NEURO-SYMBOLIC METAMODEL FOR PROGRESSIVE AI DEVELOPMENT AND ENHANCEMENT

Abstract

ABSTRACT NEURO-SYMBOLIC METAMODEL FOR PROGRESSIVE AI DEVELOPMENT AND ENHANCEMENT The present disclosure introduces a neuro-symbolic metamodel for progressive AI development and enhancement 100, which integrates neural network subsystem 102 and symbolic reasoning subsystem 104 to enable advanced learning, reasoning, and decision-making. The integration and control module 106 facilitates seamless interaction between the two subsystems, while the feedback loop mechanism 108 continuously monitors and improves system performance. The hybrid inference engine 110 combines insights from neural network with rule-based logic from the symbolic subsystem. The other components are knowledge graph or ontology system 112, contextual rule-based system 114, task delegation system 116, dual-path knowledge representation 118, real-time knowledge sharing mechanism 120, self-evolving ontology 122, cross-domain transfer learning 124, rule-based error detection and optimization system 126, transparent knowledge traceability pipeline 128, modular architecture 130, contextual task delegation mechanism 132, human-AI collaboration interface 134, contextual rule updating based on neural discoveries 136 and cross-domain transfer learning capability 138. Reference Fig 1

Specification

Description:Neuro-Symbolic Metamodel for Progressive AI Development and Enhancement
TECHNICAL FIELD
[0001] The present innovation relates to a neuro-symbolic metamodel for artificial intelligence (AI) development, integrating neural networks and symbolic reasoning for enhanced interpretability, reasoning, and knowledge representation.

BACKGROUND

[0002] Artificial intelligence (AI) has seen remarkable growth, particularly in areas driven by machine learning (ML) techniques such as deep learning. While neural networks have proven successful in tasks like image recognition, natural language processing, and speech recognition, they often function as "black boxes," lacking transparency and interpretability. This opacity raises concerns about accountability, especially in critical applications such as healthcare, finance, and autonomous systems. Additionally, neural networks struggle with logical reasoning and handling symbolic knowledge, limiting their effectiveness in tasks that require structured knowledge representation and contextual understanding.

[0003] Symbolic AI, in contrast, excels in logical reasoning and manipulation of abstract concepts through explicit rules and structured knowledge. However, it lacks the ability to learn from large datasets, limiting its scalability and adaptability to real-world complexities. Existing options for AI users typically involve choosing between neural networks for data-driven tasks or symbolic AI for rule-based tasks. The main drawback is the inability of either approach to perform well across both unstructured data processing and structured reasoning tasks, leading to inefficiencies and reduced effectiveness in complex, dynamic environments.


[0004] The proposed neuro-symbolic metamodel differentiates itself by integrating both neural networks and symbolic reasoning frameworks into a unified system, combining the strengths of both approaches. The novelty of this invention lies in its dual-path architecture, where the neural network subsystem handles pattern recognition, while the symbolic reasoning subsystem manages logical inference and decision-making. The invention introduces a progressive feedback loop, allowing continuous learning and improvement of both subsystems over time. This hybrid approach not only enhances interpretability and reasoning capabilities but also provides modular adaptability across diverse domains, making the system scalable, explainable, and more efficient than traditional AI models.

OBJECTS OF THE INVENTION

[0005] The primary object of the invention is to provide a neuro-symbolic metamodel that integrates neural networks and symbolic reasoning to enhance AI development and performance.

[0006] Another object of the invention is to improve the interpretability of AI systems by combining symbolic reasoning with neural learning, allowing for transparent and traceable decision-making processes.

[0007] Another object of the invention is to offer a modular and scalable AI framework that can be applied across various domains, including healthcare, natural language processing, and autonomous systems.

[0008] Another object of the invention is to enhance the reasoning capabilities of AI systems by incorporating symbolic knowledge representation, allowing for logical inference and contextual understanding.

[0009] Another object of the invention is to enable continuous learning and self-improvement in AI systems through a feedback loop between neural and symbolic components.

[00010] Another object of the invention is to overcome the limitations of traditional neural networks by integrating structured, rule-based knowledge to improve decision-making in complex scenarios.

[00011] Another object of the invention is to provide a dual-path architecture that supports both data-driven pattern recognition and structured reasoning, improving the adaptability and accuracy of AI systems.

[00012] Another object of the invention is to offer a hybrid AI system that can learn from large datasets while maintaining the ability to reason through explicit rules and ontologies.

[00013] Another object of the invention is to ensure that AI systems are more reliable and accountable, particularly in critical applications such as healthcare, finance, and autonomous vehicles.

