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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1. 17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR l.l 7(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on April 22, 2026 has been entered.
Response to Arguments
Applicant's arguments filed 10/20/2025 have been fully considered but they are not entirely persuasive.
Applicant argues that the features of claim 1 are specifically directed to generating a set of customized guidelines using a machine learning (ML) model, which is not merely generic or conventional functions, or a setup of a basic computer system.
In response, the Examiner respectfully disagrees, the claimed limitations (i.e.,
generating a set of customized guidelines) pertaining to machine learning (ML) model amount merely to the very definition of (the training aspect of) supervised machine learning. As such, the independent claims do not reflect any improvement in machine learning (or in another technology/functioning of a computer), and the machine learning limitations are merely generic computer elements.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Subject Matter Eligibility Standard
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
Specifically, claim 1 is directed to a method. Claims 17 is directed to a system claim. Each of the claims falls under one of the four statutory classes of invention.
If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea).
Under Step 1 of the analysis, claim 1 is directed to a method claim. Claim 17 is directed to a system. The claims fall under one of the four statutory classes of invention under step 1.
Representative claim 1 recites the limitations in the abstract idea highlighted in non-bold and the “additional elements” in bold below.
generating, by a computing device, a set of customized guidelines using by utilizing a machine learning (ML) model which is trained on at least historical user data and a current set of known historical guidelines;
improving, by the computing device, the set of customized guidelines using a refined customer profile comprising user data of a current client;
providing, by the computing device, a maturity assessment of the current client using the improved set of customized guidelines and known maturity assessments; and
providing a recommendation to the current client based on the improved set of customized guidelines and the maturity assessment, wherein the maturity assessment of the current client comprises a zero-shot assessment.
Claim 2 further claims wherein the set of customized guidelines are customized FinOps guidelines generated using at least one of customer data, known maturity assessments and industry metrics and key performance indicators and subject matter experts.
Claim 3 further claims wherein the improved set of customized FinOps guidelines are customized for the current client.
Claim 4 further claims generating customized prompts using the user data of the current client of the refined customer profile, and using answers of the customized prompts to customize the guidelines for the current client.
Claim 5 further claims wherein the maturity assessment of the current client uses the improved set of customized guidelines and known maturity assessments.
Claim 6 further claims wherein the zero-shot assessment comprises training data and the improved set of customized guidelines, without context or examples.
Claim 7 further claims wherein the maturity assessment of the current client includes a few-shot assessment comprising examples and the set of improved customized guidelines.
Claim 8 further claims wherein the maturity assessment of the current client comprises a hybrid assessment that comprises the zero-shot assessment and a few- shot assessment.
Claim 9 further claims wherein the recommendation to the current client is based on a recommendation of a service provider.
Claim 10 further claims wherein the providing the maturity assessment uses a large language model.
Claim 11 further claims wherein the generating the set of customized guidelines uses training data on a large language model, the training data comprising client profiles and the current set of known guidelines.
Claim 12 further claims software provided as a service in a cloud environment.
Claim 13 recites:
generate a set of customized guidelines using by utilizing a machine learning (ML) large language model which is trained on training data comprising at least historical user data and a set of historical guidelines with a large language model;
improve the set of customized guidelines using a customer profile comprising user data of a current client;
generate a maturity assessment of the current client using the improved set of customized guidelines and additional maturity assessments; and
provide a recommendation to the current client based on the improved set of customized guidelines and the maturity assessment, wherein the maturity assessment of the current client comprises a few-shot assessment, and the ML large language model comprises a deep learning model.
Claim 14 further claim wherein the training data further comprises at least one of data from a group of users, a set of known guidelines, known maturity assessments, industry metrics and key performance indicators and subject matter experts.
Claim 15 further claims wherein the set of customized guidelines are customized FinOps guidelines.
Claim 16 further claims wherein the improved set of customized guidelines are customized for the current client using at least one of customized prompts and additional user data of the current client.
Claim 17 further claims wherein the maturity assessment of the current client further comprises a zero-shot assessment comprising the training data and the improved set of customized guidelines, without context or examples, and the few-shot assessment comprises examples and the set of improved customized guidelines.
Claim 18 further claims wherein the recommendation to the current client is generated using a service provider recommendation and the maturity assessment.
Claim 19 recites:
a processor, a computer readable memory, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to: generate a set of customized guidelines using by utilizing a machine learning (ML) large language model which is trained on at least historical user data and a set of historical guidelines a large language model;
improve the set of customized guidelines using a customer profile comprising user data of a current client;
generate a maturity assessment of the current client using the improved set of customized guidelines and stored maturity assessments of other entities; and
provide a recommendation to the current client based on a combination of the improved set of customized guidelines and service provider recommendations, wherein the maturity assessment of the current client comprises a hybrid assessment, and the ML large language model comprises a deep learning model which utilizes a set of neural networks that comprise an encoder and a decoder with self-attention capabilities.
