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 .
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 1-20 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Under step 1, claim 1 belongs to a statutory category, namely it is a method claim. Likewise, claim 16 is a system claim.
Under step 2A, prong 1: this part of the eligibility analysis evaluates whether the claim recites a judicial exception as explained in MPEP 2106.4, subsection II, a claim recites a judicial exception when the judicial exception is set forth or described in the claim.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. “mathematical relationships/algorithms/concepts” or “mental process and concepts performed in the human mind” which the court has identified as abstract) without significantly more. Claim 1 is directed to the abstract idea of calculating a plurality of fault-loop impedance signals from the first plurality of voltage signals and the first plurality of current signals;
comparing the plurality of fault-loop impedance signals and a plurality of reference impedance values to generate a plurality of difference values; inputting the plurality of difference values to a convolutional neural network (CNN) - gated recurrent unit (GRU) hybrid model and generating a reference tripping signal; inputting the plurality of difference values to a processing circuitry of the distance relay and generating a relay tripping signal; defining a deep reinforcement learning (DRL) agent for a deep reinforcement learning (DRL) model using a plurality of magnitude values and a plurality of phase angle values of the plurality of fault-loop impedance signals;
inputting the reference tripping signal and the relay tripping signal to the deep reinforcement learning (DRL) agent of the deep reinforcement learning model; performing an action-reward process using the deep reinforcement learning (DRL) agent for training the deep reinforcement learning model; processing a second plurality of voltage signals and a second plurality of current signals of the microgrid using the trained deep reinforcement learning (DRL) model for detecting one or more fault signals; and classifying the one or more fault signals into one or more fault types using the convolutional neural network (CNN) - gated recurrent unit (GRU) hybrid model.. These limitations fall under mathematical concepts (i.e. calculating impedance signals) and mental processes (i.e. comparing, defining, classifying data etc.). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the only additional elements are measuring a first plurality of voltage signals and a first plurality of current signals of the 5 microgrid, wherein each of the first plurality of voltage signals and each of the first plurality of current signals are a set of data points sequenced in time; which is mere data gathering and a microgrid, which is conventional or generic equipment which does not add anything significant to the judicial exception because this element is needed in order to detect a fault in the system. The claim as a whole does not amount to significantly more than the abstract idea itself.
Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. “mathematical relationships/algorithms/concepts” without significantly more. Claim 16 is directed to the abstract idea of implementing a first process using a deep reinforcement learning model; implementing a second process using a convolutional neural network - gated recurrent unit (CNN-GRU) hybrid model; wherein the first process comprises detection of one or more faults of the microgrid; and wherein the second process comprises classification of the one or more faults of the microgrid into one or more fault types.. These limitations fall under mathematical concepts. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the only additional elements are a first processing circuitry, a second processing circuit, distance relay unit and a microgrid, which are conventional or generic equipment which does not add anything significant to the judicial exception because these elements are needed in order to classify faults in the microgrid. The claim as a whole does not amount to significantly more than the abstract idea itself.
The data gathering and processing steps, and other elements, are recited so generically (no details whatsoever are provided other than e.g., “wherein the second process comprises classification of the one or more faults of the microgrid into one or more fault types.”) that it represents no more than mere instructions to apply the judicial exceptions on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exceptions to the technological environment of a computer. Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claims are directed to the judicial exception. No specific practical application is associated with the claimed system. For instance, nothing is done with the classification of the faults.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above, merely amount to a general purpose computer system that attempts to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use, and/or merely insignificant extra-solution activity.
Dependent claims 2-15 and 17-20 merely expand upon the abstract idea further defining the abstract steps of claims 1 and 16 respectively, and therefore stand rejected under 35 USC 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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 for establishing a background for determining obviousness under 35 U.S.C. 103 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 non-obviousness.
Claim(s) 16- is/are rejected under 35 U.S.C. 103 as being unpatentable over Dehghanian et al. (US 2020/0212676 A1, hereinafter Deh), and further in view of Blood et al. (US 2022/0407311 A1, hereinafter Blo).
Regarding claim 16, Deh discloses a fault detection system coupled to a microgrid, comprising:
a first processing circuitry configured to implement a first process using a deep reinforcement learning model (see abstract and para. 0008);
a second processing circuitry configured to implement a second process using a convolutional neural network - gated recurrent unit (CNN-GRU) hybrid model (see para. 0035);
wherein the first process comprises detection of one or more faults (i.e. event detection) of the microgrid (see abstract and para. 0031); and wherein the second process comprises classification of the one or more faults of the microgrid into one or more fault types (see para. 0031 and 0035).
However, Deh fails to disclose a distance relay unit.
Blo discloses a distance relay unit (see para. 0176).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Deh’s invention to include a distance relay as taught by Blo for the benefit of detecting faults on transmission and distribution lines.
Regarding claim 17, Deh in view of Blo discloses the fault detection system of claim 16, configured to perform an action-reward training process for the deep learning model employing the convolutional neural network - gated recurrent unit (CNN-GRU) hybrid model and the distance relay (see Deh para. 0008, 0054 and 0088-0089).
Regarding claim 18, Deh in view of Blo discloses the fault detection system of claim 16, wherein the microgrid comprises a three- phase power network including a high voltage transmission network (see Deh para. 0033).
Regarding claim 19, Deh in view of Blo discloses the fault detection system of claim 16, wherein the microgrid comprises renewable energy sources (see Blo para. 0171).
Regarding claim 20, Deh in view of Blo discloses the fault detection system of claim 16, further comprises a directional overcurrent relay (DIR) coupled to the first processing circuitry and the second processing circuitry (see Blo para. 0176 and 0206).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANUEL A RIVERA VARGAS whose telephone number is (571)270-7870. The examiner can normally be reached M-F 9:00-6:00.
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/MANUEL A RIVERA VARGAS/Primary Examiner, Art Unit 2857
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