DETAILED ACTION
Non-Final Rejection
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/25/2026 has been entered.
Response to Amendment
Applicant’s amendments, filed 03/25/2026 to claims are accepted. In this amendment, Claims 1, 13, 20 and 22 have been amended.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-18 and 20-22 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Each of claims 1, 3-18 and 20-22 falls within one of the four statutory categories. See MPEP § 2106.03. For example, each of claims 1, 3-12 and 21-22 fall within category of process. For example, each of claim 13-18 and 20 falls within category of machine, i.e., a “concrete thing, consisting of parts, or of certain devices and combination of devices.” Digitech, 758 F.3d at 1348–49, 111 USPQ2d at 1719 (quoting Burr v. Duryee, 68 U.S. 531, 570, 17 L. Ed. 650, 657 (1863)).
Regarding Claims 1-12
Step 2A – Prong 1
Exemplary claim 1 is directed to an abstract idea of determining at least one prognostic.
The abstract idea is set forth or described by the following italicized limitations:
1. A method for prognosis of an installed high voltage equipment (HVE) by a monitoring system, the method comprising:
dynamically selecting one or more machine learning models from a plurality of machine learning models based on a periodic evaluation of the plurality of the machine learning models with one or more performance criteria for evaluation, the plurality of machine learning models being trained from data obtained from a plurality of HVEs communicatively connected with the monitoring system;
predicting a failure mode of the installed HVE, based on input parameters associated with the installed HVE, using the one or more machine learning models;
determining at least one prognostic response for the installed HVE, based on the predicted failure mode, using the one or more machine learning models; and
based on the determined at least one prognostic response and a stored pre-defined action event, providing, by a prognostic based action selector, at least one prognostic response for the installed HVE, wherein providing the at least one prognostic response comprises initiating at least one service for the HVE..
The italicized limitations above represent mental steps (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment). Therefore, the italicized limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance.
For example, the limitations “predicting a failure […]; determining at least one prognostic response [..]; based on the determined at least one prognostic response and a stored pre-defined action event, providing, by a prognostic [..]” are mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment), see 2106.04(a)(2). Limitations are considered together as a single abstract idea for further analysis. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)).
Step 2A – Prong 2
Claims 1 does not include additional elements (when considered individually, as an ordered combination, and/or within the claim as a whole) that are sufficient to integrate the abstract idea into a practical application.
For example, first additional first element is “dynamically selecting one or more machine learning models from a plurality of machine learning models based on a periodic evaluation of the plurality of the machine learning models with one or more performance criteria for evaluation, the plurality of machine learning models being trained from data obtained from a plurality of HVEs communicatively connected with the monitoring system” to be performed, at least in-part, these additional elements appear to only add insignificant extra-solution activity (e.g., data gathering) and only generally link the abstract idea to a particular field. Therefore, this element individually or as a whole does not provide a practical application. See MPEP 2106.05(g)
The 2nd additional element is “high voltage equipment (HVE)”. This element amounts to mere use of a generic power line monitoring system, which is well understood routine and conventional (see background of current discloser and IDS and PTO 892) and this element individually does not provide a practical application. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. see MPEP 2106.05(d).
The 3rd additional element of “machine learning models; dynamically selecting one or more machine learning models from a plurality of machine learning models ” in limitations are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept and it is recited a computer component at a high level of generality. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. See MPEP 2106.05(f).
In view of the above, the three “additional elements” individually do not provide a practical application of the abstract idea. Furthermore, the “additional elements” in combination amount to a generic power line system with computer software with high level of generality , where such computers and software amount to mere instructions to implement the abstract idea on a computer(s) and/or mere use of a generic computer component(s) as a tool to perform the abstract idea. Therefore, these elements in combination do not provide a practical application. The combination of additional elements does no more than generally link the use of the abstract idea to a particular technological environment, and for this additional reason, the combination of additional elements does not provide a practical application of the abstract idea.
Step 2B
Claims1 does not include additional elements, when considered individually and as an ordered combination, that are sufficient to amount to significantly more than the abstract idea. For example, the limitation of Claim 1 contains additional elements that is, i.e. high voltage equipment (HVE)”, generic devices, which are well understood, routine and conventional (see background of current discloser and IDS and PTO 892) and MPEP 2106.05(d)). The reasons for reaching this conclusion are substantially the same as the reasons given above in § Step 2A – Prong 2. For brevity only, those reasons are not repeated in this section. See MPEP §§ 2106.05(g) and MPEP §§2106.05(II).
