Detailed Action
This action is in response to the application filed on 07/21/2025. Claims 1-22 are
pending and have been fully examined.
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 .
Status of the Claims
Claims 1, and 4-14 are rejected under 35 U.S.C. 102
Claims 2-3 and 15-22 are rejected under 35 U.S.C. 103
Claim 6 is objected
Claim Objections
Claim 6 is objected to because of the following informalities: “…and at least one second peak of a different shape or amplitude or duration than said at least one first peak, no peak.” Should read “…and at least one second peak of a different shape or amplitude or duration than said at least one first peak, or no peak.”
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, and 4-14 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Lee et al. (U.S. Publication No. 2019/0129821 A1), hereinafter referred to as Lee.
Regarding Claim 1, Lee teaches:
An iterative method for monitoring a computing device, said computing device comprising one or more resources and being characterized by metric data to be monitored, said iterative method comprising: ([0063]);
collecting said metric data over a predetermined interval of time at each iteration, ([0072]; regarding, “the database module 202 stores performance metrics associated with operations performed on or by the computer systems 104a and 104b.”; [0077]; regarding, “the performance metric can include a series of measurements obtained continuously, intermittently, or according to some other pattern.”);
wherein said metric data comprises total central processing unit consumption of the computing device, memory usage of the computer device, database transactions, network traffic, or a number of applications running on the computing device, ([0072]; regarding, “metrics can include an amount of computational resources being consumed by a particular computer system 104a or 104b over a period of time (e.g., a percentage of the computer system's processors' that is being utilized, an amount of data that is being written to the computer systems' data storage devices, an amount of data that is being transmitted or received using the computer systems' network connection, etc.)”);
wherein said metric data is generated by a virtual machine installed on the computing device, ([0219]; regarding, “implementations described in this specification can be… one or more modules of computer program instructions, encoded on computer storage medium for execution by, or to control the operation of, data processing apparatus.”; [0220]; regarding, “The term “data processing apparatus” encompasses all kinds of apparatus, devices, and machines for processing data, including… a virtual machine”);
wherein said virtual machine collects values from variables of interest to analyze, ([0063]; regarding, “the operations performed on or by the computer system can be monitored to identify periods of time in which the computer system is experiencing a higher than expected load, consuming an unexpected amount of computational resources, and/or behaving in an unexpected manner.”);
detecting a seasonality pattern of said metric data over said predetermined interval of time, ([0088]; regarding, “The forecast range 304 can be determined such that it accounts for a “seasonality” of the performance metric. The seasonality can refer, for example, to a recurring or periodic pattern”);
obtaining a set of optimized parameters using said metric data to be used in a modelling phase to determine an interval-specific model, ([0097]; regarding, “The user interface 400a enables the user to modify various parameters of the monitoring process. For example, the user can select and modify a first field 416a to specify a particular technique that should be used for determining an expected range of the performance metric.”);
determining said interval-specific model, using said set of optimized parameters, representing the seasonality pattern that is detected, ([0097]; regarding, “Example techniques include a “robust” technique, an “agile” technique, an “adaptive” technique, and a “standard” technique.”);
defining a time-series modelling method and said set of optimized parameters once the seasonality pattern of the metric data is established, ([0121]; regarding, “a performance metric may exhibit a one day seasonality, as well as a one week seasonality… the server computer can determine a seasonality metric identifying the one day seasonality, and another seasonality metric identifying the one week seasonality.”; [0122]; regarding, “The server device identifies at least one anomaly related to the operations being performed on or by the one or more computing devices over the first period of time… for example, using a “robust” technique,” an “agile” technique, or an “adaptive” technique”);
wherein historical time-series of said metric data are used to optimize said set of optimized parameters, ([0174]; regarding, “a monitoring system can obtain historical measurement data regarding a computer system, and use the historical measurement data to forecast future trends associated with the computer system.”);
calculating modelled data using said interval-specific model that is determined and the metric data that is collected, ([0124]; regarding, “This technique can be advantageous, for example, as it is relatively stable, and generates forecast ranges that relatively constant (e.g., even during the course of long lasting anomalies)”);
comparing the modelled data that is calculated with the metric data that is collected to calculate a score characterizing a difference between the modelled data that is calculated and the metric data that is collected, ([0214]; regarding, “The one or more server devices can determine a forecast based on a beginning portion of the sequence, and compare the forecast to the end portion of the sequence (e.g., to “test” the accuracy of the forecast technique based on historical data).”; [0162]; regarding, “each average forecast series and additional average forecast series can be initially given a weight of one.”; [0163]; regarding, “To weight each of these series, the following steps can be performed for each of M+W steps, where W is the number of points or data items that are being considered during the anomaly detection process, and M is the number of points or data items in the “runway” of the “adaptive” model (e.g., the number of points or data items that are input into the model to initialize the model, such as to create an initial set of forecast values).”);
