Description:FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003
Complete Specification
(See section10 and rule13)

1. Title of the Invention: AI-DRIVEN NETWORK INTELLIGENCE SYSTEM FOR PREDICTIVE FAULT DETECTION AND REMEDIATION
2.Applicants: -
Name Nationality Address
Rahul Tavva Indian Sr Network Engineer, Plot no 30, Radhika Colony, West Marredpally, Secunderabad, RangaReddy, Telangana- 500026, India.
3. Preamble to the description:
The following specification particularly describes the invention and the manner in which it is to be performed.

4. DESCRIPTION
FIELD OF THE INVENTION
The invention concerns artificial-intelligence-based network management, particularly systems and methods that analyze operational data to anticipate faults, identify causes, recommend corrective actions, and automatically restore performance across communication infrastructures with minimal human intervention and downtime.
BACKGROUND OF THE INVENTION
Modern communication networks support critical services across telecommunications, cloud computing, industrial automation, healthcare, transportation, and public infrastructure. As these networks become larger, virtualized, software-defined, and distributed, their operational behavior becomes more complex and difficult to monitor using conventional tools. Traditional network management systems generally rely on fixed thresholds, predefined rules, periodic diagnostics, and reactive maintenance. Such approaches often detect a fault only after service degradation, device failure, packet loss, excessive latency, or user complaints have already occurred.
Network faults may arise from hardware deterioration, configuration errors, software anomalies, congestion, environmental conditions, security events, or interactions among multiple network elements. The large volume and variety of telemetry generated by routers, switches, servers, sensors, virtual network functions, and applications can make manual analysis slow and inconsistent. Existing monitoring platforms may identify isolated alarms, but they frequently fail to correlate events across layers, distinguish root causes from secondary symptoms, or predict an impending failure with sufficient accuracy.
Artificial intelligence and machine learning techniques have been applied to network analytics; however, many available solutions remain limited to anomaly detection or advisory reporting. They may require human intervention, produce false positives, lack contextual awareness, or provide no reliable mechanism for automated corrective action. In addition, remediation workflows are often fragmented across separate systems, increasing response time and operational cost.
Accordingly, there is a need for an integrated network intelligence system capable of continuously analyzing heterogeneous network data, forecasting probable faults, determining likely causes, prioritizing risks, and initiating or recommending appropriate remediation before service disruption occurs.
SUMMARY OF THE INVENTION
The invention relates to an AI-driven network intelligence system designed to predict, detect, diagnose, and remediate faults across communication and computing networks. The system continuously collects operational data from network devices, applications, interfaces, logs, performance counters, traffic flows, and environmental sensors. A data-processing layer cleans, correlates, and converts the collected information into real-time network health indicators.
Machine-learning models analyze historical and live data to identify abnormal behavior, estimate the probability of component or service failure, and determine likely root causes before significant disruption occurs. The system may apply time-series analysis, anomaly detection, graph-based dependency mapping, and adaptive learning to recognize complex fault patterns across physical, virtual, cloud, and hybrid network environments.
When a potential fault is identified, the system generates a risk score based on severity, confidence, business impact, and predicted time to failure. A remediation engine then selects an appropriate response from predefined policies or dynamically generated actions. Such actions may include rerouting traffic, reallocating resources, restarting services, modifying configurations, isolating faulty components, or notifying authorized personnel. Automated actions can be executed with approval controls, rollback mechanisms, and audit tracking.
The invention further includes a feedback loop that measures remediation outcomes and updates the predictive models to improve future accuracy. A user interface presents network status, fault forecasts, causal relationships, recommended actions, and remediation history. By combining predictive analytics, contextual intelligence, and automated recovery, the system reduces downtime, improves service reliability, optimizes maintenance, and supports proactive network operations. It also supports scalable deployment across distributed, multi-vendor network infrastructures globally.
BRIEF DESCRIPTION OF THE DRAWINGS
Fig.1: Depicts AI driven Network Fault Management
Fig.2: Depicts AI Driven Network Intelligence System.
Fig.3: Depicts Block Diagram of an AI-Driven Network Intelligence System for Predictive Fault Detection and Automated Remediation.
BRIEF DESCRIPTION OF THE INVENTION
The invention relates to an AI-driven network intelligence system designed to predict, identify, and remediate faults across communication and data networks. The system continuously collects operational information from network devices, servers, links, applications, virtual resources, and connected endpoints. Such information may include traffic patterns, latency, packet loss, device health, error logs, configuration changes, resource utilization, and historical incident records.
DETAILED DESCRIPTION OF THE INVENTION
A data processing engine cleans, correlates, and organizes the collected information to create a real-time representation of network behavior. One or more machine-learning models analyze this representation to detect abnormal conditions, recognize developing fault patterns, and estimate the probability, location, severity, and expected time of a potential failure. The models may be trained using historical network events and may be updated continuously as new operational data becomes available.
When a predicted or detected fault exceeds a defined confidence or risk threshold, a decision engine determines an appropriate corrective action. Remediation may include rerouting traffic, restarting services, adjusting bandwidth, modifying configurations, isolating defective devices, activating backup resources, or generating guided instructions for an administrator. Before execution, the system may evaluate the expected impact of each action and select a response that minimizes service disruption, cost, and security risk.
The invention further provides dashboards, alerts, root-cause explanations, incident prioritization, and audit records for monitoring system decisions and outcomes. Feedback from completed remediation actions is returned to the learning models to improve future predictions and responses. By combining continuous monitoring, predictive analytics, explainable fault assessment, and automated recovery, the system reduces network downtime, improves reliability, shortens incident resolution time, and enables proactive management of complex physical, virtual, cloud-based, and hybrid network environments.
