1. An AI and ML System with Self-Evolving Neural Architecture for Automated Decision Making in Dynamic Environments claims that a system, comprising a data input module, preprocessing unit, adaptive neural network core, architecture evolution engine, decision-making module, and feedback unit, wherein the system is configured to continuously adapt based on real-time data and performance metrics.

2. The system as claimed in claim 1, wherein the data input module is configured to collect structured and unstructured data from multiple sources including sensors, databases, and real-time data streams.

3. The system as claimed in claim 1, wherein the preprocessing unit performs data cleaning, normalization, feature extraction, and transformation to generate optimized input for the adaptive neural network.

4. The system as claimed in claim 1, wherein the adaptive neural network core dynamically modifies its architecture by adding or removing layers, neurons, and connections based on learning requirements and environmental changes.

5. The system as claimed in claim 1, wherein the architecture evolution engine employs neural architecture search, structural mutation, and hyperparameter tuning to optimize the neural network configuration.

6. The system as claimed in claim 1, wherein the system integrates reinforcement learning to enable decision-making through interaction with an environment using state, action, and reward mechanisms.

7. The system as claimed in claim 1, wherein a meta-learning module is configured to improve learning efficiency by adapting prior knowledge to new tasks with minimal training data.

8. The system as claimed in claim 1, wherein the decision-making module generates actionable outputs based on prediction confidence, risk evaluation, and environmental conditions.

9. The system as claimed in claim 1, wherein the feedback unit continuously updates model parameters and triggers structural modifications based on evaluation of decision outcomes.

10. The system as claimed in claim 1, wherein the system is deployable across cloud, edge, and hybrid computing environments for scalable and real-time operation in applications including healthcare, finance, industrial automation, and smart infrastructure.

