DETAILED ACTION
1. Claims 1-14 have been presented for examination.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
PRIORITY
3. Acknowledgment is made of applicant's claim for foreign priority based on an application EP 21161840.0 filed 03/10/2021. It is noted, however, that applicant has not filed a certified copy of the application as required by 37 CFR 1.55.
Response to Arguments
4. Applicant's arguments filed 8/20/25 have been fully considered but they are not persuasive.
i) Following Applicants amendments and arguments the previously presented 101 rejection is
MAINTAINED. Specifically, Applicants state that the newly amended limitation of “higher order applications” provides an explicit application of any purported abstract ideas. However as noted in the previous office action and reiterated here the claims continue to merely recite a calculation which encompasses a mental process and/or mathematical concept. Specifically, the step of generating a safety report in view of its broadest reasonable interpretation encompasses a determination that can be made in the mind or with pencil and paper. Further the remaining options of “controlling a voltage, and preventative and corrective Security Constraint Optimal Power Flow (SCOPF)” are merely recited as options which use the basis of the previous calculation and do not appear to be explicitly carried out. As such the 101 rejection is MAINTAINED.
ii) Following Applicants amendments the 112 rejection of claim 14 is WITHDRAWN.
iii) Following Applicants amendments an additional prior art rejection has been presented below.
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.
5. Claims 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
i) In view of Step 1 of the analysis, claim(s) 1 is directed to a statutory category as a process and claim 14 is directed to a statutory category as a machine which each represent a statutory category of invention. Therefore, claims 1-14 are directed to patent eligible categories of invention.
ii) In view of Step 2A, Prong One, claims 1 and 14 recite the abstract idea calculating the state of an electrical grid which constitutes an abstract idea based on Mental Processes based on concepts performed in the human mind, or with the aid of pencil and paper as well as and alternatively as Mathematical Concepts including mathematical formulas or equations as well as calculations.
As to claim 1, the limitation of “calculating the state using a computing unit (see below), wherein the computing unit employs an iterative, numerical method on the basis of a plurality of measured values associated with the electrical grid;” and “wherein the numeral method begins with an initial value;” would be analogous to a person calculating a value based on a set of given measured values and thus fall under Mental Processes. In addition, the steps would constitute Mathematical Concepts as they recite a calculating step using an iterative/numerical method which falls under mathematical formulas or equations as well as calculations.
As to claim 1, the limitation “using the calculated state as the basis for a higher-order application selected from the group consisting of: generating a safety report, controlling a voltage, and preventative and corrective Security Constraint Optimal Power Flow (SCOPF)” would be analogous to a person evaluating calculated values and generating a safety report which could correspond to a mental determination and report and thus fall under Mental Processes. Further the remaining options of “controlling a voltage, and preventative and corrective Security Constraint Optimal Power Flow (SCOPF)” are merely recited as options which use the basis of the previous calculation and do not appear to be explicitly carried out. Thus, the claims recite the abstract idea of a Mental Process performed in the human mind, or with the aid of pencil and paper.
In addition as to claim 1, other than reciting “using a computing unit;” nothing in the claim element precludes the step from practically being performed in the mind as a Mental Process and or alternatively as Mathematical Concepts including mathematical formulas or equations as well as calculations.
Analogous limitations are also recited in claim 14 and addressed as claim 1 above.
In addition as to claim 14, other than reciting “a computing unit with a memory and a processor;” nothing in the claim element precludes the step from practically being performed in the mind as a Mental Process and or alternatively as Mathematical Concepts including mathematical formulas or equations as well as calculations.
Dependent claims 2-13 further narrow the abstract ideas, identified in the independent claims, specifically as Mental Processes and/or based on Mathematical Concepts including mathematical formulas or equations as well as calculations.
iii) In view of Step 2A, Prong Two, the judicial exception is not integrated into a practical application. In Claim 1, the additional element of “a computing unit”, and the “a computing unit with a memory and a processor”, in claim 14, merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitation in claim 1, and similarly recited in claim 14 of “wherein the initial value includes an initial state ascertained from the measured values by an artificial neural network” are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Additionally the limitation of “wherein the initial value includes an initial state ascertained from the measured values by an artificial neural network” in claims 1 and 14, alternatively can be viewed as insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the judicial exception is not integrated into a practical application.
