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 .
This Office Action responds to the amendment and argument filed by applicant on April 06, 2026, in response to the Office Action mailed on November 5, 2025.
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 21–40 are rejected under 35 U.S.C. 112(b) because the claims, when read in light of the specification and the prosecution history, fail to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
With respect to claim 21, the limitation: “processing the CUR to determine a need for workload coverage in the customer's organization” is indefinite because the phrase "need for workload coverage" lacks objective boundaries. The claim does not specify the criteria, threshold, or measurable condition by which such need is determined, such that one of ordinary skill in the art cannot ascertain the metes and bounds of the claimed limitation with reasonable certainty.
Further, the recitation: "automatically first generating ... a customer level optimization (CLO)", is indefinite because “customer level optimization (CLO)” is a coined term that is not recognized as having a well-established meaning in the art and the claim fails to define what constitutes the optimization, the optimization variables, or the conditions under which an optimization is achieved.
Similarly, the recitation: "automatically second generating ... a system level optimization (SLO)" is indefinite because “system level optimization (SLO)” is likewise a coined expression lacking objective boundaries. The claim does not specify the optimization methodology, constraints, or measurable criteria by which the optimization is determined.
Additionally, the recitation: "maximizing aggregate coverage across all customers"
is indefinite because it is unclear what constitutes "aggregate coverage," how it is measured, or when such coverage is considered "maximized." The limitation merely states a desired result without defining objective boundaries.
The recitation: “maximizing savings yield on all available commitment inventory”
is also indefinite because "savings yield" is not defined and could reasonably encompass multiple different financial metrics, including gross savings, net savings, discounted savings, or projected savings.
Further, the limitation: "minimizing system-wide waste arising from over-provisioned or excess commitment inventory", is indefinite because the claim fails to define what constitutes "system-wide waste" or the metric by which waste is measured.
Moreover, the limitation: "simulating the exact behavior of the customer's system"
is indefinite because the term “exact behavior” lacks objective boundaries and it is unclear whether absolute accuracy or approximation is required.
Finally, the recitation: “building a forecasted model of the customer's workload coverage”, is indefinite because neither the forecasting methodology nor the parameters of the forecast model are defined.
Claim 22 is rejected under 35 U.S.C. 112(b) because the phrase: "stable usage baseline"
lacks objective boundaries. The claim fails to define the statistical methodology, averaging period, confidence interval, or computation used to establish such baseline.
Additionally, the limitation: "forecast of expected spend", is indefinite because the claim does not specify the forecasting methodology or time horizon used to determine expected spend.
Claims 23 and 24 are rejected because the recitation: "target coverage ratio"
is indefinite in the absence of objective criteria defining how the ratio is calculated or maintained. Although claim 24 specifies numerical examples, the underlying metric remains undefined.
Claim 25 is rejected because the limitation: "workloads with the highest rate of savings"
and "workloads with highest stability" are comparative terms lacking objective standards for measurement. The claim fails to define how savings rate or stability are calculated.
Claim 26 is rejected because: "capacity of the overall system" and
"minimize underutilization" lack objective boundaries. The claim does not specify how overall system capacity or underutilization are quantified.
Claims 27–29 are rejected because the limitations: "ordering them as they would be applied", "ordering the customer's workloads in the order they would be applied"
and "determine the correct order" are indefinite. The claims fail to identify the governing algorithm, priority rules, or deterministic criteria by which such ordering occurs.
Further, the phrases: "priority over facilitator-owned commitments" and
"lower priority than facilitator-owned commitments" lack objective criteria establishing the priority hierarchy.
Claim 30 is rejected because the recitation: "the CUR is first received by the customer"
And "the CUR is received directly by the facilitator system" introduces alternative data acquisition mechanisms without clearly defining whether these alternatives are mutually exclusive or whether both may occur simultaneously, rendering the claim scope uncertain.
Claims 31–40 are rejected under 35 U.S.C. 112(b) for substantially the same reasons discussed with respect to claims 21–30 because the computer program product claims recite the same indefinite concepts, including but not limited to: "customer level optimization (CLO)";
"system level optimization (SLO)"; "need for workload coverage"; "maximize aggregate coverage"; "maximize savings yield"; "minimize system-wide waste"; "stable usage baseline";
"forecasted model"; "highest stability"; "correct order"; "priority over facilitator-owned commitments" and "lower priority than facilitator-owned commitments".
The foregoing limitations fail to provide objective boundaries sufficient to inform one of ordinary skill in the art of the scope of the invention with reasonable certainty.
