.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 .
Response to Argument
Applicant’s arguments in the Remarks, filed on 11/7/25 have been fully considered but they are not persuasive.
In the Remarks, applicant argues that:
Zhang fails to teach automating execution of the model adjusting the frequency of a processing unit from among the one or more processing units at an automation platform, wherein the automation platform is executed independent of the one or more processing units of the application platform.
Zhang fails to teach receiving an adjusted frequency at the application platform from the automation platform.
In responses to points (1) and (2), according to Applicant,
“Applicant's independent claim 1 separately recites an application platform and an automation platform where the ‘automation platform is executed independent of the one or more processing units of the application platform.’ Applicant submits that Zhang does not separately describe an application platform and an automation platform. Therefore, Zhang does not anticipate independent claim 1, including because the following claimed concepts are not found in Zhang, including: ‘automating execution of the model adjusting the frequency of a processing unit from among the one or more processing units at an automation platform, wherein the automation platform is executed independent of the one or more processing units of the application platform’ and ‘receiving an adjusted frequency at the application platform from the automation platform.’” (Remarks at 10)
Examiner respectfully disagree. Zhang teaches machine learning enables automatic learning of optimal core frequency at any system load at telemetry system 308 of figure 3, wherein telemetry system 308 is executed independent of the CPU of server 306 of figure 3 ([54][57][70][71], i.e., automating execution of the model adjusting the frequency of a processing u nit from among the one or more processing units at an automation platform, wherein the automation platform is executed independent of the one or more processing units of the application platform). Zhang further teach taking action of adjusting frequency of CPU at 306 based on the telemetry data received from 306 ([54][40]; fig. 3) It is inherent that 306 must receive the action of adjusting frequency from 308 since 306 and 308 are separate server shown in figure 3 (i.e., receiving an adjusted frequency at the application platform from the automation platform).
Objection
The following claims are objected to because of the following typographical error or inconsistence: Claim 15, line 2, “a processing unit” should be “the processing unit”.
Claim Rejections - 35 USC § 102
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 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 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 1-5, 8-13 and 16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang et al, U.S. Patent Application Publication 2019/0199602 (hereinafter Zhang).
As per claim 1, Zhang teaches the invention as claimed comprising:
receiving network packets over time at an application platform, the network packets defining a network traffic flow ([31][51][52][57][66], e.g., receiving ingress traffic/data (e.g., network packets) at 306 of fig. 3, the traffic/data defining an ingress flow);
monitoring metrics derived from one or more applications executing at one or more processing units of the application platform and processing data contained in the network data packets ([41][42][43][61][67][68], e.g., monitoring application performance metrics/system telemetry data and packet loss percentage rate (e.g., metric derived from processing of network data/packets sent) at 306, fig. 3);
formulating model training data from the metrics ([51][57][61][70][71], e.g., formulating telemetry/metrics data as input to machine learning for training/learning);
training a processor unit frequency adjustment model using the model training data ([54][55][57][59][61][69], e.g., training the ML to adjust the processor/core frequency);
automating execution of the model adjusting the frequency of a processing unit from among the one or more processing units at an automation platform, wherein the automation platform is executed independent of the one or more processing units of the application platform ([54][57][70][71], e.g., machine learning enables automatic learning of optimal core frequency at any system load at telemetry system 308 of figure 3, wherein telemetry system 308 is executed independent of the CPU of server 306 of figure 3);
receiving an adjusted frequency at the application platform from the automation platform ([54][40]; fig. 3, e.g., taking action of adjusting frequency of CPU at 306 based on the telemetry data received from 306) (It is inherent that 306 must receive the action of adjusting frequency from 308 since 306 and 308 are separate server shown in figure 3);
receiving additional network packets over time at the application platform, the additional network packets defining an additional network flow ([51][52][55][57][66]; fig. 4, e.g., after action (e.g., adjusting core frequency), continue inputting telemetry (e.g., packet loss percentage rate), which is based on receiving additional network packets); and
processing data contained in the additional network packets at the processing unit at the adjusted frequency ([51][52][55][57][66]; fig. 4, e.g., after action (e.g., adjusting core frequency), continue inputting telemetry from the received additional network packets).
As per claim 2, Zhang teaches the invention as claimed in claim 1 above. Zhang further teach wherein training the processor unit frequency adjustment model comprises training a Recurrent Neural Network (RNN) ([52][57]).
