DETAILED ACTION
Status of Claims
The present application, filed on or after 3/16/2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to Applicant’s Response to Election / Restriction filed 10/6/2025.
Claims 1-16 have been elected.
Claims 17-20 have been withdrawn from consideration.
Claims 1-16 have been examined.
Election/Restrictions
This application contains Claims 17-20 drawn to an invention nonelected in the reply filed on 10/6/2025. Applicant has not distinctly and specifically pointed out supposed errors in the restriction requirement but instead has noted: “Applicant elects to prosecute Invention I as identified by examiner, drawn towards a system and/or non-transitory CRM used to determine a metric associated with a data center. The remaining claims have been withdrawn.” Therefore, the election shall be treated as an election without traverse. A complete reply to the final rejection must include cancellation of nonelected claims or other appropriate action (37 CFR 1.144). See MPEP § 821.01.
Information Disclosure Statement (IDS)
Acknowledgement is hereby made of receipt of Information Disclosure Statements filed by applicant on 09/29/2023.
(AIA ) Examiner Note
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were effectively filed absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned at the time a later invention was effectively filed in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-16 are rejected under 35 U.S.C. 112(b) or (for pre-AIA ) 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor, a joint inventor, or (for pre-AIA ) the applicant regards as the invention.
Independent claims 1 and 9 each recite in part the following: “determine a recommendation to change the data center to improve the carbon efficiency metric based on predicting, using a machine learning model and based on a time-series dataset, whether the carbon efficiency metric is associated with a temporary event;”
Respectfully, there are multiple issues with this claim feature which introduce ambiguity and uncertainty in regards to proper interpretation.
First, this feature is a run-on sentence with multiple clauses which do not have clear relationships to each other. For example, it is not clear what the clause “using a machine learning model and based on a time-series dataset” is intended to mean. To illustrate this ambiguity, examiner notes Figs. 1, 2, 3 provided below. From these figures, it is unclear whether this phrase intended to mean: (1) there are two separate things (a) time-series data and (b) a machine learning model, each of which are used in some undisclosed prediction algorithm which predicts whether the carbon efficiency metric itself is associated with a temporary event? Or, (2) is this phrase intended to mean the machine learning model takes time-series data as input and the model predicts whether the carbon efficiency metric itself is associated with a temporary event? Or, (3) does this phrase mean the machine learning model is trained on time-series data, and the input is implied to be a carbon efficiency metric and the output of the model is a prediction of whether the carbon efficiency metric itself is associated with a temporary event?
Fig. 1
[AltContent: connector][AltContent: connector][AltContent: arrow][AltContent: textbox (A recommendation to change the data center (to improve the carbon
efficiency metric))][AltContent: connector][AltContent: textbox (Results from a machine learning model)][AltContent: textbox (?
Prediction Method:
Metric associated with temporary event?
Output = (Yes/No))][AltContent: textbox (Time-series dataset)][AltContent: textbox (?
function necessary to convert prediction (yes/no) to a recommendation)]
Fig. 2
[AltContent: arrow][AltContent: connector][AltContent: arrow][AltContent: connector][AltContent: textbox (Specific recommendation to change the data center to improve the carbon
efficiency metric)][AltContent: textbox (a machine learning model)][AltContent: textbox (Prediction:
(Yes/No)
[Metric’s value] associated with temporary event)][AltContent: textbox (Time-series dataset)][AltContent: textbox (? function necessary to convert prediction (yes/no) to a recom-mendation)]
Fig. 3
[AltContent: arrow][AltContent: arrow][AltContent: arrow][AltContent: arrow][AltContent: textbox (Specific recommendation to change the data center to improve the carbon
efficiency metric)][AltContent: textbox (a machine learning model (trained on a time-series data set))][AltContent: textbox (Prediction:
(Yes/No)
[Metric’s value] associated with temporary event)][AltContent: textbox (metric)][AltContent: textbox (? function necessary to convert prediction (yes/no) to a recom-mendation)]
Second, it is not clear whether the phrase “predicting, …, whether the carbon efficiency metric is associated with a temporary event” is to be taken literally – i.e. determine whether a metric itself such as temperature, total power usage, or power usage efficiency, is associated with a temporary event, or whether such phrase is a colloquialism intended to mean determine whether a time-series of values of a metric such as values of temperature fluctuations over a time period, or values of power usage over a time period, or values of a power usage efficiency over a time period, is associated with a temporary event.
