DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in response to an amendment filed on Jun 29, 2026, wherein claims 1, 3-4, 7-8, 11, 13-14, 17-20 are amended.
Claim Rejections - 35 USC § 103
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 5-6, 8-12, 15-16, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Albero et al. (US PGPUB 2024/0112285), in view of de Mattos et al. (US PGPUB 2023/0130420).
As for claim 1, Albero teaches a method comprising:
receiving node sustainability information [cost of producing the necessary amount of energy] for a first node [identified data center computing system] and a second node [identified data center computing system] in a plurality of nodes [identified data center computing systems] configured to execute at least part of a workload [event processing request] (paragraph 43, “…identify an amount of energy needed to process the event processing request and a corresponding cost of producing that amount of energy for each of the identified data center computing systems configured to process the event processing request …” and paragraph 45, “…identifying the cost of producing the necessary amount of energy…..identify various mixes of energy sources ….whether a given energy source produces storable energy…reduce wasted energy…or user specified a preference for green energy sources, any green energy sources maybe ranked…” Prior art teaching the cost of producing the energy includes the mixes of energy sources that include how wasteful the energy produced is or green energy source, etc., received/used by the energy optimization platform are understood as sustainability information in view of the specification. See, Specification paragraph 20.),
receiving location sustainability information for a location [weather or environmental patterns] at which the first node and the second node are respectively disposed (paragraph 44, “…energy optimization platform ….take into account any weather or environmental patterns that may impact processing of the event….for example availability of solar power …”).
for a given workload to be executed by the predetermined network function, generating, based on the node sustainability information and the location sustainability information, a sustainability metric for the first node and the second node (paragraph 47, “…once an energy mix has been identified for the identified data center computing system…rank these data center computing systems (based on cost/energy source preferences, and/or otherwise…” and paragraph 32, “…energy pricing information, outage information, intermittency information, … processing capacity…energy optimization model…may train a model configured to input parameters…and identify data centers…weather information, geographic information, climate information…” the ranking value based on cost/energy source considerations and any location/weather/geographic/etc. information, whatever format the ranking uses to indicate their relative value to each other, is understood as the sustainability metric.);
selecting, based on the sustainability metric the first node to execute a first portion of the given workload and the second node to execute a second portion of the given workload (In a first embodiment, paragraph 47, “…select the top ranked data center computing system…” in view of paragraph 21, “…jobs may be …re-routed to various …zones….by following the path of the sun, by closing locations having substantial sun….wind…and the likes…” teaching a portion of the job may be performed by a first node, before the remaining job re-routed to a different node based on factors that are part of the sustainability metric such as location and weather. The remaining portion of job re-routed is understood as the second portion of the given workload. In a second embodiment, paragraph 42, “… the event processing request may be broken into one or more containers based on a comparison of the size of the event processing request and the standard size (e.g., if the size of the event processing request is more than the standard size of a single container, but less than the standard size of two containers, the event processing request may be split, e.g., based on tasks included in the event processing request into the two containers). In instances where more than one container is used for the event processing request, these containers may be referred to as a container cluster. By packaging the event processing requests into the one or more containers, the energy optimization platform 102 may package the event processing requests within lightweight software that may be run using any type of hardware, which may provide flexibility in processing.” And paragraph 48, “…may shift the corresponding containers (or a portion thereof) to the newly identified data center computing system…”); and
steering, using a network controller [Fig. 1B - communication Interface(s) 113], the first portion of the given workload to the first node and the second portion of the given workload to the second node so that the first node executes the first portion of the given workload and the second node executes the second portion of the given workload (Fig. 3 – step 335 Send Container(s) for Processing, In the first embodiment corresponding to above limitation, “…jobs may be …re-routed to various …zones….by following the path of the sun, by closing locations having substantial sun….wind…and the likes…” teaching steering of a first portion executing on a first node before having another node(s) execute remaining portion(s) of the workload. In the second embodiment, paragraph 48, “…may shift the corresponding containers (or a portion thereof) to the newly identified data center computing system…” teaches different containers of the containers for an event processing request (thus, portions thereof), can be routed for processing to different nodes. Finally, paragraph 30, “….communication interface 113 ….communication between energy optimization platform 102 and one or more networks…” teaching interface/controller used to communicate thus, it would be obviously used to send workload to any destination nodes.),
wherein the first portion of the given workload and the second portion of the given workload comprise, respectively, at least one of compute, memory, or storage services that support at least portions of the workload (Abstract, “…process the event processing request using the energy mix…” in view of paragraph 26, “….data center computing system 104 may include one or more computing devices and/or other computer components (e.g., processors, memories, communication interfaces)…processing of event processing requests….” Examiner note “workload…comprise…at least one of compute, memory, or storage services that support at least portions of the workload” is no more than each execution at each node can work on portion of the workload with/alongside other nodes by performing computation, memory, or storage functionalities. Here, executing event processing requests clearly perform computation, clearly operates on data (which clearly involves either memories or storage; see also paragraph 0053)
Albero does not explicitly teach the workload is a predetermined network function.