[00014] Another object of the invention is to promote the development of more advanced, transparent, and adaptable AI systems that can operate effectively in real-world scenarios with minimal human intervention


SUMMARY OF THE INVENTION

[00015] In accordance with the different aspects of the present invention, neuro-symbolic metamodel for progressive AI development and enhancement is presented. It relates to a neuro-symbolic metamodel that integrates neural networks and symbolic reasoning to create a hybrid AI system for enhanced learning, reasoning, and knowledge representation. This metamodel improves interpretability, scalability, and adaptability by combining data-driven neural learning with rule-based symbolic reasoning. A feedback loop enables continuous improvement of the AI's performance. The system is applicable across diverse domains such as healthcare, natural language processing, and autonomous systems. Its modular design allows for flexible, real-time enhancements and transparent decision-making.

[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 neuro-symbolic metamodel for progressive AI development and enhancement.

[00021] FIG 2 is working methodology of neuro-symbolic metamodel for progressive AI development and enhancement.

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 neuro-symbolic metamodel for progressive AI development and enhancement 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, neuro-symbolic metamodel for progressive AI development and enhancement 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of neural network subsystem 102, symbolic reasoning subsystem 104, integration and control module 106, feedback loop mechanism 108, hybrid inference engine 110, knowledge graph or ontology system 112, contextual rule-based system 114, task delegation system 116, dual-path knowledge representation 118, real-time knowledge sharing mechanism 120, self-evolving ontology 122, cross-domain transfer learning 124, rule-based error detection and optimization system 126, transparent knowledge traceability pipeline 128, modular architecture 130, contextual task delegation mechanism 132, human-AI collaboration interface 134, contextual rule updating based on neural discoveries 136 and cross-domain transfer learning capability 138.

[00029] Referring to Fig. 1, the present disclosure provides details of neuro-symbolic metamodel for progressive AI development and enhancement 100. It integrates neural networks and symbolic reasoning to create a hybrid AI system, enhancing learning, reasoning, and decision-making capabilities. In one of the embodiments, the neuro-symbolic metamodel 100 may be provided with key components such as neural network subsystem 102, symbolic reasoning subsystem 104, and integration and control module 106, ensuring seamless coordination between neural learning and symbolic inference. The system incorporates feedback loop mechanism 108 for continuous learning and improvement, while the hybrid inference engine 110 enables complex decision-making through a combination of data-driven insights and rule-based logic. Additional components such as self-evolving ontology 122 and cross-domain transfer learning 124 ensure adaptability across various domains and dynamic scenarios.

[00030] Referring to Fig. 1, neuro-symbolic metamodel for progressive AI development and enhancement 100 is provided with neural network subsystem 102, which uses deep learning algorithms, such as CNNs or RNNs, to process unstructured data like images, text, and audio. This subsystem excels in pattern recognition and data-driven tasks, identifying key features and relationships in large datasets. It interacts with the symbolic reasoning subsystem 104, providing the necessary data for further reasoning. The neural network subsystem 102 also sends its processed outputs to the integration and control module 106 for coordination with symbolic logic.

[00031] Referring to Fig. 1, neuro-symbolic metamodel for progressive AI development and enhancement 100 is provided with symbolic reasoning subsystem 104, which performs logical reasoning and handles structured knowledge using ontologies or knowledge graphs. It interprets and processes data received from the neural network subsystem 102 and applies predefined rules and logic to derive inferences. The symbolic reasoning subsystem 104 works closely with the integration and control module 106 to ensure smooth interaction with the neural network for comprehensive decision-making.

[00032] Referring to Fig. 1, neuro-symbolic metamodel for progressive AI development and enhancement 100 is provided with integration and control module 106, which manages the interaction between the neural network subsystem 102 and the symbolic reasoning subsystem 104. It ensures that the outputs of the neural network subsystem 102 are interpreted and utilized by the symbolic reasoning subsystem 104 effectively. The integration and control module 106 also facilitates task delegation based on the nature of the problem, optimizing the use of both subsystems.

[00033] Referring to Fig. 1, neuro-symbolic metamodel for progressive AI development and enhancement 100 is provided with feedback loop mechanism 108, which continuously monitors the performance of both the neural network subsystem 102 and the symbolic reasoning subsystem 104. It collects insights from symbolic reasoning and feeds them back to the neural network subsystem 102 for further learning and refinement. This feedback loop ensures that the system progressively improves its capabilities over time.

