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 Arguments
Applicant’s arguments with respect to claims 1-16 and 18-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 15, 18, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Dome et al. (US 2019/0095265, Dome hereafter) in view of Marwah et al. (US 2023/0171268, Marwah hereafter).
RE claims 1, 19 and 20, Dome discloses a method, non-transitory computer-readable medium and device comprising: a processing system including at least one processor; and a non-transitory computer-readable medium storing instructions which, when executed by the processing system, cause the processing system to perform operations, the operations comprising: collecting a set of data from a plurality of sensors that is monitoring a system, wherein the plurality of sensors includes sensors of a plurality of different modalities (Paragraphs 50-55 and Figure 6 discloses a predictive failure analysis method performed by one or more processors and storage. Paragraph 52 discloses: “The predictive failure analysis 120 collects, correlates, and accumulates an array of sensor readings (collectively, “sensor data 126” in FIG. 1) for the cloud components 115 and utilizes big data analytics and machine learning to detect abnormal patterns and the root cause(s) thereof.”) detecting an instance of out-of-distribution data in the set of data by providing the set of data as an input to a machine learning model that generates as an output an indicator that the instance of out-of-distribution data is out-of-distribution with respect to the set of data; identifying a root cause for the instance of out-of-distribution data (Paragraph 52 discloses: “The predictive failure analysis 120 collects, correlates, and accumulates an array of sensor readings (collectively, “sensor data 126” in FIG. 1) for the cloud components 115 and utilizes big data analytics and machine learning to detect abnormal patterns and the root cause(s) thereof.”. Examiner equates “out-of-distribution data” to what Dome discloses as identification of “abnormal patterns”); and initiating an action to remediate the root cause of the first instance of out-of-distribution data (Paragraphs 52-54 discloses detection and “root cause” determination of “abnormal patterns” as well as taking proactive and preventative adjustment based on the findings of the predictive failure analysis.)
Dome does not explicitly disclose detecting, by the processing system based on another output of the machine learning model, a second instance of out-of-distribution data in the set of data; determining, by the processing system, that the second instance of out-of-distribution is consistent with a new distribution of the set of data that indicates a data shift and retraining, by the processing system in response to the determining, the machine learning model using the set of data augmented with the second instance of out-of-distribution data.
However, Marwah teaches detecting, by the processing system based on another output of the machine learning model, a second instance of out-of-distribution data in the set of data; determining, by the processing system, that the second instance of out-of-distribution is consistent with a new distribution of the set of data that indicates a data shift and retraining, by the processing system in response to the determining, the machine learning model using the set of data augmented with the second instance of out-of-distribution data (Paragraphs 37-49 teaches a machine learning based process, in this case applied to cybersecurity, whereby a system monitors for and identifies anomalies/attacks and adapts using “supervised machine learning, unsupervised machine learning, semi-supervised machine learning, and/or the like.”. It is further taught that “Machine learning models may be routinely trained for detecting threats and intrusions. However, during model deployment the data seen is usually not the same as that used during training. Although models can be trained to perform well on “out of distribution” data, at some point the machine learning model would need to be retrained as the current data distribution diverges further from the data used during training. Therefore, there is a need to closely monitor the performance of a deployed machine learning model. The Security Bot(s) 121 could proactively test the models on more recent data and monitor various performance metrics such as accuracy, recall, and precision. Synthetic data generated from more recent data could also be used to test the machine learning models.”. This appears to teach that as more “out of distribution” data occurs, anomalies, is known practice in machine learning to retrain the model with this anomalous data in order to improve performance.)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Dome with the teachings of Marwah in order to improve machine learning model performance.