Third, it is not clear what function, step, or steps is/are being claimed as necessary to perform the “determine a recommendation to change the data center”; i.e. although this determination is claimed as based on various input (e.g. based on the result of predicting… whether the carbon efficiency metric is associated with a temporary event), it is not clear what function, step, or steps are being claimed to functionally make this determination. The original disclosure does not appear to illuminate the function, step, or steps required to make this determination. Instead, there is inconsistency between the claim language being used and the original disclosure. For example, per [0009]-[0011] and Figs. 7-9, a recommendation is based upon a prediction of “whether a future carbon efficiency metric associated with the data satisfies a threshold” and/or other “instructions” but there is no functional algorithm provided for converting a prediction regarding “a future carbon efficiency metric” into a recommendation. There is no functional algorithm provided for converting “instructions” into a recommendation. Furthermore, per [0046]: “…The recommendation generator 209 may also determine a recommendation to improve the carbon efficiency metric…” So, there appears to be three different mutually exclusive ideas for making a recommendation. It is not clear which is intended to be claimed herein.
For each of the aforementioned reasons, the claims are found to lack the clarity, deliberateness, and precision required to conform to 35 U.S.C. 112(b) or (for pre-AIA ) 35 U.S.C. 112, second paragraph - see MPEP § 2141.03 and also MPEP § 2173.01-.03. e.g. “…A claim, although clear on its face, may also be indefinite when a conflict or inconsistency between the claimed subject matter and the specification disclosure renders the scope of the claim uncertain as inconsistency with the specification disclosure or prior art teachings may make an otherwise definite claim take on an unreasonable degree of uncertainty. In re Moore, 439 F.2d 1232, 1235-36, 169 USPQ 236, 239 (CCPA 1971)…”
Furthermore, claim 3 recites in part the following: “… determine the power consumption of the infrastructure device, to: gather, at a configurable gather rate, power consumption data from the infrastructure device.” Respectfully, it is not clear what is meant by “determine the power consumption… to: gather… power consumption data… “ It appears the claims draftsman has inadvertently included an extra clause. Stated another way, it is not clear whether the clause “to: gather…” is intended to be a part of the claim feature. The two clauses recited together in this manner do not make sense. Examiner notes that if the clauses are intended to be part of the same limitation, it remains unclear what they are intended to mean as currently recited. Perhaps, the clause is intended to mean a statement of an inherent consequence of the determination of power consumption? Or, perhaps the two clauses are supposed to be two separate steps? Or, perhaps the second clause is a step intended to be predicated upon determination of power consumption but how this functionally is intended to be implemented is not clear.
For the purpose of compact prosecution, the claim is interpreted as follows: “The non-transitory computer-readable storage medium of claim 1, wherein the executable computer program instructions, when executed by the processor, cause the processor, to: gather, at a configurable gather rate, power consumption data from the infrastructure device.” Nonetheless, correction or clarification is required.
Additionally, claim 16 recites, “The claim 9, wherein…” Respectfully, applicant has omitted the primary subject of claim 9 (e.g. The “system of” claim 9, wherein…?) It is not clear what object is being referenced as the primary subject of claim 9. For this reason, the claim is held to be indefinite. Additionally, the claim recites “cause the processor” twice. It appears, one of these is an inadvertent error on the part of the claim draftsman. For the purpose of compact prosecution, the Examiner interprets the claim to reference the “system of” claim 9 “…wherein the instructions, when executed, further cause the processor to: amend…”.
Claim 16 is also interpreted as being dependent upon claim 9 and not as an independent claim as presently presented.
Furthermore, claim 9 recites in part the following: “… wherein the instructions, when executed, further cause the processor to cause the processor to: amend the recommendation responsive to predicting that the carbon efficiency metric is not associated with a temporary event; and provide the recommendation to the output device responsive to amending the recommendation.” Respectfully, independent claim 9 does not require the recommendation be based on a prediction that the carbon efficiency metric is associated with a temporary event. Instead, the recommendation of claim 9 may be based on a prediction that the carbon efficiency metric is not associated with a temporary event. The language of claim 9, in pertinent part, recites the following: “…predicting,…, whether the carbon efficiency metric is associated with a temporary event;…”; in the English language, this means the prediction may be either “yes” the metric is associated with a temporary event or “no” the metric is not associated with a temporary event. Therefore, under a broadest reasonable interpretation, claim 9’s prediction may be that the carbon efficiency metric is not associated with a temporary event. Therefore, dependent claim 16 is completely unclear. For example, in this scenario, claim 16 apparently requires the prediction to be performed again, and if it has the same result (predicting that the carbon efficiency metric is not associated with a temporary event), then the recommendation is amended. In a scenario where the prediction of claim 9 resulted in a positive prediction that the carbon efficiency metric is associated with a temporary event, then claim 16 would not require all the limitations of the parent claim – i.e. the two predictions of claim 9 cannot be different predictions. Respectfully, it is not clear what applicant is attempting to claim and therefore the claim is indefinite.