The system can operate centrally or through distributed edge agents and may integrate with existing network management platforms through secure interfaces, policies, and role-based controls, thereby supporting scalable deployment across different organizations.
TECHNICAL IMPLEMENTATION, APPLICATIONS, AND FUTURE POTENTIAL
The AI-Driven Network Intelligence System for Predictive Fault Detection and Remediation is implemented as a multilayer platform that collects, analyzes, predicts, and responds to abnormal network behavior. At the data acquisition layer, the system gathers information from routers, switches, firewalls, servers, wireless access points, cloud resources, and endpoint devices. Inputs include packet loss, latency, jitter, bandwidth utilization, interface errors, CPU and memory consumption, device logs, routing changes, alarm records, configuration data, and environmental readings. Collection can be performed through SNMP, NetFlow, syslog, streaming telemetry, APIs, and software-defined networking controllers.
The data is normalized and stored in a scalable platform. A preprocessing engine removes duplicate events, handles missing values, aligns timestamps, filters noise, and converts raw measurements into meaningful features. Features include traffic variation, recurring errors, resource exhaustion, abnormal route convergence, and baseline deviations. Contextual information, such as device type, topology position, service priority, and maintenance history, is incorporated to improve accuracy.
The intelligence layer combines machine learning, deep learning, anomaly detection, and rule-based reasoning. Supervised models trained on labeled fault records identify failures such as link degradation, hardware malfunction, congestion, memory leakage, and power instability. Unsupervised models detect unknown anomalies by learning normal behavior and highlighting deviations. Time-series models forecast probable bandwidth saturation, device overload, or interface failure. Graph-based learning can represent network topology and determine how a local fault may affect connected devices and services.
A correlation engine groups related alerts and distinguishes root causes from secondary symptoms. It analyzes temporal relationships, dependency paths, and historical incident patterns. The system then assigns a risk score based on fault probability, expected time to failure, affected users, service criticality, and estimated business impact. Predictions are presented through dashboards, alerts, and explainable recommendations, explaining why a fault is expected.
The remediation layer converts predictions into preventive action. Depending on confidence and policies, the platform may reroute traffic, restart services, adjust bandwidth allocation, isolate unstable devices, modify quality-of-service settings, activate redundant links, roll back harmful configurations, or create maintenance tickets. High-risk actions can require human approval, while low-risk responses may be executed automatically. Feedback from actions returns to the learning engine, enabling continuous improvement. Security controls, audit trails, encryption, access management, and rollback mechanisms keep automated remediation controlled and accountable.
The invention can be applied in telecommunications networks to predict base-station failures, fiber degradation, congestion, and backhaul instability before customers experience interruption. Providers can use it to reduce downtime, prioritize field maintenance, and improve service-level agreement compliance. In enterprise networks, the system can monitor campuses, data centers, branch locations, and remote-work infrastructure, helping IT teams prevent application slowdowns and connectivity failures.
Cloud providers and data centers can deploy the invention in dynamic environments where workloads, virtual machines, containers, and network paths change rapidly. It can predict capacity shortages, detect faulty virtual links, and rebalance traffic across available resources. In industrial environments, the system can protect operational technology networks supporting factories, power plants, oil and gas facilities, and water systems. Early identification of communication faults can prevent production loss, safety incidents, and equipment damage.
The system is also suitable for smart cities, transportation networks, hospitals, financial institutions, defense systems, educational campuses, and emergency infrastructure. In 5G and edge computing environments, it can monitor network slices, distributed edge nodes, and latency-sensitive services. For Internet of Things deployments, it can identify unreliable gateways, abnormal device behavior, battery-related communication degradation, and radio interference. Managed service providers may use the platform to supervise multiple customer networks through one interface.
The invention’s future lies in autonomous, self-healing networks. As models become more accurate, the system can evolve from predicting isolated equipment failures to optimizing service ecosystems. Digital twins of physical and virtual networks could simulate faults and test remediation strategies before applying them to live environments. This would reduce operational risk and help select the safest response.
Federated learning may enable organizations or network regions to improve shared models without exposing sensitive data. Reinforcement learning could support adaptive remediation by evaluating the long-term effects of different actions. Integration with generative AI may allow administrators to use natural-language questions, receive simplified explanations, and generate troubleshooting procedures automatically.
The invention can also support energy-efficient networking by forecasting underused resources and recommending safe power reduction without affecting performance. With advances in edge intelligence, explainable AI, quantum-resistant security, and intent-based networking, the system may become a central control mechanism for resilient communication infrastructure. Its long-term value is the creation of networks that anticipate disruption, make context-aware decisions, recover with minimal human intervention, and continuously improve through operational experience.

, Claims:We Claim:
1. An AI-based network system that collects network data, predicts faults, and performs corrective actions before service failure.
2. The system of claim 1, wherein the network data includes latency, packet loss, bandwidth, device logs, errors, processor use, or memory use.
3. The system of claim 1, wherein the AI uses machine learning, deep learning, anomaly detection, or time-series analysis.
4. The system of claim 1, further comprising an alert correlation module that identifies the root cause of a network fault.
5. The system of claim 1, wherein each predicted fault receives a risk score based on failure probability, service importance, and expected impact.
6. The system of claim 1, wherein corrective actions include rerouting traffic, restarting services, activating backup links, isolating devices, or restoring configurations.
7. The system of claim 1, wherein low-risk actions are automatic and high-risk actions require user approval.
8. The system of claim 1, further comprising a feedback module that improves future fault prediction and remediation.

Dated this 1st August 2026