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
Status of Claims
This communication is a First Office Action on the merits in reply to application number 18/402,254 filed on 01/02/2024.
Applicant’s response filed on 03/23/2026 cancels claims 1-2. Claims 3-20 are currently pending and have been examined.
Election/Restriction
Applicant’s election without traverse of claims 3-20 (Invention III) in the reply filed on 03/23/2026 is acknowledged.
Claims 1-2 (Inventions I and II) are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 01/02/2024 has been considered.
Claim Objection
Claims 4-20 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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
Claim 3 is rejected under 35 U.S.C. §103 as unpatentable over Wang et al. (US 2016/0283201, hereinafter “Wang”) in view of Liu et al. (US 2019/0139191, hereinafter “Liu”) in view of Foroughi et al. (US 2022/0130272, hereinafter “Foroughi”).
Claim 3: Wang teaches a method for automating event orchestration in distributed DevOps environments (pars. 5-7: simulation of a UML activity diagram can be carried out only in a semi-automatic manner; UML activity diagram model to be simulated is read and parsed; system behavior simulation method based on activity diagram model) comprising the steps of:
employing image recognition to extract metadata from Unified Modeling Language (UML) diagrams (pars. 6-11, 29, and claim 1: e.g., UML activity diagram [which is an image] model to be simulated is read and parsed, important model element information is extracted from the model; various model elements parsed from the UML activity diagram model; token transition indicates a value change of token flag data, i.e., the accessed node changes; variable tables include specific value table and symbol value table of variables; node coverage is the ratio of the number of accessed nodes to the total number of nodes parsed from the UML activity diagram model; diagram model stored in XMI (XML Metadata Interchange); Like most UML models, UML activity diagram models are also saved in XMI (XML Metadata Interchange) format. In actual practice, an XML parsing tool dom4j can be used);
… interpret the UML diagrams for generating…event task rules (pars. 6-15 and 29-38: e.g., UML activity diagram model to be simulated is read and parsed, important model element information is extracted from the model, and complete model mapping is built up in the memory; then, the read-in UML activity diagram model is parsed and various model elements are parsed out from the UML activity diagram model; next, progressive actual execution, symbolic execution, and constraint solution are carried out for the model in a hybrid execution concept, till a node coverage threshold is reached [i.e., event task rules]; finally, simulated execution of the UML activity diagram model is carried out in the simulation test cases obtained in the previous step; Action node; Activity node, Decision node; Merge node; Fork node; Join node; Final node; By parsing the different types of nodes and their attributes described above, a data structure corresponding to the UML activity diagram model is constructed in the program memory).
Wang does not teach:
utilizing an Artificial Intelligence (AI) - Generative Adversarial Network (GAN) engine … for generating DevOps event task rules;
mapping the metadata that was extracted to specific DevOps event tasks using a context mapping engine; and
integrating the DevOps event task rules that were generated with existing DevOps tools for automated task creation and management.
Liu teaches:
utilizing an Artificial Intelligence (AI) - Generative Adversarial Network (GAN) engine … for generating…event task rules (pars. 79-81: In the adversarial networks, the generative neural network is used to convert an input image having an effect A into an output image having an effect B, and the discriminative neural network is used to judge whether the output image has features of the effect B, and output a discrimination label. For example, if it is judged that the output image has the features of the effect B, “1” is output, and if it is judged that the output image does not have the features of the effect B, “0” is output. The generative neural network is gradually generated, so that the discriminative neural network outputs an output image which is “1”, and the discriminative neural network can gradually more accurately judge whether the output image has converted features).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wang with Liu because the references are analogous since they are each directed to automated features for image/diagram processing, which is within Applicant’s field of endeavor of interpreting UL diagrams to extract information, and because modifying Wang with Liu’s AI based GAS engine for generating the event task rules, as claimed, would serve the motivation to improve efficiency and reduce cost of software development (Wang at par. 23); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Wang and Liu do not teach:
DevOps;
mapping the metadata that was extracted to specific DevOps event tasks using a context mapping engine; and
integrating the DevOps event task rules that were generated with existing DevOps tools for automated task creation and management.
