AI-BASED PREDICTIVE ERROR RECOVERY SYSTEM

Abstract

ABSTRACT AI-BASED PREDICTIVE ERROR RECOVERY SYSTEM The present disclosure introduces AI-based predictive error recovery system 100 which is designed to proactively detect, classify, and autonomously recover from system and network errors. The system comprises of data collection module 102 that gathers real-time data and a data processing unit 104 that preprocesses the data. An AI predictive engine 106 analyzes the data to forecast potential errors, while an error classification module 108 categorizes them for targeted recovery. A recovery action module 110 initiates appropriate protocols, monitored by a feedback and learning module 112 to improve future performance. Other key components are real-time monitoring dashboard 114, hybrid AI models 116, cross-platform integration system 118, context-aware anomaly detection module 124, predictive maintenance integration 122, distributed AI model deployment 130, modular and scalable architecture 120, behavioral analysis 134, multi-layer recovery strategy 136, error correlation 132, human-machine collaboration 126, and energy-efficient recovery 128 to ensure seamless operation and comprehensive stability. Reference Fig 1

Specification

Description:AI-based predictive error recovery system
TECHNICAL FIELD
[0001] The present innovation relates to AI-based predictive error recovery methods for detecting, classifying, and autonomously resolving system and network errors in digital infrastructures.

BACKGROUND

[0002] In today's interconnected world, maintaining the reliability, performance, and uptime of digital systems and communication networks is crucial across industries such as data centers, telecommunications, manufacturing, cloud services, and IoT infrastructure. Traditional error detection and recovery methods rely on predefined algorithms, manual intervention, or reactive approaches, where errors are identified and resolved only after they occur. While these methods can address common errors, they often result in delayed recovery, increased downtime, and financial losses. Moreover, such systems struggle to handle complex or unforeseen error scenarios, requiring continuous human oversight. Options such as backup systems, redundancy mechanisms, and anomaly detection tools offer some level of protection but are limited in their ability to predict failures proactively or recover autonomously.

[0003] These existing systems often operate reactively, which means users must wait for issues to arise before initiating recovery processes. Furthermore, these approaches are not adaptive to evolving patterns, such as new software configurations, environmental changes, or emerging cyber threats, making them less reliable in dynamic environments.

[0004] The present invention introduces a novel AI-based predictive error recovery system that overcomes these limitations by proactively predicting and recovering from errors before they escalate into critical failures. It employs machine learning algorithms to analyze real-time operational data and historical error patterns, enabling it to forecast potential issues across hardware, software, and network layers. Unlike traditional systems, the invention autonomously classifies predicted errors and initiates pre-defined recovery protocols tailored to each error type without requiring human intervention. Its adaptive learning capability allows it to improve over time, continuously refining predictions and recovery strategies based on past outcomes.

OBJECTS OF THE INVENTION

[0005] The primary object of the invention is to enhance system reliability and uptime by predicting and recovering from errors before they lead to failures.

[0006] Another object of the invention is to minimize downtime through autonomous recovery actions tailored to different types of errors.

[0007] Another object of the invention is to improve operational efficiency by reducing the need for manual intervention in error detection and recovery processes.

[0008] Another object of the invention is to offer adaptive learning capabilities, enabling the system to continuously improve its predictions and recovery strategies over time.

[0009] Another object of the invention is to support energy-efficient recovery mechanisms, optimizing resource usage and aligning with sustainability goals.

[00010] Another object of the invention is to provide seamless integration with existing digital infrastructure, such as data centers, IoT networks, and cloud platforms.

[00011] Another object of the invention is to proactively prevent cascading failures by predicting and addressing interconnected errors across multiple systems.

[00012] Another object of the invention is to enhance cybersecurity by identifying and mitigating potential threats before they disrupt operations.

[00013] Another object of the invention is to offer a scalable system that can be applied across industries, from telecommunications to industrial automation and smart city infrastructure.

[00014] Another object of the invention is to reduce operational costs by eliminating unnecessary downtime and optimizing error recovery processes.


