DETAILED ACTION
Claims 1-17 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 .
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 of this title, 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 1-7,9-15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Jung (US 20190107880 A1) and in view of Rotem et al (US 20190187775 A1).
Regarding claim 1, Jung discloses a network node comprising:
at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the network node to carry out at least:
transmit at least one measurement configuration to one or more devices for carrying out one or more measurements [abstract] [0015: generate a training set based on the collection of the input features and output measures of existing applications running on the secondary platform. Embodiments can further classify the training set into various categories representing a range of values for the output measures, such as a power consumption level and data rate] [0045: input features and output measures are collected as a training set for the application, wherein the input features are collected through direct measurement and the output measures reflect characteristics of the application. For example, collecting module 215 collects input features and output measures as a training set for the application] for determining at least one energy consumption estimate for at least one operation in a plurality of inter-related operations for network management, wherein the plurality of inter-related operations comprises one or more operations for collecting data, one or more data transfer operations to transmit the collected data to the network node, and one or more data storage operations to store the collected data and one or more data processing operations to process the collected data [0024: workload may be captured and defined as a data rate ratio of transmission rate (Tx) and receiving rate (Rx) between primary platform 102 and secondary platform 104][0028: a workload of the detected application is captured][0049: power consumption of the secondary platform with an expected performance level is predicted for a new application running on the secondary platform based on the training set. For example, power predicting module 217 predicts power consumption for a new application running on the secondary platform];
receive, from at least one of the one or more devices, at least one message comprising information on the one or more measurements and determine at least one energy consumption estimate using the received information [0049: power consumption of the secondary platform with an expected performance level is predicted for a new application running on the secondary platform based on the training set. For example, power predicting module 217 predicts power consumption for a new application running on the secondary platform],
or receive, from at least one of the one or more devices, at least one message comprising at least one energy consumption estimate determined using the one or more measurements; and determine at least one network management related configuration for carrying out one or more of the plurality of inter-related machine learning operations on the basis of the at least one energy consumption estimate for controlling energy consumption [0049: power consumption of the secondary platform with an expected performance level is predicted for a new application running on the secondary platform based on the training set. For example, power predicting module 217 predicts power consumption for a new application running on the secondary platform. Based on this prediction, an optimal power management policy may be deduced that minimizes the overall power consumption of the primary and secondary platforms] [0067].
Jung does not explicitly disclose a plurality of inter-related machine learning operations for network management, wherein the plurality of inter-related machine learning operations comprises one or more operations for collecting data, one or more data transfer operations to transmit the collected data to the network node, and one or more data storage operations to store the collected data and one or more data processing operations to process the collected data. In other words, Jung only discloses about the tasks/workload of the applications not about the machine learning operations as claimed.
In the same field, Rotem discloses a plurality of inter-related machine learning operations for network management, wherein the plurality of inter-related machine learning operations comprises one or more operations for collecting data, one or more data transfer operations to transmit the collected data to the network node, and one or more data storage operations to store the collected data and one or more data processing operations to process the collected data [0004: optimizing the power usage and/or performance of hardware-based AI accelerators based on an analysis of a substantially static (i.e., predictable) incoming instruction stream. In one example, a computing device capable of performing such a task may include a plurality of special-purpose, hardware-based functional units configured to perform AI-specific computing tasks. The computing device may also include an instruction stream analysis unit configured to (1) receive an instruction stream that includes one or more instructions for performing at least one AI-specific computing task, (2) predict, based on an analysis of at least a portion of the instruction stream, a power-usage requirement for at least one of the functional units when executing the instruction stream, and (3) modify, based on the power-usage requirement, power supplied to at least one of the functional units][0005-0013].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Jung and Rotem together because they both directed to optimize power usage based on the predicted power requirement for computing tasks. Rotem’s disclosing of predicting power requirement for AI computing tasks of the computing device would allow Jung to increase the system’s integrity by incorporating the teachings into the power intense tasks such as machine learning or AI tasks.
Regarding claim 2, Jung and Rotem disclose the network node of claim 1, further caused to: transmit, to the one or more devices, the at least one network management related configuration, or carry out the one or more of the plurality of inter-related machine learning operations (Jung: [0041-0045] [0067]) (Rotem: [0004-0013]).
