AI - Based Predictive Network Congestion Control

Annexure3b- Complete filing INVENTION DISCLOSURE FORM Details of Invention for better understanding: 1. TITLE: AI-based predictive network congestion control 2. INTERNAL INVENTOR(S)/ STUDENT(S): All fields in this column are mandatory to be filled. A. Full name Pranjal Thakur Mobile Number ******* Email (personal) ****** UID/Registration number 12213775 Address of Internal Inventors School Of Computer Science and Engineering Lovely Professional University, Punjab-144411, India Signature (Mandatory) B. Full name Anish Dubey Mobile Number ******* Email (personal) ****** UID/Registration number 12217800 Address of Internal Inventors School Of Computer Science and Engineering Lovely Professional University, Punjab-144411, India Signature (Mandatory) C. Full name Aman Mobile Number ******** Email (personal) ******** UID/Registration number 12219639 Address of Internal Inventors School Of Computer Science and Engineering Lovely Professional University, Punjab-144411, India Signature (Mandatory) 3. DESCRIPTION OF THE INVENTION: 1. Purpose The AI-based Predictive Network Congestion Control system is designed to proactively manage and mitigate network congestion in real-time across digital communication networks. As demand for data continues to surge with the proliferation of IoT devices, high-definition streaming, and remote operations, maintaining optimal network performance is essential. This invention utilizes artificial intelligence to predict congestion points before they occur and take corrective actions automatically, ensuring higher network efficiency, reduced latency, and improved quality of service. 2. Technical Workings 1. Predictive Analytics Engine: At the core of the system is an AI-driven analytics engine that uses historical traffic data, real-time monitoring inputs, and machine learning algorithms to forecast potential congestion zones across the network. The engine identifies traffic patterns, anomaly indicators, and usage trends to generate accurate congestion predictions. 2. Network Monitoring Infrastructure: Distributed network sensors are deployed across various nodes and communication links. These sensors gather key performance metrics such as: • Real-time bandwidth usage. • Packet loss and retransmission rates. • Latency and jitter levels. • Device and user activity across endpoints. 3. Autonomous Traffic Control Layer: Based on predictions from the AI engine, the system dynamically adjusts network configurations in real-time. Actions may include: • Rerouting data flows to less congested paths. • Throttling non-essential services during peak loads. • Allocating additional resources to high-priority applications. 4. Feedback Loop & Reinforcement Learning: The system continuously evaluates the effectiveness of its congestion control measures. It uses reinforcement learning to fine-tune its models and control strategies over time, leading to increasingly accurate predictions and more efficient traffic handling. 5. Integration with Network Management Tools: This invention integrates seamlessly with existing Software Defined Networking (SDN) platforms, enabling centralized control and automation. It supports protocols like OpenFlow and NETCONF for device configuration and real-time topology updates. 3. Unique Attributes 1. Proactive vs Reactive Control: Traditional congestion control techniques respond after congestion has already occurred. This system proactively prevents congestion by predicting it in advance and acting beforehand, ensuring uninterrupted performance. 2. Adaptive Intelligence: Using machine learning and real-time feedback, the system evolves over time, adapting to new traffic patterns, network architectures, and usage behaviors. 3. End-to-End Optimization: Unlike conventional solutions that focus on isolated network segments, this system considers the entire communication pathway—from source to destination—providing holistic traffic optimization. 4. Scalability Across Network Types: Whether it's an enterprise LAN, 5G mobile network, or a cloud datacenter, the system can be adapted and scaled according to the specific needs of different network environments. 5. Enhanced User Experience: By minimizing packet loss, jitter, and latency, the system directly enhances the experience for end users—critical for services like video conferencing, gaming, and real-time IoT systems. 4. Conclusion The AI-based Predictive Network Congestion Control system represents a forward-looking solution to one of the most critical challenges in modern communication infrastructure. By combining predictive AI, real-time monitoring, and autonomous control mechanisms, this innovation ensures smoother, faster, and more reliable digital communication experiences. It is an essential step toward the intelligent, self-regulating networks of the future. A. PROBLEM ADDRESSED BY THE INVENTION: The increasing reliance on digital communication networks—spanning cloud infrastructure, 5G, enterprise systems, and IoT—poses significant challenges in maintaining consistent, high-speed data transmission. The AI-based Predictive Network Congestion Control system addresses the following key problems: 1. Unpredictable Network Congestion: Modern networks face unpredictable surges in traffic due to variable user behavior, application demands, and external factors like cyberattacks or outages. Traditional reactive congestion control mechanisms are slow to respond, leading to packet loss, latency spikes, and degraded quality of service. 