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 .
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted 05/08/2025 was filed before the mailing date of this office action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a time series decomposition module” and “a result combination module” in claim 9, “a data imbalance handling module” in claim 13, “a threshold tuning module” in claim 17, “a model explainability module” in claim 18, and “a drift detection module” in claim 19.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 limitations “A system for performing anomaly detection, the system comprising:a time series decomposition module configured to perform time series decomposition on training time series data to extract residuals of the training time series data, the residuals including a plurality of data points of the training time series data; a plurality of unsupervised anomaly detection models configured to identify and label anomalous data points from among the plurality of data points contained in the residuals; a result combination module configured to, based on outputs from the plurality of unsupervised anomaly detection models including the labeled anomalous data points, obtain a combined output indicating the labeled anomalous data points; and a plurality of supervised anomaly detection models trained using the combined output and configured to generate respective outputs identifying anomalous data points in real-time time series data.”, in claim 9, “The system of claim 9, further comprising a data imbalance handling module configured to receive the combined output and adjust a sampling rate of the labeled anomalous data points indicated by the combined output.”, in claim 13, “The system of claim 14, further comprising a second result combination module configured to obtain a second combined output indicating the labeled anomalous data points in the real-time time series data.”, in claim 15, “The system of claim 9, further comprising a model explainability module configured to generate and output data indicating input metrics that resulted in the labeled anomalous data points.” in claim 18, and “The system of claim 9, further comprising a drift detection module configured to at least one of (i) detect drift in the real-time time series data and (ii) detect drift in one or more of the plurality of supervised anomaly detection models.” in claim 19, invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Paragraph [0052] of the Specification discloses the modules may be implemented as software components or circuitry without disclosing any structure that performs the function in the claim. It is not clear if the modules are software modules or hardware circuitry. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 5-9, 11-12, 14-15 and 18-20 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by US-PGPUB No. 2024/0346287 A1 to Keshva et al. (hereinafter “Keshva”)
Keshva discloses:
A system for performing anomaly detection (Abstract: “Systems and methods of anomaly detection using a generalized time-series forecasting model”, see Fig. 1), the system comprising:
a processor (see Fig. 1, Processor Subsystem 4); and
memory comprising instructions (see Fig. 1, Memory Subsystem 8) that, when executed, cause the processor to:
perform time series decomposition on training time series data (¶71: “in order to provide for anomaly detection by one or more anomaly detection models … seasonal variability is removed from signal data 252. … by decomposing a time-series. … a Seasonal and Trend decomposition using Loess (STL) model is employed to decompose time-series data.”) to extract residuals of the training time series data, the residuals including a plurality of data points of the training time series data (¶71: “a residual component from the STL model is provided to one or more anomaly detection models as a time-series signal input.”);
using an ensemble of unsupervised anomaly detection models, identify and label anomalous data points from among the plurality of data points contained in the residuals (¶73: “At step 206, anomalies in the signal data 252 are identified by applying one or more trained anomaly detection models. … anomaly detection models 260, 262a-262d can be individually applied and/or can be applied in one or more ensemble configurations.”, ¶94: “At step 208, identified anomalies are bucketed into one or more buckets 266a-266c”);
based on outputs from the ensemble of unsupervised models including the labeled anomalous data points, obtain a combined output indicating the labeled anomalous data points (¶95: “At step 210, feedback data 268 is received from each of the reviewer systems 24a, 24b regarding identification and/or resolution of the identified anomaly.”);
using the combined output, train an ensemble of supervised anomaly detection models to detect anomalies in inference time series data (¶96: “At step 212, at least one of the trained anomaly detection models 260, 262 are updated based, at least in part, on the feedback data 268. … new and/or updated models can be generated by a model training engine 270 according to an iterative training process that incorporates the feedback data 268 as part of a training dataset, such as the iterative training method 300”, ¶105: “The iterative training process can include a supervised training process …”); and
using the trained ensemble of supervised anomaly detection models, generate respective outputs identifying anomalous data points in real-time (¶85: “Individual ensemble models are configured to apply an ensemble approach, such as multiple nested trees, to generate a consensus identification of data points as anomalous or non-anomalous signals or interactions.”, ¶97: “The method 200 leverages machine learning and deep learning techniques on historical time-series data to raise alerts regarding observed anomalies in real-time … time-series data.”), inference time series data (¶72: “processing of the signal data 252 includes inference of one or more missing values. … characteristic features and/or time-based features can be inferred during a processing step and/or during model implementation, for example, by observing the time-series data over a predetermined time range and extracting features based on the observations.”).