Claims 21–40 are additionally rejected under 35 U.S.C. 112(a) because the specification, while being enabling for certain aspects of cloud commitment management, does not reasonably appear to provide sufficient written description or enablement for the full scope of the claimed subject matter.
In particular, the claims require simultaneously: maximizing aggregate coverage across multiple organizations; maximizing savings yield across all commitment inventory; minimizing system-wide waste; simulating the exact behavior of customer systems; generating customer-level optimizations; generating system-level optimizations; and dynamically recalculating and implementing such optimizations upon changing conditions.
The specification does not appear to disclose sufficient algorithms, mathematical models, optimization techniques, or implementation details that would enable one of ordinary skill in the art to practice the full scope of these multi-objective optimization limitations without undue experimentation.
Accordingly, the disclosure does not reasonably convey possession of, or enable, the full scope of the claimed invention as required by 35 U.S.C. 112(a).
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 21–40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without reciting significantly more than the judicial exception.
Specifically, the claims are directed to certain methods of organizing human activity and mathematical concepts, and the additional elements, considered individually and as an ordered combination, do not integrate the exception into a practical application or amount to significantly more than the exception itself.
Under the 35 U.S.C. §101 subject matter eligibility two-part analysis, Step 1 addresses whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. See MPEP §2106.03. If the claim does fall within one of the statutory categories, it must then be determined in Step 2A [prong 1] whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). See MPEP §2106.04. If the claim is directed toward a judicial exception, it must then be determined in Step 2A [prong 2] whether the judicial exception is integrated into a practical application. See MPEP §2106.04(d). Finally, if the judicial exception is not integrated into a practical application, it must additionally be determined in Step 2B whether the claim recites "significantly more" than the abstract idea. See MPEP §2106.05.
Examiner note: The Office's 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) is currently found in the Ninth Edition, Revision 10.2019 (revised June 2020) of the Manual of Patent Examination Procedure (MPEP), specifically incorporated in MPEP §2106.03 through MPEP §2106.07(c).
Step 1: Claims 21–40 are directed to statutory categories of invention because they recite a process or manufacture. Accordingly, the analysis proceeds to Step 2A.
Step 2A, Prong One: The claims recite judicial exceptions. Independent claim 21 recites receiving customer usage reports and commitment information, determining workload coverage needs, generating customer-level optimizations (CLOs), generating system-level optimizations (SLOs), allocating commitments among customers, maximizing aggregate coverage, maximizing savings yield, minimizing waste, monitoring usage information, recalculating optimizations, and implementing revised allocations.
These limitations, under their broadest reasonable interpretation, recite commercial management of cloud service commitments and allocation of financial or contractual resources among multiple customers to improve utilization and savings.
Such activities constitute certain methods of organizing human activity because they involve commercial interactions, contractual resource allocation, financial planning, cost optimization, and management of business relationships between customers and a facilitator.
Additionally, the claims recite mathematical concepts by requiring optimization operations including maximizing aggregate coverage, maximizing savings yield, minimizing waste, calculating target coverage ratios, forecasting expected spend, modeling workload coverage, and allocating commitments based upon optimization objectives.
Further, the claims recite mental processes because the determinations of workload needs, evaluation of customer usage reports, ordering commitments, prioritizing workloads, selecting allocations, comparing coverage ratios, and deciding commitment assignments are evaluations and judgments that can practically be performed in the human mind or with pen and paper.
Accordingly, claims 21–40 recite abstract ideas including certain methods of organizing human activity, mathematical concepts, and mental processes.
Step 2A, Prong Two: These claims do not integrate the judicial exception into a practical application. The additional elements beyond the abstract ideas include a facilitator system, processors, computer-readable storage media, cloud service provider interfaces, customer usage reports, commitment records, and computer program instructions.
These additional elements merely use generic computer technology as tools to collect information, perform optimization calculations, and implement business decisions regarding cloud commitments.
These claims do not recite any improvement to computer functionality, networking technology, cloud infrastructure, storage architecture, scheduling algorithms, processor operation, or database technology.
The claimed customer-level optimization and system-level optimization merely automate business decisions regarding allocation of cloud commitments among customers. Likewise, the simulation of workload behavior and forecasting of coverage represent mathematical analyses performed using generic computing components rather than improvements to the functioning of computers themselves.
The recited implementation of facilitator-owned commitments into customer accounts merely changes business relationships and financial allocations and does not improve any underlying technology. The claims therefore use computers as tools to automate commercial planning and optimization. Accordingly, the judicial exception is not integrated into a practical application.
