DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is sent in response to Applicant’s Communication received 11/25/2024 for application number 18/959,056. The Office hereby acknowledges receipt of the following and placed of record in file: Specification, Drawings, Abstract, Oath/Declaration, and claims.
Claims 1 – 20 are presented for examination.
Drawings
Examiner contends that the drawings filed 11/25/2024 are acceptable for examination proceedings.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-8 and 10-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin et al. (hereinafter as Lin) USPAT 7,610,497, and further in view of Lee et al. (hereinafter as Lee) PGPUB 2016/0239065.
As per claim 1, Lin teaches a system comprising:
a memory device [FIG. 1: main memory 2]; and
a processing device, operatively coupled to the memory device [FIG. 1 bridge logic 3 (logic requires processing and decision making, and thus is a processing device)], to perform operations comprising:
identify one or more power-saving parameters associated with each workload data of the plurality of workload data [col. 3 lines 25-35, col. 4 lines 9-51: (power parameters of clock and voltage are identified, and are associated with memory traffic (workload data))];
and
adjusting, based on the one or more power-saving parameters associated with the current workload data, a power state of the memory device [col. 1 line 65 – col. 2 line 7; col. 2 lines 48- 63; and col. 4 lines 9-51: (operating clocks and voltage of the main memory are adjusted based on the identified traffic/workload data)].
Lin does not explicitly teach providing current workload data of the memory device as input to a machine learning model, wherein the machine learning model is trained, using a plurality of workload data; obtaining an output of the machine learning model, the output comprising output comprising the one or more power-saving parameters associated with the current workload data.
Lee teaches identifying workload behavior such as intensive memory access and providing optimal power control [0023]. Lee is thus similar to Lin because they both analyze memory access and make power adjustments. Lee further teaches providing current workload data of the memory device as input to a machine learning model, wherein the machine learning model is trained, using a plurality of workload data [0023, 0040, 0157: (machine learning logic classifies workload behavior and thus receives as input, memory access information to determine if there is intensive memory accesses)]; obtaining an output of the machine learning model, the output comprising output comprising the one or more power-saving parameters associated with the current workload data [0023, 0026, 0127, and 0133: (machine learning model outputs an optimal configuration, which include voltage and frequency parameters associated with the workload behavior)]. Lee teaches using machine language to analyze workload behavior and outputs a configuration that would optimize power usage.
The combination of Lin would Lee allows Lin to use a machine learning model to receive as input the workload traffic associated with memory and to output an optimal power saving configuration of reduced voltage or clock frequency for the memory, when utilization is low.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Lee’s teaching of using a machine learning model to analyze memory behavior or patterns to determine the optimal voltage and frequency parameters for memory in Lin. One of ordinary skill in the art would have been motivated to use machine learning model in Lin because it allows for predictive determination of workload needs or changes, thus allowing for more optimal power adjustments and further reduction in power consumption.
As per claim 2, Lin and Lee teach the system of claim 1, wherein the current workload data indicates a lower current workload, and wherein to adjust the power state of the memory device, the processing device is to perform operations further comprising: transitioning to a lower power state [Lin FIG. 5 and col. 4 lines 19-29: (if the workload indicates a lower workload (if number doesn’t exceed a threshold), transition to low operating frequency and voltage (lower power state) for the main memory)].
As per claim 3, Lin and Lee teach the system of claim 1, wherein the current workload data indicates a higher current workload, and wherein to adjust the power state of the memory device, the processing device is to perform operations further comprising: transitioning to a higher power state [Lin FIG. 5 and col. 4 lines 43-51: (if the workload indicates a higher workload (if number exceed a threshold), transition to high operating frequency and voltage (higher power state) for the main memory)]..
As per claim 4, Lin and Lee teach the system of claim 1, wherein the current workload data corresponds to a current bandwidth of the memory device [Lin col. 4 lines 19-51: (based on traffic (bandwidth) of memory device)].
As per claim 5, Lin and Lee teach the system of claim 1, wherein the one or more power-saving parameters associated with the current workload data comprises one or more of: power per memory access operation, an application-specific integrated circuit (ASIC) clock domain frequency, or a central processing unit (CPU) frequency [Lin FIG. 5: (adjust clock frequency)].