calculating an anomaly likelihood for each data of the metric data that is collected using the score that is calculated, said anomaly likelihood being a probability that a value of said each data is an anomaly, ([0165]; regarding, “The server device identifies at least one anomaly related to the operations being performed on the one or more computing devices over the period of time (step 816). Each anomaly can correspond to a respective time interval during which the time-series data sequence deviated from the forecast range for the performance metric”);
detecting said anomaly on said metric data when said probability that the value of said each data is said anomaly is greater than a predetermined threshold, ([0140]; regarding, “if the time-series data sequence exceeds the upper bound of the forecast range and/or drop below the lower bound of the forecast range, the server device can identify that an anomaly has occurred.”);
updating said set of optimized parameters of said interval-specific model at said each iteration to dynamically adapt said anomaly to changes in values of said each data in said metric data, ([0142]; regarding, “the “agile” technique can be performed by determining a forecast range of a measured performance metric based on the immediate past values of the measured performance metric. This technique enables the forecast range to be updated quickly to account for level shifts in the measured performance metric.”);
such that said iterative method self-adjusts on real-time to said seasonality pattern that is detected, ([0142]; regarding, “the “agile” technique… can be particularly suitable, for example, for monitoring seasonal performance metrics in a manner that quickly adjust to level shifts in the performance metric.”);
wherein said seasonality pattern is a composite seasonality pattern, such that said composite seasonality pattern allows a reduction of a minimum amount of said historical time-series of said metric data that is required by half, ([0182]; regarding, “the one or more server devices can obtain a time-series data sequence that is limited to a particular number of data items (e.g., the N most recent data items). This can be useful, for example, in reducing the amount of data to be considered, which may improve the efficiency of generating forecasts (e.g., by reducing computational costs) and/or improve the accuracy of the generated forecasts (e.g., by focusing on more recently obtained measurements over older measurements).”);
transmitting said anomaly that is detected to a controller, ([0080]; regarding, “information from the computer systems 104a and 104b (e.g., data indicating the performance metrics of the computer systems 104a and 104b) can be transmitted to the platform 150 through the transmission module 204.”);
modifying one or more system operation parameters, via said controller, when the anomaly likelihood exceeds a dynamic threshold over a moving time window, ([0142]; regarding, “the “agile” technique… can be particularly suitable, for example, for monitoring seasonal performance metrics in a manner that quickly adjust to level shifts in the performance metric.”);
wherein said modifying said one or more system operation parameters, via said controller, comprises one or more of enabling or disabling features of said computing device based on the seasonality pattern that is detected or based on a frequency of said anomaly, ([0063]; regarding, “the operations performed on or by the computer system can be monitored to identify periods of time in which the computer system is experiencing a higher than expected load, consuming an unexpected amount of computational resources, and/or behaving in an unexpected manner. In response, a corrective action can be taken to address the issue”);
allocating or deallocating said one or more resources; ([0063]; regarding, “additional computer systems can be activated to alleviate the load on the computer system.”);
issuing an alert when the anomaly likelihood exceeds the dynamic threshold over the moving time window, ([0092]; regarding, “in response to identifying an anomaly, the platform 150 can generate a notification to a user alerting him to the anomaly.”);
wherein the alert comprises a severity level, a predicted metric value, an affected resource of said one or more resources, and a timestamp of the anomaly; ([0093]; regarding, “the user can specify that the platform 150 transmit notifications in a particular manner upon identifying an anomaly. In this manner, a user can closely control each aspect of the anomaly detection process to suit his specific needs.”; [0103]; regarding, “the user can adjust the condition field 418d to specify the percentage of “anomalous” values (e.g., values above the upper bound of the forecast range and/or values below the lower bound of the forecast range) that must occur over the specified span of time for a high priority “alert” notification to be transmitted. Further, the user can adjust the condition field 418e to specify the percentage of “anomalous” values (e.g., values above the upper bound of the forecast range and/or values below the lower bound of the forecast range) that must occur over the specified span of time for a lower priority “warning” notification to be transmitted.”);
generating a recommendation in response to the anomaly that is detected, the recommendation comprising a proposed action comprising one or more of scaling resources, rescheduling tasks, terminating a process, initiating a backup, retraining said interval-specific model, or throttling a service of said computing device. ([0082]; regarding, “the platform 150 can perform a corrective action (e.g., generate a notification to a user alerting him of the anomaly, initiate an automated process to address the anomaly, etc.).”; [0063]; regarding, “additional computer systems can be activated to alleviate the load on the computer system.”).