Dependent claims 2-13 further narrow the abstract ideas, identified in the independent claims and do not introduce further additional elements for consideration beyond those addressed above.
iv) In view of Step 2B, claims 1 and 14 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As to claim 1, the additional element of “a computing unit”, and the “a computing unit with a memory and a processor”, as to claim 14, merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitation in claim 1, and similarly recited in claim 14 of “wherein the initial value includes an initial state ascertained from the measured values by an artificial neural network” are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Additionally the limitation of “wherein the initial value includes an initial state ascertained from the measured values by an artificial neural network” in claim 1 and similarly recited in claim 14, alternatively can be viewed as is insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the claim as a whole does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered alone or in combination, do not amount to significantly more than the judicial exception. As stated in Section I.B. of the December 16, 2014 101 Examination Guidelines, “[t]o be patent-eligible, a claim that is directed to a judicial exception must include additional features to ensure that the claim describes a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception.”
The dependent claims include the same abstract ideas recited as recited in the independent claims, and merely incorporate additional details that narrow the abstract ideas and fail to add significantly more to the claims.
Dependent claims 2-13 further define the type of content of respective claims 1 and 14 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 2 further defines the type of methodology of calculation of respective claim 1 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 3 further defines the data values of the calculation of respective claim 1 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 4 further defines the data values of the calculation of respective claim 1 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 5 further defines the data values of the calculation of respective claim 1 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 6 further defines the type of methodology of calculation of respective claim 1 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 7 further defines the data values of the calculation of respective claim 1 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 8 further defines the data and type of methodology of calculation of respective claim 1 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 9 further defines the data values of the calculation of respective claim 1 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 10 further defines the training of the neural network of respective claim 1 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 11 further defines the training of the neural network of respective claim 1 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 12 further defines the data values of the calculation of respective claim 1 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 13 further defines the type of methodology of calculation of respective claim 1 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
v) Accordingly, claims 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without anything significantly more.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
6. Claim(s) 1-7 and 10-14 are rejected under 35 U.S.C. 103 as being unpatentable over Zivanovic, Rastko. "Artificial neural networks for state estimation of electric power systems." (1996), hereafter Zivanovic in view of U.S. Patent Publication No. 20200293032, hereafter Wang.
Regarding Claim 1: The reference discloses A method for ascertaining a state of an electrical grid, wherein the state of the electrical grid is represented by ascertained voltages and ascertained phase angles at one or more grid nodes of the electrical grid, the method comprising:
calculating the state using a computing unit, wherein the computing unit employs an iterative, numerical method on the basis of a plurality of measured values associated with the electrical grid; (Zivanovic. Page 77, Second Paragraph, “The algorithm converged in three iteration steps. At each iteration step the objective functions of active ANN, and reactive ANN are minimized.”)
wherein the numeral method begins with an initial value; (Zivanovic. Page 77, First Paragraph, “The IEEE 14-bus power system is used for the simulation study of the Decoupled ANN SE with steepest descent dynamic used in static state estimation. The algorithm was presented in Section 3.1.1. Power system parameter data, initial values and metering locations are given in Appendix D. First, we solve the load flow problem for initial load values. The obtained results are used in order to find measurement values. The measurement set is composed of 30 active and 30 reactive power measurements, and 7 voltage magnitude measurements, as shown in Appendix D. Measurement errors are simulated as normally distributed random values with standard deviations 0.007pu for voltage magnitude, and 0.02pu for power measurements. The Runge-Kutta method is used to simulate ANN in steps 4 and 8 of the algorithm. The simulation program is written in MATLAB. The simulation CPU time on PC486, 66MHz was 256s. From the results of the simulation we plot the time evolution of the voltage phase angle and the voltage magnitude at node 2 in Figures 3.1 and 3.2 respectively.”)
wherein the initial value includes an initial state ascertained from the measured values by an artificial neural network. (Zivanovic. Page 77, First Paragraph, “The IEEE 14-bus power system is used for the simulation study of the Decoupled ANN SE with steepest descent dynamic used in static state estimation. The algorithm was presented in Section 3.1.1. Power system parameter data, initial values and metering locations are given in Appendix D. First, we solve the load flow problem for initial load values. The obtained results are used in order to find measurement values. The measurement set is composed of 30 active and 30 reactive power measurements, and 7 voltage magnitude measurements, as shown in Appendix D. Measurement errors are simulated as normally distributed random values with standard deviations 0.007pu for voltage magnitude, and 0.02pu for power measurements. The Runge-Kutta method is used to simulate ANN in steps 4 and 8 of the algorithm. The simulation program is written in MATLAB. The simulation CPU time on PC486, 66MHz was 256s. From the results of the simulation we plot the time evolution of the voltage phase angle and the voltage magnitude at node 2 in Figures 3.1 and 3.2 respectively.”)
