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 .
This Office Action is in response to claims filed on 10/17/2027
Claims 1-20 are pending.
Claim Rejections - 35 USC § 112
Applicant’s arguments and amendments, see page 11 of the remarks, filed 10/17/2026, with respect to 35 USC 112 rejections have been fully considered and are persuasive. The 35 USC 112 rejections of claims 1-18 have been withdrawn.
Claim Rejections - 35 USC § 101
Applicant’s arguments and amendments, see page 11 of the remarks, filed 10/17/2026, with respect to 35 USC 101 rejections have been fully considered and are persuasive. The 35 USC 101 rejections of claims 1-20 have been withdrawn.
Claim Rejections - 35 USC § 103
Applicant's arguments filed 10/17/2026 have been fully considered but they are not persuasive. Applicant argues “each first weight being determined based at least in part on a difference between each sample and an expected value of the sample, and wherein the first weight for a sample increase when the difference decreases”. Examiner notes Chan teaches “fit a smooth spline to the seasonal factor, Bi, Cj ”, Par 464, and weights are selected appropriately depending on the autocorrelation of the time series and other factors” Par 373. Chan Further teaches “formulae for exponential moving averages, applied to, error residual of forecast and absolute deviation”, Par 372. Thus, Chan discloses fitting a spline function to seasonal factors, having weights set on the moving average depending on the autocorrelation of the time series, applied to error residual of forecast. Therefore, Chan discloses setting the weights based on measures that autocorrelated and based on the error residual of forecast. Applicant further argues the newly added limitations. Examiner notes, the newly added limitations do not have support, and these will not be given consideration for purpose of art rejection. Examiner also called the Attorney of record to seek specification paragraphs for support of the newly added limitations and left a message. Attorney called back and discussed this, pointing to the paragraphs. Examiner reviewed but still did not find support in those paragraphs [208, 305, 311, 327]. The 35 USC 103 rejections of claims 1-20 are maintained without addressing the limitations lacking support.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Applicant’s claim amendments recite “dynamically adjusting the first weights based at least in part on:
a duration of thread execution; and
a frequency of thread invocation within a predefined time window, wherein threads with shorter execution durations and higher invocation frequencies are assigned higher first weights to optimize real-time responsiveness;”, there is no support of these limitations in applicant’s specification. Therefore, these are considered to be new matter and not addressed below under claim rejections.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chan et al., US 2014/0310235 A1, (hereafter Chan), in views of Cosma Rohilla Shalizi, NPL. “Advanced Data Analysis from an Elementary Point of view”, Published Spring 2012 (hereafter Rohilla).
Regarding claim 1. Chan teaches a computer-implemented method comprising:
receiving, by one or more computer systems, a signal comprising a plurality of measures sampled over a span of time from a cloud computing environment in which one or more processes are being executed (fig 10, computer systems)(fig 13, multiple signals are received)(Par 82, entities being monitored, VM’s, network)(Par 104, type of files, illustrates signal that are being analyzed)(Par 105, feature type, specifies name, data type and range of values)(Par 107, signal trend, predict JVM, restart)(Par 365, cloud control system for JAVA);
extracting a first component of the time-series measurement having seasonal factors and a second component of the time-series measurement being de-seasonalized (Par 68, patterns the system can recognize , ability to capture patterns for a particular environment)(Par 70, filtered data, removal of seasonal trends)(Par 104, aggregates seasonal factors, level drifts, level shifts);
applying one or more spline functions to the first component to generate a first model, wherein applying the one or more spline functions to the first component comprises assigning first weights to samples of the first component, each first weight being determined based at least in part on a difference between each sample and an expected value of the sample (Par 372-373, weights are selected appropriately depending on the autocorrelation of the time series and other factors, formulae for exponential moving averages, applied to, error residual of forecast and absolute deviation)(Par 464, fit a smooth spline to the seasonal factors, seasonal trend)(Par 468, averages of the samples that fall within the 15 minutes time interval, seasonal index k, yk, is weighted by Nk)(Par 366, forecasting trends by weighted averages)(Par 377, normalized exponentially weighted sum)(Par 430, factor decreases the weight), and wherein the first weight for a sample increases when the difference decreases;
applying a linear regression technique to the second component to generate a second model (Par 490, means square error, forecast as functions of independent variables);
generating a forecast of the signal based at least in part on the first model and the second model (Par 524, seasonal trend forecast and deseasonalized forecast); and
initiating, based at least in part on the forecast, one or more actions associated with the cloud computing environment (Par 29, optimal human resources can be selected and assigned to each task, using the model)(Par 365, resource allocation, forecasting of capacity requirements).
Chan does not teach the first weight for a sample increases when the difference decreases.
