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 .
Remarks
The present application having Application No. 18/759,468 filed on 01/31/2024 presents claims 1-20 for examination.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in 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 that, in preparing responses, the applicant 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.
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.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The present application claims the benefit of Indian patent application 202341060929, filed 11 September 2023. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Drawings
The applicant’s drawings submitted are acceptable for examination purposes.
Information Disclosure Statement
As required by M.P.E.P. 609, the applicant’s submissions of the Information Disclosure Statements (IDSs) submitted on 10/04/2024, 03/19/2025, 10/29/2025 and 05/04/2026 are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending.
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.
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 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "a predicted level of greenness…being based on a current level of greenness…and a predicted scale up factor” in lines 7-9 renders claims as indefinite because claim does not particularly point out and distinctly claim the scope of the recited “predicted level of greenness” and “a current level of greenness.” The specification and claims do not define what either “level of greenness” measures. The meanings and copes of both “current level of greenness” and “predicted level of greenness” are unclear. In particular, claim 1 does not identify the metric or measurement that constitutes or indicates level of greenness. For example, it is unclear whether level of greenness is a percentage of the service’s energy consumption supplied by renewable energy, a green quotient calculated from green energy consumption relative to total energy consumption, a data-center green quotient associated with execution of the service, a node-level green rating, a carbon-emissions metric, a renewable-energy-capacity metric, or another environmental metric. The claim also does not make it clear how a greenness value for individual nodes, data centers, workloads, or service replicas is combined to produce greenness value for the first service. Claim 1 requires that the predicted level of greenness be “based on a current level of greenness for the first service and a predicted scale up factor,” but doe not identify the metric constituting level of greenness or the relationship by which that metric and the predicted scale-up factor determine the predicted level of greenness. It is unclear how the current level of greenness and predicated scale-up factor are used to calculate the predicted level of greenness.
The specification describes multiple potentially applicable and non-equivalent metrics, including a service green quotient based on green-energy consumption divided by total energy consumption, an application green quotient based on a delta between data-center green-quotient values, and node-level GQ values. Consequently, a POSITA would not be reasonably apprised of the scope of the claimed current and predicted levels of greenness, or how to determine whether a particular implementation falls within the claim scope.
Furthermore, claim 1 recites “determine the predicted level of greenness…satisfies first threshold”, in lines 10-11, also renders this claim vague and indefinite. It is not clear whether “satisfies” means greater than, greater than or equal to, less than, less than or equal to, or any other comparison.
As per claims 14 and 20, they recite similar limitations discussed above with respect to claim 1. Claims 14 and 20 are also rejected under 35 USC 112(b) as indefinite for the same reasons set forth above with respect to claim 1.
The dependent claims 2-13 and 15-19 are also rejected by virtue of their dependency from rejected independent claims 1 and 14.
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 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, 3-6, 14, 16-18, and 20 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Dutta et al. (US 2025/0037142 A1) (hereinafter Dutta) in view of Choochotkaew et al. (US 2022/0318060 A1) (hereinafter Choochotkaew) and further in view of McGuire et al. (US 2022/0398515 A1) (hereinafter McGuire).
As per claim 1, A computing device comprising: one or more memories; one or more processors communicatively coupled to the one or more memories, the one or more processors being configured to (e.g. Dutta: [Fig. 4] [0034] “processor set 410” including “processing circuitry 420, ” “cache 421,” “volatile memory 412,” and “persistent storage 413.” [0037] Computer readable program instructions loaded onto computer 401 to cause the series of operations steps to be performed by the processor set and thereby effect a computer-implemented method): determine, based on the predicted occurrence of the scale event for the first service, a predicted level of greenness for the first service (e.g. Dutta: workload allocation engine identifies servers, VMs, clusters, and data centers, predicts workload-specific carbon emissions, and assigns workloads to selected clusters, [0005, 0007, and 0016-0018]. Dutta teaches predicting a service/workload-specific environmental value. Dutta states: “For each of the clusters and the workload, carbon emissions generated by a particular cluster and for the workload are predicted”, [0005]. Dutta also teaches “In 360, carbon emission/carbon footprint Cw is predicted for at least one VM/server/cluster/data center combinations” [0025]. Also see [0020]. Dutta allocates those emissions and energy consumption to an individual service: “all emissions e.g., carbon of a data center 100 must be allocated to individual services” [0019]. “all IT devices…and the power consumed therefrom should be allocated to a particular service” [0021]. Thus, Dutta teaches predicting a workload/service-specific projected environmental value.), perform, based on whether the predicted level of greenness for the first service, a first action on a first workload of the first service (e.g. Dutta: teaches a workload action based on predicted environmental value: “The workload is assigned to the particular cluster based upon the predicted carbon emission associated with the particular cluster, and the workload is performed by the particular cluster” [0005]. Dutta further teaches “based on the carbon emission Cwi, predicted for the selected workload Wi..a particular server cluster, as a low-carbon environment, is selected to perform the selected workload Wi” [0029].).