However, de Mattos teaches a known method of energy/sustainability aware workload distribution including receiving sustainability information for a first node and a second node configured to execute at least part of a predetermined network function [VNF] (Abstract, “…energy model to identify energy consumption implied by enabling an instance of the VNF at an edge node to accept a user VNF call…” in view of paragraph 14, 21, 68. Prior teaches sustainability information of each node is obtained/received by the energy model in the process of determining where to place the predetermined network function (i.e., VNF), where VNFs are part of a service function chain, thus, are constructively understood as “at least part of a predetermined network function”), and selecting, based on the sustainability metric, one of the first node and the second node to execute the predetermined network function (paragraph 84, “…prioritizes ….the energy consumption….search for nodes that consume less energy than the other nodes…”).
In addition to Albero, de Mattos also teaches steering, using a network controller, the first portion [workload performed by a VNF] of the given workload [workload performed by the entire service chain] to the first node and the second portion [workload performed by a next VNF] of the given workload to the second node so that the first node executes the first portion of the given workload and the second node executes the second portion of the given workload (paragraph 74-78, teaching the processing of workload at a first VNF, before forwarding to a next VNF v within the current Service Function Chain. Thus, clearly teaching the workload executed at 2 different nodes with the two VNFs part of the SFC),
wherein the first portion of the given workload and the second portion of the given workload comprise, respectively, at least one of compute, memory, or storage services that support at least portions of an application programming interface (Abstract, VNF calls are understood as supporting the network API triggered function executed by the VNF.).
This known technique is applicable to the system of Albero as they both share characteristics and capabilities, namely, they are directed to sustainability based deployment of workloads in networked nodes.
One of ordinary skill in the art before the effective filing date of the application would have recognized that applying the known technique of de Mattos would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of de Mattos to the teachings of Albero would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such sustainability based workload dispatch features into similar systems. Further, applying receiving sustainability information for a first node and a second node configured to execute at least part of a predetermined network function, and selecting, based on the sustainability metric, one of the first node and the second node to execute the predetermined network function to Albero with receiving sustainability information and selecting, based on sustainability metric, one of plurality of nodes to execute workload accordingly, would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow improved multi-objective optimization for deploying network functions. (de Mattos, paragraph 3).
As for claim 2, de Mattos also teaches the predetermined network function is an application programming interface function (Abstract, VNF calls are understood as API function).
As for claim 5, de Mattos also teaches generating the sustainability metric for the first node and the second node comprises receiving runtime information for the first node and the second node (paragraph 19, the system utilizes dynamism of available resources, which is understood as the dynamic/runtime information of the resources/nodes).
As for claim 6, Albero also teaches generating the sustainability metric for the first node and the second node comprises receiving workload information for the workload (paragraph 38).
De Mattos teaches workload is predetermined network function (paragraph 19).
As for claim 8, Albero also teaches automatically selecting the one of the first node and the second node to execute the workload (paragraph 47).