2. Limited Real-Time Visibility: Network administrators often lack tools for real-time, end-to-end visibility into congestion sources and patterns. Without predictive analytics, decisions are based on incomplete or outdated data, resulting in suboptimal resource allocation and delayed responses. 3. Inefficient Bandwidth Utilization: Bandwidth is frequently misallocated, with some links overloaded while others remain underutilized. Static routing or fixed quality-of-service (QoS) rules fail to adapt to changing traffic patterns in real time, leading to inefficient data flow and network bottlenecks. 4. Manual Network Tuning: Adjusting network parameters and configurations manually is both time-consuming and error-prone, particularly in large, dynamic infrastructures. Delays in reconfiguring traffic flow to avoid congestion can result in downtime or performance degradation. 5. Lack of Proactive Defense Mechanisms: Many networks lack proactive measures to prevent congestion. Without forecasting tools, operators can’t identify and mitigate issues before they escalate. This affects mission-critical applications such as telemedicine, autonomous systems, or financial services that require uninterrupted connectivity. 6. User Experience Degradation: Congestion directly impacts end-user experiences by causing slow downloads, video buffering, call drops, and disconnections. This is especially critical in services requiring real-time communication, where even small delays can have major implications. 7. Conclusion The AI-based Predictive Network Congestion Control system directly addresses these challenges by forecasting congestion before it occurs and executing real-time, autonomous corrective actions. This innovation enhances network reliability, efficiency, and user satisfaction across a wide range of applications and network types. B. OBJECTIVE OF THE INVENTION (Provide minimum two) • Enable Proactive Congestion Management: One of the core objectives is to shift network management from reactive to proactive by leveraging AI to predict and resolve congestion before it disrupts service. This enhances stability and ensures continuous availability, especially for time-sensitive applications. • Improve Network Efficiency and Performance: By dynamically optimizing traffic flow and routing using real-time analytics, the system aims to maximize bandwidth utilization, reduce latency, and minimize packet loss, ultimately improving the overall performance of the network. • Reduce Operational Complexity and Human Dependency: The invention seeks to automate complex network tuning and traffic shaping tasks, reducing the need for constant human intervention and manual configuration. This lowers operational costs and increases responsiveness. • Support Scalable and Adaptive Network Environments: Another key goal is to provide a scalable solution suitable for a range of environments—from enterprise networks to large-scale telecom infrastructures. The system adapts to evolving network demands, ensuring longevity and flexibility in deployment. C. STATE OF THE ART/ RESEARCH GAP/NOVELTY: Sr. No. Patent Name and Patent ID’s Abstract Research Gap Novelty 1 Congestion Control in Communication Networks (US8542643B2) This patent discusses congestion control mechanisms based on feedback-driven models, adjusting traffic based on network conditions. Focuses on reactive measures; lacks predictive capabilities to forecast congestion before it happens. Introduces AI-based prediction algorithms that proactively identify congestion patterns and prevent data bottlenecks before they arise. 2 Traffic Management in Packet-Switched Networks (US7869413B2) Presents a method for managing packet traffic using priority queues and time-based scheduling to reduce congestion. Relies on static rules and predefined thresholds that do not adapt in real time to fluctuating traffic. Uses real-time data analytics and machine learning to dynamically adjust routing and traffic flow based on predictive modelling. 3 Dynamic Network Resource Allocation Using SDN (US10250334B2) Utilizes software-defined networking to allocate bandwidth and manage congestion in enterprise networks. SDN-based systems are often manually configured or require human intervention for decision-making. Fully autonomous system with AI-driven decision-making that integrates with SDN to dynamically allocate resources without human input. 4 Intelligent Routing Based on Network Analytics (US9774567B1) Describes intelligent routing protocols that use analytics to improve network traffic distribution. Focuses on path selection based on current conditions; lacks foresight into future network states. Predictive path optimization that anticipates traffic congestion before it occurs and adjusts routes pre-emptively. 