DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is made final.
Claims 1-3 filed on 12/10/2025 have been reviewed and considered by this office action.
Claims 1 and 3 have been amended.
Claims 4-10 have been cancelled.
Response to Arguments
Applicant’s amended claims, filed 12/10/2025, have overcome the rejections under 35 U.S.C. § 103 and 35 U.S.C. § 112.
Regarding the rejections under 35 U.S.C. § 101, on Page 7 of Remarks, Applicant argues that the energy management system performs the capacity configuration by using a solution result to set operational parameters and limits for the energy storage system which directly controls behavior of the future energy storage devices and integration into the physical microgrid. Examiner agrees that controlling “in real-time, the output power of the energy storage equipment and the energy exchange between the microgrid and the main grid” would overcome the rejection under 101, however, this is not claimed. Examiner therefore recommends amending claim 1 to include the configuration of operational parameters, for example to state “performing, by the energy management system, the capacity configuration of energy storage based on the solution result, comprising configuring at least one of: i) energy storage configuration capacity; ii) a charging power of the energy storage; iii) a discharging power of the energy storage; iv) state of charge limits of the energy storage; v) charge and discharge limits of the energy storage, to govern operation of the energy storage in the microgrid.”
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-3 are directed to a process.
With respect to claim 1:
2A Prong 1: The claims recite an abstract idea. Specifically:
the distributionally robust optimization model comprises: an objective function of the distributionally robust optimization model is expressed as follows:
C
=
C
1
+
C
2
;
wherein
C
is a total investment cost of energy storage;
C
1
indicates a daily average investment cost of energy storage battery;
C
2
indicates a daily operating cost;
r
e
indicates a fund recovery factor;
C
1
'
indicates an investment cost per unit capacity of energy storage battery;
S
indicates energy storage configuration capacity;
D
indicates a depth of discharge of the energy storage battery; d indicates a discount rate;
y
indicates an investment life of the energy storage battery;
p
g
,
t
indicates a price of natural gas at time
t
;
V
c
h
p
,
t
indicates the amount of natural gas purchased at time
t
;
p
t
indicates a price of purchased electricity at time
t
;
P
g
r
i
d
,
t
indicates the amount of electricity purchased at moment
t
;
p
t
c
and
p
t
d
indicate charging and discharging power of energy storage at moment
t
, respectively; and
C
e
s
s
indicates a cost per unit charge and discharge of the energy storage battery; (Mathematical concept – see MPEP § 2106.04(a)(2)(I))
constraints of the objective function comprise: an operation constraint of energy storage equipment is expressed as follows:
S
O
C
t
+
1
=
S
O
C
t
1
-
φ
+
p
t
c
⋅
η
-
p
t
d
1
-
η
Δ
t
p
m
i
n
c
≤
p
t
c
≤
p
m
a
x
c
p
m
i
n
d
≤
p
t
d
≤
p
m
a
x
d
S
O
C
t
,
m
i
n
≤
S
O
C
t
≤
S
O
C
t
,
m
a
x
;wherein
S
O
C
t
is a capacity of a battery at time
t
;
φ
is a self-discharging rate of the battery;
η
is a charging efficiency of the battery;
p
t
c
is a charging power of the battery at time
t
;
p
t
d
is a discharging power of the battery at time
t
;
p
m
i
n
c
is a minimum charging power of the battery;
p
m
a
x
c
is a maximum charging power of the battery;
p
m
i
n
d
is a minimum discharging power of the battery;
p
m
a
x
d
is a maximum discharging power of the battery; and
Δ
t
is dispatching time interval; (Mathematical concept – see MPEP § 2106.04(a)(2)(I))
an operation constraint of a gas turbine is expressed as follows:
P
g
t
,
t
=
V
c
h
p
,
t
×
J
×
ω
H
g
t
,
t
=
V
c
h
p
,
t
×
η
×
1
-
ω
P
g
t
m
i
n
≤
P
g
t
,
t
≤
P
g
t
m
a
x
;wherein
V
c
h
p
,
t
is the amount of natural gas purchased at time
t
;
ω
is an electrical efficiency of the gas turbine;
J
is a heat value of the natural gas;
H
g
t
,
t
is a thermal power output by the gas turbine at time
t
;
P
g
t
m
i
n
is an upper limit of electrical power of the gas turbine; and
P
g
t
m
a
x
is a lower limit of the electrical power of the gas turbine; (Mathematical concept – see MPEP § 2106.04(a)(2)(I))
a constraint of power balance is expressed as follows:
P
g
r
i
d
,
t
+
P
p
v
,
t
+
p
t
d
-
p
t
c
+
P
g
t
,
t
=
P
l
o
a
d
,
t
H
g
t
,
t
=
H
l
o
a
d
,
t
;wherein
P
g
r
i
d
,
t
represents the power transmission between the microgrid and a main grid at time
t
;
P
p
v
,
t
is a power output of the photovoltaic power generation at time
t
;
p
t
c
is a charging power of an energy storage battery at time
t
;
p
t
d
is a discharging power of the of the energy storage battery at time
t
,
P
l
o
a
d
,
t
is a total electrical load demand of the microgrid at time
t
;
H
g
t
,
t
is a thermal power output of the gas turbine at time
t
; and
H
l
o
a
d
,
t
is a total heat load demand of the microgrid at time
t
; and (Mathematical concept – see MPEP § 2106.04(a)(2)(I))
an ambiguity set
M
ε
used for measuring an uncertainty of the distributionally robust optimization model is shown as follows:
M
ε
=
{
P
p
^
∈
M
ξ
:
d
W
P
p
~
,
P
^
≤
ε
}
;
wherein
P
p
^
is a probability distribution of an actual output power of the photovoltaic power generation;
P
^
is an empirical distribution of the photovoltaic power generation;
M
ξ
is all probability distribution spaces defined by Wasserstein distance
d
W
; and
ε
is radius of an ambiguity set W. (Mathematical concept – see MPEP § 2106.04(a)(2)(I))
2A Prong 2: The additional elements recited in the claim do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
acquiring, by one or more sensors and one or more data collectors, time-series data related to photovoltaic power generation (Insignificant extra-solution activity – acquiring data represents pre-solution activity (mere data gathering) – see MPEP § 2106.05(g))
performing, by one or more processors, preprocessing of the time-series data related to the photovoltaic power generation to obtain preprocessed time-series data; (Insignificant extra-solution activity – preprocessing data represents pre-solution activity (selecting a particular data source or type of data to be manipulated) – see MPEP § 2106.05(g))
training a time-series generative adversarial network (Time GAN) based on the preprocessed time-series data to perform data enhancement to obtain enhanced time-series data; (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP § 2106.05(f))
wherein the Time GAN comprises an embedded network and a generative adversarial network (GAN); and (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP § 2106.05(h))