[00034] Referring to Fig. 1, neuro-symbolic metamodel for progressive AI development and enhancement 100 is provided with hybrid inference engine 110, which integrates both neural inference from the neural network subsystem 102 and logical conclusions from the symbolic reasoning subsystem 104. This engine enables the system to tackle complex problems by combining data-driven insights with rule-based reasoning. The hybrid inference engine 110 works in close conjunction with the integration and control module 106 to dynamically adjust the balance between neural learning and symbolic reasoning for optimal performance.
[00035] Referring to Fig. 1, neuro-symbolic metamodel for progressive AI development and enhancement 100 is provided with knowledge graph or ontology system 112, which stores structured knowledge such as rules, entities, and relationships. This system provides the symbolic reasoning subsystem 104 with the foundational data needed for logical inference and decision-making. The knowledge graph or ontology system 112 is updated through inputs from the neural network subsystem 102, ensuring that new data-driven insights are incorporated into the symbolic reasoning framework.

[00036] Referring to Fig. 1, neuro-symbolic metamodel for progressive AI development and enhancement 100 is provided with contextual rule-based system 114, which dynamically updates the symbolic reasoning subsystem 104 with new rules based on patterns detected by the neural network subsystem 102. This allows the system to adapt to new scenarios and refine its reasoning abilities. The contextual rule-based system 114 works in tandem with the knowledge graph or ontology system 112 to ensure that updated rules are logically consistent.

[00037] Referring to Fig. 1, neuro-symbolic metamodel for progressive AI development and enhancement 100 is provided with task delegation system 116, which intelligently assigns tasks to the neural network subsystem 102 or the symbolic reasoning subsystem 104 depending on the nature of the problem. For instance, pattern recognition tasks are delegated to the neural network, while logical reasoning tasks are handled by the symbolic reasoning subsystem 104. This system is controlled by the integration and control module 106 to ensure efficient resource utilization.

[00038] Referring to Fig. 1, neuro-symbolic metamodel for progressive AI development and enhancement 100 is provided with dual-path knowledge representation 118, which enables simultaneous processing of unstructured data by the neural network subsystem 102 and structured data by the symbolic reasoning subsystem 104. This dual-path approach allows the system to better handle complex tasks that require both data-driven insights and logical reasoning. The integration and control module 106 manages the flow between these two paths for seamless decision-making.

[00039] Referring to Fig. 1, neuro-symbolic metamodel for progressive AI development and enhancement 100 is provided with real-time knowledge sharing mechanism 120, which ensures that discoveries and updates from one instance of the system are shared across multiple instances. This mechanism supports decentralized learning and collective intelligence, allowing different systems to learn from each other's experiences. It interacts with the knowledge graph or ontology system 112 to update shared knowledge across distributed systems.

[00040] Referring to Fig. 1, neuro-symbolic metamodel for progressive AI development and enhancement 100 is provided with self-evolving ontology 122, which autonomously updates the structured knowledge within the symbolic reasoning subsystem 104. This component ensures that the system remains current by incorporating new insights from the neural network subsystem 102 into the existing knowledge graph. The self-evolving ontology 122 works in conjunction with the contextual rule-based system 114 to dynamically adapt the rules and relationships as new data is processed.

[00041] Referring to Fig. 1, neuro-symbolic metamodel for progressive AI development and enhancement 100 is provided with cross-domain transfer learning 124, which allows the neural network subsystem 102 to apply learned knowledge from one domain (e.g., image recognition) to another (e.g., language processing). This capability enables the system to adapt efficiently to new environments or tasks. The cross-domain transfer learning 124 interacts with the symbolic reasoning subsystem 104, ensuring that domain-specific rules are adjusted to accommodate new contexts.

[00042] Referring to Fig. 1, neuro-symbolic metamodel for progressive AI development and enhancement 100 is provided with rule-based error detection and optimization system 126, which validates the outputs from the neural network subsystem 102 by applying symbolic logic from the symbolic reasoning subsystem 104. If errors or inconsistencies are detected, the system feeds back these insights to optimize the neural learning process. The rule-based error detection and optimization system 126 plays a key role in ensuring the accuracy and reliability of the overall AI system.

[00043] Referring to Fig. 1, neuro-symbolic metamodel for progressive AI development and enhancement 100 is provided with transparent knowledge traceability pipeline 128, which ensures that all decision-making processes from the neural network subsystem 102 to the symbolic reasoning subsystem 104 are fully traceable. This feature is critical for applications where auditability and accountability are required, such as in healthcare or legal systems. The transparent knowledge traceability pipeline 128 works closely with the feedback loop mechanism 108 to document and analyze each step in the AI's decision-making process.

