DETAILED ACTION
This action is in response to the original filing on 05/19/2026. Claims 1-12 are pending for examination.
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 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.
Claim 10 recites the limitation "classifications for the time domain by executing the FNN on additional target time series data received from the time domain”. There is insufficient antecedent basis for the term “time domain” in the claim.
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.
Claims 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. (US 20240362461 A1, hereinafter Jain) in view of HUANG et al. (US 20230222322 A1, hereinafter Huang).
Regarding Claim 1, Jain teaches a method (Paragraph [0005] In some aspects, a method of behavior monitoring) comprising:
transferring the functional parameters of the FNN to a target domain that is separate from the source domain; and (Paragraph [0031] In some implementations, a previously generated model is trained (or re-trained) using a machine-learning technique. In this context, “training” refers to adapting the model or parameters of the model to a particular data set. Unless otherwise clear from the specific context, the term “training” as used herein includes “re-training” or refining a model for a specific data set. For example, training may include so called “transfer learning.” As described further below, in transfer learning a base model may be trained using a generic or typical data set, and the base model may be subsequently refined (e.g., re-trained or further trained) using a more specific data set. (A model which has been trained on a larger, generic dataset (source domain) has its parameters adjusted by retraining in order to fit a more specific (target domain) dataset))
tuning the functional parameters of the FNN with target time series data from the target domain, the target time series data having fewer samples than the source time series data. (Paragraph [0041] The predicted values of the training data are compared to corresponding actual values of the training data, and the computer modifies the model until the model accurately and reliably (e.g., within some specified criteria) predicts values of the training data. In this example, the model can subsequently be used (in a runtime phase) to receive data elements and predict values that have not been received. To illustrate, the model can analyze time series data, in which case, the model can predict one or more future values of the time series based on one or more prior values of the time series. (Fine tuning of a model by using time series data, wherein the target domain is smaller and more specific in scope than the original base domain))
Jain does not teach learning a functional neural network (FNN) for a source domain associated with source time series data, the learning comprising learning functional parameters of the FNN, the FNN comprising a plurality of layers of continuous neurons;
In the same field of endeavor, Huang teaches learning a functional neural network (FNN) for a source domain associated with source time series data, the learning comprising learning functional parameters of the FNN, the FNN comprising a plurality of layers of continuous neurons; (Paragraph [0008] Example implementations described herein involve an innovative method for using a functional neural network (FNN) to do vibration data analysis for predictive maintenance. An FNN may be used to automatically extract features from the time-frequency 2D representation of vibration data by regarding each frequency component in the 2D representation as a functional covariate. FNN may be used to effectively capture the important information on a multiresolution representation of the vibration data on a time-frequency plane. Paragraph [0035] The functional neurons 422 may take the functional covariates X.sub.i(t)=[X.sup.(i,1)(t), . . . , X.sup.(i,R)(t)].sup.T as input, and may calculate a set of functions 425 including a linear transformation 424 (e.g., Σ.sub.r=1.sup.R∫.sub.t∈TW.sub.r(β.sub.r, t)X.sup.(i,r)(t)dt) of the input data based on a parameter function W.sub.r(β.sub.r, t) for r=1, . . . , R (e.g., a set of trainable parameters, or weights, associated with each X.sup.(i,r) for r=1, . . . , R), where β.sub.r is a finite-dimensional vector that quantifies the parameter function W.sub.r(β.sub.r,t). The functional neurons 422 may then calculate a scalar value based on an activation function 426 (A model is learned for a source domain associated with time data, the model having trainable neurons and parameters and comprising a functional neural network FNN))
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have combined the concept of learning a functional neural; network for a source domain associated with source time series data, the learning comprising learning functional parameters of the FNN, and the FNN comprising a plurality of layers of continuous neurons as taught by Huang into Jain as both are in the same field of making predictions with regards to time series datasets, and doing so would be desirable in order to more accurately sample and analyze real world data, such as vibration data, which is subject to frequent changes in a given time-domain. This would allow for the accurate extraction of features from such data (Huang Paragraph [0003]).
Regarding Claims 5 and 9, these are non-transitory computer readable medium and apparatus claims which correspond to the method of Claim 1. As such, they are rejected for the same reasons above.