Rohilla teaches weighted are inversely proportional to the variance (Rohilla, Page 233, sec 12.3.2, weighted linear regression, with weights inversely proportional to that variance).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Chan to incorporate the teachings of Rohilla to have weighted values according the inversely proportional to the variance because it would use the model to inference from data, to analyze data better, more reliably with fewer and smaller errors (Rohilla, Page 16, sec 1.1)
Regarding claim 2. Chan and Rohilla teach the method of claim 1,
wherein the span of time spans a plurality of cycles of a period having a particular length (Chan, fig 13, different intervals);
wherein the period is divided into a plurality of regular intervals (Chan, fig 13, intervals 1304)( Chan, Par 69, first interval); and
wherein extracting the first component and the second component comprises:
for each of the plurality of regular intervals, determining an average measure of the interval (Chan, Par 175, average intensity)(Chan, Par 368, time intervals scaled proportionally, average time interval is scaled close to 1);
for each of the plurality of cycles, determining an average measure of the cycle (Chan, Par 368, tracked in parallel, independent filters running at different time scales);
determining a set of seasonal factors by, for each of the plurality of regular intervals, determining a season factor for the interval by comparing the average measure of the interval with the average measure of the cycle (Chan, Par 368, seasonal indexing to select, minimizes the normalized error residual);
applying a spline function to the set of seasonal factors to obtain the first component (Chan, Par 464, seasonal factors, renormalized, fit a smooth spline to the seasonal factor, smoothen the seasonal trend); and
de-seasonalizing the time-series measurement based at least in part on the first component to obtain the second component (Chan, Par 465, updating the process, effectively deseasonalized the average).
Regarding claim 3. Chan and Rohilla teach the method of claim 1, wherein the plurality of measures are sampled at irregular intervals over the span of time (Chan, Par 104, regular or irregular time series)( Chan, Claim 2, irregular time intervals).
Regarding claim 4. Chan and Rohilla teach the method of claim 3, wherein the irregular intervals at which the plurality of measures are sampled and the plurality of measures exhibit a dependency relationship (Chan, Par 136, dependency between classes of threads),
wherein the linear regression technique is a robust linear regression technique (Chan, Par 490, mean square error (MSE) functions), and
wherein the robust linear regression technique is applied to the second component to compensate for the dependency relationship (Chan, Par 235, dependency information, used to estimate traffic intensity)(Chan, Par 490, superimposed, the time zone relationship is accounted).
Regarding claim 5. Chan and Rohilla teach the method of claim 4, wherein applying the robust linear regression technique to the second component comprises, for each of the plurality of measures, assigning a weight to the measure based at least in part on a length of an irregular interval associated with the measure (Chan, Par 104, FSDType, irregular time series)(Par 373, time series data smoothed by normalized weighted sum).
Regarding claim 6. Chan and Rohilla teach the method of claim 5, wherein applying the robust linear regression technique to the second component further comprises, for each of the plurality of measures, trimming the measure if the length of the irregular interval associated with the measure does not exceed a threshold length (Chan, Par 490, the parameter tz, is within a range, thus trimming the interval to a corresponding date).
Regarding claim 7. Chan and Rohilla teach the method of claim 5, wherein applying the robust linear regression technique to the second component comprises, for each of the plurality of measures: predicting an expected measure that corresponds to the measure (Chan, Par 490, forecasting, optimizing the time zone, for when it overlaps ); and
assigning a weight to the measure based at least in part on a deviation between the expected measure and the measure (Chan, Par 493, expected average time interval, value depends on different time steps,).
Regarding claim 8. Chan and Rohilla teach the method of claim 1, wherein the signal is heteroscedastic (Chan, fig, 14, the signal data, is not uniform)( Chan, Par 117, N number of classes), and
wherein the linear regression technique is applied to the second component to account for the heteroscedasticity of the signal (Chan, Par 90, root mean error, in connection to fig 13, signals).
Regarding claim 9. Chan and Rohilla teach the method of claim 1, wherein the signal corresponds to a usage of a heap, over the span of time, of the cloud computing environment (Chan, Par 18, heap usage by virtual machines)(Chan, Par 372, heap usage and thread segment intensity).
Regarding claim 10. Chan and Rohilla teach the method of claim 1, wherein the one or more actions comprises providing additional resources to the cloud computing environment (Chan, par 29, assign human resources )(Chan, Par 365, elasticity in resource allocation)(Chan, Par 372, monitoring and forecasting resource utilization ).
