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 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
A method for detecting a location of a disturbance event in an electrical system, the method comprising:
acquiring, by at least one Intelligent Electronic Device (IED) of an electrical system, energy-related signals associated with the electrical system;
processing the energy-related data to identify an occurrence of at least one disturbance event in the electrical system;
in response to identifying the occurrence of the at least one disturbance event,
applying each of a plurality of disturbance event location detection algorithms to the energy-related data, wherein each of the applied disturbance event location detection algorithms generates an output representative of an independently ascertained candidate location of the at least one disturbance event relative to the IED; and
combining the outputs of the disturbance event location detection algorithms to determine a location or origin of the at least one disturbance event from the candidate locations based on an analysis of the combined outputs.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation, that covers mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, steps of “processing the energy-related data to identify an occurrence of at least one disturbance event in the electrical system (analyzing data to make a determination);
in response to identifying the occurrence of the at least one disturbance event,
applying each of a plurality of disturbance event location detection algorithms to the energy-related data, wherein each of the applied disturbance event location detection algorithms generates an output representative of an independently ascertained candidate location of the at least one disturbance event relative to the IED (determination based on decided analysis); and
combining the outputs of the disturbance event location detection algorithms to determine a location or origin of the at least one disturbance event from the candidate locations based on an analysis of the combined outputs (determination based on analysis)” are treated by the Examiner as belonging to mental process grouping.
Similar limitations comprise the abstract ideas of Claim 13.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
Claim 1: A method for detecting a location of a disturbance event in an electrical system, the method comprising: acquiring, by at least one Intelligent Electronic Device (IED) of an electrical system, energy-related signals associated with the electrical system;
Claim 13: A system for detecting a location of a disturbance event in an electrical system (Note the electrical system is not appositively recited element of the system and therefore does not carry patentable weight), the system comprising: at least one Intelligent Electronic Device (IED) communicatively coupled to an electrical system, the IED configured to acquire energy-related signals associated with the electrical system; a processor receiving and responsive to the energy-related signals acquired by the at least one IED; and a memory storing processor-executable instructions.
The additional element in the preamble of “A method/system for detecting a location of a disturbance event in an electrical system” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use. Acquiring energy-related signals associated with the electrical system represents a mere data gathering step and only adds an insignificant extra-solution activity to the judicial exception. A memory (generic memory) and a processor and Intelligent Electronic Device (generic processors) are generally recited and are not qualified as particular machines.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis).
The claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-12 and 14-24 provide additional features/steps which are part of an expanded algorithm, so these limitations should be considered part of an expanded abstract idea of the independent claims.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-5, 7—17, and 19-24 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Aleman et al. (US 20210072303 A1), hereinafter “Aleman”.
Regarding Claim 1, Aleman teaches a method for detecting a location of a disturbance event in an electrical system, the method comprising:
acquiring, by at least one Intelligent Electronic Device (IED) of an electrical system, energy-related signals associated with the electrical system (Aleman [0138] The information processing component 572 includes a communications interface 610 that, in an example, receives data reported via the communications system 570 by various monitors in the electrical distribution line 506.);
processing the energy-related data to identify an occurrence of at least one disturbance event in the electrical system (Aleman [0139] In the illustrated example, the first fault location estimation process 612 and the second fault location estimation process 614 produce fault maps that indicate probability values of the location of a detected line fault.);
in response to identifying the occurrence of the at least one disturbance event,
applying each of a plurality of disturbance event location detection algorithms to the energy-related data, wherein each of the applied disturbance event location detection algorithms generates an output representative of an independently ascertained candidate location of the at least one disturbance event relative to the IED (Aleman [0138] The communications interface 610 in an example provides the received data to one or both of a first fault location estimation process 612 and a second fault location estimation process 614. Also see Fig. 6 612-618); and
combining the outputs of the disturbance event location detection algorithms to determine a location or origin of the at least one disturbance event from the candidate locations based on an analysis of the combined outputs (Aleman [0140] The information processing component 572 further includes a composite location estimation determination process 620. The composite location estimation determination process 620 is an example of a process performed by a composite fault map processor. In an example, the composite location estimation determination process 620 receives fault maps from one or more sources, such as from either one or both of the illustrated first set of fault maps 616 and the second set of fault maps 618, and produces a composite location estimation for each determined line fault. See Fig. 6 320).
