DETAILED ACTION
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 responsive to communications: RCE filed on 8/20/2025.
Claims 1-11, and 13-21 are pending. Claims 1, 7, 11, and 17 are independent.
The rejection of claims 11, and 13-16 under 35 USC § 112(f) rejection have been withdrawn in view of the amendment.
The rejection of claims 1-11 and 13-20 under 35 USC § 101 have been maintained in view of the amendment.
The previous rejection of claims 1-11, and 13-20 under 35 USC § 103 have been withdrawn in view of the amendment.
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 1-11, and 13-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1
According to the first part of the analysis, in the instant case, claims 1-6 are directed to a non-transitory machine-readable medium, claims 7-10 are directed to a computing device, and claims 17-20 are directed towards a method. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Step 2A, Prong 1
Regarding Claim 1, claim recites
generate one or more training matrices based on the topology, each row of the one or more training matrices comprising a value corresponding to a respective sensor within the electric power delivery system, the value indicating a probability of an event location spatially associated with the respective sensor, wherein the one or more training matrices indicate possible events at respective line sections of a plurality of line sections (this step for generating training matrices appears to be practically implementable in the human mind and is understood to be a recitation of a mental process).
Regarding claim 7, claim recites
Generate one or more training matrices based on the topology, each row of the one or more training matrices comprising a value corresponding to a respective sensor of the one or more sensors within the electric power delivery system matrices, the value indicating a probability of an event occurring at a particular sensor (this step for generating appears to be practically implementable in the human mind and is understood to be a recitation of a mental process);
determine, using the machine learning prediction engine, a likelihood of the event occurring at the line section of the plurality of line sections based on the electrical parameters and the one or more training matrices (this step for determining appears to be practically implementable in the human mind and is understood to be a recitation of a mental process); and
Regarding claim 11, claim recites
Generating one or more training matrices based on the topology and a simulation of possible event locations, wherein each row of the one or more training matrices comprises a value corresponding to a respective sensor within the electric power delivery system, the value indicating a probability of an event location occurring at a location spatially associated with the respective sensor (this step for generating appears to be practically implementable in the human mind and is understood to be a recitation of a mental process and math)
determine, via a prediction model , a line section of the plurality of line sections identified as an event location based on a probability of an event occurring at the line section being greater than probabilities of other line sections of the plurality of line sections (this step for determining appears to be practically implementable in the human mind and is understood to be a recitation of a mental process).
In regards to claim 17
generating, via the processing circuitry, one or more training matrices based on the topology, wherein each row of the one or more training matrices comprises a value corresponding to a respective sensor within the electric power delivery system, the value indicating a probability of an event location within the electric power delivery system spatially associated with the respective sensor (this step for generating training matrices appears to be practically implementable in the human mind and is understood to be a recitation of a mental process).
Step 2A, Prong 2
In regards to Claim 1
receive a topology of an electric power delivery system (This limitation appears to be directed to receiving information, which is understood to be insignificant extra-solution activity);
train a machine learning engine based on the one or more training matrices to identify an event location on the electric power delivery system (This limitation recites training a machine learning engine as a mere tool to perform the abstract idea which is not indicative of integration into a practical application).
In regards to claim 7, claim recites
acquire electrical parameters of one or more sensors associated with a line section of a plurality of line sections in an electric power delivery system (This limitation appears to be directed to receiving information, which is understood to be insignificant extra-solution activity);
acquire a topology of the electric power delivery system (This limitation appears to be directed to receiving information, which is understood to be insignificant extra-solution activity);
provide, to a client device, an indication of the line section as an event location based on the likelihood of the event occurring at the line section being greater than respective likelihoods of the event occurring at other line sections of the plurality of line sections (This limitation appears to be directed to providing information, which is understood to be insignificant extra-solution activity).