Description:FIELD OF INVENTION
The invention relates to artificial intelligence systems, specifically adaptive machine learning architectures for automated decision-making in dynamic, real-time, and data-driven environments.
BACKGROUND OF INVENTION
Conventional artificial intelligence and machine learning systems rely on predefined architectures and static training procedures, which limit their ability to adapt to rapidly changing environments. These systems require manual intervention for retraining, parameter tuning, and structural modifications when faced with new data patterns or unexpected conditions. In dynamic environments such as healthcare monitoring, financial markets, and industrial automation, data characteristics frequently change, leading to performance degradation in traditional models. Existing solutions attempt to address adaptability through incremental learning or transfer learning; however, they do not provide full autonomy in structural evolution. Neural architecture search methods exist but are computationally expensive and not suitable for real-time applications. Furthermore, most systems lack the ability to balance exploration and exploitation effectively in uncertain scenarios. Therefore, there is a need for a self-evolving intelligent system capable of autonomously modifying its architecture, learning strategies, and decision-making policies to maintain optimal performance in dynamic and unpredictable environments.
The patent application number 202147023884 discloses a method and apparatus for providing a rendering engine model comprising a description of a neural network embedded in a media item. A method and apparatus that embed a neural network description within a media item to enable rendering engines to process, interpret, and dynamically generate enhanced visual or interactive content efficiently.
The patent application number 202121055943 discloses a recurrent neural network system based on ultra-low power neuron. A recurrent neural network system utilizing ultra-low power neuron circuits designed for energy-efficient computation, enabling continuous learning, real-time processing, and deployment in resource-constrained devices such as iot and embedded systems.
The patent application number 202217059612 discloses a computer-assisted method for generating training data for a neural network for predicting a concentration of pollutants at a measuring station. A computer-assisted method that generates synthetic and real data to train neural networks, enabling accurate prediction of pollutant concentrations at monitoring stations under varying environmental and atmospheric conditions.
The patent application number 202321027200 discloses a system and method for neural node-based data processing flow control in a microkernel. A microkernel-based system that manages data processing using neural nodes to dynamically control data flow, improving efficiency, scalability, and adaptability through intelligent, real-time decision-making and optimized resource allocation.
The patent application number 202341087256 discloses a system and method for performance evaluation of an activation function for training deep neural networks. A system and method that evaluates activation functions by measuring their impact on training stability, convergence speed, and accuracy in deep neural networks, enabling selection of optimal functions for improved model performance.
OBJECTIVE OF THE INVENTION
The primary objective of the invention is to develop an intelligent system capable of autonomously evolving its neural architecture and learning strategies for accurate, real-time decision-making in dynamic environments without requiring manual intervention or retraining.
SUMMARY
The invention presents a self-evolving artificial intelligence system designed to operate efficiently in dynamic environments. The system incorporates adaptive neural architecture mechanisms that allow automatic modification of network structure, including layers, nodes, and connections, based on incoming data and performance feedback. It employs reinforcement learning to optimize decision-making policies and meta-learning to improve learning efficiency across tasks. A neural architecture search module dynamically identifies optimal configurations while minimizing computational overhead. The system continuously monitors environmental changes and triggers structural adaptation when performance metrics deviate from expected thresholds. It integrates data preprocessing, feature extraction, model adaptation, and decision execution into a unified framework. The proposed system enhances robustness, scalability, and accuracy while reducing dependency on human intervention. It is applicable across multiple domains where real-time adaptability is essential, offering a significant improvement over traditional static machine learning systems.
DETAILED DESCRIPTION OF INVENTION
The proposed system is designed as a fully autonomous intelligent framework capable of adapting to dynamic and uncertain environments through continuous learning and structural evolution. The system operates by acquiring real-time data from multiple heterogeneous sources and processing it through an adaptive neural network capable of modifying its internal structure based on performance feedback. Unlike conventional machine learning systems that rely on static architectures and periodic retraining, the present system introduces a continuously evolving framework that dynamically adjusts itself to changing data distributions and environmental conditions.
The architecture is modular in nature, comprising interconnected functional units such as data acquisition, preprocessing, adaptive learning, architecture evolution, decision-making, and feedback integration. Each module performs a specific function while interacting seamlessly with other components through well-defined interfaces. The system is capable of real-time operation, ensuring that decisions are updated continuously as new data becomes available. This dynamic adaptability enhances accuracy, efficiency, and robustness, making the system suitable for complex real-world scenarios where uncertainty and variability are inherent.
System Architecture
The system architecture is designed in a layered and modular format to ensure flexibility, scalability, and ease of integration. It comprises six primary components, namely the Data Input Module, Preprocessing and Feature Engineering Unit, Adaptive Neural Network Core, Self-Evolving Architecture Engine, Decision-Making Module, and Feedback and Reinforcement Unit. These components are interconnected through a continuous data flow pipeline supported by bidirectional communication channels for feedback and optimization.
The Data Input Module serves as the entry point, capturing real-time data from various sources. The Preprocessing Unit refines the data before passing it to the Adaptive Neural Network Core, which performs learning and prediction tasks. The Self-Evolving Architecture Engine monitors performance metrics and dynamically modifies the neural structure when required. The Decision-Making Module interprets outputs and generates actionable insights, while the Feedback Unit ensures continuous improvement by providing performance-based updates to the system.
The architecture supports distributed computing environments and can be deployed on cloud, edge, or hybrid infrastructures. This ensures scalability and efficient handling of high-volume data streams.

