day load predictions.” Spec. ¶ 69. The
Specification describes using models “to improve real-time load estimates
using available data.” Id. Models for predicting loads are further described
in paragraphs 70–75 of the Specification. It is evident from this disclosure
that Specification expressly describes “predicting energy consumptive
behavior.” As far as the recited limitation of “predicting the behavior in
response to future stimuli,” the Specification teaches, for example,
determining how to achieve effective energy consumption by regulating set
points of HVAC systems (Spec. ¶ 57), which is one of the factors that are
optimized by the predictive building control systems described in the
Specification. See also Spec. ¶ 76.
Accordingly, one of ordinary skill in the art would have recognized
that the inventors were in possession of all the features of step [4] of claim
21, and the corresponding limitation in claims 37 and 48.

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CONCLUSION
In summary:
Claims
Rejected
35 U.S.C.
§
Reference(s)/Basis Affirmed Reversed
21–31 and
37–58
103 Drees, Brickfield,
Crabtree
21–31 and
37–58

21–31 and
37–58
112 Written description 21–31 and
37–58
Overall
Outcome
21–31 and
37–58

TIME PERIOD
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