As per claim 6, Lin and Lee teach the system of claim 1, wherein the processing device is to perform operations further comprising: generating training data for the machine learning model, wherein generating the training data comprises: generating first training input, the first training input comprising the plurality of workload data; and generating a first target output for the first training input, wherein the first target output identifies the one or more power-saving parameters for each workload data of the plurality of workload data; and providing the training data to train the machine learning model on (i) a set of training inputs comprising the first training input, and (ii) a set of target outputs comprising the first target output paired with the first training input [Lee 0128, 0132, and 0174: (training is performed using workload data; supervised training involves providing inputs and target outputs to a machine learning model to determine weight adjustments, and providing such data to a machine learning model so that it can take the input and generate the desired output; it is apparent that this is performed in Lee’s training process); Calugaru (referenced below) also teach this in paragraphs 0034-0035].
As per claim 7, Lin and Lee teach The system of claim 1, wherein the machine learning model comprises at least one of: a regression model, an autoregressive integrated moving average model, a supervised learning model, an unsupervised learning model, a semi-supervised learning model, a Bayesian model, or a reinforcement learning model [Lee 0132 regression model].
As per claim 8, Lin and Lee teach the system of claim 1, wherein the processing device is to perform operations further comprising: in response to adjusting the power state of the memory device, determining a power savings value associated with adjusting the power state of the memory device [Lee 0164: (selecting optimal configuration provide energy savings of 4x (power saving value))].
Claim 10 is similar in scope to claim 1 as addressed above and is thus rejected under the same rationale.
Claim 11 is similar in scope to claim 2 as addressed above and is thus rejected under the same rationale.
Claim 12 is similar in scope to claim 3 as addressed above and is thus rejected under the same rationale.
Claim 13 is similar in scope to claim 5 as addressed above and is thus rejected under the same rationale.
Claim 14 is similar in scope to claim 6 as addressed above and is thus rejected under the same rationale.
Claim 15 is similar in scope to claim 7 as addressed above and is thus rejected under the same rationale.
Claim 16 is similar in scope to claim 1 as addressed above and is thus rejected under the same rationale.
Claim 17 is similar in scope to claim 2 as addressed above and is thus rejected under the same rationale.
Claim 18 is similar in scope to claim 3 as addressed above and is thus rejected under the same rationale.
Claim 19 is similar in scope to claim 6 as addressed above and is thus rejected under the same rationale.
Claim 20 is similar in scope to claim 8 as addressed above and is thus rejected under the same rationale.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin et al. (hereinafter as Lin) USPAT 7,610,497 in view of Lee et al. (hereinafter as Lee) PGPUB 2016/0239065, and further in view of Calugaru et al. (hereinafter as Calugaru) PGPUB 2021/0294403.
As per claim 9, Lin and Lee teach the system of claim 1.
Lin and Lee do not teach wherein providing the current workload data of the memory device as input to the machine learning model is triggered in response to a change to the current workload.
Calugaru teaches using AI to determine load patterns in memory to predict workload transients, and allocating power in anticipation of these workload transients. Calugaru is thus similar to Lin and Lee. Calugaru further teach wherein providing the current workload data of the memory device as input to the machine learning model is triggered in response to a change to the current workload [0034: (responsive to a launch of an application (a change to the current workload) AI controller obtains identifier and begins its prediction)]. Calugaru teaches launching the AI controller, and thus providing it with memory workload data, whenever a change in workload occurs, such as the launch of an application.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Calugaru’s teachings of launching the AI/machine learning model when a change in workload, such as from launching a new application, occurs in Lin and Lee. One of ordinary skill in the art would have been motivated to have the machine learning model launch to analyze the memory workload data in response to a change in workload (such as from launching an app) in Lin and Lee because it allows for predictive and proactive system configuration changes whenever a different activity or application is initiated, thereby maintaining power efficiency and reducing power consumption quickly instead of being reactive to change.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is reminded that in amending in response to a rejection of claims, the patentable novelty must be clearly shown in view of the state of the art disclosed by the references cited and the objections made. Applicant must also show how the amendments avoid such references and objections. See 37 CFR §1.111(c).
Yudanov et al. (PGPUB 2020/0379914) teaches an artificial neural network that is trained using usage history to determine memory usage pattern of an application, and then use it to adjust computing power in a memory [0360].
Schluessler et al. (PGPUB 2018/0293011) teaches evaluating memory usage of application when it starts up [0244 and 0247] and machine learning operations.
Zhou et al. (PGPUB 2015/0301572) teaches analyzing memory access patterns and power consumption to provide optimization suggestions over computing nodes on a network and using machine language.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANNY CHAN whose telephone number is (571)270-5134. The examiner can normally be reached Monday - Friday 10-7 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew J. Jung can be reached at 5712703779. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANNY CHAN/Primary Examiner, Art Unit 2175
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