As per claim 3, Zhang teaches the invention as claimed in claim 1 above. Zhang further teach wherein training the processor unit frequency adjustment model comprises training an Long Short-Term Memory (LTSM) model ([52][57]).
As per claim 4, Zhang teaches the invention as claimed in claim 1 above. Zhang further teach wherein automating execution of the model comprises executing the model to predict an increase in network traffic ([54][78], e.g., increasing the processor/core frequency to process packets when workload is predicted to be high).
As per claim 5, Zhang teaches the invention as claimed in claim 4 above. Zhang further teach wherein adjusting the frequency of the processing unit comprises increasing the frequency of the processing unit ([54][78], e.g., increasing the processor/core frequency to process packets when workload is predicted to be high).
As per claim 8, Zhang teaches the invention as claimed in claim 1 above. Zhang further teach wherein adjusting the frequency of the processing unit from among the one or more processing units comprises optimizing the frequency of the processing unit to provide processor resources to process data contained in the additional network packets such that power is reduced ([57][60]).
As per claim 9, it is rejected for the same reason as set forth in claim 1 above. See figures 3 for a computer system comprising: a processor; system memory coupled to the processor and storing instructions configured to cause the processor to perform the method of claim 1.
As per claim 10, it is rejected for the same reason as set forth in claim 2 above.
As per claim 11, it is rejected for the same reason as set forth in claim 3 above.
As per claim 12, it is rejected for the same reason as set forth in claim 4 above.
As per claim 13, it is rejected for the same reason as set forth in claim 5 above.
As per claim 16, it is rejected for the same reason as set forth in claim 8 above.
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 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.
Claims 6-7, 14-15 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang.
As per claim 6, Zhang teaches the invention as claimed in claim 1 above. Although Zhang teaches wherein automating execution of the model comprises executing the model to predict network traffic and adjusting the frequency of a processing unit ([57][78], e.g., predict/forecast workload (e.g., processing of network packets/traffic) and set the processor core frequency for power saving), however, Zhang is silent in regards to decreasing in network traffic and decreasing the frequency of the processing unit. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the forecast a decrease in workload (i.e., predict a decrease in network traffic), which would require/cause decreasing the processor core frequency in Zhang’s system because by doing so it would allow Zhang’s system to optimize the processor core frequency at any system load in order to save power ([57][78]).
As per claim 7, Zhang teaches the invention substantially as claimed in claim 6 above. Although Zhang teaches wherein automating execution of the model comprises executing the model to predict network traffic and adjusting the frequency of the processing unit ([57][78], e.g., predict/forecast workload (e.g., processing of network packets/traffic) and set the processor core frequency for power saving), however, Zhang is silent in regards to decreasing in network traffic and decreasing the frequency of the processing unit. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the forecast a decrease in workload (i.e., predict a decrease in network traffic), which would require/cause decreasing the processor core frequency in Zhang’s system because by doing so it would allow Zhang’s system to optimize the processor core frequency at any system load in order to save power ([57][78]).
As per claim 14, it is rejected for the same reason as set forth in claim 6 above.
As per claim 15, it is rejected for the same reason as set forth in claim 7 above.
As per claim 17, Zhang teaches the invention as claimed in claim 1 above. Although Zhang teaches wherein adjusting the frequency of the processing unit from among the one or more processing units comprises adjusting the frequency of at least one processing unit and the at least one processing unit include Central Processing Units (CPUs), Graphical Processing Units (GPUs), general-purpose GPUs (GPGPUs), Field Programmable Gate Arrays (FPGAs), application specific integrated circuits (ASICs), Tensor Processing Units (TPUs)), and/or system memory ([54], e.g., adjusting at least one CPU core of CPU module), however Zhang does not expressly disclose adjusting two or more processing units. Zhang does teach after performing action ([54]; 406, fig. 4, e.g., adjusting at least one CPU core), continue inputting telemetry, and continue taking action (e.g., adjusting at least one CPU). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include adjusting frequency of two or more CPUs (e.g., adjusting at least one CPU and later adjusting another at least one CPU) because by doing so it would allow Zhang’s system to continue to avoid service deterioration [54].
As per claim 18, it is rejected for the same reason as set forth in claim 17 above.
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 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 Philip Lee whose telephone number is (571)272-3967. The examiner can normally be
reached on 6a-3p M-F.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor,
Glenton Burgess can be reached on 571-272-3949. The fax phone number for the organization where this
application or proceeding is assigned is 571-273-8300.
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/PHILIP C LEE/Primary Examiner, Art Unit 2454