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 .
Status of Claims
Claims 1-20 are presented for examination in this application. The application filing date on 10/23/2024. Claims 1 and 11 are independent.
Examiner notes
(A). Drawings submitted on 10/23/2024 comply with the provisions of 37 CFR 1.121(d).
(B). IDS submitted on 06/20/2025 have been fully considered by the Examiner.
(C). Limitations have been provided with the Bold fonts in order to distinguish from the cited part of the reference (Italic).
(D). Examiner has cited particular columns, line numbers, references, or figures in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses to fully consider the reference in entirety, as potentially teaching all or part of the claimed invention. See MPEP § 2141.02 VI and 2123.
The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111 (c).
Priority
The provisional application No. 63/660,387, filed on June 14, 2024 related to this application. The submission is in compliance with the provisions of 37 CFR 1.78 and 37 CFR 1.79. Examiner further acknowledged CON, CIP and plurality of other provision applications. However, other provisional applications’ as well CON, CIP applications’ date did not consider by the examiner due to subject matter does not match.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“processing device” (claim 11, line 3; claim 13-16, 20, line 1; claim 17, line 2).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
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.
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.
Claims 1-6, 8, 11-16, and 18 are rejected under 35 U.S.C. 103 as being obvious over Petrov et al (US 20240427564 A1, hereinafter Petrov) in view of Zheng et al. (US 20240427595 A1, hereinafter Zheng).
As to claim 1, Petrov discloses a method, comprising:
creating, in a storage system (par 0032, … create and/or update code. In embodiments, the code and corresponding work product may be transmitted to server 120 over network 110. Computing devices 105 may be utilized by code developers to develop code, and by code reviewers to review the code. In embodiments, the developers and reviewers may review code that has been augmented, updated, and/or documents by AI computing device 140. Responsive to approving code for updating, computing devices 105 may transmit the approved code to server 120 where the approved code can be stored within codebase repositories 130),
wherein the (par. 0033, … Server 120 may include physical computing devices residing at a particular location or may be deployed in a cloud computing network environment. In this description, “cloud computing” may be defined as a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned via virtualization and released with minimal management effort or service provider interaction, and then scaled accordingly. A cloud model can be composed of various characteristics (e.g., on-demand self-service, broad network access, resource pooling, rapid elasticity, measured service, etc.), service models [i.e. different tenant] (e.g., Software as a Service (“SaaS”), Platform as a Service (“PaaS”), Infrastructure as a Service (“IaaS”), and deployment models (e.g., private cloud, community cloud, public cloud, hybrid cloud, etc.). …);
generating, using a large language model (LLM) (pa. 0035, … AI computing devices 140 may be configured to increase the quality and efficiency of code developed [i.e. generate] and reviewed with computing devices 105, while streamlining CI/CD pipelines using Large Language Models (LLM) technology) and based on input data comprising data describing an error in the software deployment, a code update to fix the error (par. 0037, … docstring generator 220, bug identifier 225, code analyzer and fixer 230, unit test generator 235, pull request approver and summarizer 240, and note generator 250. Further, par. 0042, … when analyzing the code, bug identifier 225 may determine potential issues and vulnerabilities associated with the code before the code is compiled or deployed. If bug identifier 225 determines there are issues with the code, bug identifier 225 may determine fixes and best practices. In embodiments, bug identifier 225 may be configured to suggest fixes to the code);
verifying the code update, including updating (see, par. 0037, 0042. Further, par. 0052, At operation 320, after the AI computer device has updated the code on the pipeline with docstrings and comments, the AI computing device may propose changes to the code on the pipeline. For example, the AI computing device may determine if bugs are associated with the code, and suggest fixes for the bugs …); and
updating the software deployment based on the verified code update (par. 0032, … reviewers may review code that has been augmented, updated, and/or documents by AI computing device 140. Responsive to approving code for updating, computing devices 105 may transmit the approved code to server 120 where the approved code can be stored within codebase repositories 130. Further, par. 0033, Server 120 may be a computing resource that is configured to remotely provision, allocate, manage, and codebase repositories 130 associated with software applications to execute the software applications. Server 120 may include physical computing devices residing at a particular location or may be deployed in a cloud computing network environment. …).