Regarding claim 11. Chan teaches a system comprising:
one or more processors (Fig 11, element 1102); and
a memory accessible to the one or more processors (Fig 11, element 1108, 1110)(Par 566-577, computer readable storage media connected to a network ),
the memory storing one or more instructions that, upon execution by the one or more processors (Fig 11, element 1108, 1110)(Par 569, storage media containing code ), causes the one or more processors to:
receive a signal comprising a plurality of measures sampled over a span of time from a cloud computing environment in which one or more processes are being executed (fig 10, computer systems)(fig 13, multiple signals are received)(Par 82, entities being monitored, VM’s, network)(Par 104, type of files, illustrates signal that are being analyzed)(Par 105, feature type, specifies name, data type and range of values)(Par 107, signal trend, predict JVM, restart)(Par 365, cloud control system for JAVA);
extract a first component of the time-series measurement having seasonal factors and a second component of the time-series measurement being de-seasonalized (Par 68, patterns the system can recognize , ability to capture patterns for a particular environment)(Par 70, filtered data, removal of seasonal trends)(Par 104, aggregates seasonal factors, level drifts, level shifts);
apply one or more spline functions to the first component to generate a first model, wherein applying the one or more spline functions to the first component comprises assigning first weights to samples of the first component, each first weight being determined based at least in part on a difference between each sample and an expected value of the sample (Par 372-373, weights are selected appropriately depending on the autocorrelation of the time series and other factors, formulae for exponential moving averages, applied to, error residual of forecast and absolute deviation) (Par 464, fit a smooth spline to the seasonal factors, seasonal trend)(Par 468, averages of the samples that fall within the 15 minutes time interval, seasonal index k, yk, is weighted by Nk)(Par 366, forecasting trends by weighted averages)(Par 377, normalized exponentially weighted sum)(Par 430, factor decreases the weight ), and wherein the first weight for a sample increases when the difference decreases;
apply a linear regression technique to the second component to generate a second model (Par 490, means square error, forecast as functions of independent variables);
generate a forecast of the signal based at least in part on the first model and the second model (Par 524, seasonal trend forecast and deseasonalized forecast ); and
initiate, based at least in part on the forecast, one or more actions associated with the cloud computing environment (Par 29, optimal human resources can be selected and assigned to each task, using the model)(Par 365, resource allocation, forecasting of capacity requirements).
Chan does not teach the first weight for a sample increases when the difference decreases.
Rohilla teaches weighted are inversely proportional to the variance (Rohilla, Page 233, sec 12.3.2, weighted linear regression, with weights inversely proportional to that variance).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Chan to incorporate the teachings of Rohilla to have weighted values according the inversely proportional to the variance because it would use the model to inference from data, to analyze data better, more reliably with fewer and smaller errors (Rohilla, Page 16, sec 1.1)
Regarding claim 12. Chan and Rohilla teach the system of claim 11, wherein the span of time spans a plurality of cycles of a period having a particular length (Chan, fig 13, different intervals);
wherein the period is divided into a plurality of regular intervals (Chan, fig 13, intervals 1304)(Par 69, first interval, ); and
wherein extracting the first component and the second component comprises: for each of the plurality of regular intervals, determining an average measure of the interval (Chan, Par 175, average intensity, )( Chan, Par 368, time intervals scaled proportionally, average time interval is scaled close to 1);
for each of the plurality of cycles, determining an average measure of the cycle (Chan, Par 368, tracked in parallel, independent filters running at different time scales);
determining a set of seasonal factors by, for each of the plurality of regular intervals, determining a season factor for the interval by comparing the average measure of the interval with the average measure of the cycle (Chan, Par 368, seasonal indexing to select, minimizes the normalized error residual);
applying a spline function to the set of seasonal factors to obtain the first component (Chan, Par 464, seasonal factors, renormalized, fit a smooth spline to the seasonal factor, smoothen the seasonal trend); and
de-seasonalizing the time-series measurement based at least in part on the first component to obtain the second component (Chan, Par 465, updating the process, effectively deseasonalized the average).
Regarding claim 13. Chan and Rohilla teach the system of claim 11, wherein the plurality of measures are sampled at irregular intervals over the span of time (Chan, Par 104, regular or irregular time series)( Chan, Claim 2, irregular time intervals).
Regarding claim 14. Chan and Rohilla teach the system of claim 13, wherein the irregular intervals at which the plurality of measures are sampled exhibit a dependency relationship (Chan, Par 136, dependency between classes of threads),
wherein the linear regression technique is a robust linear regression technique configured to be applied to the second component to compensate for the dependency relationship (Chan, Par 235, dependency information, used to estimate traffic intensity)(Chan, Par 490, superimposed, the time zone relationship is accounted).