In regards to claim 11, claim recites
a plurality of sensors, wherein each of the plurality of sensors is configured to be located at respective line sections of a plurality of line sections of an electric power delivery system (The sensors are understood to be generic computer equipment); and
a controller communicatively coupled to the plurality of sensors (This limitation is understood to be generic computer equipment and mere instructions to implement an abstract idea on a computer), wherein the controller is configured to:
acquire electrical parameters, event status, or both from at least some of the plurality of sensors for the respective line sections of the plurality of line sections (This limitation appears to be directed to receiving information, which is understood to be insignificant extra-solution activity)
receiving a topology of the electrical power delivery system from user input via a graphical user interface (This limitation appears to be directed to receiving information, which is understood to be insignificant extra-solution activity);
training a machine learning engine using the one or more training matrices (This limitation recites training a machine learning engine as a mere tool to perform the abstract idea which is not indicative of integration into a practical application).
In regards to claim 17, claim recites
receiving, via processing circuitry, a topology of an electric power delivery system (This limitation appears to be directed to receiving information, which is understood to be insignificant extra-solution activity);
training, via the processing circuitry, a machine learning engine using the one or more training matrices to determine a likelihood of an event location within the electric power delivery system (This limitation recites training a machine learning engine as a mere tool to perform the abstract idea which is not indicative of integration into a practical application).
Step 2B
In regards to claim 1, claim recites
receive a topology of an electric power delivery system (This limitation appears to be directed to receiving information, which is understood to be insignificant extra-solution activity. MPEP 2106.05 (g).);
train a machine learning engine based on the one or more training matrices to identify an event location (This limitation recites training a machine learning engine as a mere tool to perform the abstract idea which taken alone or in combination, fails to amount to significantly more than the judicial exception MPEP 2105.05(f).).
In regards to claim 7, claim recites
acquire electrical parameters of one or more sensors associated with a line section of a plurality of line sections in an electric power delivery system (This limitation appears to be directed to receiving information, which is understood to be insignificant extra-solution activity, MPEP 2106.05 (g).);
acquire a topology of the electric power delivery system (This limitation appears to be directed to receiving information, which is understood to be insignificant extra-solution activity, MPEP 2106.05 (g).)
provide, to a client device, an indication of the line section as an event location based on the likelihood of the event occurring at the line section being greater than respective likelihoods of the event occurring at other line sections of the plurality of line sections (This limitation appears to be directed to providing information, which is understood to be insignificant extra-solution activity, MPEP 2106.05 (g).).
In regards to claim 11, claim recites
a plurality of sensors, wherein each of the plurality of sensors is configured to be located at respective line sections of a plurality of line sections of an electric power delivery system (The sensors are understood to be generic computer equipment, MPEP 2106.05(f)); and
a controller communicatively coupled to the plurality of sensors (This limitation is understood to be generic computer equipment and mere instructions to implement an abstract idea on a computer MPEP 2106.05(f)), wherein the controller is configured to:
acquire electrical parameters, event status, or both from at least some of the plurality of sensors for the respective line sections of the plurality of line sections (This limitation appears to be directed to receiving information, which is understood to be insignificant extra-solution activity, MPEP 2106.05(g)); and
receiving a topology of the electrical power delivery system from user input via a graphical user interface (This limitation appears to be directed to receiving information, which is understood to be insignificant extra-solution activity, MPEP 2106.05(g));
training a machine learning engine using the one or more training matrices (This limitation recites training a machine learning engine as a mere tool to perform the abstract idea which taken alone or in combination, fails to amount to significantly more than the judicial exception MPEP 2105.05(f).).
In regards to claim 17, claim recites
receiving, via processing circuitry, a topology of an electric power delivery system (This limitation appears to be directed to receiving information, which is understood to be insignificant extra-solution activity. MPEP 2106.05 (g).);
training, via the processing circuitry, a machine learning engine using the one or more training matrices to determine a likelihood of an event location within the electric power delivery system (This limitation recites training a machine learning engine as a mere tool to perform the abstract idea which taken alone or in combination, fails to amount to significantly more than the judicial exception MPEP 2105.05(f).).