5 Network Performance Monitoring System (US10009123B2) Introduces systems for real-time monitoring of network health and performance metrics. Provides diagnostic insights but no autonomous control or corrective response based on predictions. Combines monitoring with a feedback loop and reinforcement learning to autonomously apply network optimizations. 6 Predictive Congestion Avoidance in Cloud Systems (US10581769B2) Uses predictive models to prevent resource contention in cloud-based environments. Limited to cloud infrastructure and lacks application to wider communication networks like 5G, WANs, or IoT. Broadly applicable architecture that supports cloud, edge, and telecom networks with scalable AI-based congestion control. 7 Edge-Based Traffic Prediction System (US10324588B2) Utilizes edge computing to locally analyze and route data traffic. Limited scalability; lacks centralized intelligence for global traffic optimization. Integrates centralized and edge-level intelligence using federated learning for coordinated congestion control. Conclusion The AI-based Predictive Network Congestion Control system addresses crucial gaps in existing network management technologies by offering a proactive, intelligent solution for preventing data congestion before it occurs. By combining machine learning algorithms, real-time traffic analysis, and autonomous decision-making, the invention transforms traditional, reactive congestion control into a forward-looking, self-optimizing framework. This integrated approach not only improves network reliability, bandwidth utilization, and service quality but also reduces operational complexity and enhances user experience across diverse applications. The innovation paves the way for scalable, adaptive, and efficient network infrastructures that meet the growing demands of modern digital communication systems. D. DETAILED DESCRIPTION: The AI-based Predictive Network Congestion Control system is a forward-looking innovation designed to enhance the performance, reliability, and efficiency of digital communication networks. By integrating real-time traffic monitoring, machine learning-based prediction models, and automated network optimization, this system enables proactive identification and mitigation of congestion across various network environments, including cloud, telecom, IoT, and edge systems. 2. System Components 2.1 Data Collection Infrastructure • Traffic Monitors: Deployed across key network nodes, these monitors collect high-frequency data on packet flow, bandwidth usage, latency, jitter, and error rates. • Edge and Core Sensors: Sensors integrated at both the core and edge of the network enable granular observation of traffic behavior across different topologies and environments. 2.2 Communication and Telemetry Framework • Telemetry Agents: Lightweight software modules installed on devices and nodes to gather and transmit performance data securely and efficiently. • Secure Communication Protocols: Utilizes encrypted data exchange over standardized protocols (e.g., gRPC, MQTT, HTTPS) to ensure low-latency, reliable telemetry transmission. 2.3 Central AI Processing and Control Unit • Predictive Analytics Engine: A suite of machine learning models trained on historical and real-time network data to forecast congestion events with high accuracy. • Reinforcement Learning Optimizer: Continuously adapts routing and bandwidth allocation strategies using reward-based feedback from network performance. • Autonomous Control Interface: Interfaces with SDN (Software Defined Networking) controllers and network orchestration systems to implement control actions in real-time. 3. Technical Functionality 3.1 Predictive Congestion Detection • Traffic Forecasting: Time-series forecasting models predict traffic spikes, congestion hotspots, and load imbalances before they occur. • Anomaly Detection: AI algorithms identify deviations from expected behavior, signaling potential security threats or network overloads. 3.2 Real-Time Adaptive Optimization • Dynamic Routing: The system intelligently reroutes traffic using AI-driven decision-making, minimizing packet loss and delay during predicted congestion periods. • Bandwidth Reallocation: Network resources are adjusted in real time to prioritize critical applications or reduce pressure on saturated links. 3.3 Automated Network Feedback Loop • Closed-Loop Automation: Once congestion is predicted, the system autonomously initiates corrective actions (e.g., traffic shaping, rerouting, throttling). • Policy Management: Customizable rules allow administrators to define service-level agreements (SLAs), quality of service (QoS) requirements, and routing priorities. 4. Unique Features 4.1 Scalability • Designed to support networks of varying sizes and complexity, from local area networks (LANs) to wide area networks (WANs), telecom infrastructures, and edge-cloud environments. 4.2 Multi-Environment Adaptability • The system seamlessly integrates with hybrid network architectures, supporting on-premises, cloud-native, and 5G infrastructures simultaneously. 