Dependent claims 2-8, 10-16, inherit the deficiencies of their parent claim and are also rejected under 35 U.S.C. 112(b) or (for pre-AIA ) 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor, a joint inventor, or (for pre-AIA ) the applicant regards as the invention.
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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (i.e. a judicial exception) without significantly more.
Per step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed towards a process, machine, or manufacture.
Per step 2A Prong One, the claims recite specific limitations which fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG, as follows:
Per Independent claims 1 and 9 exemplified in the limitations of claim 1:
determine a recommendation to change the data center to improve the carbon efficiency metric based on predicting, …, whether the carbon efficiency metric is associated with a temporary event; and provide the recommendation…
As noted supra, these limitations fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, these limitations fall within the group Certain Methods Of Organizing Human Activity (e.g. fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
That is, the determining steps, as drafted, are a business decision to make a recommendation (e.g. recommend using more efficient equipment) which is akin to targeting advertising of equipment based on a perceived targeting criteria (e.g. inefficient data center operating equipment) and thus falling into Certain Methods of Organizing Human Activity. Furthermore, the mere nominal recitation of a generic computer components used to facilitate the abstract idea does not take the claim limitation out of the enumerated grouping. Thus, the claims recite an abstract idea.
Per step 2A Prong 2, the Examiner finds that the judicial exception is not integrated into a practical application. Although there are additional elements, other than those noted supra, recited in the claims, none of these additional element(s) or a combination of elements as recited in the claims apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception. As drafted, the claims as a whole merely describe how to generally “apply” the aforementioned concepts and/or link them to a field of use (i.e. in this case targeted advertising to data-center operators) or serve as insignificant pre-solution activity or insignificant extra-solution activity. The claimed computer components are recited at a high level of generality and are merely invoked as tools to implement the idea but are not technical in nature. Simply implementing the abstract idea on or with generic computer components is not a practical application of the abstract idea.
These additional limitations are as follows, exemplified in limitations of claim 1: “A non-transitory computer-readable storage medium having stored thereon executable computer program instructions that, when executed by a processor, cause the processor to: determine a carbon efficiency metric associated with a data center based on: determining a power consumption of an infrastructure device of the data center; and estimating a performance of the infrastructure device based on the power consumption; …using machine learning and based on time-series data... to an output device”
However, these elements do not present a technical solution to a technical problem; i.e. Applicant’s invention is not a technique nor technical solution for “determining carbon efficiency metrics” (e.g. simple power consumption data-collection and calculation) nor is it an output device to which a recommendation is provided. The additional elements do not recite a specific manner of performing any of the steps core to the already identified abstract idea. Instead, these features merely serve as insignificant pre-solution activity or insignificant extra-solution activity (e.g. gathering targeting criteria such as time-series data regarding carbon efficiency metrics of a data-center) upon which the abstract idea operates, or serve to generally “apply” the aforementioned concepts using generic computer components (e.g. an output device, a non-transitory computer-readable storage medium, etc…) and link them to a field of use (e.g. targeted advertising to data-center operators) and do not integrate the abstract idea into a practical application thereof.
