AI-POWERED ELECTRICAL CONCEPT EXPLANATION AND CIRCUIT ANALYSIS ASSISTANT WITH ADAPTIVE MULTI-MODAL LEARNING SYSTEM

Abstract

The present invention discloses an AI-powered electrical concept explanation and circuit analysis assistant designed to enhance electrical engineering education and professional practice. It employs an Adaptive Multi-Modal Learning Algorithm (AMMLA), integrating natural language processing (NLP), computer vision, and reinforcement learning to deliver personalized learning experiences. The system interprets multi-modal inputs, including text, voice, and visual representations of circuit diagrams, and provides tailored explanations based on the user’s proficiency level. A dynamic knowledge graph evolves with user interactions, enriching context and relevance. The circuit analysis engine automates problem-solving by simulating circuit behavior and calculating key parameters. Additionally, user profiling and progress tracking enable adaptive learning paths, ensuring continuous improvement in user engagement and understanding. With its cloud-based architecture, the assistant is scalable and accessible, making it a versatile tool for both students and professionals in electrical engineering. Accompanied Drawing [Figure 1]

Specification

Description:[001] The present invention relates to the field of artificial intelligence (AI) applications in education and engineering, specifically in the domain of electrical engineering. It introduces an AI-powered assistant designed to explain electrical concepts and perform circuit analysis. The invention aims to enhance educational outcomes, improve productivity in electrical engineering practice, and make complex electrical concepts more accessible to students and professionals.
BACKGROUND OF THE INVENTION
[002] Electrical engineering is a critical field that involves complex theoretical concepts and practical applications. For students and professionals, understanding and applying these concepts in real-world scenarios, such as circuit analysis, can be challenging. Traditional learning methods rely heavily on textbooks, e-learning platforms, and manual circuit analysis tools, which often lack interactivity and fail to adapt to individual learning needs. As a result, learners may struggle with grasping abstract electrical concepts, and professionals may face inefficiencies in performing error-free circuit analysis. These challenges highlight the need for an advanced, personalized educational tool that can bridge the gap between theoretical knowledge and practical application in electrical engineering.
[003] With the rapid advancements in artificial intelligence (AI), there is growing interest in leveraging AI technologies to enhance learning outcomes and improve engineering practices. AI systems that integrate natural language processing (NLP), computer vision, and machine learning have demonstrated success in various fields, but their application in electrical engineering education and circuit analysis has been limited. Existing educational platforms and simulation tools offer valuable resources, but they lack the personalization and adaptability required to cater to diverse learner profiles. There is a need for an AI-powered solution that can provide real-time, adaptive guidance for both students and professionals in the field of electrical engineering.
[004] Several prior art technologies have attempted to address the educational and practical challenges in electrical engineering. For example, US Patent 10,839,015 discloses "Systems and methods for adaptive learning," which provides personalized learning pathways based on user inputs but does not focus on electrical engineering or circuit analysis. Similarly, US Patent 10,657,884 describes an "Artificial intelligence tutor" that offers general-purpose learning assistance without the capability to interpret visual inputs like circuit diagrams.
[005] Additionally, the publication "Deep learning in electrical engineering education: A systematic review" (IEEE Transactions on Education, 2023) discusses the role of AI in enhancing learning but does not integrate multi-modal learning approaches such as combining text, voice, and visual data for real-time problem-solving in circuit analysis. Other systems, such as circuit recognition methods (Rachala, R.R. & Panicker, M.R. 2022), offer object detection in hand-drawn circuit diagrams but lack the adaptive learning features necessary for personalized explanations.
[006] The disadvantages of these prior art approaches are manifold. Traditional e-learning platforms (e.g., Coursera, edX) offer static, pre-recorded content that lacks real-time interaction and cannot adapt to the learner's pace or understanding level. Circuit simulation tools (e.g., SPICE, Multisim) require manual input and interpretation, offering little to no explanation of the underlying electrical concepts.
[007] General-purpose AI chatbots (e.g., ChatGPT, Google Bard) are effective in providing general answers but lack specialized knowledge in electrical engineering and the ability to process visual inputs such as circuit diagrams. Interactive textbooks and resources (e.g., Khan Academy) offer some degree of engagement but do not dynamically adjust to the user's learning progress. These approaches are limited in their capacity to provide a holistic learning experience that integrates theoretical explanations with practical circuit analysis in real-time.
[008] The present invention overcomes these shortcomings by introducing an AI-Powered Electrical Concept Explanation and Circuit Analysis Assistant that employs an Adaptive Multi-Modal Learning Algorithm (AMMLA). Unlike traditional systems, this AI assistant integrates NLP, computer vision, and reinforcement learning to process text, voice, and visual inputs, including circuit diagrams. It provides real-time, interactive, and personalized responses based on the user's proficiency level and learning style.