Step 2B: These claims do not recite an inventive concept sufficient to amount to significantly more than the abstract idea. The additional elements individually consist of generic computing components performing well-understood, routine, and conventional functions, including: receiving customer usage reports; storing commitment information; processing optimization calculations; generating reports; monitoring customer information; updating optimization results; implementing allocation decisions; and storing computer program instructions on non-transitory media.
The ordered combination likewise merely automates longstanding business practices involving allocation of shared contractual resources across multiple customers to maximize efficiency and reduce waste.
The recited customer-level optimization and system-level optimization merely express desired business results without reciting any specific technological mechanism for achieving those results. Similarly, generating forecast models, simulating workload behavior, maximizing savings, maximizing coverage, and minimizing waste merely represent abstract mathematical optimization goals implemented on generic computing devices.
These claims do not recite any specialized hardware, unconventional data structures, improved cloud architecture, novel scheduling mechanism, or technological improvement to cloud computing itself. Rather, the claims merely employ generic computer technology to perform business planning functions more quickly.
As such, the claims are analogous to those found patent ineligible in Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208 (2014), Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016), SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161 (Fed. Cir. 2018), and Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044 (Fed. Cir. 2017), where collecting information, analyzing information according to mathematical or business rules, and presenting or acting upon the results were held to be directed to abstract ideas implemented on generic computer components. Accordingly, these claims do not amount to significantly more than the judicial exception itself.
Dependent claims 22–40 merely add further abstract data analysis or business optimization features, including recalculation triggers, target coverage ratios, preferred allocation strategies, workload ordering rules, commitment priority rules, and computer-readable media implementing the same optimization logic.
These additional limitations merely refine the underlying abstract idea and do not integrate the judicial exception into a practical application or provide an inventive concept.
Thus, claims 21–40 are therefore rejected under 35 U.S.C. 101 because the claimed invention is directed to the abstract idea of optimizing cloud commitment allocation and workload coverage through business planning and mathematical optimization, and the claims, individually and as an ordered combination, do not recite additional elements sufficient to integrate the judicial exception into a practical application or to amount to significantly more than the judicial exception itself.
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.
Claims 21–40 are rejected under 35 U.S.C. 103 as being unpatentable over Krishnaiah (US 2025/0053903) in view of Eaton et al. (US 2015/0081880 A1, hereinafter Eaton).
With respect to claim 21, Krishnaiah discloses receiving a customer usage report ("CUR"), and data regarding commitments owned, for a first customer of a cloud service provider ("CSP"); by obtaining cloud account information, billing information, usage information, and cloud account data for managed cloud accounts (see, e.g., ¶¶ [0002], [0009], [0250]-[0252]).
processing the CUR to determine a need for workload coverage in the customer's organization; by analyzing the cloud account information to determine usage patterns, trends, anomalies, and parameter values for management actions (¶¶ [0009], [0255]-[0259], [0266]).
automatically first generating, using the CUR, a customer level optimization ("CLO") of cloud service coverage for the customer; through determining recommendations and optimization actions for an individual cloud account based on analyzed usage and budget information (¶¶ [0259], [0283]-[0285], [0289]-[0290]).
implementing the generated optimization; dynamically monitoring account information; recalculating recommendations based on updated cloud information; and implementing revised actions upon detection of triggering conditions (¶¶ [0264] – [0266], [0331]).
determining workload needs; simulating effects of changes to cloud account configuration; and forecasting future resource usage through predictive analytics and scenario evaluation (¶¶ [0266], [0275]-[0277], [0286]).
Krishnaiah does not expressly teach accessing a set of CLOs generated for a set of other customers of the CSP; automatically second generating, from the CLO for the first customer and from each respective CLO, a system level optimization ("SLO") across multiple customers specifying facilitator-owned commitments to move between customer accounts, utilizing both customer-owned commitments and facilitator-owned commitments as optimization inputs nor maximizing aggregate coverage across multiple customers while maximizing savings yield and minimizing system-wide waste.