Step 2A, Prong 1 Dependent Claims
Regarding claim 2
wherein the event location comprises a faulted line section (This step appears to be practically implementable in the human mind and are understood to be a recitation of a mental process).
Regarding claim 3
wherein the topology indicates a number of sensors located at each of the plurality of line sections and a type of phase associated with each of the number of sensors (This step for generating topology appears to be practically implementable in the human mind and are understood to be a recitation of a mental process)
Regarding claim 4
comprising machine-readable instructions that cause the one or more processors to train the machine learning engine based on a regularized one-vs-rest logistic regression model, a regularized multinomial logistic regression model, a support vector machine (SVM) regression model, or any combination thereof (This step for training appears to be practically implementable in the human mind and are understood to be a recitation of a mental process and calculation).
Regarding claim 5
receive user input to modify the topology; modify the one or more training matrices based on the user input; and update the machine learning engine based on the one or more training matrices being modified (This step for updating topology appears to be practically implementable in the human mind and are understood to be a recitation of a mental process)
Regarding claim 6
wherein modifying the topology comprises adding at least one sensor to the electric power delivery system, removing at least one sensor from the electric power delivery system, relocating at least one sensor in the electric power delivery system, or any combination thereof (This step for modify topology appears to be practically implementable in the human mind and are understood to be a recitation of a mental process)
Regarding claim 9
wherein the electrical parameters comprise an event status for the line section, wherein the event status indicates whether the event occurred at the line section according to the one or more sensors (This step for generating topology appears to be practically implementable in the human mind and are understood to be a recitation of a process).
Regarding claim 10
determine, using the machine learning prediction engine, the likelihood of the event occurring at the line section; and provide, to the client device, an indication of the line section as the event location despite at least one of the one or more sensors failing to operate or providing an inaccurate event status of the line section (This step for determining likelihood appears to be practically implementable in the human mind and are understood to be a recitation of a mental process).
Regarding claim 13
wherein the topology indicates a number of the plurality of sensors at the respective line sections and a type of phase of each of the plurality of sensors (This step for determining likelihood appears to be practically implementable in the human mind and are understood to be a recitation of a mental process).
Regarding claim 14
wherein the controller is configured to train the machine learning engine using a logistic regression model (This step for determining likelihood appears to be practically implementable in the human mind and are understood to be a recitation of a mental process and math).
Regarding claim 18
wherein the one or more training matrices are based on every potential occurrence of the event location (This step for generating training matrices appears to be practically implementable in the human mind and are understood to be a recitation of a mental process).
Regarding claim 19
wherein the topology comprises a relationship between a plurality of line sections and a corresponding plurality of line sensors associated with the electric power delivery system (This step for generating training matrices appears to be practically implementable in the human mind and are understood to be a recitation of a mental process).
Regarding claim 20
wherein the one or more training matrices are based on phases of the electric power delivery system (This step for generating training matrices appears to be practically implementable in the human mind and are understood to be a recitation of a mental process)
Regarding claim 21
wherein determining the likelihood of the event occurring at the line section is greater than respective likelihoods of the event occurring at other line sections of the plurality of line sections comprises determining that the likelihood of the event occurring at the line section exceeds a threshold probability (This limitation for determining appears to be practically implementable in the human mind and are understood to be a recitation of a mental process and math).
Step 2A Prong 2 dependent claims
Regarding claim 8
wherein the one or more sensors comprise one or more wireless line sensors, one or more intelligent electronic devices, one or more relays, or any combination thereof (This limitation appears to be directed to receiving information, which is understood to be insignificant extra-solution activity such as data gathering.)
Regarding claim 15
wherein the system comprises a first hardware component for training the machine learning engine and a second hardware component for determining the event location using the prediction model (This limitation is understood to be generic computer equipment and mere instructions to implement an abstract idea on a computer).
Regarding claim 16
wherein the machine learning engine is implemented on a cloud server (This limitation is understood to be generic computer equipment and mere instructions to implement an abstract idea on a computer).