Regarding claim 2:
Keshva discloses:
The system of claim 1, wherein the inference time series data includes network observability data (¶72: “processing of the signal data 252 includes inference of one or more missing values. … by observing the time-series data over a predetermined time range and extracting features based on the observations.”).
Regarding claim 3:
Keshva discloses:
The system of claim 2, wherein the network observability data corresponds to a telecommunications network (¶06: “receiving a plurality of historic time-series datasets, … iteratively training a generalized time-series forecasting model to generate a time-series forecast”, ¶36: “… the communications interface 10 can provide data communications functionality in accordance with a number of protocols. … CDMA cellular radiotelephone communication systems”).
Regarding claim 5:
Keshva discloses:
The system of claim 1, wherein obtaining the combined output includes obtaining the combined output using a majority voting algorithm (¶62: “… the tree-based neural network 150 applies an aggregation process 162 to combine the output of each of the trained decision
trees 154a-154c into a final output 164. … the tree-based neural network 150 can apply a majority-voting process to identify a classification selected by the majority of the trained decision trees 154a-154c.”).
Regarding claim 6:
Keshva discloses:
The system of claim 1, wherein the ensemble of unsupervised anomaly detection models include at least two of a K-means clustering model, a density- based spatial clustering of applications with noise (DBSCAN) model, a Gaussian mixture model, an isolation forest model, a local outlier factor model, a robust covariance model, and a one class support vector machine model (¶19: “… k-means clustering model, an isolation forest model, and/or a statistical profiling model can be implemented as part of the ensemble approach.”).
Regarding claim 7:
Keshva discloses:
The system of claim 1, wherein the unsupervised anomaly detection models are machine learning models (¶49: “… a model training system 30 is configured to train and/or refine machine learning models for detection of anomalies, … the model training system 30 can be configured to generate … clustering-based unsupervised models (such as a k-means model),”).
Regarding claim 8:
Keshva discloses:
The system of claim 1, the memory further comprising instructions that, when executed, cause the processor to:
using the respective outputs of the trained ensemble of supervised anomaly detection models, obtain a second combined output indicating the labeled anomalous data points in the inference time series data (¶62: “… the tree-based neural network 150 applies an aggregation process 162 to combine the output of each of the trained decision trees 154a-154c into a final output 164.”).
Regarding claim 9:
Keshav discloses:
A system for performing anomaly detection (Abstract: “Systems and methods of anomaly detection using a generalized time-series forecasting model”, see Fig. 1), the system comprising:
a time series decomposition module configured to perform (p-51: “a module/engine can be executed on the processor(s) of one or more computing platforms …”, in view of claim 5: “wherein the processor is configured to, … decompose the time-series dataset ...”) …;
a plurality of unsupervised anomaly detection models (see Fig. 7, Ensemble models 262a-b in view of p-84: “… one or more clustering models 262a are generated by an unsupervised learning process.”) configured to …;
a result combination module configured to (see Fig. 7, Module 264b), …; and
a plurality of supervised anomaly detection models trained using the combined output (see Fig. 7, Models 260 and 262c) and configured to …
In addition to the above limitations claim 9 recites substantially the same limitations as claim 1 in the form of a system comprising the functional modules and models. Therefore, it is rejected by the same rationale.
Regarding claim 10:
Keshva discloses:
The system of claim 9, wherein the real-time time series data includes network observability data of a telecommunications network (¶06: “receiving a plurality of historic time-series datasets, … iteratively training a generalized time-series forecasting model to generate a time-series forecast”, ¶36: “… the communications interface 10 can provide data communications functionality in accordance with a number of protocols. … CDMA cellular radiotelephone communication systems”).