Regarding claim 15. Chan and Rohilla teach the system of claim 14, wherein applying the robust linear regression technique to the second component further comprises, for each of the plurality of measures, trimming the measure if the length of the irregular interval associated with the measure does not exceed a threshold length (Chan, Par 104, FSDType, irregular time series)(Par 373, time series data smoothed by normalized weighted sum).
Regarding claim 16. Chan and Rohilla teach the system of claim 15, wherein applying the robust linear regression technique to the second component further comprises, for each of the plurality of measures, trimming the measure if the length of the irregular interval associated with the measure does not 4 exceed a threshold length (Chan, Par 490, the parameter tz, is within a range, thus trimming the interval to a corresponding date).
Regarding claim 17. Chan and Rohilla teach the system of claim 11, wherein the signal is heteroscedastic (Chan, fig, 14, the signal data, is not uniform)( Chan, Par 117, N number of classes), and wherein the linear regression technique is applied to the second component to account for the heteroscedasticity of the signal (Chan, Par 90, root mean error, in connection to fig 13, signals).
Regarding claim 18. Chan teaches a non-transitory computer-readable medium storing one or more instructions that (Fig 11, element 1108, 1110)(Par 569, storage media containing code ), when executed by a processor, cause the processor to perform operations comprising:
receiving, by one or more computer systems, a signal comprising a plurality of measures sampled over a span of time from a cloud computing environment in which one or more processes are being executed (fig 10, computer systems)(fig 13, multiple signals are received)(Par 82, entities being monitored, VM’s, network)(Par 104, type of files, illustrates signal that are being analyzed)(Par 105, feature type, specifies name, data type and range of values)(Par 107, signal trend, predict JVM, restart)(Par 365, cloud control system for JAVA);
extracting a first component of the time-series measurement having seasonal factors and a second component of the time-series measurement being de-seasonalized (Par 68, patterns the system can recognize , ability to capture patterns for a particular environment)(Par 70, filtered data, removal of seasonal trends)(Par 104, aggregates seasonal factors, level drifts, level shifts);
applying one or more spline functions to the first component to generate a first model, wherein applying the one or more spline functions to the first component comprises assigning first weights to samples of the first component, each first weight being determined based at least in part on a difference between each sample and an expected value of the sample (Par 372-373, weights are selected appropriately depending on the autocorrelation of the time series and other factors, formulae for exponential moving averages, applied to, error residual of forecast and absolute deviation) (Par 464, fit a smooth spline to the seasonal factors, seasonal trend)(Par 468, averages of the samples that fall within the 15 minutes time interval, seasonal index k, yk, is weighted by Nk)(Par 366, forecasting trends by weighted averages)(Par 377, normalized exponentially weighted sum)(Par 430, factor decreases the weight ), and wherein the first weight for a sample increases when the difference decreases;
applying a linear regression technique to the second component (Par 490, means square error, forecast as functions of independent variables);
generating a forecast of the signal based at least in part on the first model and the second model (Par 524, seasonal trend forecast and deseasonalized forecast ), the second model being generated by applying the linear regression (Par 490, forecasting, optimizing the time zone, for when it overlaps ); and
initiating, based at least in part on the forecast, one or more actions associated with the cloud computing environment (Par 29, optimal human resources can be selected and assigned to each task, using the model)(Par 365, resource allocation, forecasting of capacity requirements).
Chan does not teach the first weight for a sample increases when the difference decreases.
Rohilla teaches weighted are inversely proportional to the variance (Rohilla, Page 233, sec 12.3.2, weighted linear regression, with weights inversely proportional to that variance).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Chan to incorporate the teachings of Rohilla to have weighted values according the inversely proportional to the variance because it would use the model to inference from data, to analyze data better, more reliably with fewer and smaller errors (Rohilla, Page 16, sec 1.1)
Regarding claim 19. Chan and Rohilla teach the non-transitory computer-readable medium of claim 18, wherein applying the linear regression includes assigning second weights to samples of the second component, each second weight being determined based at least in part on a duration of an interval from which a sample is taken (Chan, Par 104, FSDType, irregular time series)(Par 373, time series data smoothed by normalized weighted sum), and wherein the second weight for the sample increases when the interval increases (Chan, Par 504, linear trend,).
Regarding claim 20. Chan and Rohilla teach the non-transitory computer-readable medium of claim 18, wherein the forecast of the signal comprises a growth rate forecast of the signal (Chan, Fig 13)(Chan, Par 209, projected grow trends)(Chan, Par 229, growth rate to forecast the growth trend), and wherein an amount of allocated memory resource is determined based at least in part on the growth rate forecast (Chan, Par 365, elasticity in resource allocation)(Chan, Par 372, monitoring and forecasting resource utilization).
Conclusion
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 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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/A.C./Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189
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