Regarding claim 3, Jung and Rotem disclose the network node of claim 1, wherein the at least one network management related configuration is determined on the basis of on a net energy balance between an accumulated energy consumption estimated for executing one or more of the plurality of inter-related machine learning operations and energy saved by at least one action carried out based on at least one output of at least one of the inter-related machine learning operations, wherein the accumulated energy consumption is based on the at least one energy consumption estimate (Jung: [0041-0045] [0067]) (Rotem: [0004-0013]).
Regarding claim 4, Jung and Rotem disclose the network node of claim 1, wherein the at least one network management related configuration comprises timing and/or a location in association with carrying out the at least one operation in a plurality of inter-related machine learning operations for network management (Jung: [0041-0045] [0067]) (Rotem: [0004-0013]).
Regarding claim 5, Jung and Rotem disclose the network node of claim 1, wherein the plurality of inter-related machine learning operations are part of a machine learning process based on at least one machine learning model for network management, the at least one network management related configuration comprises at least one of the following: one or more parameters of the machine learning model to be updated, an instruction to trigger retraining of the machine learning model, an instruction to trigger inference of the machine learning model, an instruction to trigger training data collection for the machine learning model, an instruction to trigger inference data collection for the machine learning model, an instruction to replace the machine learning based model by another machine learning based model, and an instruction to stop execution of one or more of the plurality of inter-related machine learning operations (Jung: [0041-0045] [0067]) (Rotem: [0004-0013]).
Regarding claim 6, Jung and Rotem disclose the network node of claim 1, wherein the one or more of the plurality of inter-related machine learning operations comprises at least one of the following: updating at least one parameter of at least one of the plurality of inter-related machine learning operations, triggering at least one of the plurality of inter-related machine learning operations, and deactivating execution of at least one of the plurality of inter-related machine learning operations (Jung: [0041-0045] [0067]) (Rotem: [0004-0013]).
Regarding claim 7, Jung and Rotem disclose the network node of claim 1, further caused, as an option to the determining the at least one network management related configuration, to: transmit the at least one energy consumption estimate to a network management entity, and receive the at least one network management related configuration from the network management entity (Jung: [0041-0045] [0067]) (Rotem: [0004-0013]).
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 of this title, 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 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Jung (US 20190107880 A1) and in view of Rotem et al (US 20190187775 A1) and in further view of Zhou et al (US Publication 20230205293 A1).
Regarding claim 8, Jung and Rotem do not disclose control validity of the at least one energy consumption estimate by using an associated timestamp.
Zhou discloses control validity of the at least one energy consumption estimate by using an associated timestamp [abstract: The PBUs communicate with an array-level power accumulator via dedicated wiring. Communication via the dedicated wiring may use timestamps to ensure time-accurate aggregate estimates] [0011: The PBUs communicate with an array-level power accumulator via dedicated wiring. Communication via the dedicated wiring may use timestamps to ensure time-accurate aggregate estimates] [0034] [0099-0100] [0110] [0112] [0145].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Jung, Rotem and Zhou together because they directed to optimize power usage based on the predicted power requirement for computing tasks. Zhou’s disclosing of control validity of the at least one energy consumption estimate by using an associated timestamp would allow Jung in view of Rotem to achieve maximum granularity and accuracy of the estimates.
Regarding claims 9-16, these claims are rejected for the same reasons as set forth in claims 1,3-8 above.
Regarding claims 17, this claim is rejected for the same reasons as set forth in claim 1 above.
Pertinent Arts
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Koorapat (US Publication 20240427396 A1) discloses the IC chip includes a processor as well integrated supporting processing devices for the processor. The hierarchical power management system controls power consumption of devices in the IC chip to achieve the desired performance in the processor-based system based on activity power events generated from local activity monitoring of devices in the IC chip. The hierarchical power management system includes a centralized power estimation and limiting (PEL) circuit that is configured to track and merge received power throttle recommendations associated with related activity power events for monitored processing devices to generate one or more power limiting management responses to throttle power consumption of related devices that may be contributing to excess power consumption.
Conclusion
Examiner's note: Examiner has cited particular paragraphs and columns and line numbers in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner (see MPEP § 2123).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHIL K NGUYEN whose telephone number is (571)270-3356. The examiner can normally be reached 9:30 a.m - 5 p.m.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jaweed Abbaszadeh can be reached at (571)270-1640. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PHIL K NGUYEN/Primary Examiner, Art Unit 2176
Prosecution Insights