Regarding Claim 4, Lee teaches the method of claim 1 as referenced above. Lee further teaches:
wherein the detecting the seasonality pattern of said metric data over said predetermined interval of time comprises retrieving a previously detected pattern or determining a new pattern. ([0085]; regarding, “the platform 150 can obtain measurements of the performance metric of the computer system for a period of time in the past. Based on this information, the platform 150 can identify one or more patterns and/or trends of the performance metric that occurred during in the past, and use these patterns and/or trends to predict the forecast range 302 for some time in the future.”).
Regarding Claim 5, Lee teaches the method of claim 1 as referenced above. Lee further teaches:
wherein the seasonality pattern is a seasonality pattern which is a periodically repeated pattern. ([0089]; regarding, “the load of the computer system may be expected to have a day-to-day seasonality (e.g., having a pattern that repeats every day).”).
Regarding Claim 6, Lee teaches the method of claim 1 as referenced above. Lee further teaches:
wherein the seasonality pattern comprises one or more of a combination of at least one first peak of values of the metric data that is collected, and at least one second peak of a different shape or amplitude or duration than said at least one first peak, no peak. ([0168]; regarding, “FIG. 9A, the forecast range 900a generated using the “standard” technique successfully identify anomalies that spike out of the normal range of values… the forecast ranges 900b, 900c, and 900d (calculated by the “robust” technique, the “agile” technique, and the “adaptive” technique, respectively) each recognize the seasonal pattern and can detect more nuanced anomalies (e.g., if the metric were to flat line near its minimum value).”).
Claims 7-8 are rejected under 35 U.S.C. 103 under the same grounds of rejection as claim 1.
Regarding Claim 8, Lee teaches the system of claim 8 as referenced above. Lee further teaches:
further comprising said computing device. ([0067]).
Regarding Claim 10, Lee teaches the system of claim 9 as referenced above. Lee further teaches:
wherein the computing device is a computer or a server or a cluster of one or more computers and servers. ([0069]).
Regarding Claim 11, Lee teaches the method of claim 1 as referenced above. Lee further teaches:
wherein said reallocating said one or more resources is in anticipation of predicted performance degradation based on historical pattern analysis. ([0085]; regarding, “The forecast range 304 can be determined based on historical information regarding the computer system… the platform 150 can obtain measurements of the performance metric of the computer system for a period of time in the past. Based on this information, the platform 150 can identify one or more patterns and/or trends of the performance metric that occurred during in the past, and use these patterns and/or trends to predict the forecast range 302 for some time in the future.”).
Regarding Claim 12, Lee teaches the method of claim 1 as referenced above. Lee further teaches:
further comprising predicting resource usage of said one or more resources of the computing device ahead of time based on historical telemetry data of said seasonality pattern of said metric data over time. ([0085]; regarding, “the platform 150 can identify one or more patterns and/or trends of the performance metric that occurred during in the past, and use these patterns and/or trends to predict the forecast range 302 for some time in the future.”).
Regarding Claim 13, Lee teaches the method of claim 12 as referenced above. Lee further teaches:
wherein said predicting said resource usage is used to trigger automatic provisioning or deprovisioning of said one or more resources. ([0082]; regarding, “anomalies can be identified by determining a forecast value (or range of forecast values) of a particular performance metric at a given time… the platform 150 can perform a corrective action (e.g., generate a notification to a user alerting him of the anomaly, initiate an automated process to address the anomaly”).
Regarding Claim 14, Lee teaches the method of claim 1 as referenced above. Lee further teaches:
wherein said anomaly is defined as a deviation from a predicted normal system usage path determined by the interval-specific model. ([0082]; regarding, “If the measured value of the performance metric at that time deviates from the forecast value (or range of forecast values), the platform 150 determines that an anomaly has occurred.”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2-3 and 15-22 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (U.S. Publication No. 2019/0129821 A1), hereinafter referred to as Lee, in view of Salunke et al. (U.S. Patent No. 10,635,563 B2), hereinafter referred to as Salunke.