Per Step 2B, the Examiner does not find that the claims provide an inventive concept, i.e., the claims do not recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception recited in the claim. As discussed with respect to Step 2A Prong Two, the additional elements in the independent claims were considered as merely serving to generally “apply” the aforementioned concepts via generically described computer components (e.g. an output device, a non-transitory computer-readable storage medium, etc…) and “link” them to a field of use (e.g. targeted advertising to data-center operators), or as insignificant pre-solution activity or insignificant extra-solution activity (e.g. gathering targeting criteria such as time-series data regarding carbon efficiency metrics of a data-center) upon which the abstract idea operates. For the same reason these elements are not sufficient to provide an inventive concept; i.e. the same analysis applies here in 2B. Mere instructions to apply an exception using a generic computer component and conventional data gathering cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. So, upon revaluating here in step 2B, these elements are determined to amount to no more than mere instructions to apply the exception using generic computer components (i.e. a server) and/or gather and transmit data which is well-understood, routine, conventional activity in the field; i.e. note the Symantec, TLI, and OIP Techs Court decisions cited in MPEP 2106.05(d)(ll) indicate that mere receipt or transmission of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Accordingly, alone and in combination, these elements do not integrate the abstract idea into a practical application, as found supra, nor provide an inventive concept, and thus the claims are not patent eligible.
As for the dependent claims, the dependent claims do recite a combination of additional elements. However, these claims as a whole, considered either independently or in combination with the parent claims, do not integrate the identified abstract idea into a practical application thereof nor do they provide an inventive concept.
For example, dependent claim 2 recites the following: “implement the change to the data center based on the recommendation, wherein the change comprises migrating a workload of the data center.” However, this is part of the abstract idea; i.e. it is a business decision to implement a recommended change which is intended to provide some type of efficiency for the business. It is a business decision to perform a typical operation (such as migrating a workload at a data center) to achieve the expected efficiency. No specific workload or mechanism of migration is being claimed. Instead, this is recited at the highest levels of generality and cannot be viewed as anything except part of the abstract idea and certainly it is not significantly more than the abstract idea.
As another example, dependent claims 3 recites following: “gather, at a configurable gather rate, power consumption data from the infrastructure device.” However, gathering data upon which the abstract idea operates is insignificant pre-solution or insignificant extra-solution activity. The type of data collected does not alter this finding. Therefore, this is not significantly more than the abstract idea.
Therefore, the Examiner does not find that these additional claim limitations integrate the abstract idea into a practical application nor provide an inventive concept. Instead, these limitations, as a whole and in combination with the already recited claim elements of the parent claims, are not significantly more than the already identified abstract idea. A similar finding is found for the remaining dependent claims.
For these reasons, the claims are not found to include additional elements that are sufficient to amount to significantly more than the judicial exception and therefore the claims are not found to be patent eligible.
Please see the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019 (found at http://www.uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials).
Claim Rejections - 35 USC § 103 (AIA )
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 non-obviousness.
Claims 1-4 and 8 are rejected under 35 U.S.C. 103 as obvious over Chen et al. (U.S. 2019/0080028 A1; hereinafter, "Chen") in view of Ramakrishnan et al. (U.S. 11,996,988 B1; hereinafter, "Ramakrishnan").
Claim 1:
Pertaining to claim 1, Chen as shown teaches the following:
A non-transitory computer-readable storage medium having stored thereon executable computer program instructions that, when executed by a processor, cause the processor to:
determine a carbon efficiency metric associated with a data center (Chen, see at least [0062]-[0068] regarding “Power usage efficiency metric” [carbon efficiency metric] of “data centers”; e.g.: “…power consumption efficiencies may be indicated by the power usage efficiency (PUE) metric [carbon efficiency metric]. A PUE of 1.5 indicates that for each Watt used by data center equipment, an additional half Watt is used for cooling, power distribution, etc. Thus, using the data center PUE, the total power demand Ptotal for a service can be determined by multiplying the power demand P for hosting the service with the data center PUE according to Equation(7)…”; see also at least Figs. 5a-e and [0014]-[0015] and [0042]) based on:
determining a power consumption of an infrastructure device of the data center (Chen, see again citations noted supra, e.g. at least Fig. 5a and [0042], teaching: “…power consumption Pi of the service can be defined as the full power usage [poser consumption] of the physical server Ps [infrastructure device] used to provide the service…”; see also Chen [0014]-[0015], teaching, e.g.: “…In an example, modeling is utilized to discover [determine] service-level power consumption [a power consumption] of individual services executing in data center(s), including cross-platform data centers such as the cloud…”; where as shown per Fig. 5a and noted per [0042] a service may execute 1:1 on a dedicated server [infrastructure device]); and
estimating a performance of the infrastructure device based on the power consumption (Chen, see again citations noted supra, including e.g. at least [0064]: “…The impact on service-level sustainability (e.g., carbon emission, resource consumption) [carbon efficiency metrics] may depend at least in part on the efficiency [performance metric] of the infrastructure [infrastructure device]…” in view of [0014]-[0115], teaching, e.g.: “…models that account for the power consumption of their services, including detailed metrics such as, but not limited to, energy consumption, carbon emissions, regulatory issues, and associated costs [yet another performance metric]…” and [0062]-[0068]: “…power consumption efficiencies [a performance metric] may be indicated by the power usage efficiency (PUE) metric. A PUE of 1.5 indicates that for each Watt used by data center equipment [infrastructure device(s)], an additional half Watt is used for cooling, power distribution, etc…”);
Although Chen teaches the above limitations, and Chen as shown supra teaches e.g. per at least [0051]-[0064]: “…the resource usage model [a machine learning model] can be used to estimate [predict] resource usage… the resource usage of requests is estimated using the resource usage model, e.g., as expressed by Equation (4) [which depends on “time interval” data; i.e. time-series data] ... For example, water and electricity consumption may depend on the cooling and power infrastructure of the data center. In some examples, the sustainability impact may be highly dependent on [is associated with] these efficiencies [e.g. “power usage efficiency metric”] (or lack thereof)…”, and Chen teaches, per at least [0013], a reason for determining his “Power usage efficiency metric” [Applicant’s “carbon efficiency metric”] includes evaluation and comparison “to improve the understanding and management of service-level sustainability” and “provid[e] representations and output (e.g., on a display or in a report format)… for providing various services for evaluation and comparison”, all of which provides motivation to a person of ordinary skill in the art to further implement a step of providing recommendations of changes to Chen’s data center for this stated purpose and also a step of providing reports of such recommendations to Chen’s display [output device] – i.e. for the purpose of enabling Chen’s goal “to improve management of service-level sustainability”, Chen may not explicitly teach the nuance as recited below even though, as noted supra, he does provide motivation to implement such steps. However, Chen in view of Ramakrishnan teaches the following:
determine a recommendation to change the data center to improve the carbon efficiency metric based on predicting, using a machine learning model and based on a time-series dataset, whether the carbon efficiency metric is associated with a temporary event; and provide the recommendation to an output device (Note the 112(b) rejection guiding claim interpretation. Ramakrishnan, see at least [3:35 - 5:16], teaching, e.g.: “…First, the RL [reinforcement learning; i.e. machine learning] based data center power consumption [carbon efficiency metric] minimizing system in embodiments herein may train a time-series utilization [time-series data utilizing] forecasting engine [machine learning model] to predict a future utilization rate [i.e. carbon efficiency metric = power consumption utilization rate] for each of a plurality of data center hardware components by group (e.g., memory hardware, processors, PCIE card or other ASIC card, fabric network paths), based on previously recorded utilization rates for each of the plurality of component groups… Such a utilization forecasting engine [machine learning model] may then be used during a monitoring period to predict future utilization rates [carbon efficiency metric value = power utilization rates] for each of these hardware component groups and to identify [predict] low-utilization windows [temporary events associated with metric of power consumption utilization] when such utilization rates may fall below a user-defined low-utilization threshold specific to each hardware component group… For example, a notification that memory hardware may be underutilized during an upcoming time window [temporary event] may indicate a need to switch off [a need for a recommended change to data center operation] one or more managed drives, or one or more storage arrays. In tum, such a power-throttling of these managed drives or storage arrays may indicate a further need to monitor and potentially reroute some of the minimal workload expected or any unexpected workload at these managed drives or storage arrays to which power may be ceased to other managed drives, storage arrays, or even data centers expected to maintain full power during the upcoming time window [temporary event], according to load-balancing instructions [recommended change to data center]… The RL based data center power consumption minimizing system in embodiments herein may then transmit a recommendation or instruction to the data center…”)
