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 .
Claims 1-20 are pending.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The disclosure is objected to because of the following informalities:
--Kwaspd -- should be -kswapd -- in [0002].
--belows -- should be --below -- in [0002].
-- kernal -- should be -- kernel -- in [0011].
Appropriate correction is required.
The use of the term Linux, Android etc., which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-20 are rejected under 35 U.S.C. 112 (b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or joint inventor regards as the invention.
The following claim language is not clearly understood:
Claim 1 recites “size of reclaiming memory” and later recite “reclaim amount of memory”. It is unclear if size and amount is referring to same metric of memory or different metric of memory i.e. size of memory and amount of memory is same or different. Applicant is requested to use the same term to refer to same metric for consistency.
Claim 1 recites “weights of the first function and the second function”. It is unclear weights are referring to what and if the second function is also a predicting function and has associated weight as well independent of first function.
Claim 6 recites “a trend” without clearly reciting trend of what e.g. trend of amount of memory being reclaimed or trend of predict amount of reclaimed memory..
Claims 11 and 20 recite elements of claim 1 and have similar deficiency as claim 1. Therefore, they are rejected for the same rational. Remaining dependent claims 2-10 and 12-19 are also rejected due to similar deficiency inherited from the rejected independent claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Independent claim 20 recites a “storage medium”, which is not defined by the specification. The broadest reasonable interpretation of a claim drawn to a “storage medium” covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer usable program product, particularly when the specification is silent. Transitory propagating signals are non-statutory subject matter.
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.
Claims 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rangole (US 10,771,330 B1) in view of Kwon et al. (US 2023/0214262 A1, hereafter Kwon).
As per claim 1, Rangole teaches the invention substantially as claimed including a method comprising:
training, by a server using a neural network, a first function and a second function (col 2 lines 26-37 construct and train a machine learning model configured to output a set of generated tunable parameter settings), the first function used to predict a reclaim size for reclaiming memory on a user device (col 2 lines 26-37 machine learning model, output, tunable parameter settings; col 5 lines 60-67 generated tunable parameter settings, prediction based on probabilities, col 6 lines 7-12 generated tunable parameter setting, recommended to a customer col 7 lines 4-12 tunable parameter settings, memory size, memory recovery properties col 3 lines 22-35 computer memory size parameters, memory recovery parameters) and the second function used to adjust a reclaim amount for the memory (col 7 lines 4-12 tunable parameter settings, adjusted, memory size, memory recovery properties; col 1 lines 35-40 tunable parameters, memory allocation parameter col 3 lines 22-35 computer memory size parameters, memory recovery parameters);
in response to completion of the training, offloading weights of the first function and the second function to the user device (col 5 lines 60-67 generated tunable parameter settings, prediction based on probabilities, col 6 lines 7-23 notification, provided, customer, advising the customer of the second set of tunable parameter setting, customer accept the recommended second set of tunable parameter settings, immediately applied without user intervention col 7 lines 4-12 tunable parameter settings, memory size, memory recovery properties col 3 lines 22-35 computer memory size parameters, memory recovery parameters), the weights causing an adjustment to the reclaim size and reclaim amount associated with kswapd at the user device (col 6 lines 24-30 applying the second set of tunable parameter settings, result in improved performance; col 7 lines 4-12 tunable parameter settings, adjust memory size, memory recovery properties col 3 lines 22-35 computer memory size parameters, memory recovery parameters);
causing the user device to predict the reclaim size based on the first function (col 6 lines 7-23 set of tunable parameter settings, immediately applied without user intervention; col 5 lines 60-67 generated tunable parameter settings, prediction based on probabilities col 7 lines 4-12 tunable parameter settings, memory recovery properties col 3 lines 22-35 computer memory size parameters, memory recovery parameters);
causing the user device to adjust the reclaim amount based on the second function (col 6 lines 7-23 set of tunable parameter settings, immediately applied without user intervention; col 5 lines 60-67 generated tunable parameter settings, col 7 lines 4-12 tunable parameter settings, adjust memory size, memory recovery properties col 3 lines 22-35 computer memory size parameters, memory recovery parameters); and
causing the user device to reclaim memory based on the predicted reclaim size and adjusted reclaim amount when kswapd is woken (col 5 lines 60-67 generated tunable parameter settings, prediction based on probabilities col 6 lines 7-23 set of tunable parameter settings, immediately applied without user intervention; col 7 lines 4-12 tunable parameter settings, adjust memory size, memory recovery properties col 3 lines 22-35 computer memory size parameters, memory recovery parameters; col 8 lines 50-55 second set, tunable parameter settings, tunable parameter service, automatically applied i.e. configurable).
Rangole doesn’t specifically teach reclaim memory when kswapd is woken.
Kwon, however, teaches reclaim memory when Kswapd is woken ([0059] memory retrieval operation, kswapd).
It would have been obvious to one of ordinary skills in the art before the effective filing date of the invention was made to combine the teachings of Rangole with the teachings of Kwon of memory retrieval operation performed by kernel swap daemon to improve efficiency and allow reclaim memory when Kswapd is woken to the method of Rangole as in the instant invention.
The combination of cited prior art would have been obvious because applying the known method of reclaiming memory using Kswapd daemon as taught by Kwon to the method of Rangole of predict/adjusting tunable parameter including memory size / recovery parameter to yield expected result with improved memory efficiency.
As per claim 2, Kwon teaches wherein the neural network is a recurrent neural network ([0031] recurrent neural network).