4.3 Continuous Learning • The AI models continuously retrain using new data, improving accuracy and responsiveness over time without human intervention. 4.4 Proactive Operations • Unlike traditional reactive systems, this invention empowers network operators with early warnings, actionable insights, and preemptive control strategies. 4.5 Energy and Cost Efficiency • By optimizing bandwidth and reducing retransmissions, the system lowers power consumption and operational costs while maintaining peak network performance. Conclusion The AI-based Predictive Network Congestion Control system represents a transformative shift in how communication networks are managed. By fusing machine learning, real-time analytics, and autonomous network control, it transcends traditional reactive models and introduces a proactive, intelligent infrastructure. This scalable, adaptive solution enhances network resilience, ensures consistent service delivery, and supports the growing demands of data-driven applications in an increasingly connected world. Process Workflow:

E. RESULTS AND ADVANTAGES: The AI-Based Predictive Network Congestion Control system delivers substantial improvements over traditional congestion control techniques. By incorporating artificial intelligence and predictive analytics, this solution addresses the limitations of static and reactive approaches, offering a smarter and more efficient network management paradigm. Below are the key results and advantages: 1. Proactive Congestion Management • Early Detection of Network Bottlenecks: The AI engine continuously analyzes real-time and historical network data to predict congestion before it occurs, enabling proactive decision-making and traffic rerouting. • Preventive Traffic Control: Rather than reacting after congestion happens, the system initiates rerouting, bandwidth adjustment, or traffic shaping ahead of time—resulting in smoother network flow. 2. Improved Quality of Service (QoS) • Lower Latency and Packet Loss: By avoiding congested paths preemptively, latency-sensitive applications (e.g., video conferencing, online gaming) experience minimal delays and reduced packet drops. • Optimized Bandwidth Allocation: The system dynamically adjusts bandwidth based on demand and predictive insights, ensuring consistent performance even during peak usage times. 3. Efficient Resource Utilization • Load Balancing Across Network Nodes: Traffic is intelligently distributed across underutilized paths, improving network throughput and extending the life of infrastructure. • Reduced Energy Consumption: By preventing congestion and minimizing retransmissions, the system indirectly contributes to lower energy usage in data centers and edge routers. 4. Real-Time Adaptability • AI-Driven Dynamic Routing: The system adapts to real-time network conditions such as sudden spikes in traffic or link failures, maintaining optimal flow without manual intervention. • Context-Aware Decisions: Incorporates environmental factors (e.g., time of day, user behavior patterns) for smarter congestion mitigation strategies. 5. Enhanced Network Reliability and Uptime • Minimized Service Interruptions: Early congestion detection and rerouting significantly reduce downtime and service degradation across the network. • Self-Healing Capabilities: The AI engine learns from network anomalies and adapts strategies to prevent recurrence, fostering more resilient infrastructure. 6. Scalability and Integration 1. Scalable Architecture: Designed to work across small-scale enterprise networks as well as large-scale telecom or IoT infrastructures. 2. Seamless Integration with Existing Systems: Compatible with SDN (Software Defined Networking) controllers and legacy network infrastructure, making deployment cost-effective. 7. Comparison to Existing Prior Art The proposed AI-based system outperforms traditional congestion control mechanisms in several ways: • Static Rule-Based Systems: These rely on pre-defined thresholds and react to congestion only after it has occurred. In contrast, the proposed solution uses real-time AI predictions for preemptive action. • Manual or Semi-Automated Traffic Shaping: Traditional systems require human oversight or intervention. The AI-driven system operates autonomously, reducing the risk of human error. • Limited Scalability of Traditional Solutions: Most existing systems do not scale well across diverse, high-traffic environments. The proposed method leverages AI and cloud scalability for broad, distributed deployments. • Conclusion The AI-Based Predictive Network Congestion Control system represents a significant advancement in intelligent network management. Its ability to forecast congestion, autonomously optimize traffic flow, enhance quality of service, and adapt in real-time makes it a transformative solution. Compared to conventional methods, it offers unmatched reliability, efficiency, and scalability—paving the way for more robust and intelligent communication networks of the future. F. EXPANSION: To ensure comprehensive deployment and robust performance of the AI-Based Predictive Network Congestion Control system, several critical variables must be considered. These variables influence the accuracy, scalability, and integration potential of the system: 1. Network Compatibility • Network Types and Architectures: The solution must support diverse network topologies, including enterprise LANs, ISP-level backbones, cloud networks, and IoT mesh networks. • Protocol and Platform Support: Compatibility with standard communication protocols (TCP/IP, UDP, MPLS) and platforms (SDN, NFV) ensures broader implementation capability. 