Zivanovic does not explicitly recite using the calculated state as the basis for a higher-order application selected from the group consisting of: generating a safety report, controlling a voltage, and preventative and corrective Security Constraint Optimal Power Flow (SCOPF).
However Wang recites using the calculated state as the basis for a higher-order application selected from the group consisting of: generating a safety report, (Wang. “[0062] Report generator 326 may generate reports relating to status information relating to the power system component(s), on command (e.g., from a user). Report generator 326 may also generate reports automatically in response to detected event(s), or periodically, wherein the report may be generated and provided (e.g., transmitted) to a desired destination (e.g., a destination address such as an email address of an operator, etc.). Report generator 326 may also generate alarms indicating an abnormal condition (e.g., fault, power system parameter outside of predefined threshold parameter value or range of parameter values, etc.), using a visual, audio, and/or vibrational indicator, e.g., which is detectable via other senses (e.g., touch).”) controlling a voltage, ([0104] “SCADA component 1110 may also allow operators at a central control center to perform or facilitate management of energy flow in the power grid system. For example, operators may use a SCADA component (e.g., using a computer such as a laptop or desktop) to facilitate performance of certain tasks such opening or closing circuit breakers, or other switching operations which might divert the flow of electricity.”) and preventative and corrective Security Constraint Optimal Power Flow (SCOPF)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the report and control aspect of Wang with the calculation of Zivanovic since it would “generate alarms indicating an abnormal condition” and “allow for operators at a central control center to perform or facilitate management of energy flow in the power grid system” (Wang, [0104])
Regarding Claim 2: The reference discloses The method as claimed in claim 1, wherein the numerical method includes Newton's method. (Zivanovic. Page 40, Second Paragraph, “In order to minimize the objective function J (X) given in (2.3) a Gauss-Newton iterative algorithm [69],[74] is commonly used.”)
Regarding Claim 3: The reference discloses The method as claimed in claim 1, wherein the measured values include a measurement vector comprising voltages, currents, real powers, and/or reactive powers associated with and acquired by grid nodes and/or cables of the electrical grid. (Zivanovic. Page 77, First Paragraph, “The IEEE 14-bus power system is used for the simulation study of the Decoupled ANN SE with steepest descent dynamic used in static state estimation. The algorithm was presented in Section 3.1.1. Power system parameter data, initial values and metering locations are given in Appendix D. First, we solve the load flow problem for initial load values. The obtained results are used in order to find measurement values. The measurement set is composed of 30 active and 30 reactive power measurements, and 7 voltage magnitude measurements, as shown in Appendix D. Measurement errors are simulated as normally distributed random values with standard deviations 0.007pu for voltage magnitude, and 0.02pu for power measurements. The Runge-Kutta method is used to simulate ANN in steps 4 and 8 of the algorithm. The simulation program is written in MATLAB. The simulation CPU time on PC486, 66MHz was 256s. From the results of the simulation we plot the time evolution of the voltage phase angle and the voltage magnitude at node 2 in Figures 3.1 and 3.2 respectively.” See also Paragraph 22-23, Section 1.1, “A State Estimator algorithm calculates the state of a power system using the information on the status of breakers/isolators and on analog measurements, such as bus voltages, active and reactive power flows, and generator/load active and reactive power injections [6], [7]. It also uses the status of transformer/phase shifter 22 23 tap positions, and in some recent cases voltage and current phasor measurements.”)
Regarding Claim 4: The reference discloses The method as claimed in claim 1, wherein the state is formed by a state vector indicating the voltages and phase angles at respective grid nodes of the electrical grid. (Zivanovic. Page 23, Second Paragraph, “The output of a SE algorithm is the state vector X which contains the bus voltage phasors, magnitude V and angle(), at all the buses except one bus which is used as a reference for the angles. The angle of the reference bus is arbitrarily excluded from the state vector. Another possible option is to use an external (fictitious) reference, i.e. all angles are included in the state vector [10].” Each bus represents a node of the electrical grid.)