Figure 1: Overall system architecture and the interaction between modules.
Data Acquisition and Preprocessing
The system is designed to handle both structured and unstructured data obtained from diverse sources such as IoT sensors, enterprise databases, user inputs, and real-time streaming platforms. The Data Acquisition Module continuously collects and aggregates incoming data while ensuring minimal latency.
The preprocessing stage plays a crucial role in enhancing data quality and reliability. It performs several operations including noise filtering, removal of redundant and irrelevant information, normalization of data values, and handling of missing or inconsistent entries. Feature engineering techniques are applied to extract meaningful attributes that improve model performance. This includes dimensionality reduction, encoding of categorical variables, and transformation of raw data into representative feature vectors.
The preprocessing pipeline is adaptive in nature and can modify its operations based on the characteristics of incoming data. This ensures that the neural network receives optimized inputs, thereby improving learning efficiency and prediction accuracy.
Adaptive Neural Network Core
The Adaptive Neural Network Core forms the central intelligence of the system. Unlike traditional neural networks with fixed architectures, this core is capable of dynamically adjusting its structure and parameters during operation. It begins with a baseline configuration and evolves over time by adding or removing layers, neurons, and connections based on performance requirements.
The network supports multiple learning paradigms including supervised learning, unsupervised learning, and reinforcement learning. Advanced optimization techniques such as gradient descent, evolutionary algorithms, and hybrid optimization strategies are employed for weight adjustment. Activation functions are dynamically selected to suit specific tasks, enhancing the model’s ability to capture complex patterns.
The adaptive nature of the neural network ensures that it remains relevant even as data distributions change, thereby eliminating the need for frequent manual retraining. This significantly improves efficiency and reduces operational overhead.
Self-Evolving Neural Architecture Mechanism
A key feature of the invention is the self-evolving neural architecture mechanism, which enables the system to autonomously modify its structure in response to changing conditions. The system continuously monitors performance indicators such as accuracy, loss, response time, and computational efficiency. When deviations from expected performance are detected, the architecture evolution engine is activated.
The evolution process involves techniques such as Neural Architecture Search (NAS), structural mutation, and hyperparameter optimization. New layers or nodes may be added to increase learning capacity, while redundant components may be removed to improve efficiency. Connection weights and network topology are also optimized to enhance performance.
This adaptive evolution occurs without interrupting system operation, ensuring seamless real-time performance.

Figure 2: Iterative process of neural architecture evolution.
Reinforcement Learning Integration
The system integrates reinforcement learning to enable intelligent decision-making through interaction with the environment. In this framework, the system acts as an agent that observes the current state of the environment, selects actions, and receives rewards based on the outcomes of those actions.
The reinforcement learning module continuously updates its policy to maximize cumulative rewards over time. It balances exploration and exploitation to ensure optimal decision-making even in uncertain conditions. Advanced techniques such as Q-learning, policy gradients, and deep reinforcement learning are incorporated to improve learning efficiency.
This integration allows the system to adapt to complex scenarios where predefined rules are insufficient, making it highly suitable for dynamic and unpredictable environments.
Meta-Learning Capability
The system incorporates meta-learning techniques that enable it to learn from prior experiences and apply that knowledge to new tasks. This “learning to learn” approach significantly reduces the time required for training and improves generalization across different problem domains.
Meta-learning algorithms analyze patterns in learning processes and adjust model parameters accordingly. This allows the system to quickly adapt to new data with minimal training samples. It also enhances robustness by preventing overfitting and improving adaptability to unseen scenarios.
The inclusion of meta-learning ensures that the system remains efficient and effective even when dealing with rapidly changing environments or limited data availability.
Decision-Making Module
The Decision-Making Module is responsible for generating actionable outputs based on processed data and learned patterns. It evaluates predictions from the neural network and considers additional factors such as confidence levels, risk assessments, and environmental constraints before making decisions.
The module supports both automated and semi-automated decision-making processes, allowing integration with external systems where human oversight may be required. It ensures that decisions are optimized for accuracy, efficiency, and reliability.
In complex scenarios, the module can prioritize multiple objectives and provide ranked decision options, enhancing its applicability across diverse domains.
Feedback and Continuous Learning
The system incorporates a closed-loop feedback mechanism that enables continuous learning and improvement. After a decision is executed, the outcome is evaluated and fed back into the system. This feedback is used to update model parameters, refine learning strategies, and trigger architectural modifications when necessary.
The feedback loop ensures that the system learns from both successes and failures, leading to progressive improvement in performance. It also allows the system to adapt to changes in the environment without requiring external intervention.

Figure 3: Feedback loop mechanism and its role in continuous learning.
Performance Optimization
To ensure efficient operation in real-time environments, the system employs various performance optimization techniques. These include parallel processing for handling large datasets, model pruning to remove unnecessary components, and resource-aware computation to optimize energy and processing requirements.
The system dynamically allocates computational resources based on workload demands, ensuring optimal utilization of available hardware. This results in reduced latency, improved throughput, and enhanced scalability.
Security and Robustness
The system is designed with robust mechanisms to handle challenges such as noisy data, adversarial inputs, and system failures. It incorporates anomaly detection techniques to identify and mitigate potential threats. Data validation and encryption methods are used to ensure data integrity and security.
The system also includes redundancy and fail-safe mechanisms to maintain operation in the event of component failures. This ensures reliability and consistency, making the system suitable for critical applications.
Implementation Framework
The implementation framework provides a structured approach for deploying the system across various platforms. It supports integration with existing infrastructures and can be customized based on application requirements.
Table 1: System Components and Functions
Component Function
Data Module Collects and aggregates real-time data
Preprocessing Unit Cleans, transforms, and prepares data
Neural Core Performs learning and prediction
Evolution Engine Dynamically modifies architecture
Decision Module Generates actionable decisions
Feedback Unit Enhances performance through learning