RE claim 15, Dome in view of Marwah discloses the method of claim 1 as set forth above. Note that Marwah further teaches augmenting, by the processing system, a set of training data used to train the machine learning model with the first instance of out-of-distribution data (Paragraphs 37-49 teaches a machine learning based process, in this case applied to cybersecurity, whereby a system monitors for and identifies anomalies/attacks and adapts using “supervised machine learning, unsupervised machine learning, semi-supervised machine learning, and/or the like.”. It is further taught that “Machine learning models may be routinely trained for detecting threats and intrusions. However, during model deployment the data seen is usually not the same as that used during training. Although models can be trained to perform well on “out of distribution” data, at some point the machine learning model would need to be retrained as the current data distribution diverges further from the data used during training. Therefore, there is a need to closely monitor the performance of a deployed machine learning model. The Security Bot(s) 121 could proactively test the models on more recent data and monitor various performance metrics such as accuracy, recall, and precision. Synthetic data generated from more recent data could also be used to test the machine learning models.”. This appears to teach that as more “out of distribution” data occurs, anomalies, is known practice in machine learning to retrain the model with this anomalous data in order to improve performance.)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Dome with the teachings of Marwah in order to improve machine learning model performance.
RE claim 18, Dome in view of Marwah discloses the method of claim 1 as set forth above. Note that Marwah further teaches wherein the set of data is further augmented, prior to the retraining, with additional instances of data that were collected after a last training of the machine learning model (Paragraphs 37-49 teaches a machine learning based process, in this case applied to cybersecurity, whereby a system monitors for and identifies anomalies/attacks and adapts using “supervised machine learning, unsupervised machine learning, semi-supervised machine learning, and/or the like.”. It is further taught that “Machine learning models may be routinely trained for detecting threats and intrusions. However, during model deployment the data seen is usually not the same as that used during training. Although models can be trained to perform well on “out of distribution” data, at some point the machine learning model would need to be retrained as the current data distribution diverges further from the data used during training. Therefore, there is a need to closely monitor the performance of a deployed machine learning model. The Security Bot(s) 121 could proactively test the models on more recent data and monitor various performance metrics such as accuracy, recall, and precision. Synthetic data generated from more recent data could also be used to test the machine learning models.”. This appears to teach that as more “out of distribution” data occurs, anomalies, is known practice in machine learning to retrain the model with this anomalous data in order to improve performance.)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Dome with the teachings of Marwah in order to improve machine learning model performance.
Claims 2-5 are rejected under 35 U.S.C. 103 as being unpatentable over Dome in view of Marwah and further in view of Nair et al. (US 11, 876, 858, Nair hereafter).
RE claim 2, Dome in view of Marwah discloses the method of claim 1 as set forth above. Note that Dome further discloses wherein the system comprises one of: a communications network (Figure 13).
Dome in view of Marwah does not explicitly disclose wherein the system comprises one of: an autonomous vehicle, a piece of artwork, a physical location at which a crowd is gathered, or a piece of software that is under development.
However, Nair teaches wherein the system comprises one of: an autonomous vehicle, a piece of artwork, a physical location at which a crowd is gathered, or a piece of software that is under development (Table 1 in column 20, “Example use case scenarios and corresponding compute requirements and parameters for various AI and ML applications at the edge.”. These usage scenarios include AL/ML applications utilizing sensors deployed for use in autonomous vehicles, healthcare such as wearable monitors, industrial and media/entertainment which includes security cameras and motion detectors).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Dome in view of Marwah with the teachings of Nair in order to provide for anomaly detection across a variety of usage scenarios.
RE claim 3, Dome in view of Marwah discloses the method of claim 1 as set forth above. Note that Dome further discloses wherein the plurality of sensors includes at least two of: a network sensor and a temperature sensor (Paragraph 36, sensor data is collected from “cloud component(s)” and can include “hardware sensor readings, environmental sensor readings, and/or utilization sensor readings”. Paragraph 28 discloses “environmental temperature of a critical application”).
Dome in view of Marwah does not explicitly disclose wherein the plurality of sensors includes at least two of: an imaging sensor, an audio sensor, a weather sensor, a medical sensor, or a biometric sensor.
However Nail further teaches wherein the plurality of sensors includes: an imaging sensor, an audio sensor, a weather sensor, a medical sensor, or a biometric sensor (Table 1 in column 20, “Example use case scenarios and corresponding compute requirements and parameters for various AI and ML applications at the edge.”. These usage scenarios include AL/ML applications utilizing sensors deployed for use in autonomous vehicles, healthcare such as wearable monitors, industrial and media/entertainment which includes security cameras and motion detectors).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Dome in view of Marwah with the teachings of Nair in order to provide for anomaly detection across a variety of usage scenarios and associated sensor types.
