DETAILED ACTION
This is in response to the amendment filed on February 27th 2026.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/23/25 and 5/4/26 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Response to Arguments
Applicant’s arguments, see pg. 9, filed 2/27/26, with respect to the claim objection have been fully considered and are persuasive. The objection of claim 20 has been withdrawn.
Applicant’s arguments, see pg. 9-11, with respect to the rejection(s) of claim(s) 1 under 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Chen US 2022/0318065 A1.
Applicant’s arguments, see pg. 11, with respect to claims 5 and 16, have been fully considered and are persuasive. The 103 rejection of claims 5 and 16 has been withdrawn.
Applicant’s arguments with respect to claim(s) 9-10, pg. 12, have been considered but are moot because the new ground of rejection.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-4, 6-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta et al. US 2025/0037142 A1 in view of Jin et al. US 10,841,236 B1 and Chen US 2022/0318065 A1.
Regarding claim 1, Dutta discloses a computing system (computer - Fig. 4) comprising:
one or more memories (memory - Fig. 4);
one or more processors communicatively coupled to the one or more memories (processor - Fig. 4), the one or more processors being configured to:
determine a measure of first carbon emission associated with the first workload (determine carbon emissions generated by workload – abstract, paragraphs 5, 25);
determine a predicted measure of second carbon emission associated with the one or more other workloads (there are a set of workloads, determine carbon emissions for other workloads – Figs. 1, 3A, paragraphs 16, 25);
determine a combined emission, the combined emission including the measure of the first carbon emission and the predicted measure of the second carbon emission (consider carbon emissions generated by the combination of the workloads – paragraph 27);
determine [replicas for] the first workload based on the combined emission and an emission threshold (workload allocation engine identifies servers for workload based on carbon emission – Figs. 1-2, paragraphs 7, 18, 29; and consider sustainability/ESG goal, i.e. “emission threshold” – see paragraphs 2, 4; also see Fig. 3B which shows a limit “CMAX” when allocating servers);
schedule spawning of … replicas of the first workload or destruction of replicas of the first workload to implement the replica count (workload allocation engine generates server clusters for workload based on carbon emission – Fig. 3A, paragraphs 7, 18; the process is dynamic – paragraph 29).
Dutta does not explicitly disclose determine that a first workload depends on one or more other workloads, however it does teach that workloads may have an affinity or relationship (paragraph 6, Fig. 3A). However, it is well known in the art to consider dependency when scheduling tasks/jobs in a network computer environment. Jin explicitly discloses a task dependency topology that contains dependency relationships between jobs in order to distribute/schedule jobs in a cloud computing environment (see abstract, Figs. 1, 4, col. 1 ln. 56-64 and col. 14 ln. 60-65). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dutta to consider workload dependency as taught by Jin for the purpose of allocating workloads to a cloud. As mentioned above, Dutta itself already teaches considering whether workloads have an affinity or relationship. Task dependency is simply a type of relationship. Jin teaches that by considering dependency many benefits may be realized including reducing processing time, reducing cost, increasing resource utilization and satisfying SLA (col. 2 ln. 37-40).
The combination of Dutta and Jin does not explicitly disclose a replica count of replicas or a first set of the replicas / a second set of the replicas. But this is taught by Chen as a workload distribution management system (abstract) that explicitly discloses replica deployment for workloads and counting a number of replicas including a set of replicas (see Figs. 1-2, paragraphs 4, 19, 27-28 and 30). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Dutta and Jin to determine a count of replicas for a specific workload and scaling that workload using a set of replicas as taught by Chen. Chen discloses many advantages for using a technique including auto-scaling, robustness, and reduced resource consumption (paragraphs 3-4 and 12).
Regarding claim 2, Dutta discloses wherein the first carbon emission is a direct carbon emission attributable to the first workload, the first carbon emission not including any emission attributable to the one or more other workloads (power/carbon is allocated to a particular service – paragraph 21, thus it does not include any emission attributable to other workloads).
Regarding claim 3, Dutta discloses wherein the second carbon emission is an indirect carbon emission attributable to supporting scale up of the one or more other workloads due to a scale up of the first workload (the second carbon emission is the emission for the other workload – paragraph 25, Fig. 3A, thus it is “indirect” to the first workload and attributable to scale up the servers required to process the workloads when there is a workload relationship (Fig. 3A, paragraphs 25-29; also see paragraph 19 which further discusses “indirect” emissions).
Regarding claim 4, Dutta discloses wherein the one or more processor are further configured to determine a scale factor as a linear function of the direct carbon emission and the indirect carbon emission (server cluster provide for application scaling – paragraph 23; determine whether load to energy is linear or non-linear – paragraphs 6, 26; consider indirect emission – paragraph 19).