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Ramakrishnan (directed towards determining recommendations to change operations of a data center to improve power consumption efficiency based upon predictions regarding temporary events such as time windows of underutilized data center equipment) which is applicable to a known base device/method of Chen (already directed towards a device/method determining a “Power usage efficiency metric” [carbon efficiency metric] of a data center and determining its relationship to temporary events such as resource calls and allocations during various “time intervals”, etc…; e.g. per Equation (4)) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the technique of Ramakrishnan to the device/method of Chen in order to perform the limitations as claimed, i.e. determine recommendations to change parameters of Chen’s data center, as suggested by Ramakrishnan, based on Chen’s own estimates [predictions] of resource usage, using his usage models [machine learning] by a service over time intervals [time series data], etc… and then report such recommendations, because Chen and Ramakrishnan are analogous art in the same field of endeavor (at least G06F 11/3062) and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claims 2:
Chen/ Ramakrishnan, teach the above limitations upon which this claim depends. Furthermore, as shown Ramakrishnan, teaches the following: … implement the change to the data center based on the recommendation, wherein the change comprises migrating a workload of the data center (Ramakrishnan, see citations noted supra, including again at least [3:35 – 5:16], teaching recommendation to move workload from underutilized infrastructure to alternative hardware components; e.g.: “…The RL based data center power consumption minimizing system may also transmit the optimal load-balancing instruction to divert any expected workload away from the powered down hardware component(s) toward fully powered alternative hardware components…”)
Claims 3:
Chen/ Ramakrishnan, teach the above limitations upon which this claim depends. Furthermore, as shown Ramakrishnan, teaches the following: … determine the power consumption of the infrastructure device, to: gather, at a configurable gather rate, power consumption data from the infrastructure device. (Note 112(b) rejection guiding claim interpretation. Ramakrishnan, see citations noted supra, including again also at least [5:16 – 50]; e.g. “…Data storage system/data center(s) 150 hardware components operational telemetry measurements including hardware component utilization rates, performance analytics, available load-balancing actions, user-specified low-utility threshold values, and user specified Quality of Service (QoS) requirements may be gathered during routine monitoring periods from a plurality of data storage system/data center(s) 150 hardware components, and the data storage system/data center(s) 150 managing such components to the UEM platform 100 executing the RL based data center power consumption minimizing system 180…”)
Claims 4:
Chen/Ramakrishnan teach the above limitations upon which this claim depends. Furthermore, as shown Chen teaches the following: …determine the carbon efficiency metric associated with the data center further based on: determining a second power consumption of a different, second infrastructure device of the data center; and estimating a second performance of the second infrastructure device based on the second power consumption (Chen, see again citations noted supra, e.g. [0042]-[0044] in view of [0060]-[0064], teaching that the “Power Usage Efficiency (PUE)” of the data center is determined from the aggregate determined power consumption of the infrastructure devices on which the services are executing, e.g. power consumption of first and second infrastructure devices needed to run the services. Note per [0044]: “…Power consumption of a physical server [infrastructure device] can be separated into two parts. One part is the idle power (e.g., power usage when the server is idle), which can be determined based on the configuration (e.g., processor model, number of processors and speed, memory type and size, network interface cards, and power supply). Another part is dynamic power, which can be determined based on resource activity levels, (e.g., CPU utilization)…” Furthermore, as already noted per citations provided supra, Chen determines “energy consumption” [a performance metric], “carbon emissions” [another performance metric], etc… for each operating infrastructure device, e.g. including first and second devices. The “energy consumption” and “carbon emissions” [performance metrics] of each device are both based on the “power consumption” of each respective device)
Claims 8:
Chen/Ramakrishnan teach the above limitations upon which this claim depends. Furthermore, Chen/Ramakrishnan teaches the following: …wherein the infrastructure device comprises a server (Chen, see citations noted supra, e.g.: [0042] physical server Ps used to provide the service), and wherein the recommendation identifies a workload suitable to be migrated from the server to a different, second server in the data center to improve the carbon efficiency metric (Ramakrishnan, see citations noted supra, including again at least [3:35 – 5:16], teaching recommendation to move workload from underutilized infrastructure to alternative hardware components; e.g.: “…The RL based data center power consumption minimizing system may also transmit the optimal load-balancing instruction to divert any expected workload away from the powered down hardware component(s) toward fully powered alternative hardware components…”)
Claims 5-7, 9-13, and 15-16 are rejected under 35 U.S.C. 103 as obvious over Chen in view of Ramakrishnan in view of Tung (U.S. 2009/0201293 A1; hereinafter, "Tung").