Regarding claim 11:
Keshva discloses:
The system of claim 9, wherein the result combination module is configured to execute a majority voting algorithm to obtain the combined output (¶62: “… the tree-based neural network 150 applies an aggregation process 162 to combine the output of each of the trained decision trees 154a-154c into a final output 164. … the tree-based neural network 150 can apply a majority-voting process to identify a classification selected by the majority of the trained decision trees 154a-154c.”).
Regarding claim 12:
Keshva discloses:
The system of claim 9, wherein the plurality of unsupervised anomaly detection models include at least two of a K-means clustering model, a density- based spatial clustering of applications with noise (DBSCAN) model, a Gaussian mixture model, an isolation forest model, a local outlier factor model, a robust covariance model, and a one class support vector machine model (¶19: “… k-means clustering model, an isolation forest model, and/or a statistical profiling model can be implemented as part of the ensemble approach.”).
Regarding claim 14:
Keshva discloses:
The system of claim 9, further comprising a second time series decomposition module configured to perform time series decomposition on the real-time time series data to extract residuals of the real-time time series data, the residuals including a plurality of data points of the real-time time series data, wherein the plurality of supervised anomaly detection models is configured to generate the respective outputs using the residuals of the real-time time series data (¶71: “a residual component from the STL model is provided to one or more anomaly detection models as a time-series signal input.”).
Regarding claim 15:
Keshva discloses:
The system of claim 14, further comprising a second result combination module configured to obtain a second combined output indicating the labeled anomalous data points in the real-time time series data (¶62: “… the tree-based neural network 150 applies an aggregation process 162 to combine the output of each of the trained decision trees 154a-154c into a final output 164.”).
Regarding claim 18:
Keshva discloses:
The system of claim 9, further comprising a model explainability module configured to generate and output data indicating input metrics that resulted in the labeled anomalous data points (¶50: “the model training system 30 configures a trained generalized time-series model to identify one or more of a plurality of characteristic features in time-series input provided to the model. The characteristics features can be provided as an input to a forecasting portion of a model, such as, for example, a LSTM forecasting model configured to receive the time-series data and the characteristic features.”).
Regarding claim 19:
Keshva discloses:
The system of claim 9, further comprising a drift detection module configured to at least one of (i) detect drift in the real-time time series data and (ii) detect drift in one or more of the plurality of supervised anomaly detection models (¶82: “the anomaly detection engine 256 is configured to implement … one or more statistical profiling models 262c (e.g., standard deviation models),”).
Regarding claim 20:
Keshva discloses:
A system for performing anomaly detection (Abstract: “Systems and methods of anomaly detection using a generalized time-series forecasting model”, see Fig. 1), the system comprising:
a processor (see Fig. 1, Processor Subsystem 4); and
memory comprising instructions (see Fig. 1, Memory Subsystem 8) that, when executed, cause the processor to:
receive training time series data corresponding to network observability data of a telecommunications network (¶06: “receiving a plurality of historic time-series datasets, … iteratively training a generalized time-series forecasting model to generate a time-series forecast”, ¶36: “… the communications interface 10 can provide data communications functionality in accordance with a number of protocols. … CDMA cellular radiotelephone communication systems”);
using an ensemble of unsupervised anomaly detection models, identify and label anomalous data points from among a plurality of data points contained in the training time series data (¶73: “At step 206, anomalies in the signal data 252 are identified by applying one or more trained anomaly detection models. … anomaly detection models 260, 262a-262d can be individually applied and/or can be applied in one or more ensemble configurations.”, ¶94: “At step 208, identified anomalies are bucketed into one or more buckets 266a-266c”);
based on outputs from the ensemble of unsupervised models including the labeled anomalous data points, obtain, using a majority voting algorithm, a combined output indicating the labeled anomalous data points (¶62: “… the tree-based neural network 150 applies an aggregation process 162 to combine the output of each of the trained decision trees 154a-154c into a final output 164. … the tree-based neural network 150 can apply a majority-voting process to identify a classification selected by the majority of the trained decision trees 154a-154c.”);
using the combined output, train an ensemble of supervised anomaly detection models to detect anomalies in real-time time series data received from the telecommunications network (¶96: “At step 212, at least one of the trained anomaly detection models 260, 262 are updated based, at least in part, on the feedback data 268. … new and/or updated models can be generated by a model training engine 270 according to an iterative training process that incorporates the feedback data 268 as part of a training dataset, such as the iterative training method 300”, ¶105: “The iterative training process can include a supervised training process …”); and
using the trained ensemble of supervised anomaly detection models, generate respective outputs identifying anomalous data points in the real-time time series data (¶85: “Individual ensemble models are configured to apply an ensemble approach, such as multiple nested trees, to generate a consensus identification of data points as anomalous or non-anomalous signals or interactions.”, ¶97: “The method 200 leverages machine learning and deep learning techniques on historical time-series data to raise alerts regarding observed anomalies in real-time … time-series data.”).