based on the enhanced time-series data, performing capacity configuration of energy storage by using a distributionally robust optimization model. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP § 2106.05(f))
wherein the step of “using a distributionally robust optimization model to perform capacity configuration of energy storage” comprises: solving the distributionally robust optimization model by one or more processors to obtain a solution result comprising an optimal configuration scheme for an energy storage system (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP § 2106.05(f))
wherein the optimal configuration scheme minimizes a total cost of the energy storage system subject to constraints; (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP § 2106.05(h))
transmitting the solution result to an energy management system of the microgrid; (Receiving or transmitting data over a network and performing repetitive calculations have been deemed as well‐understood, routine, and conventional functions – see MPEP § 2106.05(d))
performing, by the energy management system, the capacity configuration of energy storage based on the solution result (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP § 2106.05(f))
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
acquiring, by one or more sensors and one or more data collectors, time-series data related to photovoltaic power generation (Receiving or transmitting data over a network have been deemed as well‐understood, routine, and conventional functions – see MPEP § 2106.05(d))
performing, by one or more processors, preprocessing of the time-series data related to the photovoltaic power generation to obtain preprocessed time-series data; (Performing repetitive calculations has been deemed as a well‐understood, routine, and conventional function – see MPEP § 2106.05(d))
training a time-series generative adversarial network (Time GAN) based on the preprocessed time-series data to perform data enhancement to obtain enhanced time-series data; (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP § 2106.05(f))
wherein the Time GAN comprises an embedded network and a generative adversarial network (GAN); and (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP § 2106.05(h))
based on the enhanced time-series data, performing capacity configuration of energy storage by using a distributionally robust optimization model. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP § 2106.05(f))
wherein the step of “using a distributionally robust optimization model to perform capacity configuration of energy storage” comprises: solving the distributionally robust optimization model by one or more processors to obtain a solution result comprising an optimal configuration scheme for an energy storage system (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP § 2106.05(f))
wherein the optimal configuration scheme minimizes a total cost of the energy storage system subject to constraints; (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP § 2106.05(h))
transmitting the solution result to an energy management system of the microgrid; (Receiving or transmitting data over a network and performing repetitive calculations have been deemed as well‐understood, routine, and conventional functions – see MPEP § 2106.05(d))
performing, by the energy management system, the capacity configuration of energy storage based on the solution result (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP § 2106.05(f))
Therefore, claim 1 is ineligible.
With respect to claim 2:
2A Prong 2: The additional elements recited in the claim do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
wherein the time- series data related to the photovoltaic power generation comprises photovoltaic power data, global horizontal radiation and diffuse horizontal radiation data, temperature data, and humidity data; the preprocessing comprises data cleaning processing, data integration processing, data transformation processing, data reduction processing, and data standardization processing (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP § 2106.05(h))
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the time- series data related to the photovoltaic power generation comprises photovoltaic power data, global horizontal radiation and diffuse horizontal radiation data, temperature data, and humidity data; the preprocessing comprises data cleaning processing, data integration processing, data transformation processing, data reduction processing, and data standardization processing (Generally linking the use of a judicial exception to a particular technological environment or field of use – see MPEP § 2106.05(h))
Therefore, claim 2 is ineligible.
With respect to claim 3:
2A Prong 2: The additional elements recited in the claim do not integrate the abstract idea into a practical application, individually or in combination.
Additional elements:
the step of “training a time-series generative adversarial network (Time GAN) based on the preprocessed time-series data to perform data enhancement to obtain enhanced time-series data” comprises: (S21) training the embedded network based on the time-series data; and the embedded network is formed by embedding a function used for dimensionality reduction of the time-series data into an autoencoder; (S22) training a generator and a discriminator in the GAN based on the time-series data; and (S23) performing the data enhancement on the time-series data via joint-training of the embedded network and the GAN (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP § 2106.05(f))
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
the step of “training a time-series generative adversarial network (Time GAN) based on the preprocessed time-series data to perform data enhancement to obtain enhanced time-series data” comprises: (S21) training the embedded network based on the time-series data; and the embedded network is formed by embedding a function used for dimensionality reduction of the time-series data into an autoencoder; (S22) training a generator and a discriminator in the GAN based on the time-series data; and (S23) performing the data enhancement on the time-series data via joint-training of the embedded network and the GAN (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP § 2106.05(f))
Therefore, claim 3 is ineligible.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Magdalena Kossek whose telephone number is (571)272-5603. The examiner can normally be reached Mon-Fri 9:00-5:00 EST.
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/M.I.K./Examiner, Art Unit 2117
/ROBERT E FENNEMA/Supervisory Patent Examiner, Art Unit 2117