SUMMARY OF THE INVENTION

[00015] In accordance with the different aspects of the present invention AI-based predictive error recovery system is presented. It is designed to proactively detect, classify, and autonomously recover from system and network errors. The system leverages machine learning algorithms to analyze real-time data and historical patterns, enabling preemptive actions before failures occur. The system features adaptive learning to improve recovery strategies over time, ensuring resilience to evolving error scenarios. It offers seamless integration across various platforms, enhancing operational efficiency and reducing downtime.

[00016] Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.

[00017] It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.

BRIEF DESCRIPTION OF DRAWINGS
[00018] The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.

[00019] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

[00020] FIG. 1 is component wise drawing for AI-based predictive error recovery system.

[00021] FIG 2 is working methodology of AI-based predictive error recovery system.

DETAILED DESCRIPTION

[00022] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.

[00023] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of AI-based predictive error recovery system and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.

[00024] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[00025] The terms "comprises", "comprising", "include(s)", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by "comprises... a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[00026] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[00027] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.

[00028] Referring to Fig. 1, AI-based predictive error recovery system 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of data collection module 102, data processing unit 104, AI predictive engine 106, error classification module 108, recovery action module 110, feedback and learning module 112, real-time monitoring dashboard 114, hybrid AI models 116, cross-platform integration system 118, modular and scalable architecture 120, predictive maintenance integration 122, context-aware anomaly detection module 124, human-machine collaborative interface 126, energy-efficient recovery mechanism 128, distributed ai model deployment system 130, error correlation module 132, behavioural analysis component 134 and multi-layer recovery strategy 136.

[00029] Referring to Fig. 1, the present disclosure provides details of AI-based predictive error recovery system 100 which is designed to proactively detect, classify, and autonomously recover from system and network errors. In one of the embodiments, the system leverages AI predictive engine 106 to forecast potential failures by analyzing real-time data collected by the data collection module 102 and preprocessed through the data processing unit 104. The error classification module 108 categorizes predicted errors, triggering tailored recovery actions via the recovery action module 110. Continuous improvement is ensured through the feedback and learning module 112, which refines predictions based on past outcomes. The system also features hybrid AI models 116 for enhanced adaptability, a real-time monitoring dashboard 114 for transparency, and context-aware anomaly detection module 124 to reduce false positives. Its modular and scalable architecture 120 ensures compatibility across platforms, making it suitable for various industries and infrastructure types.

[00030] Referring to Fig1, AI-based predictive error recovery system 100 is provided with data collection module 102 that continuously gathers real-time operational data from various system components, including logs, performance metrics, and resource usage. It ensures that the AI predictive engine 106 has access to the most recent system information for accurate predictions. The collected data is then passed to the data processing unit 104 for preprocessing.

[00031] Referring to Fig1, AI-based predictive error recovery system 100 is provided with Data processing unit 104 which ensures the incoming data from data collection module 102 is cleansed by filtering out noise, normalizing data formats, and filling any missing values. It structures the data in a suitable format for analysis by the AI predictive engine 106, improving the accuracy of predictions. Proper preprocessing ensures seamless data flow between components and enhances the reliability of the entire system.

[00032] Referring to Fig1, AI-based predictive error recovery system 100 is provided with AI predictive engine 106 which is the core component responsible for predicting potential errors by analyzing both historical and real-time data. It uses hybrid AI models 116 to forecast failures, correlating operational patterns with error likelihood. It interacts with error classification module 108 to categorize predicted issues, ensuring that relevant recovery protocols are triggered through recovery action module 110.

[00033] Referring to Fig1, AI-based predictive error recovery system 100 is provided with error classification module 108 which identifies and classifies errors into predefined categories such as hardware failures, software glitches, or network issues. It works closely with AI predictive engine 106 to understand the type and severity of the predicted error. This classification ensures that recovery action module 110 can deploy appropriate corrective actions to prevent system downtime or cascading failures.

[00034] Referring to Fig1, AI-based predictive error recovery system 100 is provided with recovery action module 110 which automatically initiates predefined recovery actions such as system reboots, backup switching, or traffic rerouting. It leverages inputs from error classification module 108 to determine the appropriate recovery plan. The actions taken are monitored by feedback and learning module 112 to assess their success and optimize future responses.