Claim 5:
Although Chen/Ramakrishnan teach the above limitations upon which this claim depends, they may not explicitly teach the nuance as recited below. However, regarding this feature, Chen/Ramakrishnan in view of Tung teaches the following:
…determine the recommendation further based on: identifying, based on operational data, a different, second infrastructure device estimated to have a greater carbon efficiency metric than the carbon efficiency metric associated with the data center. (Tung, see at least [0108]-[0111], e.g.: “…At block 620 the system 100 may identify the first component [first solution] of the data center equipment, such as a server. At block 630 the system 100 may search the historical dataset for a component [a different, second infrastructure device] that is more efficient estimated to have a greater carbon efficiency metric], either cost-wise or carbon
emissions-wise, than the component currently used in the data center… at block 650 the system 100 may add the more efficient component to a recommended configuration…”)
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Tung which is applicable to a known base device/method of Chen/Ramakrishnan to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the technique of Tung to the device/method of Chen/Ramakrishnan in order to perform the limitation in question because Chen/Ramakrishnan and Tung are analogous art in the same field of endeavor (at least G06F 11/3062) and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claims 6:
Chen/Ramakrishnan/Tung teach the above limitations upon which this claim depends. Furthermore, Chen/Ramakrishnan in view of Tung teaches the following: The non-transitory computer-readable storage medium of claim 5, wherein the executable computer program instructions, when executed by the processor, cause the processor to determine the recommendation further based on: determine the recommendation further based on: determining that a capital expense of the second infrastructure device will be offset by an operational cost reduction associated with the second infrastructure device (Tung, see citations noted supra, e.g. per at least [0108]-[011], e.g.: “…At block 670 the system 100 may display a comparison of the current configuration and the recommended configuration to the user A 120A. The comparison may display to the user A 120A the cost savings and the carbon emissions reductions achieved by the current data center configuration of the user A 120A and those achieved by the recommended configuration. At block 680 the system 100 may display the future forecasted energy and cost scenarios of the current configuration of the user A 120A and the configuration recommended by the system 100. At block 690 the system 100 may offer the user A 120A the option to purchase the equipment necessary to upgrade their data center to the recommended configuration…”).
Claims 7:
Chen/Ramakrishnan/Tung teach the above limitations upon which this claim depends. Furthermore, Chen/Ramakrishnan in view of Tung teaches the following: …wherein the recommendation identifies a second infrastructure device as suitable to replace the infrastructure device in the data center to improve the carbon efficiency metric (Tung, again see citations noted supra, e.g. Tung, per at least [0108]-[0111], e.g.: “…At block 620 the system 100 may identify the first component of the data center equipment, such as a server. At block 630 the system 100 may search the historical dataset for a component [a different, second infrastructure device] that is more efficient estimated to have a greater carbon efficiency metric], either cost-wise or carbon emissions-wise, than the component currently used in the data center… at block 650 the system 100 may add the more efficient component to a recommended configuration…”).
Claim 9
Pertaining to claim 9, Chen as shown teaches the following:
A system, comprising: a processor to perform processing of operations (Chen, see at least Fig. 1, 2, [0018], [0029], [0073] teaching such “processor”); and
a memory to store data (Chen, see at least Figs. 1, 2, [0018], [0029], teaching such “Each of the computing devices may include memory, storage, etc….”), including operational data associated with one or more solutions (Chen, see citations noted supra, in view of at least [0023] and [0034]-[0060] teaching the system stores data such as CPU usage, transactions, and resource usage, etc… [operational data] which is used to calculate power use of infrastructure devices, e.g. servers, of data centers), a time-series dataset associated with a data center (Chen, see citations noted supra, e.g. per [0023] and [0052] “…The analyzer 105 may have access to at least one source 115 of information for the data center(s)… The source 115 may include databases for providing historical information, and/or monitoring for providing real-time data….” And “time interval” of request rate of transaction types are known), and instructions that, when executed, cause the processor to:
determine a carbon efficiency metric associated with a data center (Chen, see at least [0062]-[0068] regarding “Power usage efficiency metric” [carbon efficiency metric associated with a data center] of “data centers”; e.g.: “…power consumption efficiencies may be indicated by the power usage efficiency (PUE) metric [carbon efficiency metric]. A PUE of 1.5 indicates that for each Watt used by data center equipment, an additional half Watt is used for cooling, power distribution, etc. Thus, using the data center PUE, the total power demand Ptotal for a service can be determined by multiplying the power demand P for hosting the service with the data center PUE according to Equation(7)…”; see also at least Figs. 5a-e and [0014]-[0015]);
[…]
Although Chen teaches the above limitations, Chen may not explicitly teach the below recited nuance. However, regarding this feature, Chen in view of Tung teaches the following:
determine a recommendation to change the data center to improve the carbon efficiency metric based on: identifying, based on the carbon efficiency metric and the
operational data, a solution from the one or more solutions; […] and provide the recommendation to an output device. (Tung, see at least [0108]-[0111], e.g.: “…At block 630 the system 100 may search the historical dataset for a component [identify a solution from one or more solutions] that is more efficient [based on carbon efficiency metric and operational data], either cost-wise or carbon emissions-wise, than the component currently used [i.e. current use is a type of operational data] in the data center… at block 650 the system 100 may add the more efficient component [i.e. the solution] to a recommended configuration…”)
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Tung which is applicable to a known base device/method of Chen to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the technique of Tung to the device/method of Chen in order to perform the limitation in question because Chen and Tung are analogous art in the same field of endeavor (at least G06F 11/3062) and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Although Chen/Tung teaches the above limitations, and Chen as shown supra teaches e.g. per at least [0051]-[0064]: “…the resource usage model [a machine learning model] can be used to estimate [predict] resource usage… the resource usage of requests is estimated using the resource usage model, e.g., as expressed by Equation (4) [which depends on “time interval” data; i.e. time-series data] ... For example, water and electricity consumption may depend on the cooling and power infrastructure of the data center. In some examples, the sustainability impact [e.g. per [0064] carbon emission or resource consumption, where resource consumption is e.g. per [0003] electricity usage, and per [0031] energy usage, water usage, and other natural resource consumption] may be highly dependent on [is associated with] these efficiencies [e.g. “power usage efficiency metric”] (or lack thereof)…”; all of which suggests Chen’s “Power usage efficiency metric” [Applicant’s “carbon efficiency metric”] is a “usage” which is intended to be estimated [predicted] over Chen’s time intervals, e.g. to enable Chen’s teaching at [0013]: “…The systems and methods may also provide representations and output (e.g., on a display or in a report format) of the power consumption for providing various services for evaluation and comparison.”, nonetheless, he may not explicitly disclose all the nuances as recited below. However, Chen/Tung in view of Ramakrishnan teaches the following:
and predicting, using a machine learning model and based on the time-series dataset, whether the carbon efficiency metric is associated with a temporary event; (Note the 112(b) rejection guiding claim interpretation. Ramakrishnan, see at least [3:35 - 5:16], teaching, e.g.: “…First, the RL [reinforcement learning; i.e. machine learning] based data center power consumption [carbon efficiency metric] minimizing system in embodiments herein may train a time-series utilization [time-series data utilizing] forecasting engine [machine learning model] to predict a future utilization rate [i.e. carbon efficiency metric = power consumption utilization rate] for each of a plurality of data center hardware components by group (e.g., memory hardware, processors, PCIE card or other ASIC card, fabric network paths), based on previously recorded utilization rates for each of the plurality of component groups… Such a utilization forecasting engine [machine learning model] may then be used during a monitoring period to predict future utilization rates [carbon efficiency metric value = power utilization rates] for each of these hardware component groups and to identify [predict] low-utilization windows [temporary events associated with metric of power consumption utilization] when such utilization rates may fall below a user-defined low-utilization threshold specific to each hardware component group… For example, a notification that memory hardware may be underutilized during an upcoming time window [temporary event] may indicate a need to switch off [a need for a recommended change to data center operation] one or more managed drives, or one or more storage arrays. In tum, such a power-throttling of these managed drives or storage arrays may indicate a further need to monitor and potentially reroute some of the minimal workload expected or any unexpected workload at these managed drives or storage arrays to which power may be ceased to other managed drives, storage arrays, or even data centers expected to maintain full power during the upcoming time window [temporary event], according to load-balancing instructions [recommended change to data center]… The RL based data center power consumption minimizing system in embodiments herein may then transmit a recommendation or instruction to the data center…”)
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Ramakrishnan (directed towards determining recommendations to change operations of a data center to improve power consumption efficiency based upon predictions regarding temporary events such as time windows of underutilized data center equipment) which is applicable to a known base device/method of Chen (already directed towards a device/method determining a “Power usage efficiency metric” [carbon efficiency metric] of a data center and determining its relationship to temporary events such as resource calls and allocations during various “time intervals”, etc…; e.g. per Equation (4)) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the technique of Ramakrishnan to the device/method of Chen in order to perform the limitations as claimed, i.e. determine recommendations to change