Regarding Claim 2, Aleman further teaches applying weighted balloting to each output of the applied disturbance event location detection algorithms to generate weighted outputs before combining the outputs (Aleman [0146] In an example, determining a composite line fault likelihood value for a particular segment includes computing a weighted average of the likelihood values for that particular segment contained in two more fault maps. For example, a likelihood values in a fault map from the second set of fault maps 618 may be given more weight than likelihood values in a fault map from the first set of fault maps 616.).
Regarding Claim 3, Aleman further teaches wherein combining the outputs of the disturbance event location detection algorithms comprises aggregating the weighted outputs of the disturbance event location detection algorithms (Aleman [0146] In an example, determining a composite line fault likelihood value for a particular segment includes computing a weighted average of the likelihood values for that particular segment contained in two more fault maps. For example, a likelihood values in a fault map from the second set of fault maps 618 may be given more weight than likelihood values in a fault map from the first set of fault maps 616.).
Regarding Claim 4, Aleman further teaches wherein the weighted balloting is implicit or explicit (Aleman [0146] In an example, determining a composite line fault likelihood value for a particular segment includes computing a weighted average of the likelihood values for that particular segment contained in two more fault maps. For example, a likelihood values in a fault map from the second set of fault maps 618 may be given more weight than likelihood values in a fault map from the first set of fault maps 616.).
Regarding Claim 5, Aleman further teaches wherein the weighted balloting takes into account at least one of a specific algorithm used, availability of relevant data, applications, customer segments, installations, loads, and risks associated with the electrical system (Aleman [0145] the respective composite line fault likelihood value for a particular line segment is determined based on a mathematical combination of a number of likelihood values that a line fault is located in that particular line segment as are indicated in a number of various fault maps. Also see [0147-0148]. The likelihood values are determined based on a number of factors, including installation locations of the monitors).
Regarding Claim 7, Aleman further teaches wherein the weighted balloting takes into account at least one of a specific algorithm used, availability of relevant data, applications, customer segments, installations, loads, and risks associated with the electrical system (Aleman [0145] the respective composite line fault likelihood value for a particular line segment is determined based on a mathematical combination of a number of likelihood values that a line fault is located in that particular line segment as are indicated in a number of various fault maps. Also see [0147-0148]. The likelihood values are determined based on a number of factors, including installation locations of the monitors).
Regarding Claim 8, Aleman further teaches wherein the disturbance event location detection algorithms are selected from the group consisting of: simple aggregated balloting; confidence-weighted balloting; algorithm-weighted ballot; algorithm and confidence weighting; highest confidence; best algorithm; partial weighting with machine learning; and dynamic weighting to machine learning (Aleman [0145] the respective composite line fault likelihood value for a particular line segment is determined based on a mathematical combination of a number of likelihood values that a line fault is located in that particular line segment as are indicated in a number of various fault maps. Also see [0147-0148]. The composite estimation determination may simply combine its inputs or weight them if decided).
Regarding Claim 9, Aleman further teaches wherein combining the outputs of the disturbance event location detection algorithms to determine the location of the at least one disturbance event from the candidate locations includes providing a measure of confidence in the determined location (Aleman [0146] In an example, determining a composite line fault likelihood value for a particular segment includes computing a weighted average of the likelihood values for that particular segment contained in two more fault maps. For example, a likelihood values in a fault map from the second set of fault maps 618 may be given more weight than likelihood values in a fault map from the first set of fault maps 616. A composite likelihood value is derived from the individual likelihood values).
Regarding Claim 10, Aleman further teaches taking at least one action to address the at least one disturbance event (Aleman [0141] The composite location estimation determination process 620 produces information to present line fault locations estimates on a location estimation display 630. In an example, the location estimation display 630 is able to present a display similar to the above described example user interface 400, which presents the data defining a composite fault map. The composite location estimation determination process 620 in some examples also provides estimated line fault locations to a service dispatch interface 632. The service dispatch interface 632 in an example is then able to provide the estimated line fault location to the service dispatch component 574 to allow the service dispatch system to, for example, more efficiently direct physical inspection of feeder lines or lateral lines to determine the location of the line fault and perform needed repairs.).
Regarding Claim 11, Aleman further teaches wherein the disturbance event location detection algorithms are applied simultaneously to the energy-related data (Aleman [0138] The information processing component 572 includes a communications interface 610 that, in an example, receives data reported via the communications system 570 by various monitors in the electrical distribution line 506. The communications interface 610 in an example provides the received data to one or both of a first fault location estimation process 612 and a second fault location estimation process 614. Also see [0139] In some examples, a separate fault map is generated based upon data produced by and received from each individual monitoring device along feeder lines, such as the illustrated feeder line section 590, and lateral lines, such as the illustrated lateral line 592. Fault maps may also be generated based upon the systems and sensors including those described with respect to FIG. 1. Fault maps may be generated separately based on the data received through the communications interface, see Fig. 6 610. Additionally, applying the algorithms “simultaneously” does not change their outputs, and combining the outputs requires both to be available).