Regarding Claim 5: The reference discloses The method as claimed in claim 1, wherein the measured values are provided by a control system of the electrical grid. (Zivanovic. Page 23, First Paragraph, “Measurements and status data are collected during cyclic scans of remote terminals performed by the Supervisory Control and Data Acquisition (SCADA) system (7].”)
Regarding Claim 6: The reference discloses The method as claimed in claim 1, wherein the numerical method minimizes a target function including a weighted, quadratic deviation between the measured values and a measurement model function depending on the state to be ascertained. (Zivanovic. Page 40, equations 2.8-2.9 and the section following reciting “At each iteration step the linearized quadratic error function J (AX) (given by Eq. (2.4)) is minimized… When the algorithm converges, the estimate X minimizes J (X), and J (X.) is the minimum of weighted sum of the squared measurement residuals for one snapshot of measurements.”)
Regarding Claim 7: The reference discloses The method as claimed in claim 6, wherein the initial state is used as the initial value if the target function value is greater than or equal to a threshold value. (Zivanovic. Page 73, Step 10, whereby the system only proceeds to the next state of the system if the values calculated in step 10 are greater than or equal to a threshold value which corresponds to the initial state of the next step.)
Regarding Claim 10: The reference discloses The method as claimed in claim 1, wherein the artificial neural network is trained and designed to ascertain states of the electrical grid from measured values associated with the electrical grid. (Zivanovic. Page 77, First Paragraph, “The IEEE 14-bus power system is used for the simulation study of the Decoupled ANN SE with steepest descent dynamic used in static state estimation. The algorithm was presented in Section 3.1.1. Power system parameter data, initial values and metering locations are given in Appendix D. First, we solve the load flow problem for initial load values. The obtained results are used in order to find measurement values. The measurement set is composed of 30 active and 30 reactive power measurements, and 7 voltage magnitude measurements, as shown in Appendix D. Measurement errors are simulated as normally distributed random values with standard deviations 0.007pu for voltage magnitude, and 0.02pu for power measurements. The Runge-Kutta method is used to simulate ANN in steps 4 and 8 of the algorithm. The simulation program is written in MATLAB. The simulation CPU time on PC486, 66MHz was 256s. From the results of the simulation we plot the time evolution of the voltage phase angle and the voltage magnitude at node 2 in Figures 3.1 and 3.2 respectively.”)
Regarding Claim 11: The reference discloses The method as claimed in claim 1, wherein the artificial neural network was trained using a training data set of measured values associated with the electrical grid and states of the electrical grid belonging to the measured values. (Zivanovic. Page 77, First Paragraph, “The IEEE 14-bus power system is used for the simulation study of the Decoupled ANN SE with steepest descent dynamic used in static state estimation. The algorithm was presented in Section 3.1.1. Power system parameter data, initial values and metering locations are given in Appendix D. First, we solve the load flow problem for initial load values. The obtained results are used in order to find measurement values. The measurement set is composed of 30 active and 30 reactive power measurements, and 7 voltage magnitude measurements, as shown in Appendix D. Measurement errors are simulated as normally distributed random values with standard deviations 0.007pu for voltage magnitude, and 0.02pu for power measurements. The Runge-Kutta method is used to simulate ANN in steps 4 and 8 of the algorithm. The simulation program is written in MATLAB. The simulation CPU time on PC486, 66MHz was 256s. From the results of the simulation we plot the time evolution of the voltage phase angle and the voltage magnitude at node 2 in Figures 3.1 and 3.2 respectively.”)
Regarding Claim 12: The reference discloses The method as claimed in claim 11, wherein the training data set was formed by one or a plurality of simulations and/or historic measured values and associated historic states of the electrical grid. (Zivanovic. Page 77, First Paragraph, “The IEEE 14-bus power system is used for the simulation study of the Decoupled ANN SE with steepest descent dynamic used in static state estimation. The algorithm was presented in Section 3.1.1. Power system parameter data, initial values and metering locations are given in Appendix D. First, we solve the load flow problem for initial load values. The obtained results are used in order to find measurement values. The measurement set is composed of 30 active and 30 reactive power measurements, and 7 voltage magnitude measurements, as shown in Appendix D. Measurement errors are simulated as normally distributed random values with standard deviations 0.007pu for voltage magnitude, and 0.02pu for power measurements. The Runge-Kutta method is used to simulate ANN in steps 4 and 8 of the algorithm. The simulation program is written in MATLAB. The simulation CPU time on PC486, 66MHz was 256s. From the results of the simulation we plot the time evolution of the voltage phase angle and the voltage magnitude at node 2 in Figures 3.1 and 3.2 respectively.” Measurements values read on the historic measured values and simulation reads on simulations.)