RE claim 4, Dome in view of Marwah discloses the method of claim 1 as set forth above. Dome in view of Marwah does not explicitly disclose wherein at least one sensor of the plurality of sensors is mounted in a fixed location.
However, Nair teaches wherein at least one sensor of the plurality of sensors is mounted in a fixed location (Table 1 in column 20, “Example use case scenarios and corresponding compute requirements and parameters for various AI and ML applications at the edge.”. These usage scenarios include AL/ML applications utilizing sensors deployed for use in autonomous vehicles, healthcare such as wearable monitors, industrial and media/entertainment which includes security cameras and motion detectors. The sensors listed are a combination of fixed, wearable and those attached to vehicles).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Dome in view of Marwah with the teachings of Nair in order to provide for anomaly detection across a variety of usage scenarios and associated sensor types.
RE claim 5, Dome in view of Marwah discloses the method of claim 1 as set forth above. Dome in view of Marwah does not explicitly disclose wherein at least one sensor of the plurality of sensors is mounted to a moving object.
However, Nair teaches wherein at least one sensor of the plurality of sensors is mounted to a moving object (Table 1 in column 20, “Example use case scenarios and corresponding compute requirements and parameters for various AI and ML applications at the edge.”. These usage scenarios include AL/ML applications utilizing sensors deployed for use in autonomous vehicles, healthcare such as wearable monitors, industrial and media/entertainment which includes security cameras and motion detectors. The sensors listed are a combination of fixed, wearable and those attached to vehicles).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Dome in view of Marwah with the teachings of Nair in order to provide for anomaly detection across a variety of usage scenarios and associated sensor types.
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Dome in view of Marwah and further in view of Schell et al. (US 2024/0403662, Schell hereafter).
RE claim 6, Dome in view of Marwah discloses the method of claim 1 as set forth above. Dome in view of Marwah does not explicitly disclose wherein a value of the instance of out-of-distribution data deviates from a mean value for the set of data by more than a predefined threshold value.
However, Schell teaches wherein a value of the instance of out-of-distribution data deviates from a mean value for the set of data by more than a predefined threshold value (Paragraphs 18 teaches “the univariate machine learning models are statistical models and predict a potential anomaly when any one input feature (such as any single monitored metric of a configuration item) exceeds a configured threshold, such as one, two, or three standard deviations (or another threshold value) from the mean value or another baseline value of an input feature.”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Dome in view of Marwah with the teachings of Schell since such a modification would have involved the mere application of a known technique to a piece of prior art ready for improvement.
Where a claimed improvement on a device or apparatus is no more than "the simple substitution of one known element for another or the mere application of a known technique to a piece of prior art ready for improvement," the claim is unpatentable under 35 U.S.C. 103(a). Ex Parte Smith, 83 USPQ.2d 1509, 1518-19 (BPAI, 2007) (citing KSR v. Teleflex, 127 S.Ct. 1727, 1740, 82 USPQ2d 1385, 1396 (2007)).
RE claim 7, Dome in view of Marwah and further in view of Schell discloses the method of claim 6 as set forth above. Note that Schell further teaches wherein the predefined threshold value is different for each modality of data that is collected by the plurality of sensors (Paragraphs 18 teaches “the univariate machine learning models are statistical models and predict a potential anomaly when any one input feature (such as any single monitored metric of a configuration item) exceeds a configured threshold, such as one, two, or three standard deviations (or another threshold value) from the mean value or another baseline value of an input feature.”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Dome in view of Marwah with the teachings of Schell since such a modification would have involved the mere application of a known technique to a piece of prior art ready for improvement.