[00035] Referring to Fig1, AI-based predictive error recovery system 100 is provided with feedback and learning module 112. It monitors the outcome of recovery actions initiated by recovery action module 110 and feeds the results back into AI predictive engine 106. This continuous learning mechanism improves the accuracy and efficiency of future predictions. Over time, the system becomes more adaptive, refining recovery strategies to suit evolving error patterns.

[00036] Referring to Fig1, AI-based predictive error recovery system 100 is provided with real-time monitoring dashboard 114 which provides operators with a visual interface displaying system status, predictions, and recovery actions. It ensures transparency, allowing users to oversee the automated processes of AI predictive engine 106 and error classification module 108. The dashboard also supports manual oversight and intervention, if required.

[00037] Referring to Fig1, AI-based predictive error recovery system 100 is provided with hybrid AI models 116. It enhance the prediction capabilities of AI predictive engine 106 by integrating supervised, unsupervised, and reinforcement learning techniques. These models improve system adaptability to new error patterns. The hybrid nature ensures that predictive performance remains robust across various industries and system configurations.

[00038] Referring to Fig1, AI-based predictive error recovery system 100 is provided with cross-platform integration system 118 which ensures the seamless operation of the invention across cloud platforms, data centers, IoT networks, and industrial systems. It facilitates communication between components like data collection module 102 and recovery action module 110, allowing smooth deployment across diverse infrastructures.

[00039] Referring to Fig1, AI-based predictive error recovery system 100 is provided with modular and scalable architecture 120 which allows the system to expand horizontally, supporting deployments across distributed environments such as smart city networks and cloud infrastructures. It ensures that each instance of AI predictive engine 106 and recovery action module 110 can operate efficiently, regardless of scale.

[00040] Referring to Fig1, AI-based predictive error recovery system 100 is provided with predictive maintenance integration 122 to identify early signs of hardware wear and enables preventive maintenance before system failures occur. It utilizes insights from data processing unit 104 and AI predictive engine 106 to forecast maintenance needs, minimizing downtime.

[00041] Referring to Fig1, AI-based predictive error recovery system 100 is provided with context-aware anomaly detection module 124 which incorporates external factors such as time of day and seasonal trends to reduce false positives. It refines predictions generated by AI predictive engine 106 and ensures that recovery action module 110 responds accurately based on the specific context of the anomaly.

[00042] Referring to Fig1, AI-based predictive error recovery system 100 is provided with human-machine collaborative interface 126 which provides a mechanism for operators to interact with the system, review predictions from AI predictive engine 106, and override automated processes if necessary. This interface enhances trust and allows manual intervention during critical scenarios.

[00043] Referring to Fig1, AI-based predictive error recovery system 100 is provided with energy-efficient recovery mechanism 128 to optimize resource consumption during recovery, ensuring that system resources like CPU and bandwidth are used efficiently. It works with recovery action module 110 to selectively engage failover mechanisms only when needed, aligning with sustainability goals.

[00044] Referring to Fig1, AI-based predictive error recovery system 100 is provided with distributed AI model deployment system 130 to ensure that predictions and recovery actions occur close to the source of potential errors, reducing latency. It allows AI predictive engine 106 to operate locally across various network nodes, improving response times in large-scale infrastructures.

[00045] Referring to Fig1, AI-based predictive error recovery system 100 is provided with error correlation module 132 to identify relationships between predicted errors across interconnected systems to prevent cascading failures. It enables AI predictive engine 106 to address root causes spanning multiple systems and ensures that recovery action module 110 resolves underlying issues effectively.

[00046] Referring to Fig1, AI-based predictive error recovery system 100 is provided with behavioral analysis component 134 which monitors user behavior to predict and mitigate human-generated errors. It works alongside data collection module 102 and AI predictive engine 106 to identify patterns that may lead to errors, enhancing overall system stability.