Step 2B dependent claims
Regarding claim 8
wherein the one or more sensors comprise one or more wireless line sensors, one or more intelligent electronic devices, one or more relays, or any combination thereof (This limitation appears to be directed to receiving information, which is understood to be insignificant extra-solution activity such as data gathering. MPEP 2106.05(g).)
Regarding claim 15
wherein the system comprises a first hardware component for training the machine learning engine and a second hardware component for determining the event location using the prediction model (This limitation is understood to be generic computer equipment and mere instructions to implement an abstract idea on a computer MPEP 2106.05(f)).
Regarding claim 16
wherein the machine learning engine is implemented on a cloud server (This limitation is understood to be generic computer equipment and mere instructions to implement an abstract idea on a computer MPEP 2106.05(f)).
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.
Claim(s) 1-10, and 17-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tong et al. (“Detection and Classification of Transmission Line Transient Faults Based on Graph Convolutional Neural Network”) in view of Li et al. (“Physics-Informed Graph Learning for Robust Fault Location in Distribution Systems”) and Yuan et al. (US7,035,763).
In regards to claim 1, Tong et al. discloses a non-transitory machine-readable medium, comprising machine-readable instructions that, when executed by one or more processors, cause the one or more processors to:
receive a topology of an electric power delivery system (Tong et al. fig. 4 pg458 section III.B para1-2, receive topology of IEEE 9-bus system);
generate one or more training matrices based on the topology, wherein the one or more training matrices indicate possible events at respective line sections of a plurality of line sections (Tong et al. pg460 section III.C para2, generates an input matrix based on the topology); and
Tong et al. does not explicitly disclose train a machine learning engine based on the one or more training matrices to identify an event location.
However Li et al. discloses train a machine learning engine based on the one or more training matrices to identify an event location on the electric power delivery system(Li et al. pg3 section II.B para2, trains system to classify fault location from topology).
It would have been obvious to one of ordinary skill in the art before the filing date of invention to have combined the fault detection method of Tong et al. with the Fault location system of Li et al. in order to find and repair faults in a power grid with a limited number of sensors (Li et al. pg1 abstract para1).
Tong et al. does not explicitly disclose each row of the one or more training matrices comprising a value corresponding to a respective sensor within the electric power delivery system, the value indicating a probability of an event location spatially associated with the respective sensor.
However Yuan et al. discloses each row of the one or more training matrices comprising a value corresponding to a respective sensor within the electric power delivery system, the value indicating a probability of an event location spatially associated with the respective sensor (Yuan et al. col6 ln50 to col7 ln11, generates probability distribution for each sensor vector, and generates a covariance matrix from sensor vectors).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined fault detection method of Tong et al. with the fault model generation method of Yuan et al. in order to detect and localize faults to be corrected in a timely manner (Yaun et al. col1 ln53-60).
In regards to claim 2, Tong et al. as modified by Li et al. and Yuan et al. discloses the machine-readable medium of claim 1, wherein the event location comprises a faulted line section (Tong et al. pg458 section III.A.2 para1-2).
In regards to claim 3, Tong et al. as modified by Li et al. and Yuan et al. discloses the machine-readable medium of claim 1, wherein the topology indicates a number of sensors located at each of the plurality of line sections and a type of phase associated with each of the number of sensors (Li et al. pg1 section I para3, topology indicates number and location of wide area sensors such as phasor measurement units (PMUs)).
It would have been obvious to one of ordinary skill in the art before the filing date of invention to have combined the fault detection method of Tong et al. with the Fault location system of Li et al. in order to find and repair faults in a power grid with a limited number of sensors (Li et al. pg1 abstract para1).
In regards to claim 4, Tong et al. as modified by Li et al. and Yuan et al. discloses the machine-readable medium of claim 1, comprising machine-readable instructions that cause the one or more processors to train the machine learning engine based on a regularized one-vs-rest logistic regression model, a regularized multinomial logistic regression model, a support vector machine (SVM) regression model, or any combination thereof (Tong et al. pg462 section IV.C para3).