Where a claimed improvement on a device or apparatus is no more than "the simple substitution of one known element for another or the mere application of a known technique to a piece of prior art ready for improvement," the claim is unpatentable under 35 U.S.C. 103(a). Ex Parte Smith, 83 USPQ.2d 1509, 1518-19 (BPAI, 2007) (citing KSR v. Teleflex, 127 S.Ct. 1727, 1740, 82 USPQ2d 1385, 1396 (2007)).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Dome in view of Marwah and further in view of Hatch et al. (US 2014/0058615, Hatch hereafter).
RE claim 8, Dome in view of Marwah discloses the method of claim 1 as set forth above. Dome in view of Marwah does not explicitly disclose wherein a value of the instance of out-of-distribution data deviates from a median value for the set of data by more than a predefined threshold value.
However, Hatch teaches wherein a value of the instance of out-of-distribution data deviates from a median value for the set of data by more than a predefined threshold value (Paragraph 15 teaches “calculating a set of deviations corresponding to the set of mean values relative to a median value of the set of mean values; (g) determining if anomalous behavior exists based on the set of deviations; (h) calculating a characteristic value representative of the set of deviations; (i) calculating a set of normalized deviations for the calculated characteristic value for each of the machines in the fleet; and (j) comparing the set of normalized deviations to a predefined threshold.”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Dome with the teachings of Hatch since such a modification would have involved the mere application of a known technique to a piece of prior art ready for improvement.
Where a claimed improvement on a device or apparatus is no more than "the simple substitution of one known element for another or the mere application of a known technique to a piece of prior art ready for improvement," the claim is unpatentable under 35 U.S.C. 103(a). Ex Parte Smith, 83 USPQ.2d 1509, 1518-19 (BPAI, 2007) (citing KSR v. Teleflex, 127 S.Ct. 1727, 1740, 82 USPQ2d 1385, 1396 (2007)).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Dome in view of in view of Marwah, in view of Elad et al. (US 7,480,640, Elad hereafter) in view of Tung et al. (US 2017/0206680) and further in view of Klimov et al. (US 2021/0081816, Klimov hereafter).
RE claim 9, Dome in view of Marwah discloses the method of claim 1 as set forth above. Dome in view of Marwah does not explicitly disclose wherein the machine learning model comprises at least one of: a density-based algorithm, a reconstruction-based algorithm, a classification-based algorithm, or a distance-based algorithm.
However, Elad teaches a density-based algorithm and a distance-based algorithm (Claim 40 teaches a plurality of machine-learning methods. Among them are “density-based clustering” and “distance-based outlier detection”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Dome in view of Marwah with the teachings of Elad since such a modification would have involved the mere application of a known technique to a piece of prior art ready for improvement.
Where a claimed improvement on a device or apparatus is no more than "the simple substitution of one known element for another or the mere application of a known technique to a piece of prior art ready for improvement," the claim is unpatentable under 35 U.S.C. 103(a). Ex Parte Smith, 83 USPQ.2d 1509, 1518-19 (BPAI, 2007) (citing KSR v. Teleflex, 127 S.Ct. 1727, 1740, 82 USPQ2d 1385, 1396 (2007)).
Dome in view of Marwah and further in view of Elad does not explicitly disclose a reconstruction-based algorithm or a classification-based algorithm.
However, Tung teaches a reconstruction-based algorithm (Paragraph 28 teaches “For example, a rule can be to use a reconstruction with specific settings for a patient with a high weight. In another embodiment, the AI engine 6 uses a Bayesian network that codifies expert knowledge of reconstruction algorithms or workflows are deployed…)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Dome in view of Marwah and further in view of Elad with the teachings of Tung since such a modification would have involved the mere application of a known technique to a piece of prior art ready for improvement.
Where a claimed improvement on a device or apparatus is no more than "the simple substitution of one known element for another or the mere application of a known technique to a piece of prior art ready for improvement," the claim is unpatentable under 35 U.S.C. 103(a). Ex Parte Smith, 83 USPQ.2d 1509, 1518-19 (BPAI, 2007) (citing KSR v. Teleflex, 127 S.Ct. 1727, 1740, 82 USPQ2d 1385, 1396 (2007)).
Dome in view of Marwah, in view of Elad and further in view of Tung does not explicitly disclose a classification-based algorithm.