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.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Keshva, and further in view of US-PGPUB No. 2025/0203459 A1 to Tofighbakhsh et al. (hereinafter “Tofighbakhsh”)
Regarding claim 4:
Keshva discloses the system of claim 3, but does not explicitly disclose the following limitation taught by Tofighbakhsh:
wherein the telecommunications network includes a 5G cellular network (Tofighbakhsh, ¶71: “… the cellular network is a 5G wireless network”), and wherein the network observability data corresponds to the 5G cellular network (Tofighbakhsh, ¶84: “… model 800 can generate observability data … model 800 can provide the ability to dynamically program probes by the enterprise customer as a service for their own dedicated physical and logical 5G resources and infrastructures.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Keshva to incorporate the functionality of the probe-as-a-service architecture model to generate observability data in a cellular 5G wireless network, as disclosed by Tofighbakhsh, such modification would enable the system to collect data regarding throughput, delay, and/or flow and congestion statistics, and also define security-related probes that can trigger diagnostics through.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Keshva, and further in view of US-PGPUB No. 2025/0363304 A1 to Fortkort
Regarding claim 13:
Keshva discloses the system of claim 9, but does not explicitly disclose the following limitation taught by Fortkort:
further comprising a data imbalance handling module configured to receive the combined output and adjust a sampling rate of the labeled anomalous data points indicated by the combined output (Fortkort, ¶36: “an adaptation module configured to adjust the sampling rate …”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Keshva to incorporate the system architecture to implement an adaption module to adjust a sampling rate, as disclosed by Fortkort, such modification would enable the system to adjust the sampling rate in real-time based on monitored characteristics to maintain optimal performance.
Claims 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Keshva, and further in view of US-PGPUB No. 2022/0195861 A1 to Patino Virano et al. (hereinafter “Patino Virano”)
Regarding claim 16:
Keshva discloses the system of claim 9, but does not explicitly disclose the following limitation taught by Patino Virano:
wherein the plurality of supervised anomaly detection models is configured to generate the respective outputs by determining respective probabilities associated with the anomalous data points in the real-time time series data (Patino Virano, ¶78: “the anomaly detection system 104 determines a mean probability of all of the features and then sets a threshold relative to the mean probability.”) and comparing the respective probabilities to a threshold (Patino Virano, ¶78: the anomaly detection system 104 compares each of the feature probabilities to the anomaly threshold such that the anomaly detection system 104 can indicate (via an anomaly visualization) each anomalous operation feature contributing to the overall anomaly of the time-series data.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Keshva to incorporate the functionality of the anomaly detection system to compare feature probabilities to an anomaly threshold to identify operation anomalies, as disclosed by Patino Virano, such modification would enable the system to indicate (via an anomaly visualization) each anomalous operation feature contributing to the overall anomaly of the time-series data.
Regarding claim 17:
17. The system of claim 16, further comprising a threshold tuning module configured to adjust the threshold (Patino Virano, ¶25: “the anomaly detection system dynamically determines anomalies and updates clusters of feature curves based on an adjustable (e.g., slidable) anomaly threshold.”).
The same motivation which is applied to claim 16 with respect to Patino Virano applies to claim 17.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang et al. (US 2026/0095466 A1)- disclosed an automated system receives various types of unlabeled data and determines, through an unsupervised machine learning model, a label for the data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHIAS HABTEGEORGIS whose telephone number is (571)272-1916. The examiner can normally be reached M-F 8am-5pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William R. Korzuch can be reached at (571)272-7589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHIAS HABTEGEORGIS/ Examiner, Art Unit 2491
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