Invented by Singh; Shailendra, Gupta; Saurabh

Let’s explore how new technology can take DevOps to a new level. This article explains a patent application for automating DevOps task orchestration using advanced AI and GANs (Generative Adversarial Networks). We’ll look at why the market needs this, the science and older technology behind it, and what is truly new about this invention.

Background and Market Context

DevOps is about making software faster and better by connecting development and operations teams. In today’s world, teams are spread out across cities, countries, and even continents. They build, test, and deploy software together. But when people work in different places and time zones, things can get messy fast.

Manual work is a big problem in DevOps. Teams have to create tasks, check systems, set up environments, and keep everyone in the loop. This often means filling out tickets, tracking tasks by hand, or double-checking if systems are ready. These steps can slow down work, lead to mistakes, and cause confusion. In big teams, these problems only grow. A simple forgotten step or miscommunication can break the whole chain, causing bugs or delays in launching new features.

The software world is moving fast. Every company wants to update their apps and websites often. Faster updates mean happier users and more business. So, there’s pressure on teams to go faster while still keeping things stable and safe. But with so many manual steps, it’s hard to keep up. Mistakes creep in, especially when people are tired or in a rush. Each manual task is a chance for something to go wrong.

That’s why there’s a big push for automation. If computers can take over the boring, repeatable jobs, teams can focus on the important stuff—like building new features and fixing bugs. Automation can also mean fewer errors, better security, and smoother teamwork. But making this happen is not easy, especially in big, distributed teams who use lots of different tools.

Another key factor is complexity. Modern apps are made up of many parts that talk to each other. Each part might be built by a different team or even in a different country. Keeping all these moving parts working together is tough. The more teams and tools you have, the harder it is to keep track of what’s going on. Manual steps just don’t scale. They slow down progress and make it hard to spot problems early.

The invention we’re talking about steps in right here. It promises a way to use smart AI to read system diagrams, understand how everything fits together, and then create and manage the tasks needed to keep the software moving forward. It even learns over time, getting better as it goes. This is a big deal for companies that want to stay ahead, deliver faster, and avoid costly mistakes.

Scientific Rationale and Prior Art

Let’s break down the science and what has been done before. DevOps automation has always been a goal. Early solutions used scripts or rule-based systems. These tools could help with simple, repeatable tasks—like setting up a new environment or running standard tests. However, they needed lots of setup and didn’t handle change well. If the system changed, people had to rewrite scripts by hand.

Another important tool is the UML diagram. UML diagrams are like blueprints for software. They show how parts of a system connect and talk to each other. Developers use them to plan big systems, track changes, and explain ideas to others. But until now, these diagrams have mostly been used by people, not by machines. Most software tools can’t “read” a diagram and understand what it means for the way tasks should be managed.

Image recognition and natural language processing (NLP) have made it possible for computers to “see” and “read” diagrams and documents. With these tools, AI can pull out details from pictures or text, like which parts are connected or what each part does. This is a big step forward, but it’s only the start. Just knowing what’s in a diagram isn’t enough—you need to understand the context and what should happen next.

Traditional automation tools in DevOps include Jenkins, GitLab CI/CD, and others. These tools can run tests and deploy code, but they need people to set up the rules. They don’t learn over time or adapt to new workflows automatically. When things change, someone has to update the settings.

AI and machine learning have started to make inroads. Some solutions can predict problems, suggest fixes, or optimize resource use. But most AI in DevOps today is used for monitoring or analytics, not for orchestrating complex workflows based on system design.

Generative AI takes the next step. GANs, or Generative Adversarial Networks, are a special kind of AI that can create new things by learning from examples. They are often used in images, art, and language, but using them to generate DevOps rules and workflows is new. GANs can learn from real-world feedback. If a workflow fails, the AI can try something different next time. This kind of learning is powerful because it means the system can adapt as teams, tools, or projects change.

Previous attempts at using AI in this space have hit limits. They could not fully automate complex, changing workflows. They could not “see” and understand diagrams and use that knowledge to create new, working DevOps tasks. They couldn’t easily integrate with many different tools used by teams around the world. And they didn’t learn from mistakes or get smarter over time.

The invention in this patent application changes the game by bringing together image recognition, GANs, and smart workflow management. It automates not just the simple steps, but the whole chain—from reading diagrams to managing tasks, creating rules, checking for errors, and learning from what happens.

