Automated production recovery system with APIs and intelligent alerts

Abstract

An automated production recovery system (100) using utility APIs and intelligent alerts, comprising: (a) an intelligent monitoring and alarm recording module configured to receive and prioritize alarms from multiple monitoring sources; b) a root cause analysis module configured to correlate alerts with logs, metrics, and topology data to identify root causes (c) a decision intelligence module configured to determine one or more recovery actions based on predefined policies and historical incident data; (d) a utility API integration layer configured to autonomously execute the specified recovery actions through infrastructure and application-level APIs; and (e) a feedback system configured to collect incident data and refine alarm classification and decision-making models, f) the system autonomously detects, diagnoses and resolves production incidents with minimal human intervention.

Description

[0001] The present invention relates to automated IT operations, particularly to systems and methods for intelligent production recovery. It utilizes utility APIs and intelligent alerting mechanisms to detect, diagnose, and autonomously resolve system failures. The invention aims to increase uptime, reduce manual intervention, and ensure rapid recovery of critical services.

[0002] In modern IT infrastructures, system and production outages pose a significant risk to business continuity, customer satisfaction, and operational efficiency. Traditional recovery processes often rely on manual intervention by IT teams, which can be time-consuming and error-prone and depend on the availability and responsiveness of human resources. These delays in diagnosing problems and initiating recovery actions lead to extended downtime, financial losses, and poorer service-level agreements (SLAs).

[0003] Despite the availability of monitoring tools and alerting systems, most existing solutions lack the intelligence needed to distinguish between critical incidents and non-urgent anomalies. This often leads to alarm fatigue, where important signals are lost in a flood of non-critical messages. Furthermore, many current systems are not tightly integrated with utility APIs that could automate recovery actions such as restarting services, clearing queues, or reallocating resources. As a result, valuable time is lost in identifying and resolving recurring issues that could otherwise be handled automatically.

[0004] The invention addresses these limitations by introducing an intelligent alerting and recovery system that not only identifies and classifies incidents in real time but also provides interfaces to utility APIs to autonomously perform context-dependent recovery actions. This approach minimizes downtime, reduces human workload, and ensures a more resilient and responsive IT environment. By automating decision-making and execution of recovery workflows in production, the system significantly improves the stability and reliability of business operations.

[0005] An objective of the present disclosure is to minimize downtime by enabling autonomous and rapid recovery from failures.

[0006] Another objective of this disclosure is to reduce manual intervention through intelligent decision making and execution.

[0007] Another objective of this disclosure is to improve the accuracy of alerts by filtering out false positives and irrelevant signals.

[0008] Another goal of this disclosure is to accelerate root cause identification through automated correlation and analysis.

[0009] Another objective of this disclosure is to improve the recovery success rate through adaptive learning from past incidents.

[0010] Another goal of this disclosure is seamless integration into existing tools via API-based automation.

[0011] Another objective of this disclosure is to enable audit-proof reporting and real-time insight into system operations.

[0012] Another goal of this disclosure is to scale across complex environments to support enterprise-level resiliency.

[0013] Further objects and advantages of the present disclosure will become apparent from the following description, which is not intended to limit the scope of the present disclosure.

[0014] The present invention relates to an automated system for detecting, diagnosing and resolving production disruptions using intelligent alerts and APIs.

[0015] It reduces the dependence on manual intervention by enabling self-healing capabilities that ensure faster recovery and improved system availability in IT environments.

[0016] Another embodiment of the present invention is the intelligent alert capture module, which filters and classifies alerts from various monitoring sources.

[0017] It uses machine learning to prioritize high-impact events and suppress noise. Only actionable incidents are forwarded for further diagnosis and resolution.

[0018] Another embodiment of the present invention is the root cause analysis module, which correlates alerts with logs, metrics, and system dependencies. It identifies the underlying source of failure using historical data and pattern recognition. This enables targeted recovery actions instead of symptom fixes.

[0019] Another embodiment of the present invention is the API execution layer, which performs recovery operations through interfaces to infrastructure and application APIs. It can restart services, reallocate resources, trigger rollbacks, or execute custom workflows.

[0020] Another embodiment of the present invention is the feedback and learning module, which collects telemetry data from each incident and recovery operation. It refines classification models and recovery strategies based on success rates and operator input. This self-improvement loop increases the system's adaptability over time.

[0021] Another embodiment of the present invention is the reporting and notification module, which provides an overview of system actions and incident trends. It creates audit logs, real-time dashboards, and sends notifications to stakeholders. The system can be integrated with tools such as PagerDuty, Slack, or ServiceNow to optimize collaboration.

[0022] Another embodiment of the present invention is that these modules form a closed automated recovery system for modern IT operations.

[0023] The invention ensures minimal downtime, optimized resource utilization, and increased service reliability. It is scalable, intelligent, and adaptable to various enterprise technology stacks.