Regarding Claim 2, Lee teaches the method of claim 1 as referenced above. Lee fails to explicitly disclose but Salunke teaches:
wherein the modelled data…
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(Col. 9-10, lines 35-65, 1-47; regarding, “The process continues by fitting a baseline model and computing uncertainty intervals using the extracted data points for the seasonal pattern (Operation 350). In one or more embodiments, an additive or multiplicative Holt-Winters model may be fit to the data points…. where X.sub.t, L.sub.t, T.sub.t, and S.sub.t denote the observed level, local mean level, trend, and seasonal index at time t, respectively. Parameters α, γ, δ denote smoothing parameters for updating the mean level, trend, and seasonal index, respectively, and p denotes the duration of the seasonal pattern. An expected value at future time t+k may be given… the Multiplicative Holt-Winters models…”). Examiners note: See formulas in Col. 9 and Col. 10 of Salunke.
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Lee with the teachings of Salunke. Doing so could mitigate performance degradation by detecting and treating anomalies as efficiently as possible (Salunke, Col. 1, lines 50-55).
Regarding Claim 3, Lee teaches the method of claim 1 as referenced above. Lee fails to explicitly disclose but Salunke teaches:
wherein the score deviates from a…
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(Col. 10, lines 19-50; regarding, “where x is the sample mean, s.sup.2 is the sample variance, σ is the standard deviation, t is the sample time, and γ is the prescribed confidence.”; Col. 13, lines 27-60; regarding, “a cumulative sum (CUSUM) control chart is used to determine whether a deviation is statistically significant. A CUSUM control chart is a model that may be trained to model (a) the expected mean and standard deviation of a time-series signal; (b) the size of a shift from the historical mean and standard deviation; and (c) a control limit or threshold (e.g., five standard deviations) for classifying the time-series as statistically significant.”).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Lee with the teachings of Salunke. Doing so could mitigate performance degradation by detecting and treating anomalies as efficiently as possible (Salunke, Col. 1, lines 50-55).
Regarding Claim 15, Lee teaches the method of claim 1 as referenced above. Lee fails to explicitly disclose but Salunke teaches:
further comprising transmitting outputs of the modelled data to an enterprise dashboard in real time. (Col. 7, lines 53-56; regarding, “the process generates an alert in response to detecting an anomaly in the first seasonal pattern or the second seasonal pattern. The alert may cause display of information regarding the detected anomaly.”; Col. 15, lines 50-60; regarding, “responsive to an anomaly being detected, an initial chart may be displayed with a temporal region being highlighted where an anomaly was detected. Additional details about the anomaly may be stored in data repository 140 without being initially displayed. Responsive to clicking on the temporal region, the system may access the additional details from data repository 140 and display them to the end user”).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Lee with the teachings of Salunke. Doing so could mitigate performance degradation by detecting and treating anomalies as efficiently as possible (Salunke, Col. 1, lines 50-55).
Regarding Claim 16, Lee teaches the method of claim 1 as referenced above. Lee fails to explicitly disclose but Salunke teaches:
wherein said enabling or disabling features of said computing device comprises one or more of enabling or disabling processor cores or adjusting processor frequency; activating or suspending swap memory or cache flush operations; toggling GPU acceleration or compute offload modes; enabling, disabling, or throttling network interfaces or communication protocols; modifying operating system-level policies including scheduling, logging, or process isolation; activating or suspending background services or job schedulers; enabling enhanced security protocols, restricting network access, or disabling application-level modules. (Col. 13, lines 45-60; regarding, “a responsive action may include, but is not limited to generating an alert, deploying additional resources to satisfy unexpected increases in resource demand (e.g., to service additional client requests), bringing resources offline due to unexpected decreases in demand or to prevent potential compromising behavior (e.g., to prevent denial of service attacks), and updating resource configurations (e.g., shifting requests from a resource experiencing unexpected overload to a more available resource).”).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Lee with the teachings of Salunke. Doing so could mitigate performance degradation by detecting and treating anomalies as efficiently as possible (Salunke, Col. 1, lines 50-55).
