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 1-5, 7, 9-14, 16, and 18-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims 1, 10, and 19
Claim 1 recites “A method…” which is a series of steps and is therefore a process. Claim 10 recites “A computing apparatus…” and is a machine. Claim 19 recites “A non-transitory computer readable storage medium…” and is therefore a manufacture.
Independent claims 1, 10, and 19 recite limitations of:
receiving…
extracting…
perturbing…
training…
adjusting…
Claims 1, 10, and 19 recite the limitations of “perturbing…”, “training…”, and “adjusting…” which are processes that, under its broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting a processor, a memory, a communication interface, or a non-transitory computer readable storage medium; nothing in the claim elements preclude the step from practically being performed in a human mind.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including observation, evaluation, judgement, and opinion).
The claims recite the additional elements of “receiving…” and “extracting…”. Both these elements amount to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
The processor, memory, communication interface, and non-transitory computer readable storage medium are recited at a high level of generality (i.e. as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (see MPEP 2106.05(f)). The claims are directed to an abstract idea.
Claims 2-5, 7, 9, 11-14, 16, 18, and 20 recite additional limitation of “selecting…” which is a process that, under its broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting a processor, a memory, a communication interface, or a non-transitory computer readable storage medium; nothing in the claim elements preclude the step from practically being performed in a human mind.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including observation, evaluation, judgement, and opinion).
The judicial exception is not integrated into a practical application. Claims 2-5, 7, 9, 11-14, 16, 18, and 20 recite no additional elements.
The processor, memory, communication interface, and non-transitory computer readable storage medium are recited at a high level of generality (i.e. as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (see MPEP 2106.05(f)). The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception
Claims 6, 8, 15, and 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Valpola U.S. Patent No. 11,568,208
Disclosed is a computer-implemented method for estimating an uncertainty of a prediction generated by a machine learning system, the method including: receiving first data; training a first machine learning model component of a machine learning system with the received first data, the first machine learning model component is trained to generate a prediction; generating an uncertainty estimate of the prediction; training a second machine learning model component of the machine learning system with second data, the second machine learning model component is trained to generate a calibrated uncertainty estimate of the prediction. Also disclosed is a corresponding system.
Cohen et al. U.S. Publication No. 2019/0236447
A method of generating a controller for a continuous process. The method includes receiving from a storage memory, off-line stored values of one or more controlled variables and one or more manipulated variables of the continuous process over a plurality of time points. The off-line stored values are used to train a first neural network to operate as a predictor of the controlled variables. Then, the method includes training a second neural network to operate as a controller of the continuous process using the first neural network after it was trained to operate as the predictor for the continuous process and employing the second neural network as a controller of the continuous process.
Choi et al. U.S. Publication No. 2021/0117774
A method of machine learning model development includes building an autoencoder including an encoder trained to map an input into a latent representation, and a decoder trained to map the latent representation to a reconstruction of the input. The method includes building an artificial neural network classifier including the encoder, and a classification layer partially trained to perform a classification in which a class to which the input belongs is predicted based on the latent representation. Neural network inversion is applied to the classification layer to find inverted latent representations within a decision boundary between classes in which a result of the classification is ambiguous, and inverted inputs are obtained from the inverted latent representations. Each inverted input is labeled with a class that is its ground truth, and thereby producing added training data for the classification, and the classification layer is further trained using the added training data.
Deng et al. U.S. Publication No. 2022/0383109
A system for machine learning architecture for time series data prediction. The system may be configured to: maintain a data set representing a neural network having a plurality of weights; obtain time series data associated with a data query; generate, using the neural network and based on the time series data, a predicted value based on a sampled realization of the time series data and a normalizing flow model, the normalizing flow model based on a latent continuous-time stochastic process having a stationary marginal distribution and bounded variance; and generate a signal providing an indication of the predicted value associated with the data query.
Flores et al. Data Augmentation for Short-Term Time Series Prediction with Deep Learning
In this paper, a hybrid data augmentation technique for short-term time series prediction is proposed in order to overcome the underfitting problem in deep learning models based on recurrent neural networks such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The proposal hybrid technique consists of the combination of two basic data augmentation techniques that are generally used for time series classification, these are: time-warping and jittering. Time-warping allows the generation of synthetic data between each pair of values in the time series, extending its length, while jittering allows the synthetic data generated to be non-linear. To evaluate the proposal technique, it’s experimented with three non-seasonal short-term time series of Perú: CO2 emissions per capita, renewable energy consumption and Covid-19 positive cases, it is considered that predicting non-seasonal time series is more difficult than seasonal ones. The results show that the regression models based on recurrent neural networks using the selected time series with data augmentation improve results between 16.318% and 42.1426%.
Wen et al. Time Series Data Augmentation for Deep Learning: A Survey
Deep learning performs remarkably well on many time series analysis tasks recently. The superior performance of deep neural networks relies heavily on a large number of training data to avoid overfitting. However, the labeled data of many real-world time series applications may be limited such as classification in medical time series and anomaly detection in AIOps. As an effective way to enhance the size and quality of the training data, data augmentation is crucial to the successful application of deep learning models on time series data. In this paper, we systematically review different data augmentation methods for time series. We propose a taxonomy for the reviewed methods, and then provide a structured review for these methods by highlighting their strengths and limitations. We also empirically compare different data augmentation methods for different tasks including time series classification, anomaly detection, and forecasting. Finally, we discuss and highlight five future directions to provide useful research guidance.
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/KRIS E MACKES/ Primary Examiner, Art Unit 2153