Invention Description and Key Innovations

This invention is a new system that brings AI into every step of DevOps task management. Let’s walk through how it works in plain language.

The heart of the system starts with UML diagrams and design documents. These are fed into an AI engine that acts like a super-smart reader. Using computer vision, the AI scans the diagrams and pulls out key details: what parts exist, how they connect, and how data flows. This process is much more advanced than just reading text—it understands shapes, lines, arrows, and even words, no matter the language or diagram type.

Once it has this information, the AI creates “metadata.” Think of metadata as notes about what’s in the diagram. It lists the parts, how they interact, and what each part is supposed to do. This metadata forms the basis for everything that follows.

Next, another engine analyzes this metadata in context. It goes beyond just names and lines; it tries to grasp what the system is for and how it should work. For example, it might notice that two parts must work together or that one part must finish before another can start. This understanding is key to building the right tasks and workflows.

The system then uses a special AI engine—powered by GANs—to generate the actual DevOps tasks and rules. The GAN engine doesn’t just spit out random rules. It looks at the context, past workflows, and feedback from earlier runs. If something worked well last time, it uses that as a guide. If something failed, it tries a new approach. Over time, the AI gets better at creating rules that fit each project and team.

The system also includes a dependency analyzer. This part of the AI checks which tasks depend on others. It figures out what must happen first, what can run at the same time, and where problems might pop up. This helps avoid bottlenecks and keeps things running smoothly.

After generating the tasks and rules, the system validates them. It checks if the rules make sense, are safe, and will actually work in the current DevOps setup. Only after passing these checks do the rules get used in real projects.

The orchestration engine is where everything comes together. It manages the whole workflow, making sure each task runs at the right time, in the right order, and using the right resources. It connects with the tools teams already use—like Git, Jenkins, Jira, and others. This means teams don’t have to throw away what they know; the AI simply makes those tools smarter and faster.

A unique part of this invention is real-time learning. The AI watches how tasks go, what works, what fails, and how long things take. It uses this feedback to improve future workflows. If a new tool is added, or if the team changes how they work, the AI adapts. This is called meta-learning—it means the AI doesn’t just follow old rules; it gets better over time as it learns from experience.

Security and compliance are built in. Every rule and task the AI creates is checked for safety and follows company or industry rules. This is critical for teams working with sensitive data or in regulated industries.

The AI also handles communication. It can send out automatic messages to stakeholders, letting them know when tasks start, finish, or if something goes wrong. This keeps everyone on the same page without flooding inboxes with useless messages.

Another key feature is documentation. Every task, rule, and workflow the AI creates is documented automatically. This means teams can see what was done, why it was done, and how to repeat or change it in the future. This is a huge help for audits, training, and troubleshooting.

Finally, the system runs in the cloud. This makes it easy for teams in different places to work together. It scales up or down as needed, handling big projects or small ones just as easily. It can also work with multiple languages and types of diagrams, which is important for global teams.

To sum it up, the key innovations in this invention are:

1. Smart diagram reading and context understanding: The AI can “see” diagrams, understand what they mean, and use this to drive automation.

2. GAN-powered rule generation: The system uses cutting-edge AI to create, test, and improve DevOps task rules automatically.

3. Real-time learning and adaptation: The AI gets smarter with each project, learning from success and failure.

4. Full workflow automation: From reading diagrams to managing tasks, checking dependencies, ensuring security, and keeping everyone informed, the system covers it all.

5. Seamless integration: It works with existing tools and systems, so teams don’t have to start from scratch.

6. Cloud-based and globally ready: The platform supports teams anywhere in the world, handling different languages and diagram types.

Conclusion

The invention described in this patent application is a leap forward for DevOps automation. By harnessing the power of AI, GANs, and computer vision, it removes the manual, error-prone steps from distributed DevOps workflows. Teams save time, reduce mistakes, and deliver better software, faster. The system learns as it goes, adapts to changes, and helps teams of any size or location work together smoothly. This is not just another automation tool—it’s a smarter, learning platform designed for the future of software development.

For businesses looking to stay ahead, adopting such technology means less time worrying about process and more time delivering value to customers. This is the new face of DevOps—intelligent, automated, and always improving.

Click here https://ppubs.uspto.gov/pubwebapp/ and search 20250217740.