Regarding Claim 17, Lee teaches the method of claim 1 as referenced above. Lee fails to explicitly disclose but Salunke teaches:
wherein said modifying said one or more system operation parameters, via said controller, further comprises modifying one or more of hardware subsystems of the computing device, operating system configuration parameters, active or scheduled processes, access control policies, application service states. (Col. 13, lines 45-60; regarding, “a responsive action may include, but is not limited to generating an alert, deploying additional resources to satisfy unexpected increases in resource demand (e.g., to service additional client requests), bringing resources offline due to unexpected decreases in demand or to prevent potential compromising behavior (e.g., to prevent denial of service attacks), and updating resource configurations (e.g., shifting requests from a resource experiencing unexpected overload to a more available resource).”).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Lee with the teachings of Salunke. Doing so could mitigate performance degradation by detecting and treating anomalies as efficiently as possible (Salunke, Col. 1, lines 50-55).
Regarding Claim 18, Lee teaches the method of claim 1 as referenced above. Lee fails to explicitly disclose but Salunke teaches:
wherein said modifying said one or more system operation parameters, via said controller, further comprises modifying said system operation parameters based on whether the anomaly that is detected is classified as transient, persistent, or predictive in nature. (Col. 13, lines 9-26; regarding, “If the evaluation data point is classified as anomalous, the process determines whether the deviation is statistically significant… a single anomalous data point may be classified as significant and trigger a responsive action. However, in other cases, an evaluation data point may be permitted to cross the limit without automatically triggering an alert. The process may account for the magnitude of the deviation of the evaluation data point, the number of data points in a sequence that have crossed the limits, and/or the cumulative magnitude of deviation fort the sequence of data points. One or more of these factors may compared to threshold values. If the thresholds are exceeded, then the deviation may be classified as statistically significant. If the deviation is not statistically significant, then monitoring may continue without triggering a responsive action.”).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Lee with the teachings of Salunke. Doing so could mitigate performance degradation by detecting and treating anomalies as efficiently as possible (Salunke, Col. 1, lines 50-55).
Regarding Claim 19, Lee teaches the method of claim 1 as referenced above. Lee fails to explicitly disclose but Salunke teaches:
further comprising a closed-loop feedback mechanism wherein an outcome of said modifying said one or more system operation parameters from the controller is fed back into the monitoring module to refine future forecasts. (Col. 4, lines 41-44; regarding, “As more data points are received, the system may adapt the baseline model to newly learned seasonal behavior.”; Col. 16, lines 15-23; regarding, “Responsive to receiving new time-series data, the training process may be re-executed to adjust a previously generated baseline model. For example, the new data points may be appended to the end of the previously used training set of data. The Additive or Multiplicative Holt Winters model may then be fit to the updated training dataset.”).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Lee with the teachings of Salunke. Doing so could mitigate performance degradation by detecting and treating anomalies as efficiently as possible (Salunke, Col. 1, lines 50-55).
Regarding Claim 20, Lee teaches the method of claim 1 as referenced above. Lee fails to explicitly disclose but Salunke teaches:
wherein said allocating or deallocating said one or more resources, via said controller, comprises using an orchestration platform API. (Col. 6, lines 5-27).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Lee with the teachings of Salunke. Doing so could mitigate performance degradation by detecting and treating anomalies as efficiently as possible (Salunke, Col. 1, lines 50-55).
Regarding Claim 21, Lee teaches the method of claim 1 as referenced above. Lee fails to explicitly disclose but Salunke teaches:
wherein said allocating said one or more resources comprises instantiating one or more virtual machines, containers, or computing nodes. (Col. 13, lines 45-60; Col. 18, lines 6-12).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Lee with the teachings of Salunke. Doing so could mitigate performance degradation by detecting and treating anomalies as efficiently as possible (Salunke, Col. 1, lines 50-55).
Regarding Claim 22, Lee teaches the method of claim 1 as referenced above. Lee fails to explicitly disclose but Salunke teaches:
wherein said deallocating said one or more resources comprises terminating low-priority services or migrating workloads to lower-utilization hardware. (Col. 13, lines 45-60; regarding, “a responsive action may include, but is not limited to… bringing resources offline…”).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Lee with the teachings of Salunke. Doing so could mitigate performance degradation by detecting and treating anomalies as efficiently as possible (Salunke, Col. 1, lines 50-55).
References Cited
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kalekar et al. “Time series Forecasting using Holt-Winters Exponential Smoothing” discloses the analysis of seasonal time series data using Holt-Winters exponential smoothing methods, discussing the Multiplicative Seasonal Model and the Additive Seasonal Model.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATHEW GUSTAFSON whose telephone number is (571)272-5273. The examiner can normally be reached Monday-Friday 8:00-4:00.
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/M.D.G./Examiner, Art Unit 2113 /BRYCE P BONZO/Supervisory Patent Examiner, Art Unit 2113
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