However, Klimov teaches a classification-based algorithm (Paragraph 30 teaches “For classification-based calibrations, the machine learning algorithm may comprise linear or nonlinear machine learning classifiers, including classifiers based on least squares, nearest-neighbour methods, linear discriminant analysis, quadratic discriminant analysis, logistic regression, support vector machines or neural networks.”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Dome in view of Marwah, in view of Hatch and further in view of Tung with the teachings of Klimov since such a modification would have involved the mere application of a known technique to a piece of prior art ready for improvement.
Where a claimed improvement on a device or apparatus is no more than "the simple substitution of one known element for another or the mere application of a known technique to a piece of prior art ready for improvement," the claim is unpatentable under 35 U.S.C. 103(a). Ex Parte Smith, 83 USPQ.2d 1509, 1518-19 (BPAI, 2007) (citing KSR v. Teleflex, 127 S.Ct. 1727, 1740, 82 USPQ2d 1385, 1396 (2007)).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Dome in view of Marwah and further in view of Arditi (US 2019/0147331).
RE claim 10, Dome in view of Marwah discloses the method of claim 1 as set forth above. Dome does not explicitly disclose wherein the set of data is transferred into a set of latent representations after the collecting, but prior to the detecting.
However, Arditi teaches wherein the set of data is transferred into a set of latent representations after the collecting, but prior to the detecting (Paragraph 28 teaches: “In alternative embodiments, the CNN 450 may be configured as multiple discrete neural networks, with each discrete neural network receiving and processing a different type of sensor data.”, and “In embodiments where multiple discrete neural networks are used, the machine-learning model may be configured to pool, concatenate, and/or aggregate the outputs of those networks to form the latent representation).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Dome in view of Marwah with the teachings of Arditi since such a modification would have involved the mere application of a known technique to a piece of prior art ready for improvement.
Where a claimed improvement on a device or apparatus is no more than "the simple substitution of one known element for another or the mere application of a known technique to a piece of prior art ready for improvement," the claim is unpatentable under 35 U.S.C. 103(a). Ex Parte Smith, 83 USPQ.2d 1509, 1518-19 (BPAI, 2007) (citing KSR v. Teleflex, 127 S.Ct. 1727, 1740, 82 USPQ2d 1385, 1396 (2007)).
Claims 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Dome in view of Marwah further in view of Gupta et al. (US 11,816,550, Gupta hereafter).
RE claim 11, Dome in view of Marwah discloses the method of claim 1 as set forth above. Dome in view of Marwah does not explicitly disclose wherein the instance of out-of-distribution data is assigned a score, wherein the score comprises an indication as to how closely the instance of out-of-distribution data fits a distribution of the set of data.
However, Gupta teaches wherein the instance of out-of-distribution data is assigned a score, wherein the score comprises an indication as to how closely the instance of out-of-distribution data fits a distribution of the set of data (Column 5 lines, 59-67, teaches outlier detection wherein “a kNN detector may be used for outlier prediction model 144. In kNN, for any data point, the distance to its kth nearest neighbor may be viewed as the outlying score. In an example embodiment, there are three kNN detectors: a) “Largest,” which uses the distance of the kth neighbor as the outlier score, b) “Mean,” which uses the average of all k neighbors as the outlier score, and c) “Median,” which uses the median of the distance to k neighbors as the outlier score.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Dome in view of Marwah with the teachings of Gupta since such a modification would have involved the mere application of a known technique to a piece of prior art ready for improvement.
Where a claimed improvement on a device or apparatus is no more than "the simple substitution of one known element for another or the mere application of a known technique to a piece of prior art ready for improvement," the claim is unpatentable under 35 U.S.C. 103(a). Ex Parte Smith, 83 USPQ.2d 1509, 1518-19 (BPAI, 2007) (citing KSR v. Teleflex, 127 S.Ct. 1727, 1740, 82 USPQ2d 1385, 1396 (2007)).