In regards to claim 5, Tong et al. as modified by Li et al. and Yuan et al. discloses the machine-readable medium of claim 1, comprising machine-readable instructions that cause the one or more processors to:
receive user input to modify the topology (Li et al. pg8 section V.E para3);
modify the one or more training matrices based on the user input (Li et al. pg8 section V.E para1); and
update the machine learning engine based on the one or more training matrices being modified (Li et al. pg8 section V.E para1).
It would have been obvious to one of ordinary skill in the art before the filing date of invention to have combined the fault detection method of Tong et al. with the Fault location system of Li et al. in order to find and repair faults in a power grid with a limited number of sensors (Li et al. pg1 abstract para1).
In regards to claim 6, Tong et al. as modified by Li et al. and Yuan et al. discloses the machine-readable medium of claim 5, wherein modifying the topology comprises adding at least one sensor to the electric power delivery system, removing at least one sensor from the electric power delivery system, relocating at least one sensor in the electric power delivery system, or any combination thereof (Li et al. pg9 section6 para2, modify topology by relocating sensors (PMUs) to optimal locations).
It would have been obvious to one of ordinary skill in the art before the filing date of invention to have combined the fault detection method of Tong et al. with the Fault location system of Li et al. in order to find and repair faults in a power grid with a limited number of sensors (Li et al. pg1 abstract para1).
In regards to claim 7, Tong et al. discloses a computing device, comprising:
processing circuitry that comprises a machine learning prediction engine (Tong et al. pg456 section I para3), wherein the processing circuitry is configured to:
acquire electrical parameters of one or more sensors associated with a line section of a plurality of line sections in an electric power delivery system (Tong et al. fig. 4 pg458 section III.B para1-2, receive topology of IEEE 9-bus system and acquire electrical parameters (nodal voltage waveforms));
acquire topology of the electric power delivery system (Tong pg457 section I para5, acquire power system topology for use in GCN);
provide, to a client device, an indication of the line section as an event location based on the likelihood of the event occurring at the line section being greater than respective likelihoods of the event occurring at other line sections of the plurality of line sections (Tong et al. pg461 section III.D para1, generate visualization of predicted fault data).
Tong et al. does not explicitly disclose determine, using the machine learning prediction engine, a likelihood of an event occurring at the line section of the plurality of line sections based on the electrical parameters and the one or more training matrices.
However Li et al. discloses determine, using the machine learning prediction engine, a likelihood of an event occurring at the line section of the plurality of line sections based on the electrical parameters (Li et al. pg3 section II.B para2, machine learning system to predicts fault location from topology);
It would have been obvious to one of ordinary skill in the art before the filing date of invention to have combined the fault detection method of Tong et al. with the Fault location system of Li et al. in order to find and repair faults in a power grid with a limited number of sensors (Li et al. pg1 abstract para1).
Tong et al. does not explicitly disclose generate one or more training matrices based on the topology, each row of the one or more training matrices comprising a value corresponding to a respective sensor of the one or more sensors within the electric power delivery system, the value indicating a probability of an event occurring at a particular location spatially associated with the respective sensor.
However Vilim et al. discloses generate one or more training matrices based on the topology, each row of the one or more training matrices comprising a value corresponding to a respective sensor of the one or more sensors within the electric power delivery system, the value indicating a probability of an event occurring at a particular location spatially associated with the respective sensor (Yuan et al. col6 ln50 to col7 ln11, generates probability distribution for each sensor vector, and generates a covariance matrix from sensor vectors).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined fault detection method of Tong et al. with the fault model generation method of Yuan et al. in order to detect and localize faults to be corrected in a timely manner (Yaun et al. col1 ln53-60).