[00044] Referring to Fig. 1, neuro-symbolic metamodel for progressive AI development and enhancement 100 is provided with modular architecture 130, which allows independent enhancements and updates to the neural network subsystem 102 and symbolic reasoning subsystem 104 without overhauling the entire system. This modular architecture 130 enables scalability and flexibility, ensuring that the system can evolve over time as new technologies or methods become available. It works in conjunction with the integration and control module 106 to facilitate smooth updates and enhancements.

[00045] Referring to Fig. 1, neuro-symbolic metamodel for progressive AI development and enhancement 100 is provided with contextual task delegation mechanism 132, which dynamically assigns tasks to the neural network subsystem 102 or symbolic reasoning subsystem 104 based on the task's requirements. For example, data-heavy tasks are directed to the neural network subsystem 102, while logical inference tasks are handled by the symbolic reasoning subsystem 104. This mechanism ensures that each component is utilized optimally for the specific nature of the task.

[00046] Referring to Fig. 1, neuro-symbolic metamodel for progressive AI development and enhancement 100 is provided with human-AI collaboration interface 134, which facilitates interaction between human users and the AI system. This interface allows users to understand the reasoning process of the symbolic reasoning subsystem 104 and make informed decisions based on AI suggestions. The human-AI collaboration interface 134 is crucial in domains where human oversight is essential, such as healthcare, ensuring transparency and trust in AI-assisted decision-making.

[00047] Referring to Fig. 1, neuro-symbolic metamodel for progressive AI development and enhancement 100 is provided with contextual rule updating based on neural discoveries 136, which allows the symbolic reasoning subsystem 104 to modify its rules dynamically in response to new patterns identified by the neural network subsystem 102. This component ensures that the system remains adaptable and continuously improves its reasoning capabilities as new data is processed. The contextual rule updating based on neural discoveries 136 enhances the overall system's responsiveness to evolving data.

[00048] Referring to Fig. 1, neuro-symbolic metamodel for progressive AI development and enhancement 100 is provided with cross-domain transfer learning capability 138, which enables the system to apply knowledge from one domain to another, ensuring adaptability across diverse fields such as healthcare, cybersecurity, and autonomous systems. The cross-domain transfer learning capability 138 interacts with the task delegation system 116 to ensure that the knowledge transfer is contextually appropriate, enhancing the system's versatility and efficiency.



[00049] Referring to Fig 2, there is illustrated method 200 for neuro-symbolic metamodel for progressive AI development and enhancement 100. The method comprises:

At step 202, method 200 includes the neural network subsystem 102 receiving unstructured data such as images, text, or audio and processing it to identify patterns and features;

At step 204, method 200 includes the neural network subsystem 102 sending the processed data to the integration and control module 106 for coordination with the symbolic reasoning subsystem 104;

At step 206, method 200 includes the symbolic reasoning subsystem 104 receiving the processed data and applying predefined rules, logic, or knowledge from the knowledge graph or ontology system 112 to derive inferences;

At step 208, method 200 includes the integration and control module 106 facilitating the exchange of information between the neural network subsystem 102 and symbolic reasoning subsystem 104, ensuring smooth collaboration;

At step 210, method 200 includes the hybrid inference engine 110 combining the neural data-driven insights with symbolic logic to make comprehensive decisions;

At step 212, method 200 includes the feedback loop mechanism 108 continuously monitoring the performance of both subsystems, identifying any inconsistencies or areas of improvement;

At step 214, method 200 includes the feedback loop mechanism 108 sending these insights back to the neural network subsystem 102 for further learning and refinement, allowing the system to progressively improve its capabilities;

At step 216, method 200 includes the contextual rule-based system 114 updating the symbolic reasoning subsystem 104 with new rules or relationships based on patterns identified by the neural network subsystem 102 to ensure adaptability;

At step 218, method 200 includes the real-time knowledge sharing mechanism 120 ensuring any new discoveries or rule updates are shared across multiple instances of the system, enhancing the collective intelligence of the network


[00050] 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.

[00051] 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.

