System for AI-driven dynamic cloud process orchestration engine for contextual workflow automation

Abstract

System (100) for an AI-driven dynamic cloud processor orchestration engine for contextual workflow automation, comprising: - a Context Intelligence Engine configured to collect, aggregate and analyze real-time data from internal and external sources to create operational context; - an Orchestration Decision Engine operatively coupled with the Context Intelligence Engine and configured to interpret the operational context using artificial intelligence to determine optimal workflow execution paths; - a workflow execution engine operatively coupled to the orchestration decision engine and configured to deploy, manage, and scale cloud-native services or tasks in response to the specific workflow execution paths; - an Adaptive Workflow Modeling Engine configured to enable goal-oriented workflow definition and generate dynamically executable workflows based on user-defined goals and context analysis; - a learning and feedback engine configured to monitor workflow performance and apply continuous learning models to improve orchestration decisions over time; and - a security and compliance engine configured to enforce access control, audit logging, and data governance throughout workflow execution; the system autonomously adapts and executes cloud-based workflows based on real-time contextual data, without requiring manual intervention.

Description

[0001] The present invention relates to cloud computing and artificial intelligence, in particular to systems for dynamic cloud process orchestration. It enables context-aware, real-time workflow automation with AI-driven decision-making. The invention is applicable across industries and requires adaptive, scalable, and intelligent process execution.

[0002] Today, companies face significant challenges managing complex, dynamic workflows across multiple cloud platforms. Traditional workflow orchestration systems often rely on static rules or pre-programmed scripts that lack the flexibility to adapt to rapidly changing business environments. This leads to inefficiencies, errors, and missed opportunities, as workflows struggle to respond to unpredictable factors such as market shifts, system performance, or regulatory changes. The lack of real-time adaptability further complicates tasks such as optimizing cross-functional processes, managing disruptions, and ensuring timely responses to evolving customer needs. Existing tools are often limited to specific platforms or require manual monitoring, slowing operations and increasing the risk of errors.

[0003] The AI-driven Dynamic Cloud Process Orchestration Engine addresses these problems by providing a flexible, context-aware solution that automates and orchestrates workflows in real time. Leveraging machine learning, predictive analytics, and natural language processing, the system dynamically adapts to changing work demands, external conditions, and organizational requirements, ensuring optimal performance at all times. It integrates seamlessly with various cloud services and platforms, eliminating the limitations of static, platform-specific tools. This intelligent orchestration engine reduces the need for manual intervention, minimizes errors, and improves the scalability and efficiency of cloud-based operations, enabling organizations to thrive in an increasingly complex and volatile digital landscape.

[0004] One goal of the present disclosure is to enable real-time, context-dependent workflow automation without manual intervention.

[0005] Another objective of this disclosure is to reduce operational costs by dynamically adapting work processes based on changing conditions.

[0006] Another objective of this disclosure is to improve scalability and efficiency through the optimized use of cloud-native services.

[0007] Another objective of the present disclosure is to provide a flexible, no-code / low-code interface for business users to define workflows.

[0008] Another objective of this disclosure is the continuous improvement of decision and orchestration strategies through AI-driven feedback loops.

[0009] Another goal of this disclosure is to support multi-cloud and hybrid cloud environments and ensure seamless integration across platforms.

[0010] Another objective of this disclosure is to improve security and compliance by integrating enterprise-level data governance mechanisms.

[0011] Another objective of this disclosure is to optimize resource utilization and process performance through reinforcement learning models.

[0012] The present invention relates to AI-driven dynamic orchestration of cloud processes for automating and optimizing workflows based on real-time context data. It comprises several integrated engines: a Context Intelligence Engine that collects and analyzes operational data, an Orchestration Decision Engine that uses AI to determine the most efficient execution paths, and a Workflow Execution Engine that deploys and manages cloud services accordingly. Furthermore, the system features an Adaptive Workflow Modeling Engine that enables users to define targeted workflows, a Learning and Feedback Engine that continuously improves orchestration decisions based on performance data, and a Security and Compliance Engine that ensures data governance and access control.This system enables autonomous, scalable, and context-aware workflow automation in cloud environments, optimizing resource utilization and operational efficiency while adapting to changing conditions.

[0013] The invention presents a modular and intelligent system for AI-driven dynamic orchestration of cloud processes, composed of multiple specialized engines that collectively enable context-aware workflow automation. Each engine within the system performs a unique function and contributes to the adaptive, real-time orchestration of cloud processes in response to dynamic operating contexts. Context Intelligence Engine:

[0014] This engine serves as the system's sensory layer, continuously collecting, aggregating, and interpreting data from various sources, including application telemetry, user input, environmental data, IoT sensors, and external APIs. Using machine learning models, natural language understanding (NLU), and data correlation techniques, the Context Intelligence Engine extracts situational awareness in real time and transforms raw data into structured context maps. These context maps form the basis for decision-making throughout the orchestration process. Orchestration Decision Engine:

