Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Final Office action to Application Serial Number 18/667,978, filed on May 17, 2024. In response to Examiner’s Non-Final Office Action of October 29, 2025, Applicant, on February 10, 2026, amended claims 1, 19, 27 and 28; and cancelled claim 18; added claim 29. Claims 1-17 and 19-29 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are acknowledged.
Regarding 35 U.S.C. § 101 rejection, the amendment has been considered and is insufficient to overcome the rejection.
The 35 U.S.C. § 103 rejections are withdrawn.
Response to Arguments
Applicant’s arguments filed February 10, 2026 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed February 10, 2026.
On page 8-9 of the Remarks regarding 35 U.S.C. § 101, Applicant states the claims could not be performed mentally or with pen and paper, but a practical application that integrates the alleged judicial exception into a technological solution. In response, regarding the 35 U.S.C. § 101 rejection, Examiner finds under the broadest reasonable interpretation modelling and generating a forecast falls within the Abstract idea grouping of “Mental Processes” – evaluation. The amended claim language of implementing an active measure fall within Method of Organizing Human Activities – managing interactions. The claims primarily recite the additional element of using computer components to perform each step. The “system”, “memory”, “processor”, “computer program product”, and “computer readable medium” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). The general use of a machine learning analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Examiner recommends to expound upon the ‘active measure’ with a use case (i.e. Par. 111 of Applicant’s Specification).
On page 9 of the Remarks regarding 35 U.S.C. § 101, Applicant states amended claims improve the technical field of telemetry analysis by providing approximately 100-fold reductions in noise and time complexity through max pooling to extract daily maximum values from data intervals ranging from 20 minutes to 24 hours, and by automating multi-trend segmentation that conventional methods like simple regression (sensitive to minor changes) or deep neural networks (prone to overfitting and high computational cost) fail to achieve efficiently.. Further, the claims reflect an improvement to computer functionality itself. In response, regarding the 35 U.S.C. § 101 rejection, Examiner recommends to amend claims to include additional max pooling features and/or algorithm in the independent claim (i.e. claims 19 and 20).
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- 17 and 19-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-28 are directed to resource usage forecasting.
Claim 1 recites a system for resource usage forecasting, Claim 27 recites a method for resource usage forecasting and Claim 28 recites an article of manufacture for resource usage forecasting, which include process and recursively model a set of resampled metric data in connection with segmenting the metric data into relevant data and non-relevant data to obtain a forecast model for a system activity, wherein the set of resampled metric data pertains to the system activity, and wherein the set of resampled metric data is obtained based at least in part on performing a feature pooling with respect to metric data obtained from a metric data pipeline; generate a forecast based at least in part on the forecast model for the system activity; determine, based at least in part on the forecast indicating that resource utilization is projected to exceed a predefined percentage of available capacity within a predetermined future time period, an active measure comprising procuring and allocating additional computing resources; and cause the active measure to be automatically implemented to prevent capacity exhaustion in the system.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Mental Processes” – evaluation and Methods of Organizing Human Activities- managing interactions; business relations. The recitation of “system”, “memory”, “processor”, “computer program product”, and “computer readable medium”, provide nothing in the claim elements to preclude the step from being “Mental Processes”- evaluation and Methods of Organizing Human Activity- managing interactions and business relations. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “system”, “memory”, “processor”, “computer program product”, and “computer readable medium” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Furthermore, the claim 1, claim 27 and claim 18 recite using one or more machine learning analysis techniques [max pooling). The specification discloses the semantic analysis at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning processing is solely used a tool to perform the instructions of the abstract idea.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in forecasting.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “system”, “memory”, “processor”, “computer program product”, and “computer readable medium” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-17 and 19-26 and 29 processing and recursively modelling the resampled metric data to obtain the forecast model for the system activity comprises iteratively (a) selecting random subsets of the resampled metric data, (b) fitting a set of models to the random subsets, and (c) evaluating a quality of each of the set of models; wherein processing and recursively modelling the resampled metric data to obtain the forecast model comprises recursively segmenting the resampled metric data into a set of segments, and performing a regression analysis with respect to the set of segments; provide the forecast; wherein the forecast is provided to a user interface configured to be displayed on a client system; wherein the one or more processors are further configured to: determine an active measure based at least in part on the forecast; and cause the active measure to be implemented; wherein the active measure comprises providing an alert to a user associated with the system; wherein processing and recursively modelling a set of resampled metric data is performed using an iterative Random Sample Consensus (RANSAC) forecast model to obtain a forecast model for the system activity; wherein the iterative RANSAC forecast model implements (a) selecting random subsets of the resampled metric data, (b) fitting of the set of models to the random subsets, and (c) evaluating of a quality of each of the set of models; wherein the iterative RANSAC forecast model iteratives until a predetermined convergence threshold is satisfied; wherein the forecast model for the system activity is obtained in response to the predetermined convergence threshold being satisfied; wherein a kernel function for the iterative RANSAC forecast model is linear regression; wherein the relevant data and non-relevant data corresponding to inliers and outliers obtained by the iterative RANSAC forecast model; wherein the iterative RANSAC forecast model performs multi-trend segmentation of the set of resampled metric data; wherein the forecast model for the system activity is determined based at least in part on selection of a set of most probable inliers; wherein the forecast model for the system activity is determined based at least in part on performing a regression analysis with the selected set of most probable inliers; wherein generating the forecast comprises estimating a long-term forecast with a predefined confidence interval threshold; wherein the feature pooling comprises a max pooling.; wherein the max pooling is performed to obtain daily maximum values for a device metric comprised in the metric data; wherein the forecast comprises a long-term capacity resource forecast; wherein the set of resampled metric data is obtained based at least in part on resampling system log data; wherein the forecast model for system activity is based at least in part on performing a removal of outliers from the set of resampled metric data; wherein the forecast comprises a security service forecast for network capacity; wherein the forecast comprises a security service forecast for network demand; wherein the forecast corresponds to a security service forecast comprising one or more of (i) a per tenant forecast, (ii) a per device forecast, (iii) a next-generation firewall (NGFW) service forecast, and (iv) a secure access service edge (SASE) capacity forecast; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 27 and 28. Regarding Claim 19-20 and the additional element of max pooling- the machine learning is tool to perform the abstract idea.