RE claim 12, Dome in view of Marwah and further in view of Gupta discloses the method of claim 11 as set forth above. Note that Gupta further teaches wherein the score is proportional to a distance between the score and a mean score for the set of data (Column 5 lines, 59-67, teaches outlier detection wherein “a kNN detector may be used for outlier prediction model 144. In kNN, for any data point, the distance to its kth nearest neighbor may be viewed as the outlying score. In an example embodiment, there are three kNN detectors: a) “Largest,” which uses the distance of the kth neighbor as the outlier score, b) “Mean,” which uses the average of all k neighbors as the outlier score, and c) “Median,” which uses the median of the distance to k neighbors as the outlier score.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Dome in view of Marwah with the teachings of Gupta since such a modification would have involved the mere application of a known technique to a piece of prior art ready for improvement.
Where a claimed improvement on a device or apparatus is no more than "the simple substitution of one known element for another or the mere application of a known technique to a piece of prior art ready for improvement," the claim is unpatentable under 35 U.S.C. 103(a). Ex Parte Smith, 83 USPQ.2d 1509, 1518-19 (BPAI, 2007) (citing KSR v. Teleflex, 127 S.Ct. 1727, 1740, 82 USPQ2d 1385, 1396 (2007)).
RE claim 13, Dome in view of Marwah and further in view of Gupta discloses the method of claim 11 as set forth above. Note that Gupta further teaches wherein the score is proportional to a distance between the score and a median score for the set of data (Column 5 lines, 59-67, teaches outlier detection wherein “a kNN detector may be used for outlier prediction model 144. In kNN, for any data point, the distance to its kth nearest neighbor may be viewed as the outlying score. In an example embodiment, there are three kNN detectors: a) “Largest,” which uses the distance of the kth neighbor as the outlier score, b) “Mean,” which uses the average of all k neighbors as the outlier score, and c) “Median,” which uses the median of the distance to k neighbors as the outlier score.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Dome in view of Marwah with the teachings of Gupta since such a modification would have involved the mere application of a known technique to a piece of prior art ready for improvement.
Where a claimed improvement on a device or apparatus is no more than "the simple substitution of one known element for another or the mere application of a known technique to a piece of prior art ready for improvement," the claim is unpatentable under 35 U.S.C. 103(a). Ex Parte Smith, 83 USPQ.2d 1509, 1518-19 (BPAI, 2007) (citing KSR v. Teleflex, 127 S.Ct. 1727, 1740, 82 USPQ2d 1385, 1396 (2007)).
Claims 14 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Dome in view of Marwah and further in view of Givental et al. (US 2021/0281592, Givental hereafter).
RE claim 14, Dome in view of Marwah discloses the method of claim 1 as set forth above. Dome in view of Marwah does not explicitly disclose wherein the identifying is performed using a supervised machine learning technique in which a human operator labels instances of out-of-distribution data in a set of training data with root causes.
However, Givental teaches wherein the identifying is performed using a supervised machine learning technique in which a human operator labels instances of out-of-distribution data in a set of training data with root causes (Paragraph 18 teaches: “While machine learning based anomaly detection mechanism may be implemented, in order to achieve high detection accuracy, supervised machine learning algorithms still need a large amount of effort from the human security analysts to manually label historical computer system logs and potential threats for up-front model training, i.e. for generation of the training data.”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Dome in view of Marwah with the teachings of Givental since such a modification would have involved the mere application of a known technique to a piece of prior art ready for improvement.
Where a claimed improvement on a device or apparatus is no more than "the simple substitution of one known element for another or the mere application of a known technique to a piece of prior art ready for improvement," the claim is unpatentable under 35 U.S.C. 103(a). Ex Parte Smith, 83 USPQ.2d 1509, 1518-19 (BPAI, 2007) (citing KSR v. Teleflex, 127 S.Ct. 1727, 1740, 82 USPQ2d 1385, 1396 (2007)).