[00047] Multi-layer recovery strategy 136 ensures that recovery actions occur simultaneously across hardware, software, and network layers. It allows recovery action module 110 to manage complex failure scenarios and prevents error propagation across different layers of the system

[00048] Referring to Fig 2, there is illustrated method 200 for AI-based predictive error recovery system 100. The method comprises:

At step 202, method 200 includes data collection module 102 gathering real-time operational data, including system logs, performance metrics, and resource usage;

At step 204, method 200 includes data processing unit 104 preprocessing the collected data by filtering noise, normalizing formats, and filling any missing values;

At step 206, method 200 includes AI predictive engine 106 analyzing the preprocessed data alongside historical patterns to predict potential errors;

At step 208, method 200 includes error classification module 108 categorizing the predicted error into predefined types, such as hardware failures, software issues, or network congestion;

At step 210, method 200 includes recovery action module 110 initiating appropriate recovery actions based on the classified error, such as switching to backup servers, rerouting network traffic, or performing system reboots;

At step 212, method 200 includes feedback and learning module 112 monitoring the effectiveness of the recovery actions and feeding the results back to AI predictive engine 106 to improve future predictions;

At step 214, method 200 includes real-time monitoring dashboard 114 displaying the system status, error predictions, and ongoing recovery actions, ensuring operator transparency;

At step 216, method 200 includes hybrid AI models 116 refining the prediction accuracy by integrating supervised, unsupervised, and reinforcement learning techniques;

At step 218, method 200 includes context-aware anomaly detection module 124 enhancing predictions by incorporating environmental factors, such as time of day and seasonal trends, to reduce false positives;

At step 220, method 200 includes cross-platform integration system 118 ensuring seamless operation of the invention across diverse infrastructures, such as cloud platforms, IoT networks, and industrial systems;

At step 222, method 200 includes modular and scalable architecture 120 supporting the horizontal scaling of the system across distributed environments;

At step 224, method 200 includes predictive maintenance integration 122 identifying early signs of hardware degradation and recommending preventive maintenance before failure occurs;

At step 226, method 200 includes human-machine collaborative interface 126 allowing operators to intervene manually when necessary and override automated processes during critical scenarios;

At step 228, method 200 includes distributed AI model deployment system 130 enabling predictions and recovery actions at network nodes for faster response times;

At step 230, method 200 includes error correlation module 132 identifying cascading failures across interconnected systems to prevent further issues;

At step 232, method 200 includes behavioural analysis component 134 monitoring user behaviour to predict and mitigate human-induced errors;

At step 234, method 200 includes multi-layer recovery strategy 136 ensuring simultaneous recovery actions across hardware, software, and network layers for comprehensive stability


[00049] In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms "fixed" "attached" "disposed," "mounted," and "connected" are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.

[00050] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.

[00051] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
, Claims:WE CLAIM:
1. An AI-based predictive error recovery system 100 comprising of
data collection module 102 to gather real-time operational data, including system logs and performance metrics;
data processing unit 104 to preprocess collected data by filtering noise and normalizing input formats;
AI predictive engine 106 to analyze data and predict potential errors based on real-time and historical inputs;
error classification module 108 to categorize predicted errors into hardware, software, or network issues;
recovery action module 110 to initiate appropriate recovery protocols, such as system reboots or traffic rerouting;
feedback and learning module 112 to monitor recovery effectiveness and refine predictive algorithms continuously;
real-time monitoring dashboard 114 to display system status, error predictions, and recovery actions;
hybrid ai models 116 to integrate multiple learning techniques for enhanced prediction accuracy;
cross-platform integration system 118 to ensure seamless operation across cloud platforms and iot networks;
modular and scalable architecture 120 to support horizontal scaling across distributed environments;
predictive maintenance integration 122 to detect hardware wear and enable preventive maintenance;
context-aware anomaly detection module 124 to reduce false positives using environmental factors;
human-machine collaborative interface 126 to allow manual overrides during critical scenarios;
energy-efficient recovery mechanism 128 to optimize resource usage and reduce energy consumption;
distributed AI model deployment system 130 to enable localized predictions and faster recovery;
error correlation module 132 to identify cascading failures across interconnected systems;
behavioral analysis component 134 to monitor user behavior and mitigate human-induced errors; and
multi-layer recovery strategy 136 to provide simultaneous recovery across hardware, software, and network layers

2. The AI-based predictive error recovery system 100 as claimed in claim 1, wherein data collection module 102 is configured to continuously gather real-time operational data, including system logs, performance metrics, and resource usage, ensuring accurate inputs for error prediction and recovery.