Regarding Claim 13: The reference discloses The method as claimed in claim 1, carried out repeatedly in accordance with specified time intervals. (Zivanovic. Page 77, First Paragraph, “The IEEE 14-bus power system is used for the simulation study of the Decoupled ANN SE with steepest descent dynamic used in static state estimation… The simulation program is written in MATLAB. The simulation CPU time on PC486, 66MHz was 256s. From the results of the simulation we plot the time evolution of the voltage phase angle and the voltage magnitude at node 2 in Figures 3.1 and 3.2 respectively.” See Figure 3.1 and 3.2 for additional time intervals)
Regarding Claim 14: The reference discloses An apparatus for ascertaining a state of an electrical grid, the apparatus comprising:
a computing unit with a memory and a processor; (Zivanovic. Page 77, First Paragraph, “The IEEE 14-bus power system is used for the simulation study of the Decoupled ANN SE with steepest descent dynamic used in static state estimation…”)
wherein the state of the electrical grid is defined by voltages and phase angles at one or a plurality of grid nodes of the electrical grid; (Page 77, First Paragraph, “The IEEE 14-bus power system is used for the simulation study of the Decoupled ANN SE with steepest descent dynamic used in static state estimation… The simulation program is written in MATLAB. The simulation CPU time on PC486, 66MHz was 256s. From the results of the simulation we plot the time evolution of the voltage phase angle and the voltage magnitude at node 2 in Figures 3.1 and 3.2 respectively.”)
and the memory stores a set of instructions and, when the processor accesses and executes the set of instructions, the processor causes the computing unit to calculate the state of the electrical grid on the basis of a plurality of measured values associated with the electrical grid by an iterative numerical method starting from an initial value; (Page 77, Second Paragraph, “The algorithm converged in three iteration steps. At each iteration step the objective functions of active ANN, and reactive ANN are minimized.”)
wherein the initial value is previously ascertained by using an artificial neural network from the measured values. (Page 77, First Paragraph, “The IEEE 14-bus power system is used for the simulation study of the Decoupled ANN SE with steepest descent dynamic used in static state estimation. The algorithm was presented in Section 3.1.1. Power system parameter data, initial values and metering locations are given in Appendix D. First, we solve the load flow problem for initial load values. The obtained results are used in order to find measurement values. The measurement set is composed of 30 active and 30 reactive power measurements, and 7 voltage magnitude measurements, as shown in Appendix D. Measurement errors are simulated as normally distributed random values with standard deviations 0.007pu for voltage magnitude, and 0.02pu for power measurements. The Runge-Kutta method is used to simulate ANN in steps 4 and 8 of the algorithm. The simulation program is written in MATLAB. The simulation CPU time on PC486, 66MHz was 256s. From the results of the simulation we plot the time evolution of the voltage phase angle and the voltage magnitude at node 2 in Figures 3.1 and 3.2 respectively.”)
Zivanovic does not explicitly recite use the calculated state as the basis for a higher-order application selected from the group consisting of: generating a safety report, controlling a voltage, and preventative and corrective Security Constraint Optimal Power Flow (SCOPF).
However Wang recites use the calculated state as the basis for a higher-order application selected from the group consisting of: generating a safety report, (Wang. “[0062] Report generator 326 may generate reports relating to status information relating to the power system component(s), on command (e.g., from a user). Report generator 326 may also generate reports automatically in response to detected event(s), or periodically, wherein the report may be generated and provided (e.g., transmitted) to a desired destination (e.g., a destination address such as an email address of an operator, etc.). Report generator 326 may also generate alarms indicating an abnormal condition (e.g., fault, power system parameter outside of predefined threshold parameter value or range of parameter values, etc.), using a visual, audio, and/or vibrational indicator, e.g., which is detectable via other senses (e.g., touch).”) controlling a voltage, ([0104] “SCADA component 1110 may also allow operators at a central control center to perform or facilitate management of energy flow in the power grid system. For example, operators may use a SCADA component (e.g., using a computer such as a laptop or desktop) to facilitate performance of certain tasks such opening or closing circuit breakers, or other switching operations which might divert the flow of electricity.”) and preventative and corrective Security Constraint Optimal Power Flow (SCOPF)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the report and control aspect of Wang with the calculation of Zivanovic since it would “generate alarms indicating an abnormal condition” and “allow for operators at a central control center to perform or facilitate management of energy flow in the power grid system” (Wang, [0104])
7. Claim(s) 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Zivanovic in view of Wang and further in view of Zakerian, A., et al. "Bad data detection in state estimation using Decision Tree technique." 2017 Iranian Conference on Electrical Engineering (ICEE). IEEE, 2017, hereafter Zakerian.