Dutta does not expressly teach “predict an occurrence of a scale event for a first service,” “the predicted level of greenness being based on a current level of greenness…and a predicted scale up factor,” “determine whether the predicted level of greenness for the first service satisfies a first threshold” and performing a first action…based on the determination.
However, Choochotkaew discloses “predict an occurrence of a scale event for a first service” (e.g. Choochotkaew: teaches predictive microservice scaling. It states: “method for scheduling and scaling a cloud system for microservice applications is provided including…generating a model for predicting resource usage among the plurality of nodes and automatically deciding on a number of replicated containers” [0003]. It further teaches “As demand for each microservice increases or decreases, the resources…assigned to that microservice may also be increased or decreased as needed to meet the demand” [0029]. The planner creates a plan including: “the number of replicated containers, the nodes to bind, resource usage requests, and a weight for each replica container” [0037].); “the predicted level of greenness being based on… a predicted scale up factor” (e.g. Choochotkaew: teaches the predicted scale-up input. It teaches “mapping each microservice to a clustered group and expected load from measured features or normal behaviors for the initial deployment” [0044]. It also teaches finding a model: “to predict target resource usage for each group” [0042]. Choochotkaew then performs scaling: “the scheduler 78 performs scaling on ReplicSet 88” [0060]; and teaches “predicting replicas, node to bind and weight for each replica…scaling the system…scheduling the system” [0099]); “perform…a first action on a first workload of the first service” (e.g. Choochotkaew teaches workload action including scaling and scheduling: “Scale, schedule, and balance deployment complying to the decision by scaler, scheduler, and load balancer” [0067]. It further teaches “patch replica-set controller,” “bind the scheduled container and node,” and “leverage routing mechanism for workload partitioning” [0082-0084].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta and Choochotkaew. Dutta predicts service/workload-specific carbon footprint using workload and cluster variables, energy consumption, PUE, carbon intensity, and tracked time-series data [0020,0024-0026]. Choochotkaew predicts microservice resource use, expected load, replica count, and node binding [0037-0038, 0042-0045, and 0098-0099]. A POSITA would have recognized that an expected increase in replica count predictably changes processor usage, memory usage, cluster occupancy, total workload energy consumption, and thus the projected carbon/environmental value of the service. It would have been obvious to use Choochotkaew’s forecast replica/resource configuration as input to Dutta’s projected workload environmental calculation. The predicted result is a forecast environmental calculation for the service at its expected scaled configuration rather than at only its current configuration.
The combination of Dutta and Choochotkaew does not expressly disclose “the predicted level of greenness being based on a current level of greenness for the first service,” “determine whether the predicted level…satisfies a first threshold” and “perform, based on whether the predicted level of greenness for the first service satisfies the first threshold, a first action…”. Specifically, the combination doe not expressly teach comparing a predicted service green metric with a threshold, or threshold-controlled green-energy placement.