In regards to claim 8, Tong et al. as modified by Li et al. and Yuan et al. discloses the computing device of claim 7, wherein the one or more sensors comprise one or more wireless line sensors, one or more intelligent electronic devices, one or more relays, or any combination thereof (Li et al. pg1 section I para3, wide area sensors such as advanced metering infrastructure (AMIs), and phasor measurement units (PMUs) ).
It would have been obvious to one of ordinary skill in the art before the filing date of invention to have combined the fault detection method of Tong et al. with the Fault location system of Li et al. in order to find and repair faults in a power grid with a limited number of sensors (Li et al. pg1 abstract para1).
In regards to claim 9, Tong et al. as modified by Li et al. and Yuan et al. discloses the computing device of claim 7, wherein the electrical parameters comprise an event status for the line section, wherein the event status indicates whether the event occurred at the line section according to the one or more sensors (Li et al. pg2 section II para1, once a fault occurs according to sensors (PMUs), faulty position is treated as label of event).
It would have been obvious to one of ordinary skill in the art before the filing date of invention to have combined the fault detection method of Tong et al. with the Fault location system of Li et al. in order to find and repair faults in a power grid with a limited number of sensors (Li et al. pg1 abstract para1).
In regards to claim 10, Tong et al. as modified by Li et al. and Yuan et al. discloses the computing device of claim 7, wherein the processing circuitry is configured to:
determine, using the machine learning prediction engine, the likelihood of the event occurring at the line section (Tong et al. pg463 section IV.D para3); and
provide, to the client device, an indication of the line section as the event location despite at least one of the one or more sensors failing to operate or providing an inaccurate event status of the line section (Tong et al. pg464 section IV.E para2-3).
In regards to claim 17, Tong et al. discloses a method, comprising:
receiving, via processing circuitry, a topology of an electric power delivery system (Tong et al. fig. 4 pg458 section III.B para1-2, receive topology of IEEE 9-bus system);
generating, via the processing circuitry, one or more training matrices based on the topology (Tong et al. pg460 section III.C para2, generates an input matrix based on the topology); and
Tong et al. does not explicitly disclose training, via the processing circuitry, a machine learning engine using the one or more training matrices to determine a likelihood of an event location within the electric power delivery system.
However Li et al. discloses training, via the processing circuitry, a machine learning engine using the one or more training matrices to determine a likelihood of an event location within the electric power delivery system (Li et al. pg3 section II.B para2, trains system to classify fault location from topology).
It would have been obvious to one of ordinary skill in the art before the filing date of invention to have combined the fault detection method of Tong et al. with the Fault location system of Li et al. in order to find and repair faults in a power grid with a limited number of sensors (Li et al. pg1 abstract para1).
Tong et al. does not explicitly disclose wherein each row of the one or more training matrices comprises a value corresponding to a respective sensor within the electrical power delivery system, the value indicating a probability of an event occurring at a particular location spatially associated with the respective sensor.
However Yuan et al. discloses wherein each row of the one or more training matrices comprises a value corresponding to a respective sensor within the electrical power delivery system, the value indicating a probability of an event occurring at a particular location spatially associated with the respective sensor (Yuan et al. col6 ln50-61, generates probability distribution for sensor).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined fault detection method of Tong et al. with the sensor evaluation method of Vilim et al. in order to identify and correct sensor output (Vilim et al. para[0008]).
In regards to claim 18, Tong et al. as modified by Li et al. and Yuan et al. disclose the method of claim 17, wherein the one or more training matrices are based on every potential occurrence of the event location (Tong et al. pg457 section II.A para2).
In regards to claim 19, Tong et al. as modified by Li et al. and Yuan et al. disclose the method of claim 17, wherein the topology comprises a relationship between a plurality of line sections and a corresponding plurality of line sensors associated with the electric power delivery system (Li et al. pg1 section1 para3, topology indicates number and location of wide area sensors such as phasor measurement units (PMUs)).