[0015] Based on inputs from the Context Intelligence Engine, the Orchestration Decision Engine forms the cognitive core of the system. It applies AI-driven decision models, including rule-based logic, probabilistic reasoning, and reinforcement learning, to determine the most efficient execution path for a given workflow. This engine can dynamically adjust process flows, enable or disable cloud services, and reroute operations based on changing contexts, system loads, or business priorities. Workflow Execution Engine:

[0016] This engine is responsible for executing the decisions made by the Orchestration Decision Engine. It interfaces with cloud-native orchestration platforms such as Kubernetes, serverless frameworks, and multi-cloud APIs to deploy and manage microservices and containers. The engine ensures high availability, scalability, and parallel execution of tasks, and supports rollback and failover mechanisms in the event of process exceptions or failures. Adaptive Workflow Modeling Engine:

[0017] The Adaptive Workflow Modeling Engine enables users to design workflows in an abstract, goal-oriented format. Instead of hard-coded task sequences, users define intent, constraints, and business goals. The engine leverages historical data, process mining, and predictive modeling to create executable workflows that can evolve over time. It provides a no-code / low-code interface for business users and supports technical customization for developers. Learning & Feedback Engine:

[0018] This engine creates a closed feedback loop that monitors workflow results, execution metrics, and user feedback. It applies continuous learning techniques such as supervised and reinforcement learning to refine orchestration strategies, improve prediction accuracy, and optimize resource utilization. This engine ensures that the system becomes increasingly intelligent and efficient with each execution cycle. Security & Compliance Engine

[0019] To ensure enterprise-wide governance, this engine ensures data protection, secure communications, and regulatory compliance. It manages identity and access management, encryption, audit logging, and policy enforcement. The engine can also be integrated with third-party security platforms to ensure consistent enforcement across the entire cloud ecosystem.

[0020] The invention is explained again below with reference to the figure. It shows: Fig. : an illustration of the AI-driven dynamic cloud processor orchestration system (100).

[0021] Fig.shows the AI-driven dynamic cloud processor orchestration system (100). The system works by first collecting real-time context data from various sources such as user input, application logs, sensor networks, and third-party APIs via the Context Intelligence Engine. This data is analyzed using AI and machine learning to generate a comprehensive understanding of the current operating environment. Once the context is established, it is passed to the Orchestration Decision Engine, which interprets this information to determine the optimal workflow path. This decision-making process considers factors such as priority, resource availability, compliance requirements, and historical patterns to adaptively orchestrate workflows that match the current scenario.

[0022] Following the decision phase, the Workflow Execution Engine dynamically deploys, scales, and manages cloud-native services or tasks as defined by the orchestration logic. These workflows are generated and continuously refined by the Adaptive Workflow Modeling Engine, which adjusts the execution plans based on evolving goals and conditions. Meanwhile, the Learning & Feedback Engine monitors workflow performance and feeds insights into the system to improve future decisions. Security and compliance are enforced end-to-end via the Security & Compliance Engine, which ensures that operations are secure, auditable, and compliant with regulatory standards. This closed-loop process enables the system to operate autonomously, contextually, and intelligently across distributed cloud environments.

Claims

[1] System (100) for an AI-driven dynamic cloud processor orchestration engine for contextual workflow automation, comprising: - a Context Intelligence Engine configured to collect, aggregate and analyze real-time data from internal and external sources to create operational context; - an Orchestration Decision Engine operatively coupled with the Context Intelligence Engine and configured to interpret the operational context using artificial intelligence to determine optimal workflow execution paths; - a workflow execution engine operatively coupled to the orchestration decision engine and configured to deploy, manage, and scale cloud-native services or tasks in response to the specific workflow execution paths; - an Adaptive Workflow Modeling Engine configured to enable goal-oriented workflow definition and generate dynamically executable workflows based on user-defined goals and context analysis; - a learning and feedback engine configured to monitor workflow performance and apply continuous learning models to improve orchestration decisions over time; and - a security and compliance engine configured to enforce access control, audit logging, and data governance throughout workflow execution; the system autonomously adapts and executes cloud-based workflows based on real-time contextual data, without requiring manual intervention. [2] The system (100) of claim 1, wherein the Context Intelligence Engine uses natural language processing and machine learning algorithms to interpret unstructured data from sources such as logs, emails, or chat inputs. [3] The system (100) of claim 1, wherein the orchestration decision engine uses rule-based logic, probabilistic reasoning, and reinforcement learning to determine execution paths. [4] The system (100) of claim 1, wherein the workflow execution engine interfaces with container orchestration platforms, serverless computing frameworks, or hybrid multi-cloud infrastructures. [5] The system (100) of claim 1, wherein the Adaptive Workflow Modeling Engine includes a no-code or low-code interface that enables users to define workflows using natural language or visual modeling tools. [6] The system (100) of claim 1, wherein the learning and feedback engine uses historical workflow data and outcome analysis to refine future decision models and orchestration strategies. [7] The system (100) of claim 1, wherein the security and compliance engine integrates with external identity and access management systems to enforce enterprise security policies during orchestration.

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