However, McGuire teaches “the predicted level of greenness being based on a current level of greenness for the first service” (e.g. McGurie teaches current renewable-energy and workload-energy inputs: “energy consumption of the DC,” “renewable energy capacity,” “workload capacity and energy consumption” [0022]. McGurie also teaches predictive placement: “predictively places one or more workloads…based on anticipated power generation and capacity” [0025]. Its placement prediction uses: “DC capacity, anticipated power generation of a workload, workload data e.g., size, estimated power consumption…type of energy used to power DC e.g., green energy, and number of workloads within a DC” [0026]. Thus, McGuire teaches current and future/predicted renewable-energy/workload-energy factors for calculating service greenness forecast. Combining those data with Choochotkaew’s forecast replicas yields the projected green-energy portion of the service’s scaled energy demand.); “determine whether the predicted level…satisfies a first threshold” (e.g. McGuire states: “the monitored DC features and tagged DC data enable component 122 to identify if a DC is above or below a predetermined threshold” [0022]. The relevant data include: “energy consumption of the DC,” “renewable energy capacity,” “workload capacity and energy consumption” [0022].); and “perform, based on whether the predicted level of greenness for the first service satisfies the first threshold, a first action…” (e.g. McGuire teaches “Responsive to identifying a DC is below a predetermined threshold, component 122 schedules and/or actively migrates workloads or a portion of a workload to DCs with higher renewable energy capacities” [0022]. It also teaches “migrating…workloads between one or more data centers automatically to maximize a usage of renewable energy based on a predetermined threshold core of input power and a combination of renewable energy sources” [0004]. Also see [0033].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply McGuire’s predictive renewable-energy placement and predetermined-threshold migration policy to the Dutta-Choochotkaew scaled-workload configuration. Dutta supplies projected workload-specific environmental impact, Choochotkaew supplies forecast resource/replica demand, and McGuire supplies current/future renewable-energy capacity, energy-source type, anticipated generation, threshold comparison, and workload migration based on the comparison. The predictable result is a system that forecasts the service’s scaled energy demand, evaluates the renewable-energy portion available for the at forecast demand, compares that projected green condition with a threshold, and schedules or migrates the workload accordingly.
As per claim 3, the combination of Dutta, Choochotkaew and McGuire discloses The computing device of claim 1 [See rejection to claim 1 above], McGuire further discloses wherein the predicted level of greenness for the first service does not satisfy the first threshold, and wherein the first action comprises rescheduling the first workload to a green node (e.g. McGuire teaches” “Responsive to identifying a DC is below a predetermined threshold, component 122 schedules and/or actively migrates workload to DCs with higher renewable energy capacity” [0022]. Also see [0027].).
As per claim 4, the combination of Dutta, Choochotkaew and McGuire discloses The computing device of claim 1 [See rejection to claim 1 above], discloses wherein to predict the occurrence of the scale event for the first service, the one or more processors are configured to execute a machine learning model (e.g. McGuire states “via analytics in a prediction model, component 122 generates predictions for workloads, wherein component 122 collects data over time, then uses machine learning techniques to generate predictions for workloads” [0026]. Also see [0034]. Choochotkaew further discloses, using a model, predicting microservice resource use and expected load and automatically deciding a number of replicated containers. See [0003-0004, 0025, 0037-0038, 0042-0045, and 0098-0099]).
As per claim 5, the combination of Dutta, Choochotkaew and McGuire discloses The computing device of claim 1 [See rejection to claim 1 above], Choochotkaew further discloses wherein to predict the occurrence of the scale event for the first service, the one or more processors are configured to: predict a workload replica count for the first service; and determine that the predicted workload replica count for the first service is higher than a current workload replica count for the first service (Choochotkaew teaches: “automatically deciding on a number of replicated containers” [0003]. It also teaches that planner output includes: “the number of replicated containers” [0037]. Choochotkaew determines replica count according to: “application requests and current usage status of a cluster” [0003]. It then executes scale-out through the ReplicaSet: “the scheduler 78 performs scaling on ReplicaSet 88” [0060].).
As per claim 6, the combination of Dutta, Choochotkaew and McGuire discloses The computing device of claim 5 [See rejection to claim 5 above], Choochotkaew further discloses wherein the predicted scale up factor is based on the predicted workload replica count for the first service and the current workload replica count for the first service (e.g. Choochotkaew teaches predicted resource use, expected load, current cluster state, and a further number of replicated container [0003, 0037-0038, 0042-0045, 0098-0099]. The scale-up factor is based on the relationship between that future replicated-container count and the current replica deployment.).
As per claims 14, 16, 17 and 18, these are method claims having similar limitations as cited in system/device claims 1, 3, 5, and 6, respectively. Thus, claims 14, 16, 17 and 18 are also rejected under the same rationale as cited in the rejection of rejected claims 1, 3, 5, and 6, respectively.
As per claim 20, this is a computer readable media claim having similar limitations as cited in system claim 1. Thus, claim 20 is also rejected under the same rationale as cited in the rejection of rejected claim 1.
Claims 2, 11-13 and 15 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Dutta in view of Choochotkaew and McGuire and further in view of Arsovski et al. (US 2010/0228861 A1) (hereinafter Arsovski).