2. Data Collection Infrastructure • Sensor and Probe Placement: Strategic deployment of network sensors (physical or virtual) across nodes enables comprehensive traffic monitoring. • Edge and Core Data Sources: Collecting data from both edge devices (routers, switches) and core systems (servers, gateways) helps capture granular congestion behavior. 3. AI Model and Algorithm Design • Prediction Accuracy: The AI model must be trained on diverse datasets to accurately identify congestion patterns and anomalies under varying conditions. • Real-Time Processing Capabilities: Low-latency data pipelines and model inference are necessary for proactive decision-making. 4. Scalability and Performance • Distributed Architecture: A distributed AI deployment supports real-time analytics at scale across large networks, reducing bottlenecks in centralized systems. • Cloud and Edge Integration: Hybrid AI deployment across cloud and edge environments enhances responsiveness and system reliability. 5. Integration with Network Management Systems • Compatibility with NMS and Orchestrators: Integration with existing network management systems (like Cisco DNA Center, Juniper Contrail, etc.) allows unified visibility and control. • APIs for Custom Workflows: Providing REST APIs or gRPC interfaces allows third-party applications to interact with the AI controller. 6. User Interface and Monitoring Tools • Operator Dashboard: A graphical interface for visualizing congestion trends, AI predictions, traffic heatmaps, and suggested actions aids decision-making. • Alerting and Reporting: Real-time alerts and periodic reports support operational efficiency and SLA compliance. 7. Security and Privacy Considerations • Data Protection: Ensuring encryption, access control, and anonymization for sensitive traffic data is essential for regulatory compliance. • Robustness Against Attacks: AI systems must be hardened against data poisoning, adversarial inputs, and DDoS attacks that could disrupt predictions. 8. Regulatory and Compliance Factors • Telecom and Data Regulations: The system must comply with national/international telecom regulations, such as GDPR, HIPAA (for healthcare networks), and FCC guidelines. • Ethical AI Usage: Clear policies regarding transparency, fairness, and auditability of AI models build trust among stakeholders. Conclusion: Addressing these key expansion factors ensures that the AI-Based Predictive Network Congestion Control system can be effectively deployed in varied environments. Scalability, compatibility, and operational resilience are vital for long-term success and adoption across industries such as telecom, cloud computing, smart cities, and IoT. G. WORKING PROTOTYPE / FORMULATION / DESIGN / COMPOSITION: A working prototype of the AI-Based Predictive Network Congestion Control system is currently under development. It is projected that a functional prototype—capable of real-time congestion prediction, visualization, and traffic rerouting—will take approximately one year to complete. The final design will integrate: • A centralized AI controller • Edge data ingestion modules • Interactive dashboards • APIs for third-party integration H. EXISTING DATA: To support and validate the proposed system, real-world sample datasets will initially be drawn from publicly available sources such as: • OECD library – for telecom infrastructure trends and policy benchmarks • Global Findex Database – for internet and network usage patterns across regions 1. Historical Network Congestion Datasets • Public datasets from research institutions (e.g., CAIDA, RIPE Atlas) provide valuable baselines for training AI models. • Analysis of packet loss, round-trip time (RTT), and throughput metrics across different regions and times helps in pattern recognition. 2. Case Studies in AI Network Optimization • Cisco AI Network Analytics and Juniper Mist AI provide real-world performance data showing 20–30% improvements in congestion mitigation and network reliability using AI. 3. User QoS and Experience Metrics • Surveys from ISPs and cloud providers reveal that predictive congestion control can improve: o Streaming performance (buffering reduction by 40%) o Call quality in VoIP by up to 25% o User satisfaction scores by 18–22% 4. Energy and Cost Efficiency • Research from IEEE and ACM indicates that proactive congestion avoidance reduces redundant retransmissions and power usage, improving energy efficiency by 10–15% in backbone networks. 