Regarding claim 6, Dutta discloses determine the corresponding relative scale factor by executing a machine learning model, wherein the machine learning model is trained on historical workload metrics (allocation engine uses modeling engine that has been trained on historical data – paragraph 26, Fig. 2).
Regarding claim 7, Dutta discloses wherein the one or more processors are configured to determine that the first workload [is related to] the one or more other workloads by executing a machine learning model, wherein the machine learning model is trained on historical workload metrics (allocation engine uses modeling engine that has been trained on historical data – paragraph 26, Fig. 2).
Dutta does not explicitly disclose the workload depends on one or more other workloads but this is taught by Jin as discussed above. The motivation to combine is the same.
Regarding claim 8, the combination of Dutta and Jin does not explicitly disclose output an indication that the first workload is certified against emission criteria. However, Dutta teaches that investors are scrutinizing ESG goals including energy consumption and that it is important to meet said goals for regulations and compliance (paragraphs 2-4). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Dutta and Jin to output workload certification emission criteria as this would directly enhance the ability to meet ESG goals, regulations and compliance, which Dutta teaches are important. Furthermore, there are many well-known standards related to carbon certification, see pertinent art.
Regarding claim 9, Dutta discloses determine [replicas for] the first workload based on at least one of resource metrics, service metrics, or application metrics (paragraphs 23-24); determine that the second replica is greater than the first replica, and determine to implement the first replica based on the second replica being greater than the first replica (switch to more efficient server cluster – paragraph 26). Dutta does not explicitly disclose a second replica count of replicas for the first workload but this is taught by Chen as discussed above (see Chen Figs. 1-2). The motivation to combine is the same.
Regarding claim 10, Dutta discloses determine a measure of carbon emission associated with the second workload (Fig. 3A);
determine a first … of the second workload based on at least one of resource metrics, service metrics, or application metrics (paragraphs 23-24);
determine a second replica … of the second workload based on the measure of carbon emissions (paragraphs 25-26, Fig. 3A);
determine that the first replica is greater than the second replica; and determine to implement the second replica count based on the first replica count being greater than the second replica count (switch to more efficient server cluster – paragraph 26, select low carbon servers – paragraph 29); and
schedule spawning of … replicas of the second workload or destruction of replicas of the second workload to implement the second replica count (workload allocation engine generates server clusters for workload based on carbon emission – Fig. 3A, paragraphs 7, 18; the process is dynamic – paragraph 29).
Dutta does not explicitly disclose determine that a second workload does not depend one the one or more other workloads but this is taught by Jin as discussed above. The motivation to combine is the same.
Dutta does not explicitly disclose a replica count of replicas or a second replica count of replicas or a first set/second set of replicas but this is taught by Chen as discussed above. The motivation to combine is the same.
Regarding claim 11, Dutta does not explicitly disclose a service level agreement. However, this is taught by Jin (abstract, col. 14 ln. 15-19). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dutta to use a SLA as taught by Jin. This is merely the combination of a well-known technique according to its established function in order to yield a predictable result.
Regarding claim 12, it is a method claim that corresponds to the system of claim 1; therefore it is rejected for the same reasons.
Regarding claims 13-15 and 17-19 they correspond to previously presented dependent claims 2-4, 6-7 and 9-10 respectively. Thus, they are also rejected for the same reasons.
Regarding claim 20, it is a non-transitory computer readable medium that corresponds to the system of claim 1; therefore it is rejected for the same reasons.
Allowable Subject Matter
Claims 5 and 16 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.
The following is a statement of reasons for the indication of allowable subject matter: in response to the arguments filed 2/27/26, the previous rejection has been withdrawn. Although the art teaches determining emissions associated with a workload, scaling workloads, and workload dependency, thus allowing one of ordinary skill in the art to sum the emissions from a plurality of workloads; the specific features recited by the claims when considered as a whole differentiate over these known techniques.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Choochotkaew et al. US 2022/0318060 A1 discloses a method for scaling applications including automatically deciding a number of replicas and workload partitioning (abstract), a primary purpose is to declare “how many replicas” should be running at a time (paragraph 39).
Swierc et al. US 2020/0082289 A1 discloses scheduling work based on carbon emissions (abstract, Fig. 1).
Sheridan US 2022/0012751 A1 discloses certifying sustainable products for financial rewards including many carbon certifications (abstract, paragraphs 4, 70).
Naidu et al. US 2021/0342185 A1 discloses relocation of workloads across data centers based on power, carbon footprint, etc. (abstract, Fig. 1, paragraph 52).
Liu et al. US 2021/0342184 A1 discloses determining dependency relationships (abstract, Fig. 3) and determining the resources suitable for the combination of dependent tasks (paragraph 92).
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 JASON D RECEK whose telephone number is (571)270-1975. The examiner can normally be reached Flex M-F 9-5.
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/JASON D RECEK/Primary Examiner, Art Unit 2458
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