Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 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 accelerated life testing (ALT) of a proton exchange membrane fuel cell (PEMFC), comprising the following steps: step 1, conducting data collection and processing: collecting a voltage-time sequence data of the PEMFC through a sensor to allow Gaussian filtering to filter out noise and abnormal peaks to obtain a processed voltage-time sequence data; subjecting the processed voltage-time sequence data to empirical mode decomposition (EMD), such that a voltage data is decomposed to obtain K intrinsic mode functions, wherein K is an integer of greater than or equal to 1; and dividing the K intrinsic mode functions into a training data set and a test data set according to a ratio of user demand, and normalizing the training data set and normalizing the test data set based on a normalization standard of the training data set to smoothly map into [0,1]; step 2, constructing a bidirectional long short-term memory-based artificial neural network (BiLSTM), wherein the BiLSTM comprises an input layer, a hidden layer, and an output layer, a number of input eigenvalues of the BiLSTM is determined according to a number of the intrinsic mode functions, and a matrix and a vector of the BILSTM are initialized to 0; step 3, training the BiLSTM: subjecting the BiLSTM to network training based on an input data, selecting t time steps as a prediction interval, and using a data before each of the t time steps as an input training data at a current moment; selecting a root mean square error as an error function, calculating a gradient of each weight according to a corresponding error term using an adaptive matrix estimation algorithm as an optimizer when an error is greater than a default threshold, wherein the error term is propagated in a reverse direction along time and the weight is updated through stochastic gradient descent; conducting gradient evaluation, wherein if a gradient accuracy meets a stopping criterion, a corresponding value of the gradient accuracy is output as a prediction result; if the gradient accuracy does not meet the stopping criterion, the gradient is re-updated; and generating an initial sample point X.sub.i with an initial learning rate, a number of iterations, and a number of neurons in the hidden layer according to a range of model parameters, inputting the initial sample point X.sub.i into a sparrow search algorithm to allow automatic optimization on network parameters of the BiLSTM comprising the initial learning rate, the number of iterations, and the number of the neurons in the hidden layer, and then outputting optimal network parameters to obtain a trained and optimized BiLSTM; step 4, testing the BiLSTM: inputting the test data set into the trained and optimized BiLSTM to allow testing to determine whether a newly selected sample point meets a model accuracy requirement; wherein if the newly selected sample point meets the model accuracy requirement, the testing is terminated through a termination algorithm to output an optimal BILSTM; if the newly selected sample point does not meet the model accuracy requirement, whether the automatic optimization reaches a maximum number of iterations is determined; if the automatic optimization reaches the maximum number of iterations, the optimal BILSTM is output, otherwise the sparrow search algorithm is iterated in a loop until the newly selected sample point meets the model accuracy requirement; and step 5, applying the optimal BILSTM to the ALT of the PEMFC, denormalizing an obtained prediction result, and converting a resulting predicted remaining useful life data into a remaining useful life-time sequence data through the output layer.”
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 groupings of subject matter that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations.
Similar limitations comprise the abstract ideas of Claims 8 and 15.
Next, under the Step 2A, Prong Two, we consider whether the above claims that recites a judicial exception are integrated into a practical application.
The above claims comprise the following additional elements:
In Claim 1: A method for obtaining a capacity of a power battery, comprising: collecting, by sensors, sample data of the power battery; acquiring, by the processor, battery state parameters of the power battery;
In Claim 7: A system for ALT of a PEMFC, comprising a data acquisition and processing module, a neural network (NN) construction module, an NN training module, an NN optimization module, and an NN application module; wherein the data acquisition and processing module is configured to conduct: collecting a voltage-time sequence data of the PEMFC through a sensor.
The additional elements in the preambles are recited in generality and represent insignificant extra-solution activity (field-of-use limitations) that is not meaningful to indicate a practical application.
The limitations of Claim 1, “conducting data collection … collecting a voltage-time sequence data of the PEMFC through a sensor” and Claim 7, “the data acquisition and processing module is configured to conduct: collecting a voltage-time sequence data of the PEMFC through a sensor” represent generically recited insignificant extra-solution activity of mere data gathering. According to the October update on 2019 SME Guidance such steps are “performed in order to gather data for the mental analysis step, and is a necessary precursor for all uses of the recited exception. It is thus extra-solution activity, and does not integrate the judicial exception into a practical application”. The limitations of Claim 7, “processing module, a neural network (NN) construction module, an NN training module, an NN optimization module, and an NN application module” are examples of generic computer equipment (components) that are generally recited and not meaningful and, therefore, are not qualified as particular machines to indicate 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) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record.
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, Claims 2-6 and 8 provide additional features/steps which are part of an expanded abstract idea of the independent claims (additionally comprising abstract idea steps) and, therefore, these claims are not eligible without meaningful additional elements that reflect a practical application and/or additional elements that qualify for significantly more for substantially similar reasons as discussed with regards to Claim 1.
