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 .
DETAILED ACTION
This Office Action is in response to the Amendment filed on 05/13/2026.
In the instant Amendment, claims 10 and 20 have been amended. Claims 1 and 11 are independent claims. Claims 1-20 have been examined and are pending. This Action is made FINAL.
Information Disclosure Statement
The information disclosure statement (IDS), submitted on 05/25/2026, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
The Objection of claims 10 and 20 are withdrawn as the claims have been amended.
The rejection of claims 11-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, are withdrawn as the claims have been amended.
Applicants’ arguments in the instant Amendment, filed on 05/13/2026, with respect to limitations listed below, have been fully considered but they are not persuasive.
On page 6, Applicant arguing that Applicant submits that the proposed combination of references fails to teach or suggest "generating, by the generative AI model and based on the data, a policy for the cloud deployment" as recited in claim 1.
The Examiner disagrees with the Applicants. The Examiner respectfully submits that the combination of Hassan and Nguyen does disclose generating, by the generative AI model and based on the data, a policy for the cloud deployment. As seen in abstract, par. 0007-0008 and 0079 of Hasan. Hasan teaches accordingly, the systems and methods generate a feature input for an artificial intelligence model that is trained on validated usage data for standardized cloud architecture patterns, in which the feature input comprises specific requirements and priorities. Moreover, the trained artificial intelligence model may be trained specifically on standardized cloud architecture patterns that provide the specific cloud architecture processing requirements and priorities and/or a threshold result for the specific cloud architecture processing requirements and priorities. Outputs of the first artificial intelligence model comprise recommendations for potential cloud architecture patterns corresponding to inputted cloud architecture processing requirements. Further as seen in par. 0002 and 0027 of Nguyen. Nguyen teaches that receiving, via a multimedia generation module of the system, a user selection of an AI model for a multimedia, receiving, via the multimedia generation module, a reference input for the multimedia from the user; and in response to a determination that the reference input complies with system policies, generating, via the multimedia generation module, an AI-generated multimedia from the reference input using the AI model. Determining if the reference input complies with system policies occurs automatically using the AI model and/or using human intervention. Thus, the combination of Hassan and Nguyen does disclose generating, by the generative AI model and based on the data, a policy for the cloud deployment.
Independent claim 11 is directed to a system that perform a method at least similar to that disclosed in claim1 and claims 2-10 and 12-20 depend from claims 1 and 11. Thus Applicants’ arguments with respect to claims 1-20, have been fully considered but they are not persuasive for at least the reasons explained above with respect to claim 1.
Thus, all the limitations are still obvious over the prior art of record. Please see the rejection below.
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 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 of this title, 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hassan (US 2025/0138856) and in view of Nguyen (US 2022/0309131).
Regarding claim 1, Hassan discloses a method, comprising:
accessing data describing one or more requirements for a cloud deployment (Hassan abstract and par. 0079; Systems and methods are described herein for optimizing cloud architectures using artificial intelligence models trained on standardized cloud architecture patterns corresponding to specific requirements. Receiving a first cloud architecture processing requirement; receiving a first set of available cloud resources; generating a first feature input based on the first cloud architecture processing requirement and the first set of available cloud resources);
providing the data as input to a generative artificial intelligence (AI) model (Hassan par. 0079; Receiving a first cloud architecture processing requirement; receiving a first set of available cloud resources; generating a first feature input based on the first cloud architecture processing requirement and the first set of available cloud resources; inputting the first feature input into a first artificial intelligence model to generate a first output, wherein the first artificial intelligence model is trained on historical usage data for cloud resources in known cloud architecture patterns, wherein the known cloud architecture patterns comprise respective arrangements of used and unused cloud resources and their interconnectivity, and wherein outputs of the first artificial intelligence model comprise recommendations for potential cloud architecture patterns corresponding to inputted cloud architecture processing requirements).
Hassan discloses inputting the first feature input into a first artificial intelligence model to generate a first output (Hassan par. 0079). However, Hassan does not explicitly disclose generating, by the generative AI model and based on the data, a policy for the cloud deployment.
However, in an analogous art, Nguyen discloses generating, by the generative AI model and based on the data, a policy for the cloud deployment (Nguyen par. 0027; Receiving, via a multimedia generation module of the system, a user selection of an AI model for a multimedia, receiving, via the multimedia generation module, a reference input for the multimedia from the user; and in response to a determination that the reference input complies with system policies, generating, via the multimedia generation module, an AI-generated multimedia from the reference input using the AI model. Determining if the reference input complies with system policies occurs automatically using the AI model and/or using human intervention. See also abstract and claim 1).
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 generating, by the generative AI model and based on the data, a policy for the cloud deployment of Hassan using the generating, by the generative AI model and based on the data, a policy for the cloud deployment taught in Nguyen in order to create and the exchange of a copyright for each AI-generated multimedia via a blockchain (Nguyen par. 0002).
Regarding claim 2, Hassan and Nguyen disclose the method of claim 1,
Hassan further discloses wherein the data describes at least one of: one or more business requirements or one or more technical requirements (Hassan par. 0034; In some embodiments, the system may receive one or more processing requirements for a potential cloud architecture pattern. As described herein, a processing requirement may comprise computational capabilities and resources necessary to perform specific tasks or run software applications).