As per claim 2, the combination of Dutta, Choochotkaew and McGuire discloses The computing device of claim 1 [See rejection to claim 1 above], wherein the predicted level of greenness for the first service satisfies the first threshold, and wherein the first action comprises rescheduling the first workload to a non-green node (e.g. McGuire discloses migrating workloads between DCs based on a predetermined threshold [0022-0024] [0031]. McGuire also discloses hybrid environment where both renewable energy and non-renewable energy sources are available [0012] [0033].).
The combination implies but does not expressly disclose wherein the first action comprises rescheduling the first workload to a non-green node.
However, Arsovski discloses wherein the first action comprises rescheduling the first workload to a non-green node (e.g. Arsovski: disclose GWAE monitors changing conditions to determine when work in progress should be moved from one server farm to another…” [0040]. Arsovski teaches moving a workload that does not require fully renewable processing from renewable capacity when a new workload requires the renewable resource: “Should a new workload requiring the renewable resource be submitted, a workload which does not require fully renewable processing may be checkpointed and moved to the next available initiator as ranked” [0053]. Arsovski teaches that renewable suppliers are ranked above fossil-fuel suppliers…[0051].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Arsovski into the combination of Dutta, Choochotkaew and McGuire because migrating or moving workload to lower-ranked or non-green resource as taught by Arsovski provides means for risk mitigation and improves processing reliability (see Arsovski [0037]). The predictable result also improves overall renewable-resource utilization.
As per claim 11, the combination of Dutta, Choochotkaew and McGuire discloses The computing device of claim 1 [See rejection to claim 1 above], wherein the one or more processors are further configured to: predict an occurrence of a scale event for a second service (e.g. Choochotkaew teaches predictive scaling for each microservice application. It teaches: “generating a model for predicting resource usage among the plurality of nodes” and “automatically deciding on a number of replicated containers, node bindings, and weight for each replicated container according to application requests and current usage status of a cluster” [0003]. Choochotkaew further teaches that each microservice may be independently scaled according to demand: “As demand for each microservice increases or decreases, the resources…assigned to that microservice may also be increased or decreased as needed to meet the demand” [0029]. Thus, Choochotkaew discloses predicting an occurrence of a scale event for each of multiple services, including the claimed second service.); determine, based on the predicted occurrence of the scale event for the second service, a predicted level of greenness for the second service, the predicted level of greenness for the second service being based on a current level of greenness for the second service and a predicted scale up factor for the second service (e.g. Dutta teaches prediction of a workload/service-specific environmental value. It predicts carbon emissions for a workload at candidate VM/server-cluster/data centers [0005, 0018, and 0024-0026]. Choochotkaew teaches forecast resource use, expected load, replicated-container count, and node binding for a microservice [0003, 0037-0038, 0042-0045, and 0098-0099]. McGuire further teaches current and forecast renewable-energy/workload-energy conditions use for predictive workload placement, including: “energy consumption of the DC,” “renewable energy capacity,” “workload capacity and energy consumption” [0022]; and “DC capacity, anticipated power generation of a workload, workload data e.g., size estimated power consumption…type of energy used to power the DC e.g., green energy, and number of workloads withing a DC” [0026]. The same claim 1 combination of references applies independently to a second service. A skilled artisan would use the second service’s forecast replica/resource configuration from Choochotkaew with Dutta’s projected workload-specific environmental calculation and McGuires’ renewable-energy data to calculate the forecast green condition for the second service.). Thus, the combination determines forecast environmental/green conditions for both the first service and the second service.
The combination does not expressly disclose, but Arsovski teaches compare, the predicted level of greenness for the first service to the predicted level of greenness for the second service; and assign a first priority to the first service and a second priority to the second service for use in scaling based on the comparison (e.g. Arsovski teaches that power providers and server farms are ranked according to renewable versus fossil-fuel energy source and generation mix: “Suppliers operating from renewable resources are ranked above suppliers operating from fossil fuels” [0051]. It further teaches “Where suppliers operate using a mix of renewable and non-renewable sources, supplier ranking is influenced by the percentage mix of the sources” [0051]. Most significantly, Arsovski teaches “workloads jobs marked for assignment on only those computing resource which are 100 percent renewable will be placed on nodes which have the required energy source profile.” “Should a new workload requiring the renewable resource be submitted, a workload which does not require fully renewable processing may be checkpointed and moved to the next available initiator as ranked” [0053]. Arsovski also calculates a server farm net green rating from power-provider green ratings and their respective power contribution to the server farm [0063-0070]. Arsovski also teaches workload priority tracking and priority based workload reassignment. When a server farm’s energy usage exceeds threshold, the system transfers a low priority job to GWAE for reassignment…GWAE begins to checkpoint lower priority workload for re-assignment to other SFs [0060, 0062-0063]. Thus, Arsovski implicitly teaches that a workload requiring 100% renewable processing has a higher priority than a workload that does not require fully renewable processing. When the higher-green-requirement workload arrives, the lower-green-requirement workload is moved to another available lower-ranked resource or less green resource. In the combined system, the forecast service-level green conditions of the first and second services are compared. The service having lower forecast green conditions or a greater need for renewable-backed capacity to satisfy its green target, is assigned a higher priority. The service/workload without fully renewable capacity is assigned a lower priority and may be moved from the renewable resource when high priority job arrives.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Arsovski’s priority-based reassignment and green-resource ranking mechanism into the combination of Dutta, Choochotkaew and McGuire.