The framework supports cloud-based, edge-based, and hybrid deployments, ensuring flexibility and scalability.
Comparative Analysis
Table 2: Comparison with Traditional Systems
Feature Traditional Systems Proposed System
Adaptability Limited High
Architecture Static Dynamic
Learning Approach Periodic Continuous
Human Intervention Required Minimal

The proposed system demonstrates superior performance in dynamic environments due to its ability to evolve and adapt continuously.
Applications of the Invention
The invention has wide applicability across multiple domains where real-time decision-making is critical. In healthcare, it can be used for patient monitoring and diagnosis. In finance, it supports market prediction and risk analysis. Industrial automation benefits from predictive maintenance and process optimization. Smart city applications include traffic management and resource allocation, while autonomous vehicles rely on such systems for navigation and safety.
The system’s adaptability makes it suitable for any environment characterized by variability and uncertainty.
Advantages of the Invention
The proposed system offers several advantages over conventional methods. It operates autonomously without requiring manual retraining, ensuring continuous performance improvement. Its real-time adaptability allows it to handle dynamic environments effectively. The system provides high accuracy and efficiency due to its evolving architecture and advanced learning techniques.
It is scalable and can be deployed across different platforms, making it suitable for a wide range of applications. The reduction in human intervention also lowers operational costs and improves reliability.
The invention introduces a novel and efficient approach to automated decision-making through a self-evolving neural architecture. By integrating adaptive learning, reinforcement learning, and meta-learning, the system achieves high levels of performance, flexibility, and scalability. It overcomes the limitations of traditional machine learning systems and provides a robust solution for dynamic environments.
The system represents a significant advancement in artificial intelligence technology, offering practical benefits across various industries and applications.
DETAILED DESCRIPTION OF DIAGRAM
Figure 1: Overall system architecture and the interaction between modules.
Figure 2: Iterative process of neural architecture evolution.
Figure 3: Feedback loop mechanism and its role in continuous learning. , Claims:1. An AI and ML System with Self-Evolving Neural Architecture for Automated Decision Making in Dynamic Environments claims that a system, comprising a data input module, preprocessing unit, adaptive neural network core, architecture evolution engine, decision-making module, and feedback unit, wherein the system is configured to continuously adapt based on real-time data and performance metrics.
2. The system as claimed in claim 1, wherein the data input module is configured to collect structured and unstructured data from multiple sources including sensors, databases, and real-time data streams.
3. The system as claimed in claim 1, wherein the preprocessing unit performs data cleaning, normalization, feature extraction, and transformation to generate optimized input for the adaptive neural network.
4. The system as claimed in claim 1, wherein the adaptive neural network core dynamically modifies its architecture by adding or removing layers, neurons, and connections based on learning requirements and environmental changes.
5. The system as claimed in claim 1, wherein the architecture evolution engine employs neural architecture search, structural mutation, and hyperparameter tuning to optimize the neural network configuration.
6. The system as claimed in claim 1, wherein the system integrates reinforcement learning to enable decision-making through interaction with an environment using state, action, and reward mechanisms.
7. The system as claimed in claim 1, wherein a meta-learning module is configured to improve learning efficiency by adapting prior knowledge to new tasks with minimal training data.
8. The system as claimed in claim 1, wherein the decision-making module generates actionable outputs based on prediction confidence, risk evaluation, and environmental conditions.
9. The system as claimed in claim 1, wherein the feedback unit continuously updates model parameters and triggers structural modifications based on evaluation of decision outcomes.
10. The system as claimed in claim 1, wherein the system is deployable across cloud, edge, and hybrid computing environments for scalable and real-time operation in applications including healthcare, finance, industrial automation, and smart infrastructure.