It would have been obvious to one of ordinary skill in the art before the filing date of invention to have combined the fault detection method of Tong et al. with the Fault location system of Li et al. in order to find and repair faults in a power grid with a limited number of sensors (Li et al. pg1 abstract para1).
In regards to claim 20, Tong et al. as modified by Li et al. and Yuan et al. disclose the method of claim 17, wherein the one or more training matrices are based on phases of the electric power delivery system (Li et al. pg3 section II.B para1).
It would have been obvious to one of ordinary skill in the art before the filing date of invention to have combined the fault detection method of Tong et al. with the Fault location system of Li et al. in order to find and repair faults in a power grid with a limited number of sensors (Li et al. pg1 abstract para1).
In regards to claim 21, Tong et al. as modified by Li et al. and Yuan et al. disclose the computing device of claim 7, wherein determining the likelihood of the event occurring at the line section is greater that respective likelihoods of the event occurring at other line sections of the plurality of the plurality of line sections comprises determining that the likelihood of the event occurring at the line section exceeds a threshold probability (Tong et al. pg461 section III.C para6).
Claim(s) 11, and 13-15, is/are rejected under 35 U.S.C. 103 as being unpatentable over Tong et al. in view of Li et al., Vilim et al. (US2015/0177030) and Yuan et al.
In regards to claim 11, Tong et al. discloses a system comprising:
a controller communicatively coupled to the plurality of sensors (Tong et al. pg456 section1 para3), wherein the controller is configured to:
acquire electrical parameters, event status, or both from at least some of the plurality of sensors for the respective line sections of the plurality of line sections (Tong et al. fig. 4 pg458 section III.B para1-2, receive topology of IEEE 9-bus system and acquire electrical parameters (nodal voltage waveforms));
training a machine learning engine using the one or more training matrices (Tong et al. pg458 section II.B para3, uses matrix Xi to train hidden layer neurons).
Tong et al. does not explicitly disclose a plurality of sensors, wherein each of the plurality of sensors is configured to be located at respective line sections of a plurality of line sections of an electric power delivery system;
determine, via a prediction model , a line section of plurality of line sections identified as an event location based on a probability of an event occurring at the line section being greater than probabilities of other line sections of the plurality of line sections.
However Li et al. discloses a plurality of sensors, wherein each of the plurality of sensors is configured to be located at respective line sections of a plurality of line sections of an electric power delivery system (Li et al. section I para3, installs wide area sensors at grid nodes);
determine, via a prediction model , a line section of plurality of line sections identified as an event location based on a probability of an event occurring at the line section being greater than probabilities of other line sections of the plurality of line sections (Li et al. pg3 section II.B para2, machine learning system to predicts fault location from topology).
It would have been obvious to one of ordinary skill in the art before the filing date of invention to have combined the fault detection method of Tong et al. with the Fault location system of Li et al. in order to find and repair faults in a power grid with a limited number of sensors (Li et al. pg1 abstract para1).
Tong et al. does not explicitly disclose receive a topology of the electric power delivery system from user input via a graphical user interface.
However Vilim et al. discloses receive a topology of the electric power delivery system from user input via a graphical user interface (Vilim et al. fig. 3 para[0143], GUI used to create model of system based on training data).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined fault detection method of Tong et al. with the sensor evaluation method of Vilim et al. in order to identify and correct sensor output (Vilim et al. para[0008]).
Tong et al. does not explicitly disclose generating one or more training matrices based on the topology and a simulation of possible event locations, wherein each row of the one or more training matrices comprises a value corresponding to a respective sensor within the electric power delivery system the value indicating a probability of an event occurring at a particular location spatially associated with the respective sensor.
However Yuan et al. discloses generating one or more training matrices based on the topology and a simulation of possible event locations, wherein each row of the one or more training matrices comprises a value corresponding to a respective sensor within the electric power delivery system the value indicating a probability of an event occurring at a particular location spatially associated with the respective sensor (Yuan et al. col6 ln50 to col7 ln11, generates probability distribution for each sensor vector, and generates a covariance matrix from sensor vectors).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined fault detection method of Tong et al. with the fault model generation method of Yuan et al. in order to detect and localize faults to be corrected in a timely manner (Yaun et al. col1 ln53-60).