Petrov does not explicitly disclose cloned software, however,
Zheng discloses cloned software update (par. 0089, At block 414, the configuration tool 102 can determine configuration details associated with a previous computing environment indicated in application files [i.e. software] 112, based on the cloned copy of the existing project data 132 … . Further, par. 0099, at block 422, the configuration tool 102 can update the cloned stock infrastructure configuration data 130 received at block 406, based on the user input 110 and/or the application files 112, to generate the infrastructure configuration data 114 to be included in the application package 104. For example, the configuration tool 102 can add a Terraform infrastructure configuration file, or other type of infrastructure configuration data 114, to the application package 104 based on the cloned stock infrastructure configuration data 130. …).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Petrov to include create cloned software, as disclosed by Zheng, for the purpose of modifying the cloned copy of the existing project data (see paragraph 87 of Zheng).
As to claim 2, Petrov discloses the method wherein the data describing the error in the software deployment comprises data describing one or more instances of the error detected in the software deployment (par. 0045, … analyzing code changes quicker than developers; maintaining a consistent level of review quality regardless of the number of pull requests it handles, unlike developers who may become fatigued or distracted leading to less accurate reviews and wasted computing resources; reducing the likelihood of human errors during code review, minimizing the need for expensive compute resources to handle bugs [i.e. error], crashes, or other issues related to incorrect approvals, concurrently reviewing multiple pull requests, eliminating potential bottlenecks when there's a high volume of changes to be reviewed by a limited number of developers; identifying resource-intensive code before it is merged, …).
As to claim 3, Petrov discloses the method further comprising detecting one or more instances of the error in the (par. 0025, FIG. 6 depicts AI computing device determining a bug and determining a fix for the bug for code on a pipeline …), wherein the data describing the error in the software deployment comprises data describing the one or more instances of the error detected in the (par. 0042, Bug identifier 225 may be a hardware computing device that is configured to analyze the code without executing the code. In embodiments, when analyzing the code, bug identifier 225 may determine potential issues and vulnerabilities associated with the code before the code is compiled or deployed. If bug identifier 225 determines there are issues with the code, bug identifier 225 may determine fixes and best practices. In embodiment …).
Petrov does not explicitly disclose cloned software, however,
Zheng discloses cloned software update (par. 0089).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Petrov to include cloned software update, as disclosed by Zheng, for the purpose of modifying the cloned copy of the existing project data (see paragraph 87 of Zheng).
As to claim 4, Petrov discloses the method further comprising generating, by the LLM (par. 0035, … AI computing device 140 may be a cloud-based computing device coupled to multiple computing devices 105 and/or server 120 that leverage large language models to supplement the code development process … ), one or more test cases for the error (par. 0037, … docstring generator 220, bug identifier 225, code analyzer and fixer 230, unit test generator 235, pull request approver and summarizer 240, and note generator 250).
As to claim 5, Petrov discloses the method further comprising executing, in the (par. 0042, Bug identifier 225 may be a hardware computing device that is configured to analyze the code without executing the code. In embodiments, when analyzing the code, bug identifier 225 may determine potential issues and vulnerabilities associated with the code before the code is compiled or deployed. … ),
the one or more test cases for the error, wherein the input data to the LLM further comprises an output from the one or more test cases for the error (pa. 0035, … AI computing devices 140 may be configured to increase the quality and efficiency of code developed [i.e. generate] and reviewed with computing devices 105, while streamlining CI/CD pipelines using Large Language Models (LLM) technology, Further, par. 0037, … docstring generator 220, bug identifier 225, code analyzer and fixer 230, unit test generator 235, pull request approver and summarizer 240 [i.e. output], and note generator 250. Further, par. 0042, … when analyzing the code, bug identifier 225 may determine potential issues and vulnerabilities associated with the code before the code is compiled or deployed. If bug identifier 225 determines there are issues with the code, bug identifier 225 may determine fixes and best practices. In embodiments, bug identifier 225 may be configured to suggest fixes to the code).
Zhang discloses cloned software deployment (par. 0089, … application files 112, based on the cloned copy of the existing project data 132 retrieved at block 412. As discussed above, the existing project data 132 can be configured such that the software application can execute in an on-premise computing environment or other computing environment different from the cloud computing environment 106. The existing project data 132 may accordingly include configuration details associated with the previous computing environment associated with the existing application, and/or include references to computing resources and/or other elements of the previous computing environment. For example, the existing project data 132 may include dependencies that reference services provided by an on-premise computing environment. As another example, the existing project data 132 can include a project object model (POM) file that a software project management tool, such as Apache® Maven, can use to build and/or deploy the software application in the previous computing environment, plugins for the software project management tool, and/or other information relevant to configuration and/or deployment of the application in the previous computing environment).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Petrov to include cloned software deployment, as disclosed by Zheng, for the purpose of modifying the cloned copy of the existing project data (see paragraph 87 of Zheng).