Reasons Claims are Patentably Distinguishable from the Prior Art
Examiner analyzed Claims 1-17 and 19-29 in view of the prior art on record and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine these references with a reasonable expectation of success as discussed below.
In regards to Claim 1 (similarly Claim 27 and Claim 28), the prior art does not teach or fairly suggest:
“processing and recursively modelling a set of resampled metric data in connection with segmenting the metric data into relevant data and non-relevant data to obtain a forecast model for a system activity, wherein the set of resampled metric data pertains to the system activity, and wherein the set of resampled metric data is obtained based at least in part on performing a feature pooling with respect to metric data obtained from a metric data pipeline; and generating a forecast based at least in part on the forecast model for the system activity, determining, based at least in part on the forecast indicating that resource utilization is projected to exceed a predefined percentage of available capacity within a predetermined future time period, an active measure comprising procuring and allocating additional computing resources; and causing the active measure to be implemented to prevent capacity exhaustion in the system. ”
Examiner finds that Toledano, US Publication No. 20200233774A1 teaches A system includes a metric data store configured to receive and store a time-series of values of a first metric, a seasonal trend identification module configured to determine a periodicity profile for the first metric, and a modeling module configured to generate an autoregressive moving average (ARMA) model. [Abstract]. In particular, Toledano discloses t When establishing a normal operating envelope for a metric, from which anomalies can be detected, recognizing the periodic nature of the metric will allow for a more detailed and accurate operating envelope to be created. For example, an increase in a corporation's network activity from Sunday to Monday may indicate a weekly cycle rather than a large anomaly. Without recognizing periodicity, the normal operating envelope would need to have an uncertainty large enough to allow for a large deviation from day to day. This may increase the risk that an actual anomaly will not be detected. Once the periodic nature of the metric is determined, a model of the metric is generated. The model predicts a value of the metric at a given point in time and is used to determine the normal operating envelope. With respect to time-series data, the autoregressive moving average (ARMA) model is an efficient linear Gaussian model.(Par. 41-42).
Raguram et al., "USAC: A Universal Framework for Random Sample Consensus," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 2022-2038, Aug. 2013 teaches A computational problem that arises frequently in computer vision is that of estimating the parameters of a model from data that have been contaminated by noise and outliers. More generally, any practical system that seeks to estimate quantities from noisy data measurements must have at its core some means of dealing with data contamination. The random sample consensus (RANSAC) algorithm is one of the most popular tools for robust estimation. Recent years have seen an explosion of activity in this area, leading to the development of a number of techniques that improve upon the efficiency and robustness of the basic RANSAC algorithm. (Abstract) .
Islam et al., "Secure Access Service Edge: A Multivocal Literature Review," 2021 21st International Conference on Computational Science and Its Applications (ICCSA) discloses SASE is a new approach aimed to tackle these challenges. SASE is a solution that Gartner proposed to support organizations using cloud and connectivity by offering safety networks and services. SASE was designed to serve organizations in leveraging a standard cloud-based architecture to support cloud and mobility by delivering security services for networks and the network. It was developed to ensure that all cloud systems are equipped with reliable security services by means of a common framework. The elimination of multiple point items and using the SASE solution offered in the cloud will minimize complexity while saving substantial technological, human and financial resources [8]. SASE integrates networking and security technologies into a single cloud-delivered system. SASE provides a solution by combining security as a service and network as a Service (NaaS) (Introduction; Section IV)
Higginson et al., (US Publication No. 20230205664A1) teaches Techniques for predicting anomalies in forecasted time-series data are disclosed. A system. A system predicts whether a monitored computing system will experience anomalies by comparing forecasted values associated with components in the monitored computing system to threshold values. The system utilizes time-series machine learning models to forecast workloads of computing resources in the monitored computing system. The system trains and tests multiple different versions of a time-series model and selects the most accurate version to generate forecasts for a particular workload in the computing system. The system compares the forecasts to threshold values to predict anomalies. Based on detecting anomalies, the system generates recommendations for remediating predicted anomalies. ( Abstract).
Although Toledano, Raguram, Islam and Higginson teach elements of the claim, none of the cited prior art, singularly or in combination, teach or fairly suggest, the combination of, modelling analysis techniques. Therefore, for at least these reasons, Claim 1 (similarly Claim 27 and Claim 28) is eligible over the prior art.
The dependent claims 2-17 and 19-26 and 29 are eligible under 35 U.S.C. 102 and 35 U.S.C. 103 because they depend on claim 1 that is determined to be eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Publication No. 20230367842A1 to Liu et al.- Abstract A process for time-series forecasting is described that decouples stationary conditional distribution modeling from non-stationary dynamic modeling. The forecasting can be applied to non-stationary time-series and calculates, using the second model, a relevance score for the second set of items, which are ranked based on the respective relevance score and presented on a display.”
THIS ACTION IS MADE FINAL. 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 extension fee 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Applicants are invited to contact the Office to schedule an in-person interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
Sincerely,
/CHESIREE A WALTON/Examiner, Art Unit 3624
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