RE claim 16, Dome in view of Marwah and further in view of Givental discloses the method of claim 15 as set forth above. Note that Givental further teaches wherein the instance of out-of-distribution data is labeled as out-of-distribution before being added to the set of training data (Paragraph 18 teaches: “While machine learning based anomaly detection mechanism may be implemented, in order to achieve high detection accuracy, supervised machine learning algorithms still need a large amount of effort from the human security analysts to manually label historical computer system logs and potential threats for up-front model training, i.e. for generation of the training data.”. Paragraph 21 teaches: “The human security analyst response is stored in a training data database along with the unlabeled input log data for use as training data for training an anomaly detection machine learning model, i.e. performing supervised machine learning of an anomaly detection machine learning model.”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Dome in view of Marwah with the teachings of Givental since such a modification would have involved the mere application of a known technique to a piece of prior art ready for improvement.
Where a claimed improvement on a device or apparatus is no more than "the simple substitution of one known element for another or the mere application of a known technique to a piece of prior art ready for improvement," the claim is unpatentable under 35 U.S.C. 103(a). Ex Parte Smith, 83 USPQ.2d 1509, 1518-19 (BPAI, 2007) (citing KSR v. Teleflex, 127 S.Ct. 1727, 1740, 82 USPQ2d 1385, 1396 (2007)).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Dome in view of Marwah and further in view of Cella et al. (US 2018/02184758, Cella hereafter).
RE claim 21, Dome in view of Marwah discloses the method of claim 1 as set forth above. Dome in view of Marwah does not explicitly disclose wherein the first instance of out-of- distribution data in the set of data is captured by a first sensor of the plurality of sensors that is of a first modality of the plurality of different modalities, and wherein the root cause is detected by correlating the first instance of out-of-distribution data in the set of data with a third instance of out-of-distribution data in the set of data that is captured by another sensor of the plurality of sensors that is of a different modality than the first modality.
However, Cella teaches wherein the first instance of out-of- distribution data in the set of data is captured by a first sensor of the plurality of sensors that is of a first modality of the plurality of different modalities, and wherein the root cause is detected by correlating the first instance of out-of-distribution data in the set of data with a third instance of out-of-distribution data in the set of data that is captured by another sensor of the plurality of sensors that is of a different modality than the first modality (Paragraphs 6-8 teach a system using a neural network to monitor, in this case, a industrial drilling environment. The neural network collects data from a plurality of sensors, stores it, and analyzes it to detect anomalous conditions. It teaches “analyzing the collected data to detect an anomalous condition associated with the industrial drilling component; and switching from a first data collection routine to a second collection routine based on the detection of the anomalous condition. In embodiments, the anomalous condition may be a pre-failure mode condition for the industrial drilling component, and wherein the switching increases data monitoring of the industrial drilling component. Detecting the anomalous condition may include determining a relative phase difference between the detection values interpreted from two of the plurality of input channels.” Furthermore, “a data analysis component structured to analyze the collected data from the plurality of input channels to detect an anomalous condition associated with one of the plurality of industrial drilling components; and a data response circuit structured to adjust at least one of the data collection routines based on the detection of the anomalous condition. In embodiments, the data response circuit may be further structured to adjust the at least one of the data collection routines by changing at least one of: the collected data such that different sensors are utilized to monitor the industrial drilling component; and sensor configuration values such that operational parameters of the sensors monitoring the industrial drilling component are changed. The anomalous condition may include a reduced operating capability of the industrial drilling component, and wherein the data response circuit is further structured to provide a drilling process adjustment to reduce a work load of the industrial drilling component.” This is interpreted as teaching the analysis of multiple data points comprised of anomalous data, which the neural network uses to correlate multiple of these data points from different sensors to determine a manner in which to adjust operation.)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Dome in view of Marwah with the teachings of Cella since such a modification would have involved the mere application of a known technique to a piece of prior art ready for improvement.
Where a claimed improvement on a device or apparatus is no more than "the simple substitution of one known element for another or the mere application of a known technique to a piece of prior art ready for improvement," the claim is unpatentable under 35 U.S.C. 103(a). Ex Parte Smith, 83 USPQ.2d 1509, 1518-19 (BPAI, 2007) (citing KSR v. Teleflex, 127 S.Ct. 1727, 1740, 82 USPQ2d 1385, 1396 (2007)).
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.
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/James P Duffy/ Primary Examiner, Art Unit 2461
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