As to claim 6, Petrov discloses the method wherein verifying the code update comprises executing, in the updated (par. 0032, … reviewers may review code that has been augmented, updated, and/or documents by AI computing device 140. Responsive to approving code for updating, computing devices 105 may transmit the approved code to server 120 where the approved code can be stored within codebase repositories 130. Further, par. 0033, Server 120 may be a computing resource that is configured to remotely provision, allocate, manage, and codebase repositories 130 associated with software applications to execute the software applications. Server 120 may include physical computing devices residing at a particular location or may be deployed in a cloud computing network environment. …).
Zhang discloses cloned software deployment (see par. 0089).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Petrov to include cloned software deployment, as disclosed by Zheng, for the purpose of modifying the cloned copy of the existing project data (see paragraph 87 of Zheng).
As to claim 8, Petrov discloses the method wherein the input data comprises a code base of the software deployment (par. 0013, document the code upon the code being placed on a pipeline by adding docstrings and comments to the code, reducing the effort required for maintainability. AI may prepare the code on the pipeline for human review by analyzing the content of a pull request, wherein a pull request is associated with a piece of code to be added [i.e. input] to the database without merging the code. The AI analysis may include determining potential issues and vulnerabilities before the code is compiled or deployed. ... Further, par. 0014, Docstring generator 220 may be a computing device that is configured to analyze changes to code and insert missing documentation into the code. For example, docstring generator 220 may be configured to add docstring and comments to the code. …).
As to 11, Petrov discloses system comprising:
a memory (Petrov at par. 0033, … server 120 may include a computer-readable medium including one or more of a portable computer diskette, a hard disk, a random access memory (RAM) device, a read-only memory (ROM) device, an erasable programmable read-only memory (EPROM or Flash memory) device, a portable compact disc read-only memory (CDROM), an optical storage device, and a magnetic storage device.); and
a processing device, operatively coupled to the memory, the processing device configured to (par. 0032, Computing devices 105 may be a tablet computer, laptop computer, a computer, personal data assistant, or any other type of device with a hardware processor that are configured to process instructions and connect to one or more portions of network 110. Computing devices 105 may have a graphical user interface that is configured to allow a user to interact with a processor of client computing device 105 to create and/or update code. …):
For remaining limitations see remarks regarding claim 1.
As to claim 12, it is the method claim, having similar limitations of claim 2. Thus, claim 12 is also rejected under the same rationale as cited in the rejection of claim 2.
As to claim 13, it is the method claim, having similar limitations of claim 3. Thus, claim 13 is also rejected under the same rationale as cited in the rejection of claim 3.
As to claim 14, it is the method claim, having similar limitations of claim 4. Thus, claim 14 is also rejected under the same rationale as cited in the rejection of claim 4.
As to claim 15, it is the method claim, having similar limitations of claim 5. Thus, claim 15 is also rejected under the same rationale as cited in the rejection of claim 5.
As to claim 16, it is the method claim, having similar limitations of claim 6. Thus, claim 16 is also rejected under the same rationale as cited in the rejection of claim 6.
As to claim 18, it is the method claim, having similar limitations of claim 8. Thus, claim 18 is also rejected under the same rationale as cited in the rejection of claim 8.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being obvious over Petrov and Zheng as applied to the claim 1 and 11 above and in view of Balasubramanian et al. (US 20110154314 A1, hereinafter Balasubramanian).
As to claim 7, Petrov discloses the method wherein updating the software deployment based on the verified code update comprises:
requesting an approval of the verified code update (par. 0032, … Responsive to approving code for updating, computing devices 105 may transmit the approved code to server 120 where the approved code can be stored within codebase repositories 130);
Petrov as modified by Zhen does not explicitly disclose the following but,
Balasubramanian discloses updating the software deployment in response to receiving the approval (par. 0012, … or a user acceptance test of the source code implementing the update request), one or more software release operations (e.g., a release of a software update implementing the update request, an approval of a deployment of the software update, and a verification of a release of the software update), and/or any other operation associated with the update request).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Petrov to include updating the software deployment in response to receiving the approval, as disclosed by Balasubramanian, for the purpose to receive data representative of an update request for a deployed software application (see paragraph 10 of Balasubramanian).
