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 .
Response to Amendment
The amendment filed October 20, 2025 has been entered. Claims 1 and 3 remain pending in the instant application. Applicant’s amendments to the Claims have overcome the 112(b) rejection regarding sets S and L, previously set forth in the Non-Final Office Action mailed July 28, 2025. Applicant’s arguments regarding objections to the drawings are persuasive, and the objections are withdrawn.
Response to Arguments
Applicant’s arguments, filed October 20, 2025, regarding rejections under 35 U.S.C 112(b) have been fully considered, but they are not persuasive.
Applicant argues that “trade-off,” “goodness-of-fit,” and “model complexity” are understood by those of ordinary skill in the Art, and thus the limitation “λ is a weight parameter […] whose value trade-offs between the goodness-of-fit and model complexity” is not indefinite. The Examiner does not disagree with the argument that the phrases individually are understood by those of ordinary skill in the art. However, the claim limitation as a whole is unclear as to whether λ is configured to achieve an acceptable balance between model complexity and model accuracy, wherein the degree of what constitutes an acceptable trade-off is indefinite; or whether λ can be configured to provide a trade-off between model complexity and model accuracy. The Examiner also notes that “trade-offs” is a noun, but the term appears to be used as a verb in the claims. Applicant is advised to amend the claim to “λ is a weight parameter […] whose value can be configured to provide a trade-off between goodness-of-fit and model complexity,” to overcome the 35 U.S.C 112(b) rejections and minor informalities.
Applicant’s arguments regarding rejections under 35 U.S.C 101 have been fully considered, but they are not persuasive.
Applicant argues that the instant claims provide an improvement to technology that integrates the judicial exceptions into a practical application. Specifically, Applicant argues that the claimed probabilistic wind power forecasting minimizes operating reserve costs while maintaining secure and stable operations of the power systems with a high proportion of wind power assets. Applicant further points to the claim limitation “the operating reserve optimization model of power systems using probabilistic wind power forecasting linearizes a non-smooth L1 regular term in the loss function by introducing auxiliary continuous vectors, linearizes the indicator function and the maximum value function in constraints by introducing auxiliary logical variables” as providing an improvement that is not taught by the prior art.
Regarding Applicant’s argument that independent Claim 1 integrates the judicial exception(s) into a practical application by providing an improvement in technology, the Examiner notes that “the judicial exception alone cannot provide the improvement,” see MPEP § 2106.05(a) referenced by MPEP § 2106.04(d)(1). The specific limitation pointed to by Applicant as providing the improvement describes mathematical concepts. Furthermore, while the improvement can be provided by one or more additional element(s) in combination with the judicial exception(s), the additional elements of Claim 1 merely recite generic computer components as instructions to apply the abstract idea(s) on a computer, insignificant extra-solution activity, and/or a general field of use and technological environment, see MPEP § 2106.05(f)-(h). The Examiner also notes that the instant claims do not specifically describe how the wind power forecasting models would be used to maintain stable operation of power systems; that is, the instant claims merely recite the probabilistic model, but do not expressly recite the use of the model to control operations in a wind power facility or system. Therefore, Claim 1 is ineligible under 35 U.S.C 101.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-3 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 1, the claim recites “λ is a weight parameter […] whose value trade-offs between the goodness-of-fit and model complexity.” The term “trade-offs” appears to be used to mean “[balances] goodness-of-fit and model complexity.” Thus, the terms “trade-offs,” “goodness-of-fit,” and “complexity” are relative terms which render the claim indefinite. The terms “trade-offs,” “goodness-of-fit,” and “complexity” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The claim is not clear as to what would constitute a balance between model fit and complexity. Further, the claim is not clear as to what constitutes a good fit or what constitutes a complex model. Thus, “trade-offs between goodness-of-fit and model complexity” is indefinite.
Claim 3 requires the limitations of Claim 1, on which this claim depends, and the claim is rejected under 35 U.S.C 112(b) for the same reasons.
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.
Claim(s) 1-3 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mental processes and/or mathematical concepts without significantly more.