Regarding Claim 2, the combination of Jain and Huang teaches all the limitations of Claim 1, including: generating forecasts, predictions, and classifications for the target domain by executing the FNN on additional target time series data received from the target domain. (Jain Paragraph [0030] Note that the trained model corresponds to software that has been generated and/or refined during the creation/training phase to perform particular operations, such as classification, prediction, encoding, or other data analysis or data synthesis operations. Paragraph [0085] According to some implementations, the anomaly detection model 106 can also include a time series predictor. The time series predictor may include or correspond to one or more neural networks trained to forecast future data values (such as a regression model or a generative model). The time series predictor is trained to receive as model input one or more values of the input data 308 (denoted as z.sub.t) for a particular timeframe (t) and to estimate or predict one or more values of the input data 308 for a future timeframe (t+1) to generate model output (denoted as z′.sub.t+1). (Forecasts, predictions, and classifications based on input of time series data))
Regarding Claims 6 and 10, these are non-transitory computer readable medium and apparatus claims which correspond to the method of Claim 2. As such, they are rejected for the same reasons above.
Regarding Claim 3, the combination of Jain and Huang teaches all the limitations of Claim 1, including: receiving a window size input for learning the FNN; and (Jain Paragraph [0100] In some implementations, one or more aggregation window settings indicate parameters of an aggregation window to be used for risk score and/or feature importance value calculation. For example, the aggregation window setting(s) may include a window size setting indicating a number of samples or a time duration to be represented by a window of samples used to calculate a risk score and/or a feature importance value. The aggregation window setting(s) may also, or in the alternative, include a window stride setting indicating how often a risk score or feature importance value is generated (e.g., as a multiple of a data sampling rate of the input data). Paragraph [0134] In a particular aspect, several configurable parameters are used to determine the scoring window duration, the ideality score, the false negative duration, and the false positive duration. (A window size which can be adjusted to train the network. It is then received by the network to perform training))
wherein the learning of the FNN is conducted using the window size input for the source time series data; (Jain Paragraph [0050] In FIG. 1, sensor data 102 is received and preprocessed at a preprocessor 104. The sensor data 102 includes raw time-series data, windowed or sampled time-series data, or other data representative of operation of one or more monitored assets. (The learning occurring within a window))
wherein the tuning of the functional parameters of the FNN is conducted using the window size input on the target time series data. (Jain Paragraph [0240] For typical behavior modeling, during the model training process, the model is trained and tuned on historical time series data multiple times to simulate live conditions and to determine a preferred set of model hyperparameters for an asset. This approach is called backtesting. The backtesting approach involves selecting a training window size and a backtest window size. The model is trained on sensor values from a training window and returns a risk score on sensor values from a backtest window that is subsequent to the training window. The training window is then moved forward to include the current backtest window keeping the training window size constant. The backtest window is moved forward to be subsequent to the updated training window. (Training and re-training (fine-tuning) of the model which occurs during the window))
Regarding Claims 7 and 11, these are non-transitory computer readable medium and apparatus claims which correspond to the method of Claim 3. As such, they are rejected for the same reasons above.
Regarding Claim 4, the combination of Jain and Huang teaches all the limitations of Claim 1, including: wherein the target time series data comprises insufficient samples to learn a linear regression-based model or a deep learning model. (Jain Paragraph [0027] Typically, a model is generated using a first data set to facilitate analysis of a second data set. For example, a first portion of a large body of data may be used to generate a model that can be used to analyze the remaining portion of the large body of data. As another example, a set of historical data can be used to generate a model that can be used to analyze future data. Paragraph [0029] Examples of machine-learning models include, without limitation, perceptrons, neural networks, support vector machines, regression models, decision trees, Bayesian models, Boltzmann machines, adaptive neuro-fuzzy inference systems, as well as combinations, ensembles and variants of these and other types of models. Variants of neural networks include, for example and without limitation, prototypical networks, autoencoders, transformers, self-attention networks, convolutional neural networks, deep neural networks, deep belief networks, etc. (One possible implementation of the transfer learning process is an analysis of a future data set, which would prove insufficient to train a regression or deep learning network since none of the data points have occurred yet.))
Regarding Claims 5 and 12, these are non-transitory computer readable medium and apparatus claims which correspond to the method of Claim 4. As such, they are rejected for the same reasons above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gonzalez et al. (US 12406188 B1) discusses transfer learning in the field of machine learning models.
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/JUSTIN CARDOSO/
Patent Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
