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 § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
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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 1-9 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.
Claim 1 recites that the central server aggregates model parameters of each power station client based on the power plant scale coefficient βi. The specification, in paragraphs 24 and 71, states that the central server aggregates the model parameters of each power station client based on the power station scale coefficient βi. However, the claim 1 further includes limitations that the BRAN network implements parameter aggregation using a loss function that includes the variable β. It is unclear whether these two variables are different. The claims should refer to the power station scale coefficient consistently for clarity. Claims 2-9 depend from claim 1 and are rejected for the same reason.
Claim 1 recites the limitation "the preprocessed power station operating parameters" in S2. There is insufficient antecedent basis for this limitation in the claim. In S1, claim 1, recites operating data and preprocessing operations on the data, but does not recite “operating parameters” or performing preprocessing on operating parameters.
Claim 1 recites the limitation "the feature extraction network in each power station client" in S1. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the feature extraction network model training stage" in S22. There is insufficient antecedent basis for this limitation in the claim. While the first line of S22 recites to “train feature extraction model”, there is no reference to “feature extraction network model training.” Further, S23 recites “the feature extraction model” instead of “the feature extraction network model”. Reference to the model should be consistent for clarity.
In S24, claim 1 recites to “train the feature extraction network until completion.” S24 further recites “when the model parameters of each client server are accepted to meet a suspension condition, the training of the feature extraction network of each client server is stopped.” This step appears to provide two conditions for completing or stopping training. If completion is defined by the conditions in the second part of S24, the claim should be amended to make it clear that those are the conditions for completion.
Claim 1 recites the limitation "the electric energy residual sensing module", “the Mixing module and the sensing layer”, “the weighted values of the environment parameters”, “the internal operating parameters of the battery”, “the deep operating features inside the battery”, “the deep sensing features of the sensing layer”, and “the deep features of the power station’s electric energy” in S24. There is insufficient antecedent basis for these limitations in the claim.
There is similar lack of antecedent basis for multiple elements in the last section of S24 after equations 4 and 5.
Claims 2-9 depend from claim 1 and inherit indefinite language and insufficient antecedent basis for the limitations recited in claim 1.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Per step 1 of the Subject Matter Eligibility Test (See MPEP 2106), claim 1 is directed to an method or process which falls within a statutory category (See MPEP 2106.03).
Per step 2A, prong 1, claim 1 recites:
S1. obtain the operating data of various batteries in each energy storage station and perform preprocessing operations on the data;
S2. use the preprocessed power station operating parameters to extract power station operating fault features through the feature extraction network in each power station client;
S22. train feature extraction model;
in the feature extraction network model training stage, all operating parameters of each power station are used as input data of the feature extraction network to implement the training of the feature extraction network model;
S23. update the model parameters and aggregate the feature extraction model;
the central server aggregates model parameters of each power station client based on the power plant scale coefficient βi
S24. train the feature extraction network until completion;
when the model parameters of each client server are accepted to meet a suspension condition, the training of the feature extraction network of each client server is stopped;
the feature extraction network model is the electric energy residual sensing network BRAN (Battery Residual-aware Network), the feature extraction network model includes the electric energy residual sensing module; the electric energy residual sensing module includes the Mixing module and the sensing layer; the Mixing module receives weighted values of voltage, current, battery Coulombic efficiency, and battery temperature, the weighted values of the environment parameters after passing through 5 layers of convolutional layers and 4 layers of deconvolutional layers are used to obtain weighted fusion features; the sensing layer implements feature sensing operations on voltage, current, battery Coulombic efficiency, and battery temperature, realize the feature sensing operation of the internal operating parameters of the battery, and extract the deep operating features inside the battery; finally, the weighted fusion features are deeply fused with the deep sensing features of the sensing layer to obtain the deep features of the power station's electric energy; the environmental parameters include ambient temperature, environment Humidity, ambient light data;
the BRAN network implements parameter aggregation at the central server, and uses the following loss function to update and optimize the BRAN feature extraction network;
Equation 4
Equation 5
among them, Ltot is the total loss function of the BRAN network, LDis is the feature distance loss function, LSca is the power plant scale loss function, LVar is the model parameter variance loss function; a, β, γ are the characteristic distance coefficient, power plant scale coefficient and parameter variance coefficient respectively.; N is the number of models; li is the operating parameter; Ks1, Ks2 are model output features; Gs1, GN are functions of power plant scale; Vs1, Vs2 are the variances between client model parameters;
S3. use the aggregate feature extraction network to implement feature extraction of a single operating parameter on the client server of each power station, and train a fault diagnosis classification model;
S4. collect the operating parameters of the power station and input them into the feature extraction network and fault diagnosis network to realize fault diagnosis of the power station.
These limitations are generally directed to collecting data from batteries in energy storage stations for input training and using a model for fault diagnosis. The limitations for training and using the model are defined by mathematical operations and equations and therefore the claim 1 falls into the mathematical concepts grouping (See MPEP 2106.04(a)(2)).
The additional elements are S21. establish a client-central server distributed learning framework; and after each client completes an iterative training, the model parameters are uploaded to the central server.
Per step 2A, prong 2, The abstract idea is not integrated into a practical application because the limitations to a client and central server are instructions to implement the abstract idea on a computer and amount to mere instructions to apply the abstract idea (See MPEP 2106.05(f)).
Per step 2B, claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reason.
Claims 2-9 depend from claim 1 and do not recite any further additional elements. Claims 2-9 only recite further details of the abstract idea, therefore claims 2-9 are rejected for the same reason.
Response to Arguments
Applicant's arguments filed 31 May 2026 have been fully considered but they are not persuasive. Applicant states that claim 1 provides a specific technical solution and that amended claim 1 integrates the operation of training and using the model into fault diagnosis for energy storage power stations. However, the claim limitations for fault diagnosis are defined by mathematical operations and equations and therefore the claim 1 falls into the mathematical concepts grouping (See MPEP 2106.04(a)(2)). The only additional element recited in the claim are S21. establish a client-central server distributed learning framework; and after each client completes an iterative training, the model parameters are uploaded to the central server. These limitations are instructions to implement the abstract idea on a computer and amount to mere instructions to apply the abstract idea (See MPEP 2106.05(f)).
Conclusion
THIS ACTION IS MADE FINAL. 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 MANUEL L BARBEE whose telephone number is (571)272-2212. The examiner can normally be reached M-F: 9-5:30..
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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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/MANUEL L BARBEE/Primary Examiner, Art Unit 2857
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