The following is an analysis of independent claim 1 based on the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Step 1, Statutory Category:
Yes: Claims 1 and 3are directed to a method.
Step 2A Prong I, judicial Exception:
The Examiner submits that the foregoing claim limitations constitute mathematical concepts and mental processes, as the claims cover both the performance of the limitations of the human mind and mathematical relationships, formulas, equations, and calculations, given their broadest reasonable interpretation. Abstract ideas are bolded.
Claim 1 recites the limitations:
1. An operating reserve quantification method for power systems using probabilistic wind power forecasting, the method comprising, without setting confidence level of prediction intervals and boundary quantile proportions in advance, defining a lowest confidence of the prediction intervals with respect to training samples by an inequation constraint, directly outputting the prediction intervals of wind power by extreme learning machine, determining capacity requirements of positive and negative operating reserve of the system based on boundaries of the prediction intervals, and by taking backup reserve cost and backup deficit penalty as a loss function, constructing an operating reserve optimization model of power systems using probabilistic wind power forecasting:
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in which t is a time index, T is a time index set of the training samples; ωa and ωa are weight vectors corresponding to two output neurons in the extreme learning machine; rtu and rtd are reserve deficits respectively; πu and πd are prices for the positive and negative reserve deficit penalties respectively; λ is a weight parameter of L1 regular term (||ωa|| + || ωa||1), whose value trade-offs between the goodness-of-fit and model complexity; wt is real wind power, wt is expected wind power, wc is the total quantity of wind power installations of the system; q(x; ωa) and q(xt; ωa) are upper and lower boundaries of the prediction interval output by the extreme learning machine; xt is an input feature vector of a machine learning model; 1-ε is a lowest confidence level of the prediction interval, which corresponds to reliability requirement of operating reserve of the power system; II(.) is an indicator function, and a function value is 1 when a logical expression in the parenthesis is established, otherwise the function values is 0; and max{.} is a maximum value function, which returns a largest operand;
wherein the operating reserve optimization model of power systems using probabilistic wind power forecasting linearizes a non-smooth L1 regular term in the loss function by introducing auxiliary continuous vectors, linearizes the indicator function and the maximum value function in constraints by introducing auxiliary logical variables, and transforms equivalently a quantization model of operating reserve based on probabilistic forecasting of wind power into a mixed integer linear programming problem:
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in which 1 is a vector whose elements are all 1, ηa and ηa are the introduced auxiliary vectors equal to the elementwise absolute value of ωa and ωa at the optimal solution of above optimization problem; zta, zta, zt, ztu, ztd are the introduced auxiliary logical variables, wherein zta, zta, zt linearize inequality constraint including the indicator function, ztu, ztd linearize equality constraints including the maximum value function; Mta, Mta, Mtu, Mtd, are referred to as the big-M coefficients, Mta is a constant coefficient larger than q(xt; ωa), Mtu is a constant coefficient larger than q(xt; ωa)-wt, and Mtd is a constant coefficient larger than wt -q(xt; ωa);
wherein the mixed integer linear programming problem achieves feasible region tightening of the mixed integer linear programming problem by shrinking the big-M coefficients:
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in which, sup{.} and inf{.} are operators of supremum and infimum respectively; qtε and qt1-ε indicate predictive quantiles at quantile proportions of ε and 1- ε respectively, qtε and qt1-ε are an upper estimation of the lower boundary q(xt; ωa) and a lower estimation of the upper boundary q(xt; ωa) of the prediction interval respectively;
wherein the mixed integer linear programming problem is reformulated as a reduced mixed integer linear programming problem by executing a feasible region tightening strategy in which the big-M coefficients are shrunk and the auxiliary logical variables are partly eliminated:
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in which, \ is a difference set symbol.