Regarding Claim 12, Aleman further teaches at least one voltage waveform capture and current waveform capture (Aleman [0117] In some examples, electrical current detector 509 is able to include further processing or measurement equipment to produce various types of data to characterize the electrical current during an over-current condition. Examples of these different data that are able to be produced by the electrical current detector 509 include, but are not limited to, a detailed description of the electrical current peak transient during the over-current condition, alternating current (AC) electrical current phase shifts relative to the AC voltage waveform during the over-current condition, other measurements, or combinations of these.), and further comprising preprocessing the at least one voltage waveform capture and current waveform capture to provide improved waveforms prior to processing the energy-related data (Aleman [0170] Data describing the line fault including data describing a transient current waveform associated with the line fault and protection device operations data is received, at 904. The above described transient current waveform measured and reported by the SCADA 806 to the information processing component 572 is an example of the data describing the transient current waveform. The SCADA receives the measured waveform then sends processed data to the information processing component that performs the location determination).
Regarding Claim 13, Aleman teaches a system for detecting a location of a disturbance event in an electrical system, the system comprising:
at least one Intelligent Electronic Device (IED) communicatively coupled to an electrical system, the IED configured to acquire energy-related signals associated with the electrical system (Aleman [0138] The information processing component 572 includes a communications interface 610 that, in an example, receives data reported via the communications system 570 by various monitors in the electrical distribution line 506.);
a processor receiving and responsive to the energy-related signals acquired by the at least one IED (Aleman [0138] The information processing component 572 includes a communications interface 610 that, in an example, receives data reported via the communications system 570 by various monitors in the electrical distribution line 506. Also see [0185] The processor 1200 in this example includes a CPU 1204 that is communicatively connected to a main memory 1206 (e.g., volatile memory), a non-volatile memory 1212 to support processing operations.); and
a memory storing processor-executable instructions (Aleman [0190] Each computer system may include, inter alia, one or more computers and at least a computer readable medium allowing a computer to read data, instructions, messages or message packets, and other computer readable information from the computer readable medium.) that, when executed,
configure the processor to:
process the energy-related data to identify an occurrence of at least one disturbance event in the electrical system (Aleman [0139] In the illustrated example, the first fault location estimation process 612 and the second fault location estimation process 614 produce fault maps that indicate probability values of the location of a detected line fault.);
in response to identifying the occurrence of the at least one disturbance event,
apply each of a plurality of disturbance event location detection algorithms to the energy-related data, wherein each of the applied disturbance event location detection algorithms generates an output representative of an independently ascertained candidate location of the at least one disturbance event relative to the IED (Aleman [0138] The communications interface 610 in an example provides the received data to one or both of a first fault location estimation process 612 and a second fault location estimation process 614. Also see Fig. 6 612-618); and
combine the outputs of the disturbance event location detection algorithms to determine a location or origin of the at least one disturbance event from the candidate locations based on an analysis of the combined outputs (Aleman [0140] The information processing component 572 further includes a composite location estimation determination process 620. The composite location estimation determination process 620 is an example of a process performed by a composite fault map processor. In an example, the composite location estimation determination process 620 receives fault maps from one or more sources, such as from either one or both of the illustrated first set of fault maps 616 and the second set of fault maps 618, and produces a composite location estimation for each determined line fault. See Fig. 6 320).
Regarding Claim 14, Aleman further teaches to apply weighted balloting to each output of the applied disturbance event location detection algorithms for generating weighted outputs before the outputs are combined (Aleman [0146] In an example, determining a composite line fault likelihood value for a particular segment includes computing a weighted average of the likelihood values for that particular segment contained in two more fault maps. For example, a likelihood values in a fault map from the second set of fault maps 618 may be given more weight than likelihood values in a fault map from the first set of fault maps 616.).
Regarding Claim 15, Aleman further teaches to aggregate the weighted outputs of the disturbance event location detection algorithms for combining the outputs (Aleman [0146] In an example, determining a composite line fault likelihood value for a particular segment includes computing a weighted average of the likelihood values for that particular segment contained in two more fault maps. For example, a likelihood values in a fault map from the second set of fault maps 618 may be given more weight than likelihood values in a fault map from the first set of fault maps 616.).