[00052] 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. A neuro-symbolic metamodel for progressive AI development and enhancement 100 comprising of
neural network subsystem 102 to process unstructured data and identify patterns and features from large datasets;
symbolic reasoning subsystem 104 to perform logical reasoning using predefined rules and knowledge;
integration and control module 106 to facilitate interaction between the neural network and symbolic reasoning subsystems;
feedback loop mechanism 108 to continuously monitor and provide performance feedback for system improvement;
hybrid inference engine 110 to combine data-driven insights with rule-based logical reasoning for decision-making;
knowledge graph or ontology system 112 to store structured knowledge and relationships used by the symbolic subsystem;
contextual rule-based system 114 to update the symbolic reasoning subsystem with new rules based on neural network findings;
task delegation system 116 to assign tasks dynamically between the neural and symbolic subsystems;
dual-path knowledge representation 118 to support the simultaneous handling of unstructured data and structured rules;
real-time knowledge sharing mechanism 120 to share updates and discoveries across multiple system instances;
self-evolving ontology 122 to automatically update knowledge graphs with new data-driven insights;
cross-domain transfer learning 124 to apply learned knowledge from one domain to another;
rule-based error detection and optimization system 126 to identify errors in neural outputs using symbolic logic;
transparent knowledge traceability pipeline 128 to ensure full traceability from data processing to reasoning;
modular architecture 130 to allow independent updates to the neural and symbolic components;
contextual task delegation mechanism 132 to dynamically assign tasks based on problem nature and resource optimization;
human-AI collaboration interface 134 to facilitate interaction and transparency between humans and the AI system;
contextual rule updating based on neural discoveries 136 to enhance symbolic reasoning with new data patterns; and
cross-domain transfer learning capability 138 to support adaptability across diverse fields by applying knowledge transfer.

2. The neuro-symbolic metamodel for progressive AI development and enhancement 100, wherein the neural network subsystem 102 is configured to process unstructured data, identify patterns, and extract features, enabling advanced data-driven learning and pattern recognition across various domains.

3. The neuro-symbolic metamodel for progressive AI development and enhancement 100 as claimed in claim 1, wherein the symbolic reasoning subsystem 104 is configured to perform logical reasoning and decision-making using predefined rules and structured knowledge, ensuring enhanced interpretability and context-based inference.

4. The neuro-symbolic metamodel for progressive AI development and enhancement 100 as claimed in claim 1, wherein the integration and control module 106 is configured to facilitate seamless interaction between the neural network subsystem 102 and the symbolic reasoning subsystem 104, dynamically coordinating their operations for optimal problem-solving.

5. The neuro-symbolic metamodel for progressive AI development and enhancement 100 as claimed in claim 1, wherein the feedback loop mechanism 108 is configured to continuously monitor system performance, providing real-time feedback to enhance the learning processes of the neural network subsystem 102 and symbolic reasoning subsystem 104.

6. The neuro-symbolic metamodel for progressive AI development and enhancement 100 as claimed in claim 1, wherein the hybrid inference engine 110 is configured to integrate data-driven insights from the neural network subsystem 102 with rule-based logic from the symbolic reasoning subsystem 104, enabling comprehensive decision-making and problem-solving.

7. The neuro-symbolic metamodel for progressive AI development and enhancement 100 as claimed in claim 1, wherein the knowledge graph or ontology system 112 is configured to store structured knowledge, relationships, and rules, facilitating logical inference and knowledge representation within the symbolic reasoning subsystem 104.

8. The neuro-symbolic metamodel for progressive AI development and enhancement 100 as claimed in claim 1, wherein the contextual rule-based system 114 is configured to update symbolic rules and relationships dynamically based on new insights detected by the neural network subsystem 102, ensuring continuous adaptability and contextual decision-making.

9. The neuro-symbolic metamodel for progressive AI development and enhancement 100 as claimed in claim 1, wherein the real-time knowledge sharing mechanism 120 is configured to enable multiple instances of the system to share insights, updates, and discoveries, ensuring collective learning and scalability across distributed AI systems

10. The neuro-symbolic metamodel for progressive AI development and enhancement 100 as claimed in claim 1, wherein method comprises of
neural network subsystem 102 receiving unstructured data such as images, text, or audio and processing it to identify patterns and features;
neural network subsystem 102 sending the processed data to the integration and control module 106 for coordination with the symbolic reasoning subsystem 104;
symbolic reasoning subsystem 104 receiving the processed data and applying predefined rules, logic, or knowledge from the knowledge graph or ontology system 112 to derive inferences;
integration and control module 106 facilitating the exchange of information between the neural network subsystem 102 and symbolic reasoning subsystem 104, ensuring smooth collaboration;
hybrid inference engine 110 combining the neural data-driven insights with symbolic logic to make comprehensive decisions;
feedback loop mechanism 108 continuously monitoring the performance of both subsystems, identifying any inconsistencies or areas of improvement;
feedback loop mechanism 108 sending these insights back to the neural network subsystem 102 for further learning and refinement, allowing the system to progressively improve its capabilities;
contextual rule-based system 114 updating the symbolic reasoning subsystem 104 with new rules or relationships based on patterns identified by the neural network subsystem 102 to ensure adaptability; and
real-time knowledge sharing mechanism 120 ensuring any new discoveries or rule updates are shared across multiple instances of the system, enhancing the collective intelligence of the network.

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