wherein the mixed integer linear programming problem achieves the feasible region tightening of the mixed integer linear programming problem by reducing the auxiliary logical variables:
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in which a set S contains time indexes corresponding to all the real wind power wt covered by an interval [qet, qt1-e] in training dataset, namely
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a set L contains time indexes corresponding to all the expected wind power wt greater than or equal to qte in the training dataset, namely
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a set U contains time indexes corresponding to all the expected wind power wt less than or equal to qt1-e in the training dataset, namely
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the logical variables zt, ztu, ztd whose time indexes in the sets S, L, U respectively, certainly have values of 1, and can be preset in advance before solving the optimization problem, thereby achieving reduction of the auxiliary logical variables;
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from extreme learning machine, and calculate decision results of the positive and negative reserve provision and deficits thereof by the constructing an operating reserve optimization model of power systems using probabilistic wind power forecasting:
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in which, max{.} is a maximum value function, which returns the largest operand,
evaluate reliability of the reserve quantification according to confidence margin (CM), which is defined as a difference value between the empirical probability of prediction errors covered by reserves and the nominal reliability level 100(1-ε)%;
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in which II(.) is an indicator function, and the function value is 1 when the logical expression in the parenthesis is true, otherwise the function value is 0,
The operational cost Cε of operating reserve is estimated by the sum of the reserve provision payment and the reserve provision payment and the reserve deficit penalty:
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The limitations without setting confidence level […] defining a lowest confidence, determining capacity requirements, constructing an operating reserve model, linearizes a non-smooth L1 regular term, linearizes the indicator function and the maximum value function, transforms equivalently a quantization model […] into a mixed integer linear programming problem, achieves feasible region tightening of the mixed integer linear programming problem by shrinking the big-M coefficients, and executing a feasible region tightening strategy, reducing the auxiliary logical variables, utilize test dataset, calculate decision results of the positive and negative reserve provision and deficits thereof by constructing an operating reserve optimization model, and evaluate reliability of the reserve quantification are abstract ideas because they are directed to mathematical concepts (such as mathematical relationships, formulas, equations, or calculations) and/or mental processes (such as observations, evaluations, judgements, and opinions). A user can perform the mental evaluations of defining a confidence, determining requirements, constructing a model, linearizing functions, transforming a model, shrinking coefficients, executing a region tightening strategy, reducing variables, utilizing a dataset, calculating decision results, and evaluating reliability. A user may use pen and paper to perform the required calculations. Further, the highlighted equations recite mathematical concepts.
Step 2A Prong II, Integration into a Practical Application:
Claim 1 recites the following additional claim limitations outside the abstract idea which only present general fields of use, mere instructions to apply an exception, and/or insignificant extra-solution activity:
An operating reserve quantification method for power systems using probabilistic wind power forecasting (general field of use, see MPEP § 2106.05(h)).
directly outputting the prediction intervals of wind power (insignificant extra-solution activity of data gathering, see MPEP § 2106.05(g)).
ADDITIONAL ELEMENTS:
Claim 1 recites the following additional elements:
“Extreme learning machine” and “machine learning model” are high level recitations of generic computer components, computer elements used as a tool, and represent mere instructions to apply the abstract idea on a computer as in MPEP § 2106.05(f). Therefore, the claim does not integrate the recited abstract ideas into a practical application.
Step 2B, Significantly More:
When considered individually or in combination, the additional limitations and elements of claim 1 do not amount to significantly more than the judicial exceptions for the same reasons above as to why the additional limitations do not integrate the abstract idea into a practical application.
The additional elements “extreme learning machine” and “machine learning model” reciting generic computer components as mere instructions to apply on a computer per MPEP § 2106.05(f) are carried over and do not provide significantly more than the abstract idea. The examiner also notes that the specification does not define the structures of the additional elements in any way that could be used to integrate the abstract idea into a practical application.
The additional limitations identified as mere instructions to apply an exception, insignificant extra-solution activity, or general field of use above are carried over and also do not provide significantly more than the abstract idea. See MPEP § 2106.04(d) referencing MPEP § 2106.05(f), MPEP § 2106.05(g), and MPEP § 2106.05(h).
The insignificant extra solution activity of directly outputting the prediction intervals is considered to be further well understood, routine and conventional, see MPEP § 2106.05(d)(II); “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity […] iv. Storing and retrieving information in memory.”
Considering the claim limitations in combination and the claims as a whole does not change this conclusion, and Claim 1 is ineligible under 35 U.S.C 101.
