DETAILED ACTION
Applicant amended claims 5-7, canceled claim 8, and added new claims 9-21 in the preliminary amendment dated 8/17/2024.
Claims 1-7, and 9-21 are pending.
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 (IDS) submitted on 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.
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: first reception module, extraction module, transmission module, second reception module, recovery module in claim 1, adjustment module in claim 3.
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 § 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.
Claims 1-3, 5-7, 9, 11-15, 17-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Singh Bawa (US 2021/0281486 A1).
With regards to Claim 1, Singh Bawa teaches a traffic data collecting system comprising:
a first reception module that receives time-series data having a bandwidth value of a monitored network (i.e., A network bandwidth handler of the system may access a time series distribution data including a distribution of data points…, Paragraph 27); an extraction module that extracts a feature amount of the time-series data from the time-series data having the bandwidth value by applying a seasonal adjustment method to the time-series data (i.e., For instance, the network bandwidth handler may decompose the time series distribution data to determine data components like, a trend data component, a seasonal data component, a cyclical data component, and an irregular or residual data component, from the time series distribution data, Paragraph 27); a transmission module that transmits the feature amount of the time-series data via a network for collecting traffic data of the monitored network (i.e., The trend data component may include a distribution of a first set of data points indicative of a development or inherent trend of a first portion of the network traffic over the pre-defined time period. The seasonal data component may include a distribution of a second set of data points indicative of a second portion of the network traffic…, Paragraph 28); a second reception module that receives the feature amount of the time-series data via the network for colleting traffic data of the monitored network (i.e., The trend data component may include a distribution of a first set of data points indicative of a development or inherent trend of a first portion of the network traffic over the pre-defined time period. The seasonal data component may include a distribution of a second set of data points indicative of a second portion of the network traffic…, Paragraph 28); and a recovery module that recovers the time-series data having the bandwidth value from the feature amount of the time-series data (i.e., baseline distribution traffic data may be computed. The baseline distribution traffic data may be indicative of a base count of network packets over the pre-defined time period 212. The baseline distribution traffic data may be computed based on the trend data component 216, the cyclical data component 220, and the seasonal data component 218, Paragraph 140).
With regards to Claim 2, Singh Bawa teaches wherein the extraction module decomposes, into a trend term, a seasonality term, and a residual error term, the time-series data having the bandwidth value by applying seasonal and trend decomposition using locally estimated scatterplot smoothing (Loess)(STL) decomposition to the time-series data and extracts the feature amount of the time-series data from the trend term, the seasonality term, and the residual error term (i.e., Furthermore, the network bandwidth handler may provide an estimated bandwidth for the future time interval based on a local weighted regression using a pre-defined smoothing parameter. The local weighted regression may be performed based on a local weighted scatter plot smoother model i.e. a LOESS model. In accordance with said example implementation of the present disclosure, the dynamic bandwidth allocator may allocate, network bandwidth to the server based on pre-stored network traffic data applicable for the server and the estimated bandwidth for the future time interval, Paragraph 32; Paragraphs 39, 50, 75-76)
With regards to Claim 3, Singh Bawa teaches an adjustment model that adjusts a parameter of the STL decomposition based on a change in size of the time series data when the parameter is changed, wherein the extraction module decomposes, in a trend term, a seasonality term, and a residual error term, the time-series data having the bandwidth value applying the STL decomposition based on the parameter adjusted by the adjustment m ode to the time-series data (i.e., he dynamic bandwidth estimator 208 may determine an estimated network traffic for a future time point based on multiple inputs. For instance, the estimated network traffic for the future time point may be determined based on (a) the baseline distribution traffic data, which is a combination of trend data component 216, seasonal data component 218, and cyclical data component 220 (b) a lagged covariate factor 234, and (c) the covariate metrics 214. The lagged covariate factor 234 may be computed by the dynamic bandwidth estimator 208 based on the AR data component 224 and/or the moving average data component 226 itself. The dynamic bandwidth estimator 208 may provide, the baseline distribution traffic data, the lagged covariate factor 234, and the covariate metrics 214 as inputs to the LOESS model 236…, Paragraph 76; Paragraphs 75; )
With regards to Claim 5, Singh Bawa teaches wherein the extraction module extracts an event term indicating a variation due to an event from the time-series data having the bandwidth value, and the transmission module transmits the event term as the feature amount of the time-series data (i.e., The distribution of data points, as referred herein, may indicate an average count of network packets collected over a pre-defined time period. The average count of network packets may be representative of network traffic at a server. In some examples, the pre-defined time period may be defined based on for instance, but not limited to, a user input, an application context, a business rule, etc. Furthermore, the network bandwidth handler may determine, from the time series distribution data, one or more data components indicative of portions of network traffic contributed due to various factors. For instance, the network bandwidth handler may decompose the time series distribution data to determine data components like, a trend data component, a seasonal data component, a cyclical data component, and an irregular or residual data component, from the time series distribution data…, Paragraph 27; Paragraph 28)
With regards to Claim 6, Singh Bawa teaches wherein the extraction module extracts the feature amount of the time-series data from the time-series data having the bandwidth value in a predetermined period, and the recovery module recovers the time-series data having the bandwidth value in the predetermined period, based on the feature amount of the time-series data and the predetermined period (i.e., baseline distribution traffic data may be computed. The baseline distribution traffic data may be indicative of a base count of network packets over the pre-defined time period 212. The baseline distribution traffic data may be computed based on the trend data component 216, the cyclical data component 220, and the seasonal data component 218, Paragraph 140).
The limitations of Claim 7 are rejected in the analysis of Claim 1 above, and the claim is rejected on that basis.
The limitations of Claim 9 are rejected in the analysis of Claim 2 above, and the claim is rejected on that basis.
The limitations of Claim 10 are rejected in the analysis of Claim 3 above, and the claim is rejected on that basis.
The limitations of Claim 12 are rejected in the analysis of Claim 5 above, and the claim is rejected on that basis.
The limitations of Claim 13 are rejected in the analysis of Claim 6 above, and the claim is rejected on that basis.
The limitations of Claim 14 are rejected in the analysis of Claim 1 above, and the claim is rejected on that basis.
The limitations of Claim 15 are rejected in the analysis of Claim 2 above, and the claim is rejected on that basis.
The limitations of Claim 16 are rejected in the analysis of Claim 3 above, and the claim is rejected on that basis.
The limitations of Claim 18 are rejected in the analysis of Claim 5 above, and the claim is rejected on that basis.
The limitations of Claim 19 are rejected in the analysis of Claim 6 above, and the claim is rejected on that basis.
Allowable Subject Matter
Claims 4, 10, and 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SURAJ M JOSHI whose telephone number is (571)270-7209. The examiner can normally be reached Monday - Friday 8-6 ET.
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/SURAJ M JOSHI/Primary Examiner, Art Unit 2447 January 7, 2026