Regarding Claim 16, Aleman further teaches wherein the weighted balloting is implicit or explicit (Aleman [0146] In an example, determining a composite line fault likelihood value for a particular segment includes computing a weighted average of the likelihood values for that particular segment contained in two more fault maps. For example, a likelihood values in a fault map from the second set of fault maps 618 may be given more weight than likelihood values in a fault map from the first set of fault maps 616.).
Regarding Claim 17, Aleman further teaches wherein the weighted balloting takes into account at least one of a specific algorithm used, availability of relevant data, applications, customer segments, installations, loads, and risks associated with the electrical system (Aleman [0145] the respective composite line fault likelihood value for a particular line segment is determined based on a mathematical combination of a number of likelihood values that a line fault is located in that particular line segment as are indicated in a number of various fault maps. Also see [0147-0148]. The likelihood values are determined based on a number of factors, including installation locations of the monitors).
Regarding Claim 19, Aleman further teaches wherein the disturbance event location detection algorithms are selected based on at least one of a specific algorithm used, availability of relevant data, applications, customer segments, installations, loads, and risks associated with the electrical system (Aleman [0145] the respective composite line fault likelihood value for a particular line segment is determined based on a mathematical combination of a number of likelihood values that a line fault is located in that particular line segment as are indicated in a number of various fault maps. Also see [0147-0148]. The composite estimation determination may simply combine its inputs or weight them if decided).
Regarding Claim 20, Aleman further teaches wherein the disturbance event location detection algorithms are selected from the group consisting of: simple aggregated balloting; confidence-weighted balloting; algorithm-weighted ballot; algorithm and confidence weighting; highest confidence; best algorithm; partial weighting with machine learning; and dynamic weighting to machine learning (Aleman [0145] the respective composite line fault likelihood value for a particular line segment is determined based on a mathematical combination of a number of likelihood values that a line fault is located in that particular line segment as are indicated in a number of various fault maps. Also see [0147-0148]. The composite estimation determination may simply combine its inputs or weight them if decided).
Regarding Claim 21, Aleman further teaches to provide a measure of confidence in the determined location of the at least one disturbance event (Aleman [0146] In an example, determining a composite line fault likelihood value for a particular segment includes computing a weighted average of the likelihood values for that particular segment contained in two more fault maps. For example, a likelihood values in a fault map from the second set of fault maps 618 may be given more weight than likelihood values in a fault map from the first set of fault maps 616. A composite likelihood value is derived from the individual likelihood values).
Regarding Claim 22, Aleman further teaches to take at least one action to address the at least one disturbance event (Aleman [0141] The composite location estimation determination process 620 produces information to present line fault locations estimates on a location estimation display 630. In an example, the location estimation display 630 is able to present a display similar to the above described example user interface 400, which presents the data defining a composite fault map. The composite location estimation determination process 620 in some examples also provides estimated line fault locations to a service dispatch interface 632. The service dispatch interface 632 in an example is then able to provide the estimated line fault location to the service dispatch component 574 to allow the service dispatch system to, for example, more efficiently direct physical inspection of feeder lines or lateral lines to determine the location of the line fault and perform needed repairs.).
Regarding Claim 23, Aleman further teaches wherein the disturbance event location detection algorithms are applied simultaneously to the energy-related data (Aleman [0138] The information processing component 572 includes a communications interface 610 that, in an example, receives data reported via the communications system 570 by various monitors in the electrical distribution line 506. The communications interface 610 in an example provides the received data to one or both of a first fault location estimation process 612 and a second fault location estimation process 614. Also see [0139] In some examples, a separate fault map is generated based upon data produced by and received from each individual monitoring device along feeder lines, such as the illustrated feeder line section 590, and lateral lines, such as the illustrated lateral line 592. Fault maps may also be generated based upon the systems and sensors including those described with respect to FIG. 1. Fault maps may be generated separately based on the data received through the communications interface, see Fig. 6 610. Additionally, applying the algorithms “simultaneously” does not change their outputs, and combining the outputs requires both to be available).