Regarding Claim 3, the claim recites The method of claim 1, wherein the reduced mixed integer linear programming problem is solved by branch and bound algorithm; this limitation is considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). A user can perform the mental evaluation of a branch and bound algorithm. A user may use pen and paper to perform the algorithm.
thereby achieving training of the machine learning model; this limitation is considered to be mere instructions to apply an exception under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(f).
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 3 is ineligible under 35 U.S.C 101.
Allowable Subject Matter
Claims 1 and 3 would be allowable if rewritten or amended to overcome the rejections under 35 U.S.C 112(b) and 35 U.S.C 101 set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
In light of Huang et al. (Huang, Hanyan, Ming Zhou, and Gengyin Li. "An endogenous approach to quantifying the wind power reserve." IEEE Transactions on Power Systems 35, no. 3 (2019): 2431-2442.), hereinafter Huang; Li et al. (Li, Peng, Danwen Yu, Ming Yang, and Jianhui Wang. "Flexible look-ahead dispatch realized by robust optimization considering CVaR of wind power." IEEE Transactions on Power Systems 33, no. 5 (2018): 5330-5340.), hereinafter Li; and Wan et al. (Wan, Can, Zhao Xu, Pierre Pinson, Zhao Yang Dong, and Kit Po Wong. "Optimal prediction intervals of wind power generation." IEEE Transactions on Power Systems 29, no. 3 (2013): 1166-1174.), hereinafter Wan, claim 1 would not have been anticipated or obvious to one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention.
Huang teaches a method for quantifying wind power operating reserve (e.g., page 1, column 1, abstract), defining a lowest confidence level of prediction intervals by an inequation constraint (e.g., equation (4)), determining capacity requirements of positive and negative operating reserve of the system based on boundaries of the prediction intervals (e.g., equation (2)), taking backup reserve cost and backup deficit penalty as a loss function to construct an operating reserve optimization model (e.g., page 4, col 1, paragraph 4 and equations (9)-(19)), and auxiliary constraints (e.g., page 5, column 2, paragraph 3 and equations (38)-(40). However, Huang does not appear to specifically teach an extreme learning machine, using the weights of the extreme learning machine in the optimization model, auxiliary continuous vectors that linearize an indicator function, big-M coefficients, or region tightening via big-M coefficients.
Li teaches a method for evaluating wind power risk (e.g., page 1, column 1, abstract), wind power prediction intervals (e.g., figure 2), an operational cost optimization model (e.g., page 4, column 1, paragraph 5 and equation (5)), and a big-M method for transforming a bilinear programming problem into a mixed integer linear programming problem (e.g., page 5, column 2, paragraph 4). However, Li also does not appear to specifically teach an extreme learning machine, using the weights of the extreme learning machine in the optimization model, and auxiliary continuous vectors that linearize an indicator function.
Wan teaches a method for formulating optimal prediction intervals for wind power (e.g., page 1, column 1, abstract) using an extreme learning machine (e.g., page 2, column 2, paragraph 3). However, Wan also does not appear to specifically teach using auxiliary continuous vectors to linearize an indicator function. Further, Want does not appear to teach the claimed optimization model or constraints.
In summary, the aforementioned prior art fails to teach at least the following limitations, in combination with the remaining claimed limitations: wherein the operating reserve optimization model […] linearizes a non-smooth L1 regular term in the loss function by introducing auxiliary continuous vectors, linearizes the indicator function and the maximum value function in constraints by introducing auxiliary logical variables, or different constraints for different time index sets S, U, and L, given by :
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The combination of the teachings of the closest prior art listed above would not completely teach the limitations of independent Claim 1.
Dependent Claim 3 would be allowable for depending from independent Claim 1.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE HWA-KAI TSENG whose telephone number is (571)272-3731. The examiner can normally be reached M-F 9A-5P PST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rehana Perveen can be reached at (571) 272-3676. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.H.T./ Examiner, Art Unit 2189
/REHANA PERVEEN/ Supervisory Patent Examiner, Art Unit 2189