[0024] The present invention relates to automated production recovery using utility APIs and intelligent alerting systems, designed as a modular, scalable system for detecting, analyzing, and autonomously recovering from production failures in IT environments. It consists of six primary modules, each of which contributes to a closed-loop recovery system that minimizes manual intervention and downtime. Module for intelligent monitoring and alarm recording

[0025] This module serves as the entry point into the system and is responsible for ingesting alerts from various monitoring sources such as application performance monitors, infrastructure health checks, and third-party monitoring platforms. It applies dynamic thresholds, context-aware filtering, and noise suppression algorithms to suppress redundant or irrelevant alarms. Using machine learning classifiers and historical incident data, the module prioritizes alerts by severity, frequency, and potential impact to ensure that only actionable issues progress to the next stages of the remediation pipeline. Module for root cause analysis and correlation

[0026] Once a validated alert is received, this module performs automatic root cause analysis by correlating it with logs, metrics, previous incidents, and the system topology. It uses pattern recognition, dependency diagrams, and anomaly detection to identify the most likely cause. The engine is capable of linking seemingly disparate alerts into a single incident, providing a unified view of cascading failures. This contextual understanding enables the system to determine the exact point of failure and distinguish between symptoms and root problems. Decision Intelligence and Policy Module

[0027] This module controls automated recovery actions through a set of configurable policies, rules, and AI-driven decision models. Based on the identified root cause, historical resolution strategies, and the current system state, it selects the most appropriate recovery action. These actions can range from restarting services to reallocating storage and scaling resources to triggering fallback workflows. The engine supports learning-based adaptation, allowing it to improve its decisions over time based on recovery results and administrator feedback. Utility API integration and execution layer

[0028] This layer acts as the operational backbone of the recovery system, providing a direct interface to cloud, infrastructure, and utility APIs at the application level. It executes predefined or dynamically created recovery actions securely and without human intervention. Examples include invoking Kubernetes restart commands, triggering CI / CD rollbacks, flushing the cache, or invoking cloud provider auto-healing functions. The module also verifies the success of each action and supports rollback mechanisms if recovery attempts fail or have unintended consequences. Feedback and learning module

[0029] To ensure continuous improvement and adaptability, this module captures detailed telemetry data on all incidents, decisions, and recovery outcomes. This data is used to refine alarm classification models, decision guidelines, and root cause assignment. Through feedback loops and reinforcement learning, the system evolves to handle new types of incidents more effectively over time. It can also incorporate manual operator resolution steps into its automated knowledge base, transforming tribal knowledge into reusable information. Reporting, auditing and notification module

[0030] This module ensures transparency and accountability, logging every alert, decision, and recovery action for audit purposes. It provides IT teams with real-time dashboards and detailed reports that highlight trends, system reliability metrics, and incident patterns. Notifications are intelligently routed to stakeholders based on the severity of the incident and the need for manual monitoring. Integration with incident management platforms such as PagerDuty, Slack, or ServiceNow ensures seamless collaboration when human intervention is required.

[0031] The invention is explained again below with reference to the figure. It shows: Fig. : an automated production recovery system (100) using APIs and intelligent alerts.

[0032] Fig.shows an automated production recovery system (100) that uses APIs and intelligent alerting. The system's operation begins with the intelligent monitoring and alerting module continuously ingesting alerts and performance data from various monitoring tools and telemetry sources, filtering out noise and prioritizing actionable issues. These validated alerts are forwarded to the Root Cause Analysis and Correlation module, where they are correlated with logs, metrics, and system dependencies to identify the actual source of the failure. Once the root cause is determined, the Decision Intelligence and Policy module evaluates appropriate recovery options based on predefined rules, previous problem resolution, and the current system context.It then delegates recovery execution to the Utility API integration and execution layer, which interacts with infrastructure and application APIs to autonomously perform corrective actions such as restarting services, dequeuing queues, or scaling resources. The feedback and learning system continuously collects data from the alert-to-recovery lifecycle and uses it to train and refine the system's decision logic and alert classification algorithms. Finally, the reporting, auditing, and notifications module records every operation to ensure compliance and transparency, sending intelligent notifications and summaries to relevant stakeholders, and providing analytics dashboards for trend analysis and performance optimization.This end-to-end workflow enables autonomous, intelligent, and adaptive production recovery with minimal human intervention, significantly improving system stability and uptime.

Claims

[1] An automated production recovery system (100) using utility APIs and intelligent alerts, comprising: (a) an intelligent monitoring and alarm recording module configured to receive and prioritize alarms from multiple monitoring sources; b) a root cause analysis module configured to correlate alerts with logs, metrics, and topology data to identify root causes (c) a decision intelligence module configured to determine one or more recovery actions based on predefined policies and historical incident data; (d) a utility API integration layer configured to autonomously execute the specified recovery actions through infrastructure and application-level APIs; and (e) a feedback system configured to collect incident data and refine alarm classification and decision-making models, f) the system autonomously detects, diagnoses and resolves production incidents with minimal human intervention. [2] The system (100) of claim 1, wherein the intelligent monitoring and alert input module uses machine learning algorithms to dynamically adjust alert thresholds and reduce false alarms. [3] The system (100) of claim 1, wherein the root cause analysis module uses historical incident patterns and system dependency graphs to identify the source of the failure. [4] The system (100) of claim 1, wherein the decision intelligence module includes rule-based logic and reinforcement learning to select optimal recovery actions based on the context. [5] The system (100) of claim 1, wherein the utility API integration layer interfaces with cloud services, container orchestration platforms, and infrastructure management tools to perform recovery operations. [6] The system (100) of claim 1, wherein the feedback system stores incident results and operator feedback to continuously train and improve alarm classification and recovery decision models. [7] The system (100) of claim 1 further comprises a reporting and auditing module configured to provide real-time dashboards, incident summaries, and audit logs suitable for compliance. [8] The system (100) of claim 1, wherein the system is configured to integrate with third-party incident management tools and communication platforms to provide real-time notifications and escalation messages based on the severity of the incident.

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