As to claim 17, it is the method claim, having similar limitations of claim 7. Thus, claim 17 is also rejected under the same rationale as cited in the rejection of claim 7.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being obvious over Petrov and Zheng as applied to the claim 1 and 11 above and in view of Van Emden et al. (US 20240411533 A1, hereinafter Van Emden).
As to claim 9, Petrov as modified by Zhen does not explicitly disclose the following but,
Van Emden discloses the method wherein the input data comprises a compiler intermediate representation associated with the software deployment (par. 0027, … In particular, the code may be compilable to a binary intermediate representation which may be executable at runtime by a binary interpreter, i.e., a software component running on a target device. … . Further, par. 0037, the application is configured to establish a sandbox for execution of the binary intermediate representation of the machine learned model on the device. For security purposes, the binary intermediate representation may run in a so-called sandbox. This may facilitate the adoption of deployed machine learned model …).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Petrov to include the method wherein the input data comprises a compiler intermediate representation associated with the software deployment, as disclosed by Van Emden, for the purpose of configuration of the application for the stated purpose may be established by the system and computer-implemented method generating the application accordingly (see paragraph 37 of Van Emden).
As to claim 19, it is the method claim, having similar limitations of claim 9. Thus, claim 19 is also rejected under the same rationale as cited in the rejection of claim 9.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being obvious over Petrov and Zheng as applied to the claim 1 and 11 above and in view of Kramer et al. (US 20240086164 A1, hereinafter Kramer).
As to claim 10, Petrov as modified by Zhen does not explicitly disclose the following but,
Kramer discloses the method further comprising training the LLM to generate error code (abstract, generating synthetic paired source code snippets that are semantically equivalent but syntactically distinct. In various implementations, few shot learning may be performed to prompt a large language model, based on demonstration source code snippet(s) in syntactically constrained pseudocode, to generate additional source code snippets in the syntactically constrained pseudocode. Based on additional source code snippets in additional programming language(s), the large language model may be used to generate more training source code snippets in the syntactically constrained pseudocode. Further, par. 0022, some of the training source code snippets in the syntactically constrained pseudocode may include syntactic and/or semantic errors. For example, after few shot learning, the large language model may generate, from an unpaired source code snippet in the reference programming language, a training source code snippet in the syntactically constrained pseudocode that includes one or more syntactic and/or semantic errors. …).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Petrov to include the method further comprising training the LLM to generate error code, as disclosed by Kramer, for the purpose of checking and implementing, the training source code snippets with invalid syntaxes, semantic errors. (see paragraph 23 of Kramer).
As to claim 20, it is the method claim, having similar limitations of claim 10. Thus, claim 20 is also rejected under the same rationale as cited in the rejection of claim 10.
Conclusion
Prior arts made of record are considered pertinent to applicant's disclosure. See MPEP § 707.05 (C) For Examples:
I. Klein et al. (US 20220291986 A1) discloses: “ Cloud-based monitoring of hardware components in a fleet of storage systems, including: collecting, for a plurality of hardware components that are included in a physical storage system, information describing the operation each hardware component, wherein information is collected for the hardware components of multiple physical storage systems; predicting, based on the information describing the operation each hardware component and historical information describing the operation of one or more other hardware components, the expected performance of each hardware component; and modifying, based on the expected performance of each hardware component, the utilization of at least one or more of the physical storage systems in the fleet.” (please see abstract).
II. Karr et al. (US 11093139 B1) discloses: “Servicing I/O operations in a virtual storage system, including: receiving, by the virtual storage system, a request to write data to the virtual storage system; storing, within staging memory provided by one or more virtual drives of the virtual storage system, both the data and an erasure code based on the data; and migrating, from the staging memory to more durable data storage provided by a cloud services provider, at least a portion of data stored within the staging memory without migrating the erasure code based on the data.” (please see abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD H KABIR whose telephone number is (571)270-1341. The examiner can normally be reached M-F, 8:00 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sam Sough can be reached at 571-272-6799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Mohammad Kabir/
Examiner, Art Unit 2192
/S. Sough/SPE, Art Unit 2192
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