In regards to claim 13, Tong et al. as modified by Li et al., Vilim et al., and Yuan et al. discloses the system of claim 12, wherein the topology indicates a number of the plurality of sensors at the respective line sections and a type of phase of each of the plurality of sensors (Li et al. pg1 section I para3, topology indicates number and location of wide area sensors such as phasor measurement units (PMUs)).
It would have been obvious to one of ordinary skill in the art before the filing date of invention to have combined the fault detection method of Tong et al. with the Fault location system of Li et al. in order to find and repair faults in a power grid with a limited number of sensors (Li et al. pg1 abstract para1).
In regards to claim 14, Tong et al. as modified by Li et al. Vilim et al., and Yuan et al. discloses the system of claim 12, wherein the processing circuitry is configured to train the machine learning engine using a logistic regression model (Tong et al. pg462 section IV.C para3).
In regards to claim 15, Tong et al. as modified by Li et al. Vilim et al., and Yuan et al. discloses the system of claim 11, wherein the system comprises a first hardware component for training the machine learning engine and a second hardware component for determining the event location using the prediction model (Tong et al. pg461 section III.D para1).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tong et al. in view of Li et al., Vilim et al., Yuan et al., and Cheim (WO 2019/141856 A1) as made of reference in IDS dated 12/15/2021.
In regards to claim 16, Tong et al. as modified by Li et al. Vilim et al., and Yuan et al. discloses the system of claim 11. Tong et al. does not explicitly disclose wherein the machine learning engine is implemented on a cloud server.
However Cheim discloses wherein the machine learning engine is implemented on a cloud server (Cheim pg28 ln10-17).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the fault detection method of Tong with the power monitoring system of Cheim in order to identify and mitigate the risk of power system failure (Cheim pg1 ln19-21).
Response to Arguments
Applicant’s arguments, see pg7, filed 7/28/2025, with respect to claims 11 and have been fully considered and are persuasive. The 112 rejection of 5/28/25 has been withdrawn.
Applicant's arguments filed 7/28/2025 have been fully considered but they are not persuasive. In regards to the 101 rejection of claims 1-11, and 13 -20 applicant argues on page 8 that the claims do not recite an abstract idea. However the limitations “generate one or more training matrices based on the topology, each row of the one or more training matrices comprising a value corresponding to a respective sensor withing the electric power delivery system, the value indicating a probability of an event occurring at a particular location spatially associated with the respective sensor, wherein the one or more training matrices indicate possible events at respective line sections of a plurality of line sections” are directed towards the mental process and mathematics of calculating possible probability values and placing them in a matrix.
In regards to claim 1, Applicant argues on page 14 that the judicial exception is integrated into a practical application. However the limitations “receive a topology of an electric power delivery system;” and “train a machine learning engine based on the one or more training matrices to identify an event location” are directed towards receiving information (a topology of an electric delivery system) which is regarded as insignificant extra solution activity, and training a machine learning model as a mere tool to perform the abstract idea of identifying an event location, which is not indicative of integration into a practical application.
Applicant argues on page 16 that claims add specific limitations to the claims that are not well-understood, routine or conventional activity in the field. However “receive a topology of an electric power delivery system”, well understood, routine conventional activity (Tong et al. fig. 4 pg458 section III.B para1-2) and taken alone or in combination fails to amount to significantly more than the abstract idea.
Applicant’s arguments with respect to claims 1-11 and 13-21 have been considered but are moot because the arguments do not apply the current rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bharadwaj et al. ( US11,269,752) teaches using a network of sensors to generate a probability of a failure occurring.
Srinivasan et al. (US2020/0209841) teaches determining probability of different faults.
Abrami et al. (US2020/0004655) teaches aligning a collection of independent sensors.
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/N.H/Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141