Regarding claim 3, Hassan and Nguyen disclose the method of claim 1,
Hassan further discloses wherein providing the data as input to the generative AI model comprises prompting the generative AI model to generate the policy in accordance with the one or more requirements (Hassan par. 0079; Receiving a first cloud architecture processing requirement; receiving a first set of available cloud resources; generating a first feature input based on the first cloud architecture processing requirement and the first set of available cloud resources; inputting the first feature input into a first artificial intelligence model to generate a first output, wherein the first artificial intelligence model is trained on historical usage data for cloud resources in known cloud architecture patterns, wherein the known cloud architecture patterns comprise respective arrangements of used and unused cloud resources and their interconnectivity, and wherein outputs of the first artificial intelligence model comprise recommendations for potential cloud architecture patterns corresponding to inputted cloud architecture processing requirements).
Regarding claim 4, Hassan and Nguyen disclose the method of claim 1,
Nguyen further discloses wherein the policy comprises a machine-readable encoding of the policy (Nguyen par. 0102; Specifically, the DAO is an organization represented by rules encoded as a computer program that is transparent, controlled by the organization members and not influenced by a central government. A DAO's financial transaction record and program rules are maintained on a blockchain).
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 generating, by the generative AI model and based on the data, a policy for the cloud deployment of Hassan using the generating, by the generative AI model and based on the data, a policy for the cloud deployment taught in Nguyen in order to create and the exchange of a copyright for each AI-generated multimedia via a blockchain (Nguyen par. 0002).
Regarding claim 5, Hassan and Nguyen disclose the method of claim 1,
Hassan further discloses further comprising deploying the policy for enforcement in the cloud deployment (Hassan abstract; Systems and methods are described herein for optimizing cloud architectures using artificial intelligence models trained on standardized cloud architecture patterns corresponding to specific requirements).
Regarding claim 6, Hassan and Nguyen disclose the method of claim 5,
Hassan further discloses, wherein the policy corresponds to a particular policy type and wherein deploying the policy comprises providing the policy to a deployment agent corresponding to the particular policy type (Hassan par. 0069; The system may select a particular model based on a processing requirement. For example, the system may train a different model to detect potential cloud architecture patterns (or anti-patterns). Furthermore, in some embodiments, the system may retrieve a plurality of artificial intelligence models).
Regarding claim 7, Hassan and Nguyen disclose the method of claim 1,
Nguyen further discloses further comprising providing the policy to a user for review (Nguyen par. 0117; The human validator 318 is an expert for verification. Moreover, the blind voting 320 involves comparing two objects without knowing the details or history of them. Further, aspects/components of the verification policy 312 may be aggregated from multiple sources at a. process step 322. The aggregation at the process step 322 returns a similarity score at a process step 308 and a threshold-based decision at a process step 310).
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 generating, by the generative AI model and based on the data, a policy for the cloud deployment of Hassan using the generating, by the generative AI model and based on the data, a policy for the cloud deployment taught in Nguyen in order to create and the exchange of a copyright for each AI-generated multimedia via a blockchain (Nguyen par. 0002).
Regarding claim 8, Hassan and Nguyen disclose the method of claim 1,
Nguyen further discloses wherein the policy comprises a data policy (Nguyen par. 0093; The system policies may include rules associated with restricted content, child endangerment, inappropriate content, sexual content, profanity, hate speech, violence, terrorist, bullying, harassment, dangerous products, etc. In a first example, the process step 208 may occur as an automatic method using the AI model. In a second example, the process step 208 may occur using human intervention. Such examination may also involve referencing one or more databases to perform a legal check for the reference input. See also par. 0116).
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 generating, by the generative AI model and based on the data, a policy for the cloud deployment of Hassan using the generating, by the generative AI model and based on the data, a policy for the cloud deployment taught in Nguyen in order to create and the exchange of a copyright for each AI-generated multimedia via a blockchain (Nguyen par. 0002).
Regarding claim 9, Hassan and Nguyen disclose the method of claim 1,
Nguyen further discloses wherein the policy comprises a network policy (Nguyen par. 0093; The system policies may include rules associated with restricted content, child endangerment, inappropriate content, sexual content, profanity, hate speech, violence, terrorist, bullying, harassment, dangerous products, etc. In a first example, the process step 208 may occur as an automatic method using the AI model. In a second example, the process step 208 may occur using human intervention. Such examination may also involve referencing one or more databases to perform a legal check for the reference input).
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 generating, by the generative AI model and based on the data, a policy for the cloud deployment of Hassan using the generating, by the generative AI model and based on the data, a policy for the cloud deployment taught in Nguyen in order to create and the exchange of a copyright for each AI-generated multimedia via a blockchain (Nguyen par. 0002).
Regarding claim 10, Hassan and Nguyen disclose the method of claim 4,
Hassan further discloses wherein the machine-readable encoding of the policy comprises an Infrastructure-as-code (IaC) deployment specification (Hassan par. 0037; The system may use Infrastructure as Code (IaC) to define patterns and enact patterns. For example, the system may use IaC where cloud resources and their configurations are defined using machine-readable configuration files).
Regarding claims 11-20; claims 11-20 are directed to a system associated with the method claimed in claims 1-10 respectively. Claims 11-20 are similar in scope to claims 1-10 respectively, and are therefore rejected under similar rationale.
Conclusion
THIS ACTION IS MADE FINAL. 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 SANCHIT K SARKER whose telephone number is (571)270-7907. The examiner can normally be reached M-F 8:30 AM-5:30 PM.
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/SANCHIT K SARKER/Primary Examiner, Art Unit 2495
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