Dependent Claims 2-12 and 21-22
Dependent claims 2-12 fail to cure this deficiency of independent claim 1 (set forth above) and are rejected accordingly. Particularly, claims 2-18 recite limitations that represent (in addition to the limitations already noted above) either the abstract idea or an additional element that is merely extra-solution activity, mere use of instructions and/or generic computer component(s) as a tool to implement the abstract idea, and/or merely limits the abstract idea to a particular technological environment.
For example, the limitations of Claims 3-6, 8-10, 21-22 insignificant extra-solution activity (e.g., data gathering)
For example, the limitations of Claim 3, generic battery monitoring system, which is well understood routine and conventional.
For example, the limitations of Claims 11-12 are a mental step.
For example, the limitations of Claims 6, 12-13 and 17 are a mathematical concept.
For example, the limitations of Claim 7 in limitations are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept and it is recited a computer component at a high level of generality.
Claims 13-20
Claims 13-20 contains language similar to claims 1-12 as discussed in the preceding paragraphs, and for reasons similar to those discussed above, claims 13-20 are also rejected under 35 U.S.C. § 101(abstract idea).
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) 1-8, 9-16 and 18, 20-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 2020/0210537) in view of Singh (US 2012/0242365)
Regarding Claim 1. Wang teaches a method for prognosis of an installed high voltage equipment (HVE) by a monitoring system, the method comprising([002]):
dynamically (With the increase of lead time from 1 to 3 days and consistent 30 days observation time window (lookback time−lead time), all the performance measure metrics are improved. As such, the model evaluation module 508 may select a failure prediction model with the lookback time of 33 days and lead time of 3 days as the configuration to apply for new data to predict future faults: [0107]; the performance curvature may be assessed to assist in selection of a preferred failure prediction model. The performance look-up gives an expected forecasting outcome for a given lookback and lead time requirement. The performance look-up gives a reasonable lookback and lead time that an operator can expect (e.g. if an operator wants the sensitivity of the model being greater than 50%, the curvature will give the necessary lookback and appropriate lead time). As a result, the performance look-up gives a clear and comprehensive failure prediction model to model performance evaluation for all the possible choices (e.g., thereby enabling a selection of a failure prediction model for each set of failure prediction models): [0107]-[0110]; fig.7-8) selecting (select a preferred model of the set of models. For example, two different failure prediction models of a set with different lookback time (e.g., 31 and 33 days, respectively) and different lead times (e.g., 1 and 3 days, respectively) may have different AUC (71% and 92%, respectively), different train sensitivity (54% and 83%, respectively), different train precision (63% and 83%, respectively), and train specificity (69% and 83%, respectively: [0003], [0123]) one or more machine learning models ([0022], [0067]) from a plurality of machine learning models based on a periodic evaluation (longer lead time) of the plurality of the machine learning models (set may be compared using similar metrics and/or different metrics as described) with one or more performance criteria for evaluation(higher AUC) ( [0123]; [0108]), the plurality of machine learning models being trained from data obtained from a plurality of HVEs communicatively connected with the monitoring system([0119]]);
predicting a failure mode of the installed HVE, based on input parameters associated with the installed HVE, using the one or more machine learning models([0003], [0022], [0067]);
determining at least one prognostic response for the installed HVE, based on the predicted failure mode, using the one or more machine learning models([0003], [0022], [0067]); and
providing at least one prognostic response for the installed HVE([0003], 916: fig. 9).
Wang silent about based on the determined at least one prognostic response and a stored pre-defined action event, providing, by a prognostic based action selector, at least one prognostic response for the installed HVE, wherein providing the at least one prognostic response comprises initiating at least one service for the HVE.
However, Singh teaches based on the determined at least one prognostic response and a stored pre-defined action event, providing, by a prognostic based action selector, at least one prognostic response for the installed HVE, wherein providing the at least one prognostic response comprises initiating at least one service for the HVE([0068]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the modified invention of Wang, based on the determined at least one prognostic response and a stored pre-defined action event, providing, by a prognostic based action selector, at least one prognostic response for the installed HVE, wherein providing the at least one prognostic response comprises initiating at least one service for the HVE, as taught by Singh, so as to system is well suited for performing real-time diagnostics procedure.
Regarding Claim 3 and 14. Wang further teaches the performance criteria comprise model fidelity and at least one of parameters representing time taken for computation, and resource consumed for computation([0123]).
Regarding Claim 4. Wang further teaches the resources consumed comprise one or more of memory, input data size, and number of input parameters required for the machine learning model ([0022], [0067]; [0123]).