Foroughi teaches:
DevOps (par. 73: aspects of the task context or work description can also be considered in the identification of context identifiers for the task, such as, for example the department the task is assigned from (e.g. Quality Assurance, Operations, DevOps, Software Development, Product Development, Support));
mapping the metadata that was extracted to specific DevOps event tasks using a context mapping engine (pars. 14, 30, 35, 72-73, and 118: completing the work task matching the identified work tasks to one or more training module in a training module database by comparing contextual identifiers associated with the training module comprising one or more keyword, code fragment, or metadata tag, to the technical description or the set of requirements for the work task; training module by textual code analysis of code by a code analysis engine and the textual code analysis result is matched to a contextual identifier in the training module; context analysis engine 118 can analyse the contents of the task to identify the task context; From the task description can be extracted one or more contextual identifiers, also referred to as keywords or metadata tags, which are based on the task description and/or other contextual data; task context or work description can also be considered in the identification of context identifiers for the task, such as, for example the department the task is assigned from…DevOps [i.e., DevOps event tasks]); and
integrating the DevOps event task rules that were generated with existing DevOps tools for automated task creation and management (par. 118: work tasks can be identified from a task database, can be manually selected or created, automatically generated; See also, par. 73: task, such as, for example the department the task is assigned from…DevOps [i.e., DevOps event tasks] generated with DevOps tools).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wang/Liu with Foroughi because the references are analogous since Wang/Liu are directed to automated features for image/diagram processing, which is within Applicant’s field of endeavor of interpreting UL diagrams to extract information, whereas Foroughi’s automated content generation features are reasonably pertinent to the problem with which applicant is concerned (rapid/efficient deployment of computer generated tools), and because modifying Wang/Liu with Foroughi’s context mapping and integration of DevOps event task rules for automated task creation/management, as claimed, would serve the motivation to improve efficiency and reduce cost of software development (Wang at par. 23); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Allowable over the prior art
Claims 4-20 are allowable over the prior art. These claims are not allowed, however, because they are objected to (as noted above) due to their dependency from a rejected base claim (claim 3) and would be allowable only if rewritten in independent form including all of the limitations of their base claims and any intervening claims. With particular respect to dependent claim 4, the prior art of record does not teach or render obvious the steps of: selecting, by the Al - GAN engine, a main branch that holds source code that reflects a current state of a product in production; creating, by the Al - GAN engine, a feature branch from the main branch that allows developers to work on new features without disturbing a main code base; generating, by the Al - GAN engine, a naming convention for continuous integration (CI) branches to identify a purpose of software branches and organize workflow; generating, by the Al - GAN engine, CI/ continuous development (CD) pipeline rules that trigger a CI/ CD pipeline when changes are pushed to the CI branches to enable new code commits to be built and tested without manual intervention; triggering, by the Al - GAN engine, when the Cl /CD pipeline rules are met, automated testing tasks to verify that the new code commits do not break any existing functionality; performing, by the Al - GAN engine, automatic validation on the new code commits; generating, by the AI - GAN engine, notifications based on auto-configure trigger rules; merging, by the AI - GAN engine, the feature branch to the main branch once the new code commits pass all checks; resolving, by the AI - GAN engine, any merge conflicts between the feature branch and the main branch; and executing, by the AI - GAN engine, an autodelete task to delete the feature branch, as recited by dependent claim 4, thereby rendering claim 4 and its dependent claims (5-20) and allowable over the prior art. With respect to subject matter eligibility of claims 3-20 (MPEP 2106), the claims satisfy Step 1 of the eligibility inquiry (MPEP 2106.03) because the claimed method is directed an eligible category of subject matter (a process), and when further evaluated under Step 2A Prong One (MPEP 2106.04), the claims are not directed to a judicial exception because the claims do not recite limitations reasonably considered as setting forth an abstract idea, law of nature, or natural phenomenon, and therefore claims 3-20 qualify as eligible subject matter under 35 USC §101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
E. Di Nitto et al., "An Approach to Support Automated Deployment of Applications on Heterogeneous Cloud-HPC Infrastructures," 2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Timisoara, Romania, 2020, pp. 133-140: discloses a framework to assist DevOps teams with management of cloud and HPC clusters, including an AI inference algorithm to recognize specific objects within images and automated generation of code.
Y. L.Gamage, “Automated software architecture diagram generator using natural language processing,” BSc dissertation, Dept. Computer Sci Univ. of Westminster, UK, May 2023: discloses automated generation of software architecture diagrams, including UML diagram processing using NLP techniques, and Deep-learning based diagram generation using, e.g., Generative Adversarial Networks.
Guru Charan Kakaraparthi. Building a GenAI-Powered Advanced Code Generation Assistant Integrated with CI/CD Pipelines. Technix International Journal for Engineering Research 9(2):56-63. February 2022. Volume 9, Issue 2: discloses techniques for automating software development tasks using Generative AI (GenAI) in modern DevOps environments, including integration of AI-generated output into a continuous integration and continuous delivery (CI/CD) pipeline.
Srivastava et al. (US 2024/0160418): discloses automated assessment of IT vulnerabilities, including features for integrating the DevOps event task rules that were generated with existing DevOps tools for automated task creation and management (at par. 60: Once customization of the value stream map 602 is completed, the value stream map 602 customized based on the selected value stream mapping template 212 may be saved, shared, exported, and/or executed in another application (e.g., an application in communication with the CI/CD platform 145 and/or integrated as part of the CI/CD platform 145). Referring back to FIG. 2, the other application may include the automatic task creation 224 of the DevOps toolchain third party integration).
Jia et al. (US 2021/0173638): discloses design pattern recognition features, including employing image recognition to extract metadata from Unified Modeling Language (UML) diagrams (pars. 3 and 87: e.g., class diagram in the Unified Modeling Language (UML) is a type of static structure diagram that describes the structure of a system by showing the system's classes, their attributes, operations; object recognition tools and methods may be utilized; detect objects in a diagram; image object detection of step 506 determines that a predetermined pattern comprised of a combination of UML graphical notations is present within the modified diagram).
Franchitti (US 2019/0171438): discloses features for adaptive modification of network computing architecture, including employing image recognition to extract metadata from Unified Modeling Language (UML) diagrams (pars. 191, 208, and 492: automate specific tasks…image recognition; SoaML is an open source specification project from the Object Management Group (OMG), describing a UML profile and metamodel for the modeling and design of services within a service-oriented architecture).
Lang et al. (US 2019/0258953): discloses features for determining policies, rules, and agent characteristics for automating agents, including using Generative Adversarial Network (GAN) machine learning approaches (pars. 92, 132, 196, and 289), importing a model via a standard meta model (e.g., UML), and generating technical enforceable rules/configurations (par. 162).
Hellebro et al. (US 2011/0061041): discloses software application modeling techniques, including features for retrieving information from a UML model, including pattern recognition and optical character recognition of UML diagrams (par. 61).
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Timothy A. Padot whose telephone number is 571.270.1252. The Examiner can normally be reached on Monday-Friday, 8:30 - 5:30. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Brian Epstein can be reached at 571.270.5389. The fax phone number for the organization where this application or proceeding is assigned is 571- 273-8300.
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/TIMOTHY PADOT/
Primary Examiner, Art Unit 3625
05/07/2026
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