De Mattos teaches workload is predetermined network function (paragraph 19).
As for claim 9, Albero also teaches generating the sustainability metric for the first node and the second node comprises using machine learning (paragraph 23, “energy optimization model may include one or more supervised learning models…unsupervised learning models…and/or other models…”)
As for 10, Albero also teaches publishing the sustainability metric for the first node and the second node (paragraph 45-47. All variations of sustainability metric, i.e., energy cost, energy source and/or otherwise are clearly accessible for use to rank the data center computing systems. Thus, the availability of these calculations/determinations are constructively publishing the metrics).
As for claims 11-12, 15-16, they contain similar limitations as claims 1-2, and 5-6 above. Thus, they are rejected under the same rationales.
In addition, Albero teaches a device comprising an interface configured to enable network communications; a memory; and one or more processors coupled to the interface and the memory (paragraph 3).
As for claims 18, they contain similar limitations as claims 1 above. Thus, they are rejected under the same rationales.
In addition, Albero also teaches one or more non-transitory computer readable storage media encoded with instructions (paragraph 67).
Claim(s) 3, 13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Albero et al. (US PGPUB 2024/0112285), in view of de Mattos et al. (US PGPUB 2023/0130420), further in view of Shehabi et al. (“US Data Center Energy Usage Report”, June 2016).
As for claim 3, Albero also teaches receiving the node sustainability information for the first node and the second node (paragraph 43-44).
While Albero and de Mattos teaches obtaining energy usage of each node. Albero and de Mattos do not explicitly teach the sustainability information is generated from a server efficiency rating tool information source.
However, Shehabi teaches a known method of determining the energy usage by a node including receiving information from a server efficiency rating tool (SERT) information source (Section 2.2.3, Pg. 5, “SERT was created …tool uses a set of synthetic worklets to test …providing detailed power consumption data…collected and maintained…” Section 2.3.1., Pg. 9, “Server Power Draw”, power …estimated from the SERT …values…). This known technique is applicable to the system of Albero and de Mattos as they both share characteristics and capabilities, namely, they are directed to sustainability information determination for server nodes.
One of ordinary skill in the art before the effective filing date of the application would have recognized that applying the known technique of Shehabi would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Shehabi to the teachings of Albero and de Mattos would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such sustainability information determination features into similar systems. Further, applying the sustainability information is generated from a server efficiency rating tool information source to Albero and de Mattos with determining and receiving sustainability information accordingly, would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow improved ability to make available standardized sustainability information for government based standards. (Shehabi, Section 2.2.3).
As for claims 13 and 19, they contain similar limitations as claim 3 above. Thus, they are rejected under the same rationales.
Claim(s) 4, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Albero et al. (US PGPUB 2024/0112285), in view of de Mattos et al. (US PGPUB 2023/0130420), in view of Kommula et al. (US PGPUB 2020/0037473).
As for claim 4, Albero also teaches receiving the location sustainability information for the first node and the second node (paragraph 43-44).
While Albero and de Mattos teaches obtaining energy usage of each node. Albero and de Mattos do not explicitly teach the sustainability information is generated from a leadership in energy and environmental design (LEED) information source.
However, Kommula teaches a known method of determining the energy usage by a node including receiving information from LEED information source (paragraph 23, “optimize energy usage…qualifies under LEED standards…LEED certification…” teaching LEED information is an indication of reduced energy consumption for running workloads.). This known technique is applicable to the system of Albero and de Mattos as they both share characteristics and capabilities, namely, they are directed to sustainability information determination for server nodes.
One of ordinary skill in the art before the effective filing date of the application would have recognized that applying the known technique of Kommula would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Kommula to the teachings of Albero and de Mattos would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such sustainability information determination features into similar systems. Further, applying the sustainability information is generated from LEED information source to Albero and de Mattos with determining and receiving sustainability information from LEED information source accordingly, would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow optimized energy usage in data centers for workloads (Kommula, Paragraph 23).