Claim 20 further claims wherein the ML large language model also uses training data to generate the set of customized guidelines, the training data comprises at least one of data from a group of users, a set of known guidelines, known maturity assessments, industry metrics and key performance indicators and subject matter experts.
Step 2A, Prong One, the limitations of the above claims, under their broadest reasonable interpretation, fall within the "Certain Methods of Organizing Human Activity" grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(m, because they amount to limitations specifying steps for managing commercial or legal interactions and managing personal behavior or relationships or interactions between people by describing steps involving steps involving exchanging information between parties in relation to considering potential transactions. The BR.I of these limitations describes functions of providing a recommendation to the current client based on the improved set of customized guidelines and the maturity assessment, wherein the maturity assessment of the current client comprises a zero-shot assessment.
Step 2A, Prong Two of the eligibility analysis evaluates whether the claims as a whole integrate the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.2019 PEG Section III(A)(2), 84 Fed. Reg. at 54-55. In particular, the claims recite the following bolded limitations understood to be additional limitations:
In addition to the abstract ideas recited in claims 1, 13, and 19, the claimed
recites additional elements including a computing device, and machine learning (ML) model (claim 1), machine learning (ML) large language model, and deep learning model (claim 13), and memory, and machine learning (ML) large language model, deep learning model and neural network (claim 19). See Paragraph [0067] of applicant’s specification. The claimed machine learning (ML) model amount merely to the very definition of (the training aspect of) supervised machine learning. As such, the independent claims do not reflect any improvement in machine learning (or in another technology/functioning of a computer), and the machine learning limitations are merely generic computer elements.
When considered in view of the claims as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 1, 13, and 19 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Performing steps or functions by a user device, processor or a computing system merely limits the abstraction to a computer field by execution by generic computers. See MPEP 2106.05.
As noted in MPEP 2106.04(d), limitations which amount to instructions to implement an abstract idea on a computer or merely using a computer as a tool, limitations which amount to insignificant extra-solution activity, and limitations which amount to generally linking to a particular technological environment do not integrate a practical exception into a practical application.
Consideration of the claimed steps of the claims as a combination do not change the analysis as they do not add anything compared to when the steps are considered separately. The claims recite a particular sequence of functions to efficiently perform financial operations (FinOps) to maximize business value of cloud resources, enabling a timely data-driven decision making process by integrating the use of different, customized domains, categories, principles and management of FinOps practices for a particular client.
Performance of these steps or functions technologically may present a meaningful limit to the scope of the claim does not reasonably integrate the abstraction into a practical application.
Step 2B: The elements discussed above with respect to the practical application in Step 2A, prong 2 are equally applicable to consideration of whether the claims amount to significantly more. Accordingly, the claims fail to recite additional elements which, when considered individually and in combination, amount to significantly more. Reconsideration of these elements identified as insignificant extra-solution activity as part of Step 2B does not change the analysis.
Generating, improving, and providing data by a processor or computer hardware amounts to receiving and processing data over a network has been recognized by the courts as well-understood, routine, and conventional (See MPEP 21065(d)(H), citing Symantec. 838 F.3d at ! 32 ! , 120 USPQ2d at l 362 (utilizing an intermediary computer to forward information): TU Communications LLC v. AV Auto. LLC, 823 F3d 607,610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission): OIP Techs., Inc., v, Amazon.com, Inc., 788 F3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir, 2015) (sending messages over a network: buy SAFE, Inc.v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Positively reciting a processor and machine learning model(s) and neural network, does not change the analysis as these aspects are properly considered as additional elements which amount to instructions to apply with a computer.
These claimed elements also as found in the dependent claims are also recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic component.
In processing the claims, it is noted that the recitation of these additional elements does not impact the analysis of the claims because these elements in combination are noted only to be a general purpose computer or processor for performing basic or routine computer functions. These claimed elements are noted to a be a generic computer for accessing data similar to collecting data, storing data and performing routine and conventional functions.
The judicial exception is not integrated into a practical application. In particular, the claimed “controlling unit ” is recited at a high level of generality such they amount to no more than mere instructions to apply the exception using generic components. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
As a result of the above analysis, claim 1, as well as claims 13 and 19, do not appear to be patent eligible under 101.
Accordingly, claims 1, 13 and 19 are directed to an abstract idea.
Dependent claims 2-12, 14-18 and 20 include additional elements beyond those recited by independent claims 1, 13 and 19. The claimed steps do not amount to significantly more than the abstract idea, because they are well-understood, routine, and conventional computer functions in view of MPEP 2106 .05(d)(11). The recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 2-10 and 12-20 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-20 are rejected under 35 USC. 101 as being directed to non-statutory subject matter.
The claims would be allowable if overcome the 35 USC 101 rejection.
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. As per attached PTO 892 form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROMAIN JEANTY whose telephone number is (571)272-6732. The examiner can normally be reached M-F 9AM to 5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached on 571 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROMAIN JEANTY/Primary Examiner, Art Unit 3624
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