Regarding Claims 5 and 15. Wang further teaches the input parameters comprise at least one of online monitoring data relating to performance parameters of the installed HVE, offline operational data of the installed HVE, and factory data of the installed HVE([0053]).
Regarding Claims 6 and 16. Wang further teaches simulating and forecasting performance data of the installed HVE based on the one or more machine learning model ([0022], [0067]; [0111]).
Regarding Claim 7. Wang further teaches the one or more machine learning models further comprise to at least one of a stochastic model, or an empirical model(SVM, DeepLearning (such as CNN or CHAID):[0067]).
Regarding Claim 8. Wang further teaches the one or more machine learing models comprise a first plurality of machine learning models [0022], [0067] corresponding to failure signature models for predicting the failure mode generated from historical failure data obtained from similar type of HVEs from the plurality of HVE as the installed HVE([0004], [0045]).
Regarding Claim 9. Wang further teaches the first plurality of machine learning models ([0022], [0067]) comprises models for one or more of partial discharge based failure, impulse failure, insulation failure, short circuit failure, and earth fault failure([0004], [0045]).
Regarding Claims 11 and 18. Wang further teaches determining the at least one prognostic response comprises: predicting a failure event based on the failure mode prediction and a prognosis of the installed HVE determined from the one or more machine learning models([0022], [0067]; [0126]);
predicting a schedule of the failure event ([0126]); and
determining the at least one prognostic response based on the schedule and predefined rules([0126]).
Regarding Claim 12. Wang further teaches the determining the at least one prognostic response is further based on at least one user requirement associated with servicing of the installed HVE([0109]).
Regarding Claims 13 and 20: Claims 13 and 20 are substantially the same as claim 1 therefore, rejected for the same as noted for claim 1. Wang further teaches a computer system, fig.12.
Regarding Claim 21. Wang further teaches determining, by an event communication configuration of the monitoring system, a recipient associated with the installed HVE([0126]),
wherein providing the at least one prognostic response for the installed HVE comprises transmitting the determined recipient([0126]).
Regarding Claim 22. Wang further teaches the determined recipient comprises at least one of an operator or a third party for performing at least one of servicing or maintenance of the installed HVE([0114], [0126]).
Claim(s) 10 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang and singh, further in view of Nuthi et al. (US 2020/0210268)
Regarding Claim 10. Wang silent about the one or more machine learning models comprise a second plurality of machine learning models corresponding to prognostics models for generating the at least one prognostic response based on historical behavior data of similar type of HVEs from the plurality of HVEs as the installed HVE and user requirement specification.
However, Nuthi teaches the one or more machine learning models comprise a second plurality of machine learning models corresponding to prognostics models for generating the at least one prognostic response based on historical behavior data of similar type of HVEs from the plurality of HVEs as the installed HVE and user requirement specification([0036]-[0037]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the modified invention of Wang, the one or more models comprise a second plurality of models corresponding to prognostics models for generating the at least one prognostic response based on historical behavior data of similar type of HVEs from the plurality of HVEs as the installed HVE and user requirement specification, as taught by Nuthi, so as to solve the technical problem related to a lack of historical data from an asset.
Regarding Claim 17. Wang further teaches the one or more machine learning models comprise a first plurality of machine learning models corresponding to failure signature models for predicting the failure mode generated from historical failure data obtained from similar type of HVEs from the plurality of HVE as the installed HVE([0004], [0045]) and the first plurality of machine learning models comprises models for one or more of partial discharge based failure, impulse failure, insulation failure, short circuit failure, and earth fault failure([0004], [0022], [0067]; [0045]).
Wang silent about the one or more models comprise a second plurality of machine learning models corresponding to prognostics models for generating the at least one prognostic response based on historical behavior data of similar type of HVEs from the plurality of HVEs as the installed HVE and user requirement specification.
However, Nuthi teaches the one or more models comprise a second plurality of machine learning models corresponding to prognostics models for generating the at least one prognostic response based on historical behavior data of similar type of HVEs from the plurality of HVEs as the installed HVE and user requirement specification([0036]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the modified invention of Wang, the one or more models comprise a second plurality of machine learning models corresponding to prognostics models for generating the at least one prognostic response based on historical behavior data of similar type of HVEs from the plurality of HVEs as the installed HVE and user requirement specification, as taught by Nuthi, so as to solve the technical problem related to a lack of historical data from an asset.