As per claim 3, Rangole teaches wherein the training or retraining comprises using backpropagation through time algorithm to train or retrain the first function and the second function (col 14 lines 25-32 set of performance metric, feedback loop, collect performance metrics and implementation attributes, generating tunable parameter settings col 2 lines 26-35 set of tunable parameters, distributed applications, construct and train machine learning model configured to output set of generated tunable parameter settings col 9 lines 5-20 adjustment tunable parameter settings e.g. a period of time between adjustment of the tunable parameter settings using randomized increments or heuristics).
As per claim 4, Kwon teaches wherein the training or retraining further comprises:
detecting a number of direct reclaims triggered in a same workload (fig. 8 direct reclaim i.e. second memory retrieval operation 805); and
tuning the first function and the second function to lower the number of direct reclaims ([0142] fig. 9 possible to reduce the operation time of the second operation i.e. direct reclaim from t3 to t3’).
As per claim 5, Rangole teaches wherein causing the user device to adjust the reclaim amount comprises causing an adjustment to a high watermark for the memory ( col 6 lines 7-23 set of tunable parameter settings, immediately applied without user intervention; col 5 lines 60-67 generated tunable parameter settings, col 7 lines 4-12 tunable parameter settings, adjust memory size, memory recovery properties col 3 lines 22-35 computer memory size parameters, memory recovery parameters).
Kwon teaches remaining claim elements of high watermark for the memory ([0061] high watermark, designated as values to be compared with the available capacity of the volatile memory 132 in order to perform and stop the performance of the first memory retrieval operation).
As per claim 6, Rangole teaches wherein causing the user device to predict the reclaim size based on the first function (col 2 lines 26-37 machine learning model, output, tunable parameter settings; col 5 lines 60-67 generated tunable parameter settings, prediction based on probabilities, col 6 lines 7-12 generated tunable parameter setting, recommended to a customer col 7 lines 4-12 tunable parameter settings, memory size, memory recovery properties col 3 lines 22-35 computer memory size parameters, memory recovery parameters) is based on a previous predicted reclaim size and a trend, the trend being based on current processes deployed on the user device (col 2 lines 26-48 first set of tunable parameters, distributed application, attributes, used to construct and train machine learning model, subsequent implementation, second set of tunable parameter setting, used to tune or fine tune).
As per claim 7, Rangole teaches wherein causing the user device to adjust the reclaim amount based on the second function is based on a previous high watermark, lower watermark, and a trend, the trend being based on current processes deployed on the user device (col 6 lines 7-23 set of tunable parameter settings, immediately applied without user intervention; col 5 lines 60-67 generated tunable parameter settings, col 7 lines 4-12 tunable parameter settings, adjust memory size, memory recovery properties col 3 lines 22-35 computer memory size parameters, memory recovery parameters; col 2 lines 26-48 first set of tunable parameters, distributed application, attributes, used to construct and train machine learning model, subsequent implementation, second set of tunable parameter setting, used to tune or fine tune).
Kwon teaches remaining claim elements of previous high watermark, lower watermark ([0061] low watermark, high watermark).
As per claim 8, Rangole teaches wherein the user device is an Android device (col 10 lines 57-65 customer, device, smartphone).
As per claim 9, Rangole teaches wherein the server comprises a cloud service that manages the user device (col 10 lines 58-67 cloud environment; fig. 3 server computer 316 management component 316 tunable parameter service 314).
As per claim 10, Rangole teaches wherein offloading weights of the first function and the second function to the user device comprises downloading the weights to the user device via over-the-air updates (col 6 lines 7-23 notification, provided, customer, advising the customer of the second set of tunable parameter setting, customer accept the recommended second set of tunable parameter settings, applied, as a result of customer accepting the recommended; col 9 lines 60-67 communication, network, wireless connections).
Claim 11 recites a system comprising, one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising elements similar to claim 1. Therefore, it is rejected for the same rationale.
Claim 12 recites system comprising elements similar to claim 2. Therefore, it is rejected for the same rationale.
Claim 13 recites the system for elements similar to claim 3. Therefore, it is rejected for the same rationale.
Claim 14 recites the system for elements similar to claim 4. Therefore, it is rejected for the same rationale.
Claim 15 recites the system for elements similar to claim 5. Therefore, it is rejected for the same rationale.
Claim 16 recites the system for elements similar to claim 6. Therefore, it is rejected for the same rationale.
Claim 17 recites the system for elements similar to claim 7. Therefore, it is rejected for the same rationale.
Claim 18 recites the system for elements similar to claim 8. Therefore, it is rejected for the same rationale.
Claim 19 recites the system for elements similar to claim 10. Therefore, it is rejected for the same rationale.
Claim 20 recites storage medium comprising instructions which, when executed by one or more processors of a machine, cause the machine to perform operations comprising elements similar to claim 1. Therefore, it is rejected for the same rationale.
Examiners Note
Applicant is further reminded of that the cited paragraphs and in the references as applied to the claims above for the convenience of the applicant(s) and 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 all of 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Alagumuthu (US 11,934,316 B2) teaches controlling cache size and priority using machine learning technique.
Kim (US 2022/0137868 A1) teaches system for an artificial neural network (ANN) to optimize an ANN operation of the processor by utilizing the ANN data locality of the ANN model, which operates at a processor-memory level.
Shahane et al. (US 11,714,682 A1) teaches reclaiming computing resources in an on-demand code execution system.
Vassilvitskii et al. (US 2019/0251040 A1) teaches caching using machine learned predictions.
Wang (US 2026/0079621 A1) teaches memory reclaim method based on urgency of a process and memory pressure.
Authorization for Internet Communication
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/ABU ZAR GHAFFARI/ Primary Examiner, Art Unit 2195
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