3. The AI-based predictive error recovery system 100 as claimed in claim 1, wherein data processing unit 104 is configured to preprocess collected data by filtering noise, normalizing input formats, and filling missing values, enhancing the accuracy and reliability of predictions.

4. The AI-based predictive error recovery system 100 as claimed in claim 1, wherein AI predictive engine 106 utilizes hybrid AI models to analyze real-time and historical data, predict potential system errors, and trigger preemptive recovery actions to prevent failures.

5. The AI-based predictive error recovery system 100 as claimed in claim 1, wherein error classification module 108 is configured to categorize predicted errors into predefined types such as hardware failures, software malfunctions, and network congestion, enabling precise recovery actions.

6. The AI-based predictive error recovery system 100 as claimed in claim 1, wherein recovery action module 110 is configured to automatically initiate appropriate recovery protocols, including traffic rerouting, system reboots, and failover activation, based on classified errors.

7. The AI-based predictive error recovery system 100 as claimed in claim 1, wherein feedback and learning module 112 continuously monitors the effectiveness of recovery actions, refining predictive algorithms based on outcomes to improve future error prevention and recovery.

8. The AI-based predictive error recovery system 100 as claimed in claim 1, wherein context-aware anomaly detection module 124 integrates environmental factors, such as time of day and seasonal trends, to enhance prediction accuracy and reduce false positives.

9. The AI-based predictive error recovery system 100 as claimed in claim 1, wherein modular and scalable architecture 120 supports horizontal scaling across distributed environments, ensuring seamless operation and high performance across smart city networks, cloud platforms, and industrial systems.

10. The AI-based predictive error recovery system 100 as claimed in claim 1, wherein method comprises of
data collection module 102 gathers real-time operational data, including system logs, performance metrics, and resource usage;
data processing unit 104 preprocesses the collected data by filtering noise, normalizing formats, and filling missing values to enhance prediction accuracy;
AI predictive engine 106 analyzes the preprocessed data and historical patterns to forecast potential errors before they escalate into failures;
error classification module 108 categorizes predicted errors into predefined types such as hardware failures, software glitches, or network congestion, enabling targeted recovery actions;
recovery action module 110 initiates appropriate recovery protocols based on the classified error, such as switching to backup servers, rerouting traffic, or rebooting systems;
feedback and learning module 112 monitors the effectiveness of recovery actions and refines the AI predictive engine 106 through continuous learning to improve future performance;
real-time monitoring dashboard 114 provides a transparent visual interface, displaying system status, error predictions, and recovery actions for operator oversight;
hybrid AI models 116 integrate supervised, unsupervised, and reinforcement learning techniques to enhance the adaptability of ai predictive engine 106;
context-aware anomaly detection module 124 incorporates environmental factors such as time of day and seasonal patterns to reduce false positives and ensure accurate error detection;
cross-platform integration system 118 ensures seamless operation across diverse infrastructures, including cloud platforms, data centers, and IoT networks;
modular and scalable architecture 120 supports horizontal scaling across distributed environments, maintaining performance in smart city networks and cloud infrastructures;
predictive maintenance integration 122 detects early signs of hardware wear to enable preventive maintenance, reducing downtime and enhancing system reliability;
human-machine collaborative interface 126 allows operators to monitor and intervene, providing manual overrides during critical scenarios if needed;
distributed ai model deployment system 130 ensures localized predictions and recovery actions across network nodes, reducing latency in large-scale infrastructures;
error correlation module 132 identifies cascading failures across interconnected systems to prevent widespread disruptions and enhance system stability;
behavioural analysis component 134 monitors user behaviour to predict and mitigate human-induced errors, improving overall system resilience; and
multi-layer recovery strategy 136 ensures simultaneous recovery across hardware, software, and network layers, providing comprehensive stability and preventing cascading failures

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