Regarding Claim 24, Aleman further teaches wherein the energy-related data comprise at least one voltage waveform capture and current waveform capture (Aleman [0117] In some examples, electrical current detector 509 is able to include further processing or measurement equipment to produce various types of data to characterize the electrical current during an over-current condition. Examples of these different data that are able to be produced by the electrical current detector 509 include, but are not limited to, a detailed description of the electrical current peak transient during the over-current condition, alternating current (AC) electrical current phase shifts relative to the AC voltage waveform during the over-current condition, other measurements, or combinations of these.), and to preprocess the at least one voltage waveform capture and current waveform capture for providing improved waveforms prior to processing the energy-related data (Aleman [0170] Data describing the line fault including data describing a transient current waveform associated with the line fault and protection device operations data is received, at 904. The above described transient current waveform measured and reported by the SCADA 806 to the information processing component 572 is an example of the data describing the transient current waveform. The SCADA receives the measured waveform then sends processed data to the information processing component that performs the location determination).
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 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 nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 6 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aleman (as stated above) in view of Menzel et al. (US 20200393501 A1), hereinafter “Menzel”.
Regarding Claim 6, although Aleman (as stated above) teaches updating the algorithms to improve future estimates (Aleman [0152] One or more algorithms that is used to calculate the composite line fault likelihood value for each line segment is then updated, at 714. […] Updating these algorithms in this way is able to improve the accuracy of locations estimates for line faults made in the future. Updating these algorithms by using the actual determined locations of line faults whose locations were estimated by the above processing will allow improving the accuracy of the algorithm.), Aleman is not relied upon to explicitly teach executing one or more machine learning algorithms to determine weights for the weighted balloting.
Menzel teaches executing one or more machine learning algorithms to determine weights for the weighted balloting (Menzel [0042] It is understood that the systems and methods described herein may be responsive to changes in the power system(s) in which the systems and methods are provided and/or implemented. For example, the type(s), number(s), location(s), etc. of electrical event(s) detected in the power system(s) may change in response to changes (e.g., addition of further IEDs, loads, etc.) in the power system(s). The changes in the power system(s) may be detected, for example, from the electrical signals measured by the plurality of IEDs in the power system(s). In one example implementation, the changes are detected after manually training/teaching a system to identify the changes. For example, the specific equipment (or processes) operating at a given time may be described to allow the system to learn (i.e., a form of machine learning).).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Aleman in view of Menzel to explicitly teach executing one or more machine learning algorithms to determine weights for the weighted balloting, to explicitly disclose Aleman’s method of updating the algorithm, because machine learning is a known way of improving a model/algorithm (Menzel [0042] In one example implementation, the changes are detected after manually training/teaching a system to identify the changes. For example, the specific equipment (or processes) operating at a given time may be described to allow the system to learn (i.e., a form of machine learning).).
Regarding Claim 18, although Aleman (as stated above) teaches updating the algorithms to improve future estimates (Aleman [0152] One or more algorithms that is used to calculate the composite line fault likelihood value for each line segment is then updated, at 714. […] Updating these algorithms in this way is able to improve the accuracy of locations estimates for line faults made in the future. Updating these algorithms by using the actual determined locations of line faults whose locations were estimated by the above processing will allow improving the accuracy of the algorithm.), Aleman is not relied upon to explicitly teach to execute one or more machine learning algorithms for determining weights for the weighted balloting.
Menzel teaches to execute one or more machine learning algorithms for determining weights for the weighted balloting (Menzel [0042] It is understood that the systems and methods described herein may be responsive to changes in the power system(s) in which the systems and methods are provided and/or implemented. For example, the type(s), number(s), location(s), etc. of electrical event(s) detected in the power system(s) may change in response to changes (e.g., addition of further IEDs, loads, etc.) in the power system(s). The changes in the power system(s) may be detected, for example, from the electrical signals measured by the plurality of IEDs in the power system(s). In one example implementation, the changes are detected after manually training/teaching a system to identify the changes. For example, the specific equipment (or processes) operating at a given time may be described to allow the system to learn (i.e., a form of machine learning).).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Aleman in view of Menzel to explicitly teach to execute one or more machine learning algorithms for determining weights for the weighted balloting, to explicitly disclose Aleman’s method of updating the algorithm, because machine learning is a known way of improving a model/algorithm (Menzel [0042] In one example implementation, the changes are detected after manually training/teaching a system to identify the changes. For example, the specific equipment (or processes) operating at a given time may be described to allow the system to learn (i.e., a form of machine learning).).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Menzel et al. (US 20220319299 A1) discloses Systems And Methods For Analyzing Alarms To Characterize Electrical System Issues.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTIAN T BRYANT whose telephone number is (571)272-4194. The examiner can normally be reached Monday-Thursday and Alternate Fridays 7:00-4:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, CATHERINE RASTOVSKI can be reached at (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHRISTIAN T BRYANT/Examiner, Art Unit 2857
Prosecution Insights