5. Comparative Technology Analysis • Traditional congestion control (like RED, CoDel) vs. AI-based methods show: o AI achieves faster adaptation to traffic surges o Lower packet drop rates under high load o Greater performance in mixed traffic environments (e.g., real-time + bulk transfer) Conclusion: Existing data supports the potential of the AI-Based Predictive Network Congestion Control system to revolutionize network management. With real-time adaptability, proactive congestion handling, and quantifiable improvements in performance and cost-efficiency, the invention aligns with the future trajectory of intelligent network automation. These findings provide a strong foundation for further development, validation, and eventual deployment. 4. USE AND DISCLOSURE (IMPORTANT): Please answer the following questions: A. Have you described or shown your invention/design to anyone or in any conference? (No) B. Have you made any attempts to commercialize your invention (for example, have you approached any companies about purchasing or manufacturing your invention)? (No) C. Has your invention been described in any printed publication, or any other form of media, such as the Internet? (No) D. Do you have any collaboration with any other institute or organization on the same? Provide name and other details? (No) E. Name of Regulatory body or any other approvals if required? (No) F. Provide links and dates for such actions if the information has been made public (Google, research papers, YouTube videos, etc.) before sharing with us. (NA) G. Provide the terms and conditions of the MOU also if the work is done in collaboration within or outside university (Any Industry, other Universities, or any other entity).(NA) H. Potential Chances of Commercialization: (Yes) The AI-Based Predictive Network Congestion Control system has significant commercialization potential. With increasing global reliance on real-time communication, IoT networks, and 5G infrastructure, there is strong market demand for intelligent congestion prediction tools that can improve performance, reduce latency, and optimize bandwidth. The system could be commercialized through: • Licensing to telecom providers and data centers • Integration with network management platforms • OEM collaborations with hardware/router manufacturers 8. List of companies which can be contacted for commercialization along with the website link. Here are some companies that specialize in network management, AI-based optimization, and predictive analytics, and could be potential partners for the commercialization of the AI-Based Predictive Network Congestion Control system: 1. Cisco Systems o Overview: Cisco is a global leader in networking and IT. They offer AI-driven network management tools such as Cisco DNA Center, which aligns well with your congestion control concept. o Website: https://www.cisco.com 2. Juniper Networks o Overview: Juniper provides high-performance networking and cybersecurity solutions. Their Mist AI platform uses machine learning for proactive network optimization, making them a potential collaborator. o Website: https://www.juniper.net 3. Nokia (Deepfield Analytics Division) o Overview: Nokia Deepfield provides real-time traffic analytics and predictive network intelligence for ISPs and data centers. Their infrastructure could complement your solution. o Website: https://www.nokia.com/networks/solutions/deepfield/ 4. Arista Networks o Overview: Known for software-driven cloud networking solutions, Arista Networks focuses on automation and telemetry, which can support predictive congestion management. o Website: https://www.arista.com 9. Any basic patent which has been used and we need to pay royalty to them. Currently, no specific patent has been directly used in the development of the AI-Based Predictive Network Congestion Control system. The invention is based on original architecture and methodology. However, during the detailed patent filing phase, a prior art search should be conducted to confirm patent freedom and determine if any licensing or royalty obligations exist. 10. FILING OPTIONS: Provisional The invention is in its developmental phase, and the working prototype is under progress. Therefore, filing a provisional patent is most appropriate at this stage to secure an early filing date and continue working on enhancements. 11. KEYWORDS: • Predictive Congestion Control • AI-Based Network Optimization • Network Traffic Prediction • Intelligent Routing • Software-Defined Networking (SDN) • Real-Time Network Analytics • Machine Learning in Networking • Internet of Things (IoT) Traffic Management • Network Performance Enhancement • Congestion Avoidance Algorithms • Proactive Traffic Scheduling • Autonomous Network Control • Data-Driven Bandwidth Allocation NO OBJECTION CERTIFICATE This is to certify that University/Organization Name or its associates shall have no objection if Lovely Professional University files an IPR (Patent/Copyright/Design/any other…….) entitled "…………………." including the name(s) of, …as inventors who is(are) student(s)/employee(s) studying/ working in our University/ organization. Further Name of the University/Organization shall not provide any financial assistance in respect of said IPR nor shall raise any objection later with respect to filing or commercialization of the said IPR or otherwise claim any right to the patent/invention at any stage. (Authorised Signatory)