The additional elements in Claims 9-20 (computer device, computer readable medium, processor) are generally recited and not meaningful and, therefore, are not qualified as particular machines to indicate a practical application and/or qualify for significantly more.
Examiner Note with Regards to Prior Art of Record
Claims 1-20 are distinguished over prior art of record based on the reasons below.
The following references are considered to be the closest prior art to the claimed invention:
Dongmei Dong et al. (US 11811116) discloses a proton exchange membrane fuel cell (PEMFC) including monitoring the PEMFC degradation status by sensing the emission of fluoride. An advanced DL algorithm and/or an artificial neural network (ANN) (e.g., long-short-term memory (LSTM)) can be used for sensor-based PM of a PEMFC. The DL algorithm can comprise a long-short-term memory (LSTM) artificial neural network (ANN).
NING ZHOU et al. (CN 112732797) discloses a fuel cell vehicle driving behaviour analysis method, device and storage medium, collecting the speed collected on proton exchange membrane fuel cell vehicle, current voltage of the motor; fuel cell group current voltage and so on actual data, using the combined algorithm of the convolutional neural network and bidirectional LSTM network to process the data. Bidirectional LSTM network (i.e., BiLSTM) comprises LSTM network in positive and negative directions; the first layer calculates the sequence information of the current time point; the second layer reversely reads the same sequence; the reverse sequence information is added in; in the training sequence, the forward and backward LSTM network are two; and there will be output, so it can provide complete over and future information for one point in the input sequence, when training is still by forward and reverse propagation algorithm to update the network weight, structure diagram as shown in FIG. 3. FIG. 3 and inputting the output of the hidden layer at last moment; and the input and hidden layer output is the current time; and It is the input and hidden layer output at the future time.
Yan-yan Hu (CN 114814589) discloses predicting remaining service life of PEMFC, the method comprises: collecting PEMFC current of PEMFC at different time, current density, hydrogen air inlet and outlet pressure and output voltage data, pre-processing the collected data; by introducing semi-empirical degradation model, selecting healthy state SOH index capable of characterizing PEMFC degradation; performing random degradation modeling to the SOH index, obtaining performance degradation state space model; using EM algorithm to update the parameter in the performance degradation state space model; training the initial GRU network model, predicting the remaining service life according to the trained GRU network model.
Xue-qin LV et al. (CN 104133369) discloses prediction neural network algorithm-based controller application of the control aspects in PEMFC dynamic control strategy based on neural network.
However, in regards to Claims 1 and 7, the claims differ from the closest prior art, Dong, Zhou, and Yuan, either singularly or in combination, because the references fail to anticipate or render obvious step 3, training the BiLSTM: subjecting the BiLSTM to network training based on an input data, selecting t time steps as a prediction interval, and using a data before each of the t time steps as an input training data at a current moment; selecting a root mean square error as an error function, calculating a gradient of each weight according to a corresponding error term using an adaptive matrix estimation algorithm as an optimizer when an error is greater than a default threshold, wherein the error term is propagated in a reverse direction along time and the weight is updated through stochastic gradient descent; conducting gradient evaluation, wherein if a gradient accuracy meets a stopping criterion, a corresponding value of the gradient accuracy is output as a prediction result; if the gradient accuracy does not meet the stopping criterion, the gradient is re-updated; and generating an initial sample point X.sub.i with an initial learning rate, a number of iterations, and a number of neurons in the hidden layer according to a range of model parameters, inputting the initial sample point X.sub.i into a sparrow search algorithm to allow automatic optimization on network parameters of the BiLSTM comprising the initial learning rate, the number of iterations, and the number of the neurons in the hidden layer, and then outputting optimal network parameters to obtain a trained and optimized BiLSTM; step 4, testing the BiLSTM: inputting the test data set into the trained and optimized BiLSTM to allow testing to determine whether a newly selected sample point meets a model accuracy requirement; wherein if the newly selected sample point meets the model accuracy requirement, the testing is terminated through a termination algorithm to output an optimal BILSTM; if the newly selected sample point does not meet the model accuracy requirement, whether the automatic optimization reaches a maximum number of iterations is determined; if the automatic optimization reaches the maximum number of iterations, the optimal BILSTM is output, otherwise the sparrow search algorithm is iterated in a loop until the newly selected sample point meets the model accuracy requirement; and step 5, applying the optimal BILSTM to the ALT of the PEMFC, denormalizing an obtained prediction result, and converting a resulting predicted remaining useful life data into a remaining useful life-time sequence data through the output layer, in combination with all other limitations in the claim as claimed and defined by applicant.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER SATANOVSKY whose telephone number is (571)270-5819. The examiner can normally be reached on M-F: 9 am-5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached on (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALEXANDER SATANOVSKY/
Primary Examiner, Art Unit 2857
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