Response to Argument
Applicant’s arguments with respect 101 rejection, specially claims 1, 13 and 20, the applicant did not agree with it, see pages 8-9.
In response, the Examiner respectfully disagree because current claim amendment , i.e “based on the determined at least one prognostic response and a stored pre-defined action event, providing, by a prognostic based action selector, at least one prognostic response for the installed HVE, wherein providing the at least one prognostic response comprises initiating at least one service for the HVE”, further directed to details of abstract idea of mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment)and In view of the above, the “additional elements” in combination do not provide a practical application of the abstract idea. As such 101 rejection is maintained.
Applicant’s arguments with respect to claim(s) 1, 13 and 18, the Applicant argus that prior art fail to teach the limitation, i.e. “dynamically selecting one or more machine learning models from a plurality of machine learning models based on a periodic evaluation of the plurality of the machine learning models with one or more performance criteria for evaluation”, see pages 10-11 .
In response, the Examiner respectfully disagree because the prior arts teach the limitation as (With the increase of lead time from 1 to 3 days and consistent 30 days observation time window (lookback time−lead time), all the performance measure metrics are improved. As such, the model evaluation module 508 may select a failure prediction model with the lookback time of 33 days and lead time of 3 days as the configuration to apply for new data to predict future faults(which is considered to be a dynamic process): [0107]; the performance curvature may be assessed to assist in selection of a preferred failure prediction model. The performance look-up gives an expected forecasting outcome for a given lookback and lead time requirement. The performance look-up gives a reasonable lookback and lead time that an operator can expect (e.g. if an operator wants the sensitivity of the model being greater than 50%, the curvature will give the necessary lookback and appropriate lead time). As a result, the performance look-up gives a clear and comprehensive failure prediction model to model performance evaluation for all the possible choices (e.g., thereby enabling a selection of a failure prediction model for each set of failure prediction models): [0107]-[0110]; fig.7-8) selecting (select a preferred model of the set of models. For example, two different failure prediction models of a set with different lookback time (e.g., 31 and 33 days, respectively) and different lead times (e.g., 1 and 3 days, respectively) may have different AUC (71% and 92%, respectively), different train sensitivity (54% and 83%, respectively), different train precision (63% and 83%, respectively), and train specificity (69% and 83%, respectively: [0003], [0123]) one or more machine learning models ([0022], [0067]) from a plurality of machine learning models based on a periodic evaluation (longer lead time) of the plurality of the machine learning models (set may be compared using similar metrics and/or different metrics as described) with one or more performance criteria for evaluation(higher AUC) ( [0123]; [0108]), the plurality of machine learning models being trained from data obtained from a plurality of HVEs communicatively connected with the monitoring system([0119]]). As such rejection is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
a) Sghi et al. (US 2021/0201209) disclose a method for selecting a learning model defined in particular by parameters and hyperparameters from among a plurality of learning models, implemented by a computing device, said computing device comprising a model selection module and a model repository including a plurality of series of instructions each corresponding to a learning model and each including hyperparameter values, said method comprising a step of selecting a model when the prediction performance value and the classification value are greater than predetermined second threshold and the hyperparameter value is greater than a predefined threshold value.
b) Poornaki et al. (US 20200210824) disclose utilizing different pipelines of a prediction system, comprises receiving failure data, and asset data from SCADA system(s), receiving and dividing historical sensor data from sensors of components of wind turbines into different classes of different lead times, training a set of models to predict faults for each component using the historical sensor data and lead times with a deep neural network, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.
c) Pandey (US 20130173231) disclose forming a failure estimate for one or more components of a heat recovery steam generator (HRSG) includes forming from failure models and at least one of fired hours and fired starts, factored hours and factored starts. The factored hours and/or starts are applied to failure equations for the one or more components to form the failure estimate.
d) Yosk. et al. (US 2020/0182684) disclose Analysis of data by server 160 may include the execution of algorithms for detection of a condition of generator 1304 and/or engine 1306, including detection or prediction of mechanical and electrical faults, efficiency analysis and analysis of degradation of performance of generator 1304 and/or engine 1306. Furthermore, analysis of data by server 160 may be used to identify possible security breaches in control of generator 1304 and/or engine 1306, due for example to hacking or other malicious activities directed against generator 1304 and/or engine 1306 via computerized controls thereof.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD K ISLAM whose telephone number is (571)270-0328. The examiner can normally be reached M-F 9:00 a.m. - 5:00 p.m..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A Turner can be reached at 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMMAD K ISLAM/ Primary Examiner, Art Unit 2857
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