Regarding Claim 8: Zivanovic and Wang do not explicitly recite The method as claimed in claim 1, wherein the state ascertained by the artificial neural network is not used as the initial value if the state fails a chi-squared test with a specified probability threshold.
However Zakerian recites wherein the state ascertained by the artificial neural network is not used as the initial value if the state fails a chi-squared test with a specified probability threshold. (Abstract, “State estimation is a useful study in every control and dispatching center that its outcome is used for other programs like optimal power flow and load frequency control. State estimation uses network model and a group of measurements to calculate the best estimation of state variables of system. Measurements may get amounts of error in data reading or transmitting. One of important issue of state estimator is bad data detection. A famous method for bad data detection is Chi-square method but it has some defections.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the failure of a chi-squared test with a specified probability threshold in a state estimation system as per Zakerian for the state estimation system of Zivanovic and Wang since “One of important issue of state estimator is bad data detection” and “A famous method for bad data detection is Chi-square method” even if it has some defections as noted by the art. (Zakerian. Abstract)
Regarding Claim 9: Zivanovic and Wang do not explicitly recite The method as claimed in claim 8, wherein the probability threshold is a value between 95 percent and 100 percent.
However Zakerian recites wherein the probability threshold is a value between 95 percent and 100 percent. (Page 1040, “The threshold residual for Chi-Square method calculated 171.57 for 190 measurements and 59 state variables. Chi-Squared method identified all conventional scenarios truly but distinguished 80 bad scenarios and 80 bad data scenarios were not detected, which result in accuracy 91.19% for Chi-Square method. This accuracy is promoted using DT and PNN methods.” The prior art notes that a 91% accuracy Chi-Square is not necessarily sufficient and promotes other methodologies such as DT and PNN to improve the accuracy to levels which would be between 95% and 100% such as in the tables for DT and PNN, Table 1 and 2 respectively.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the failure of a chi-squared test with a specified probability threshold in a state estimation system as per Zakerian for the state estimation system of Zivanovic and Wang since “One of important issue of state estimator is bad data detection” and “A famous method for bad data detection is Chi-square method”, Abstract of Zakerian, even if it has some defections as noted by the art. Specifically the threshold in the 95-100% range would have been obvious since Zakerian specifically notes in at least Page 1040 a level of accuracy greater than 91% and shown in other methodologies such as DT and PNN to improve the accuracy to levels which would be between 95% and 100% such as in the tables for DT and PNN, Table 1 and 2 respectively.
Conclusion
8. 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.
9. All Claims are rejected.
10. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
i) Biserica, Monica, et al. "Neural networks to improve distribution state estimation—Volt var control performances." IEEE Transactions on Smart Grid 3.3 (2012): 1137-1144, which teaches “a pseudomeasurement estimation using neural networks in order to improve the results of a distribution state estimator (DSE), used as inputs to a centralized Volt and Var control function.”
ii) Zargar, Behzad, et al. "Multiarea parallel data-driven three-phase distribution system state estimation using synchrophasor measurements." IEEE Transactions on Instrumentation and Measurement 69.9 (2020): 6186-6202, which teaches “Distribution system state estimation (DSSE) is one of the key functions used by distribution system operators (DSOs) for the management and control of the distribution grids.”)
iii) Nguyen, Ngac Ky, et al. "Neural networks for phase and symmetrical components estimation in power systems." 2009 35th Annual Conference of IEEE Industrial Electronics. IEEE, 2009 which teaches “An original three-phase neural approach for phase and symmetrical components estimation is proposed in this paper. This neural structure can be used for power quality control, in an active power filtering scheme for example. The approach is composed of a neural symmetrical voltage components extraction and a neural phase detection technique. These functional tasks are decomposed and approximated by the learning of Adaline neural networks. The whole neural architecture is implemented on a digital signal processor (DSP) and is applied to a three-phase power system.”
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SAA
/SAIF A ALHIJA/Primary Examiner, Art Unit 2186