Eaton discloses monitoring multiple customer environments, aggregating optimization information across multiple entities, comparing utilization across customers, and reallocating shared resources among multiple accounts to improve overall efficiency (see, e.g., ¶¶ [0026], [0031], [0044], [0058]), thereby teaching the claimed cross-customer optimization and facilitator-managed allocation.
using shared commitment inventories across multiple managed customers, allocating commitments to maximize overall utilization and reduce unused capacity while improving aggregate system efficiency (¶¶ [0044], [0056], [0062]).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Krishnaiah with Eaton's multi-customer commitment allocation techniques in order to improve utilization of cloud commitments across multiple organizations, maximize aggregate savings, reduce unused commitment inventory, and dynamically rebalance commitments among customers. Such modification merely applies known resource optimization techniques to predictable cloud management systems and yields the expected benefit of improved system-wide efficiency consistent with KSR Int'l Co. v. Teleflex Inc.
With respect to claims 22 and 32, Krishnaiah discloses recalculating optimization based upon changes in workload usage, thresholds, forecast information, and updated cloud account metrics (¶¶ [0009], [0255]-[0259], [0266]) and
Eaton further teaches monitoring customer utilization and triggering reallocation when monitored usage deviates from expected behavior or target utilization (¶¶ [0031], [0058]). Therefore, the combination teaches recalculation based on workload changes, forecasted spend changes, and billing deviations.
With respect to claims 23 and 33, Krishnaiah discloses allocating resources to improve utilization and optimize cloud budgets (¶¶ [0266], [0289] – [0290]); and
Eaton teaches allocating shared commitment inventory among organizations to bring customers closer to desired utilization targets while balancing overall system efficiency (¶¶ [0044], [0062]). The combination therefore teaches allocating commitment inventory toward target coverage ratios.
With respect to claims 24 and 34, Krishnaiah and Eaton do not expressly disclose a target ratio of between 75% and 95% or 85%; however, selection of a particular numerical optimization threshold constitutes an optimization variable that would have been an obvious matter of design choice absent evidence of criticality. See MPEP §2144.05.
With respect to claims 25 and 35 Krishnaiah discloses prioritizing cloud optimization based on savings opportunities and predicted resource utilization (¶¶ [0266], [0289] – [0290]) and
Eaton teaches allocating shared commitments to maximize aggregate savings and prioritizing workloads according to utilization characteristics (¶¶ [0044], [0062]). The combined references therefore teach preferential allocation to workloads providing highest savings and highest stability.
With respect to claims 26 and 36, Krishnaiah discloses minimizing excess resource allocation and optimizing cloud utilization (¶¶ [0266], [0280]-[0282]); and
Eaton teaches redistributing excess commitment inventory among remaining customers to reduce unused commitments and improve utilization (¶¶ [0056], [0062]). Therefore, the combination teaches minimizing system-wide waste through staged allocation.
With respect to claims 27 and 37, Krishnaiah discloses evaluating workloads under different resource allocation scenarios and forecasting system behavior (¶¶ [0264] – [0266], [0275] – [0277]); and
Eaton teaches evaluating commitments independently and determining allocation order among multiple accounts (¶¶ [0044], [0058]). Collectively these teachings render obvious generating optimization beginning with usage reports, removing commitments, evaluating on-demand pricing, determining owned commitments, and ordering commitment application.
With respect to claims 28 and 38, Krishnaiah discloses applying provider-specific management rules and cloud account policies when determining optimization actions (¶¶ [0264] – [0266], [0275] – [0277]); and
Eaton teaches applying provider allocation rules and commitment ordering logic across managed accounts (¶¶ [0044], [0056]). The combination teaches applying CSP rules and commitment ordering to determine application order.
With respect to claims 29 and 39, Krishnaiah discloses evaluating owned cloud resources before recommending changes (¶¶ [0264] – [0266], [0275] – [0277]); and
Eaton expressly teaches prioritizing existing customer-owned resources while supplementing with shared managed commitments to maximize overall utilization (¶¶ [0044], [0062]). Accordingly, the combination teaches applying customer-owned commitments with priority over facilitator-owned commitments, followed by applying facilitator-owned commitments to maximize coverage.
With respect to claims 30 and 40, Krishnaiah discloses receiving cloud account information directly from cloud providers or through customer interfaces and management portals (¶¶ [0005], [0250] – [0252]); and
Eaton likewise teaches obtaining information from customer systems or intermediary management systems (¶¶ [0026], [0031]). Therefore, the combination teaches receiving CUR information either directly from the customer, through the customer to the facilitator, or directly by the facilitator system.
Response to Arguments
Applicant’s arguments with respect to the new claim(s) have been considered but are moot in view of new ground of rejections.
Conclusion
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 ROKIB MASUD whose telephone number is (571)270-5390. The examiner can normally be reached Mon-Fri 8:00-5:00.
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/ROKIB MASUD/Primary Examiner, Art Unit 3627
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