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 .
The Office Action is in response to claims filed on 4/6/2026 where claims 1, 3-8, 10-15, and 17-20 are pending and ready for examination.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claims 3-7, 10 – 14, and 17-20 are 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.
Applicant's arguments filed 4/6/2026 have been fully considered but they are not persuasive.
Bernat as set forth in rejection below teaches provisioning/deprovisioning because it provides scalability of node resources tied to workloads, sustainability, and power.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 8, and 15 are rejected under 35 USC 103 as being unpatentable over Bernat (US 20240193617) in view of Talluri et al., “FootPrinter: Quantifying Data Center Carbon Footprint", May 7th, 2024
Regarding claim 1, Bernat discloses a processor-implemented method for managing a computer network based on energy sources powering nodes of the computer network, the method comprising:
identifying one or more power sources powering each of the nodes comprising the computer network (Bernat; Bernat teaches determines a GHG footprint (i.e. carbon footprint) for a selected edge device by distinguishing among different energy sources supplying the device, including batteries, on-site generation, grid electricity, uninterruptable power supplies, and environmental control systems, and by accounting for the differing carbon fractions of those energy sources. Distinguishing and accounting for the carbon fraction of each supplying energy source for purposes of determining the GHG footprint is functionality equivalent to identifying one or more power sources powering the node, because the GHG footprint cannot be determined without identifying which power sources are supplying the node;
See e.g. [0072] “ Furthermore, the selected edge device 406A may be entirely or partially powered by stored energy (e.g., batteries) or generated on-site from sources like fuel-cells. In such examples, the GHG footprint varies according to the carbon fraction of different energy sources (e.g., green hydrogen fuel, blue hydrogen fuel, brown hydrogen fuel, fossil fuels, etc.). The GHG footprint of batteries also includes the transitive GHG emissions from electricity that was used to charge the batteries. The GHG density service also considers how the use of an Uninterruptible Power Supply (UPS) and/or changes to the Heating, Ventilation, Airing, and/or Cooling (HVAC) within an environment of the selected edge device 406A effects the GHG footprint
See e.g. [0048]);
calculating a carbon footprint associated with the nodes of the computer network based on the one or more power sources (Bernat ([0072]; Bernat calculates a GHG footprint for a selected edge device by accounting for the carbon fraction of the specific power sources. Determining the device’s GHG footprint based on the carbon fractions of its supplying energy sources is functionality equivalent to calculating a carbon footprint associated with a node based on one o more power sources;
See e.g. [0048]);
receiving a sustainability goal (Bernat; [0092], [0093]; Bernat discloses operations to increase sustainability and correcting deviations from expected emissions. This constitutes receiving and acting upon a sustainability objective, which is functionally equivalent to receiving a sustainability goal;
See e.g. [0048]);
provisioning one or more nodes of the computer network based on the sustainability goal and the carbon footprint associated with the nodes goal (Bernat; [0092], [0093]; Bernat discloses assigning, adjusting, and reassigning workloads to edge devices based on GHG reports in order to increase sustainability. Assigning or reassigning workloads to specific nodes based on emissions constitutes provisioning nodes based on a sustainability objective and the carbon footprint associated with the nodes;
See e.g. [0048]);
scaling a size of the computer network by provisioning or de-provisioning nodes of the computer network based on the one or more power sources of the nodes and the sustainability goal(Bernat teaches determining workload metrics including compute scalability and component-usage estimates for edge-device execution. See Bernat at [0067], [0074]. Bernat further teaches estimating GHG emissions for a selected edge device executing a workload based on computes scalability parameters, including core scalability factor, GPU scalability factor, and frequency scalability factor, along with device telemetry such as processor cores and processor frequency. See Bernat at [0074]. Bernat also accounts for renewable availability and carbon characteristics of the power/energy sources suppling the edge device. See Bernat at [0072], [0074].
Thus, Bernat is evaluating scalable node resources in view of workload execution, power-source-derived emissions, and sustainability. Bernat then assigns, adjusts, and reassigns workloads to edge devices based on GHG/emissions reports in order to increase sustainability. See Bernat at [0092]-[0093]. Assigning, adjusting, or reassigning a workload provisions or reprovisions the selected edge nodes because it changes which nodes are used and how much scalable node resource capacity is allocated to the workload.
Accordingly, Bernat teaches scaling the effective size of the computer network by provisioning or deprovisioning nodes, because Bernat changes the active node-resource footprint used for workload execution based on sustainability and power-source derived emissions information. Bernat provides scalability of node resources tied to workloads, sustainability, and power
See e.g. [0067] In some examples, the emissions estimator circuitry 404 includes means for determining workload metrics (e.g., compute scalability and/or component usage estimates). For example, the means for determining workload metrics may be implemented by sandbox circuitry 502.
See e.g. ([0065] “... Workload metrics generated by the example sandbox circuitry 502 may be categorized as compute scalability metrics or per component usage metrics.”
See e.g. [0074] “... Examples of compute scalability parameters include but are not limited to core scalability factor, GPU scalability factor, frequency scalability factor, etc. The execution estimator circuitry 506 also uses renewable availability data as discussed above telemetry data specific to the selected edge device 406A (e.g., the number of processor cores of the selected edge device, the processor frequency of the selected edge device, etc.) when determining the emissions estimate.
).; and
managing a workload of a client based on the sustainability goal and the carbon footprint associated with the nodes Bernat; [0092], [0093]; Bernat discloses assigning and adjusting client-submitted workloads to edge devices based on GHG reports in order to increase sustainability. Managing client workloads based on device-level emissions constitutes managing a workload of a client based on a sustainability objective and the cardoon footprint associated with the nodes;
See e.g. [0048]).
As evidence of the rationale above, Talluri discloses:
receiving a sustainability goal (Talluri, Talluri (Section 2.4 teaches operating a data center using sustainability metrics and goals within a feedback loop in which operational changes are made until requirements are met. Using goals to guide iterative operational changes is functionally equivalent to receiving a sustainability goal)
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Therefore it would have bee prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Talluri’s scheme. The motivation being the combined solution provides for implementing a known technique resulting in increasing efficiencies of optimizing workload in light of energy usage. Furthermore the combined solution expressly discloses scaling the computer network per Bernat’s scalability optimization scheme and Talluri’s explicit teachings of changing infrastructure associated with the network based on sustainability metrics and goals. One of ordinary skill in the art would have been able to adjust the active computer network resources used for the workload, including increasing, decreasing, or otherwise changing the nodes/resources provisioned for the workload as part of the sustainability-based optimization.
Regarding claim 8, claim 8 comprises the same and/or similar subject matter as claim 1 and is considered an obvious variation; therefore it is rejected under the same rationale.
Regarding claim 15, claim 15 comprises the same and/or similar subject matter as claim 1 and is considered an obvious variation; therefore it is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Melquist (US 2021/0397465): Melquist teaches auto scaling networks based on power considerations (see e.g. [0079]).
Pignataro (US 12,530,235): Pignataro teaches reconfiguring networks based on sustainability factors (see e.g. Fig. 7)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to TODD L. BARKER whose telephone number is (571) 270 0257. The Examiner can normally be reached on Monday through Friday, 7:30am to 5:00pm.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner's supervisor Vivek Srivastava can be reached on (571) 272 7304.
/TODD L BARKER/Primary Examiner, Art Unit 2449
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