Dutta teaches forecast work-load specific environmental impact. Choochotkaew teaches forecast microservice resource use, replica count, and node binding. McGurie teaches predictive renewable-energy-aware workload placement/migration based on anticipated power generation, renewable-energy capacity, workload energy consumption, energy-source type, and threshold conditions.
A POSITA would have been motivated to apply this known environmental-priority based policy into the combination because the predictable result is that scarce green/renewable capacity is allocated preferentially to the service with greater renewable-energy need, while a service without renewable-energy need is assigned a lower priority for that scarce green capacity. This would improve overall compliance with workload-specific renewable-energy constraints, reduce projected carbon footprint, and avoid assigning a new high-green-requirement workload to a non-green resources when an existing lower-green-requirement workload could be moved.
As per claim 12, the combination of Dutta, Choochotkaew, McGuire and Arsovski discloses The computing device of claim 11 [See rejection to claim 1 above], Arsovski further discloses wherein the first priority is lower than the second priority, and wherein the one or more processors are further configured to reserve, based on the first priority and the second priority, one or more non-green or less-green resources for one or more replicas of the first service (Arsovski: [0051, 0053, 0060-0063].).
As per claim 13, the combination of Dutta, Choochotkaew, McGuire and Arsovski discloses The computing device of claim 11 [See rejection to claim 1 above], Arsovski further discloses The computing device of claim 11, wherein the first priority is lower than the second priority, and wherein the one or more processors are further configured to move the first workload to non-green or less-green resources and reserve vacated green resources for one or more replicas of a second workload of the second service (Arsovski: “Should a new workload requiring the renewable resource be submitted, a workload which does not require fully renewable processing may be checkpointed and moved to the next available initiator as ranked” [0053]. Also see [0051, 0053, 0060-0063].).
As per claim 15, this is a method claim having similar limitations as cited in system/device claim 2. Thus, claim 15 is also rejected under the same rationale as cited in the rejection of rejected claim 2.
Claim 7 is rejected under AIA 35 U.S.C. 103 as being unpatentable over Dutta in view of Choochotkaew and McGuire and further in view of Abdollahian Noghabi et al. (US 2023/0061136 A1) (hereinafter Noghabi).
As per claim 7, the combination of Dutta, Choochotkaew and McGuire discloses The computing device of claim 1 [See rejection to claim 1 above], but does not expressly disclose wherein the first threshold is based on a service level agreement requirement applicable to the first service.
However, Noghabi discloses wherein the first threshold is based on a service level agreement requirement applicable to the first service (e.g. Noghabi teaches: “Each software implemented renewable energy policy may include the requirements and/or target metrics for renewable energy use for the associated server-executed software program 24” [0037]. It also teaches “the authorized user may also set the target renewable energy usage 50 goal of the server-executed software program” [0040].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement McGuire’s renewable-energy threshold as a service-specific renewable-energy policy target as taught by Noghabi. The predictable result is an SLA-like green-energy threshold associated with the particular service and used in the service’s placement/scaling decisions.
Allowable Subject Matter
Claims 8-10 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, and if independent claims are amended to overcome the outstanding 112(b) rejection set forth above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hiren Patel whose telephone number is (571) 270-3366. The examiner can normally be reached on Monday-Friday 9:30 AM to 6:00 PM.
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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
If attempts to reach the above noted Examiner by telephone are unsuccessful, the Examiner’s supervisor, April Y. Blair, can be reached at the following telephone number: (571) 270-1014. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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August 19, 2026
/HIREN P PATEL/Primary Examiner, Art Unit 2196
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