As for claims 14 and 20, they contain similar limitations as claim 4 above. Thus, they are rejected under the same rationales.
Claim(s) 7, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Albero et al. (US PGPUB 2024/0112285), in view of de Mattos et al. (US PGPUB 2023/0130420), in view of Bodas et al. (US PGPUB 2017/0185132).
As for claim 7, Albero teaches generating the sustainability metric for the first node and the second node comprises generating the node sustainability information for the first node and the second node and the location sustainability information for the location at which the first node and the second node are respectively disposed (paragraphs 43-44).
Albero and de Mattos do not explicitly teach the use of geometric means calculation to generate energy efficiency information for servers.
However, Bodas teaches a known method of sustainability based evaluation of nodes for execution of workloads including using a geometric mean of performance per power value at each node (paragraph 117). This known technique is applicable to the system of Albero and de Mattos as they both share characteristics and capabilities, namely, they are directed to sustainability information determination for server nodes.
One of ordinary skill in the art before the effective filing date of the application would have recognized that applying the known technique of Bodas would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Bodas to the teachings of Albero and de Mattos would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such sustainability information determination features into similar systems. Further, applying use of geometric mean to determine sustainability metrics to Albero and de Mattos with determining sustainability metrics including sustainability information for the first and second node and sustainability information for the location at which the first and second node respectively reside including energy efficiency of the nodes to execute workloads accordingly, would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow improved dynamic measurement of energy efficiency to allow better power allocation to run the workloads efficiently (Bodas, Paragraph 6).
As for claims 17, they contain similar limitations as claim 7 above. Thus, they are rejected under the same rationales.
Response to Arguments
Applicant's arguments filed on 6/29/2026 have been fully considered but they are not persuasive.
Applicant Argues in the Remarks:
Argument I: “…in Albero’s container are not configured to be split up…as is recited by amended claim 1….as such Albero does not disclose or suggest ‘steering, …the first portion of the given workload to the first node and the second portion of the given workload to the second node….” (App. Arg. Pg. 15-16).
Examiner respectfully disagrees for the following reasons:
As for Argument I: See paragraph 7 above. In addition, Examiner note applicant assertions are directly contradicted by both the BRI of the claim limitations, and the explicit teachings of the prior art. In particular, Examiner note, the prior art explicitly teaches that in one embodiment, the workload can be partially executed on 1 node (i.e., first portion of workload), before re-routed to a different node to execute remaining portion of the workload based on the sustainability metrics (paragraph 47, “…select the top ranked data center computing system…” in view of paragraph 21, “…jobs may be …re-routed to various …zones….by following the path of the sun, by closing locations having substantial sun….wind…and the likes…”). Thus, clearly reading upon the explicit claim limitations. Applicant’s assertion to split up, is neither recited, nor implicitly required. More importantly, splitting up the workload for execution by 2 different nodes can, under the BRI include a first node executing a first sequence of computations on the workload, and a second node executing a second set of computations on the workload.
In addition, as noted in the second embodiment of prior art also reading upon the claim limitation above. The prior art can, for a single workload, split and package the request into plurality of containers, where a subset of containers is executed on one node while a portion of the containers are executed on a different node (paragraph 48, “…may shift the corresponding containers (or a portion thereof) to the newly identified data center computing system…”). Consequently, Applicant’s argument is unpersuasive.
Examiner’s Comments
Regarding previously issued 35 USC 101 rejection, Examiner note applicant submitted amendment, in particular, “steering, using a network controller, the first portion of the given workload to the first node and the second portion of the given workload to the second node so that the first node executes the first portion of the given workload and the second node executes the second portion of the given workload” is interpreted to include the first node actually executing the first portion of the given workload and the second node actually executing the second portion of the given workload, and consequently overcomes the previous rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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 KEVIN X LU whose telephone number is (571)270-1233. The examiner can normally be reached M-F 10am-6pm.
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/KEVIN X LU/Examiner, Art Unit 2199
/LEWIS A BULLOCK JR/Supervisory Patent Examiner, Art Unit 2199
