Detailed Action
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
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.
Claim(s) 1, 13, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over SZURTEI [20220100254], in view of Kegel [20220229712]
As to claim 1,
SZURTEI [20220100254] teaches An electronic apparatus, comprising: memory storing instructions; and at least one processor [0045]connected to the memory [] and configured to control the electronic apparatus using a neural network model, wherein the instructions, when executed by the at least one processor, cause the electronic apparatus to : identify whether to use the neural network model based on a state of the electronic apparatus(whether to use neural network, has a claim contingency, it is not related to invention when there a decision to not use neural network and patentability weight is not given); based on identifying that the neural network model is to be used: identify an operation mode of the electronic apparatus as one of a first performance having a first power consumption or a second performance having a second power consumption greater than the first power consumption [0026: “in some embodiments, the plurality of performance characteristics includes a plurality of power consumptions for execution of the workload. The power consumptions are an amount of power consumed to execute the workload (e.g., during a particular interval). ” and 0011: “configuring a power management system using reinforcement learning that, when executed, cause a computer system to perform steps including: receiving data indicating a plurality of performance characteristics for an execution of a workload, wherein the plurality of performance characteristics include a plurality of processing frequency modification decisions generated by a neural network; calculating, based on one or more of the performance characteristics, a reward value for the execution of the workload; and modifying one or more weights of the neural network based on the reward value ” and 0013: “The processor 104 operates at a processing frequency (e.g., a processing speed) determined by a System Management Unit 106. The System Management Unit 106 is a subsystem that regulates, based on performance and workloads, various attributes of the test system 102, including the processing frequency, voltages, and the like. For example, the System Management Unit 106 determines a particular frequency at which to operate, and selects a corresponding voltage required to drive the processor 104 at that frequency. ”- frequency with corresponding voltage is a operation mode and with different frequency and corresponding voltage it has various power setting, when there is more than one setting, one has to higher power setting or voltage setting than the other]; and
adjust power consumption of the electronic apparatus based on the identified operation mode [0016: “During execution of the workload, the System Management Unit 106 aggregates performance counters as described above and uses the neural network 108 to determine whether to adjust the frequency of the processor 108. In some embodiments, where the processing frequency modification decision is selected from a plurality of possible decisions (e.g., a “categorical output”) a processing frequency modification decision is selected from a probability distribution generated by the neural network 108, such as a softmax probability distribution.”]But do not explicitly teach power mode.
However, Kegel [20220229712] teaches power mode [Claim 19: “neural network runs known input data using a first power setting to generate first outputs from the neural network and a first power mode is adjusted based at least in part on the first outputs to cause an error rate of the neural network be within bounds, the first power setting adjusted by adjusting at least one of voltage and frequency” – power setting is equivalent to power mode]
It would have been obvious to person of ordinary skill in the art before the effective filing date of the claimed invention to combine teaching of SZURTEI and Kagel both are directed toward using Neural network to control operating parmaters. Furthermore, Kagel improves upon Szurtei by being able to adjust power mode based on the settings such that the setting are predefined in the mode and be efficiently provide desired power and frequency with mode selection of power supply.
As to claim13,
Szurtei and Kagel teach this claim according to the reasoning set forth in claim 1 supra.
As to claim 20,
Szurtei and Kagel teach this claim according to the reasoning set forth in claim 1 supra.
Allowable Subject Matter
Claim2-12, 13-19 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 KESHAB R PANDEY whose telephone number is (571)270-0176. The examiner can normally be reached Monday-Friday 9:00-5:00(ET).
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/KESHAB R PANDEY/Primary Examiner, Art Unit 2176
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