[009] The AI continuously learns from user interactions, refining its explanations and problem-solving approaches, thus offering a tailored educational experience. By seamlessly combining conceptual explanations with practical circuit analysis and simulation, the present invention significantly enhances the accessibility of complex electrical engineering concepts and improves both learning outcomes and productivity for students and professionals alike.
SUMMARY OF THE PRESENT INVENTION
[010] The present invention is an AI-Powered Electrical Concept Explanation and Circuit Analysis Assistant that utilizes a novel Adaptive Multi-Modal Learning Algorithm (AMMLA), designed to revolutionize electrical engineering education and practice. The system combines cutting-edge natural language processing (NLP), computer vision, and reinforcement learning to provide comprehensive, real-time explanations of electrical concepts and assist in circuit analysis. By interpreting text, voice, and visual inputs such as circuit diagrams, the AI offers step-by-step problem-solving guidance and personalized feedback based on the user's proficiency level. This adaptive learning system continuously refines its knowledge base through user interactions, making it an indispensable tool for both students and professionals in electrical engineering.
[011] The invention integrates seamlessly into existing educational platforms, electrical design tools, and simulation software, providing a holistic learning experience that bridges the gap between theoretical knowledge and practical applications. With support for multiple input modalities, it offers personalized learning experiences that adapt to each user's progress and understanding. The system not only accelerates the learning process but also enhances the accuracy and efficiency of circuit analysis, reducing the time required to solve complex problems. The invention's AI-powered approach to circuit analysis, coupled with its dynamic knowledge graph and multi-modal learning, ensures a deep understanding of electrical engineering principles, making it a versatile tool for education, design, and professional troubleshooting in the electrical engineering field.
[012] In this respect, before explaining at least one object of the invention in detail, it is to be understood that the invention is not limited in its application to the details of set of rules and to the arrangements of the various models set forth in the following description or illustrated in the drawings. The invention is capable of other objects and of being practiced and carried out in various ways, according to the need of that industry. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[013] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[014] When considering the following thorough explanation of the present invention, it will be easier to understand it and other objects than those mentioned above will become evident. Such description refers to the illustrations in the annex, wherein:
Figure 1 illustrates working flowchart associated with the proposed system, in accordance with an embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[015] The following sections of this article will provided various embodiments of the current invention with references to the accompanying drawings, whereby the reference numbers utilised in the picture correspond to like elements throughout the description. However, this invention is not limited to the embodiment described here and may be embodied in several other ways. Instead, the embodiment is included to ensure that this disclosure is extensive and complete and that individuals of ordinary skill in the art are properly informed of the extent of the invention.
[016] Numerical values and ranges are given for many parts of the implementations discussed in the following thorough discussion. These numbers and ranges are merely to be used as examples and are not meant to restrict the claims' applicability. A variety of materials are also recognised as fitting for certain aspects of the implementations. These materials should only be used as examples and are not meant to restrict the application of the innovation.
[017] Referring to Figure 1, the present invention relates to an AI-powered Electrical Concept Explanation and Circuit Analysis Assistant, designed to revolutionize electrical engineering education and professional practice. At its core, the system incorporates an Adaptive Multi-Modal Learning Algorithm (AMMLA), which integrates natural language processing (NLP), computer vision, and reinforcement learning. The invention provides a comprehensive solution for the interpretation and explanation of electrical concepts, as well as detailed circuit analysis. The AI assistant accepts multi-modal inputs including text, voice, and visual representations such as circuit diagrams, offering personalized and adaptive learning experiences tailored to the user's proficiency level.
[018] The natural language processing (NLP) module is capable of interpreting complex electrical engineering queries in multiple languages, providing users with the flexibility to interact in their preferred language. This enhances accessibility across diverse regions and education levels. By integrating NLP with a powerful knowledge graph, the system retrieves and presents electrical concepts, contextualizing them based on the user's current level of understanding. The NLP engine is also connected to a dynamic explanation generation module, which tailors responses to various input modalities, including text-based responses and voice outputs.
[019] The computer vision component of AMMLA plays a critical role in interpreting circuit diagrams. Users can upload or capture images of circuit diagrams through the system interface, which are then processed by a neural network-based vision module. This component detects and interprets electrical symbols, connections, and circuit elements with high accuracy. Using a pre-trained model, the vision module identifies common circuit configurations and provides real-time guidance by overlaying interactive annotations on the diagram. This visual feedback is particularly beneficial for users who are engaged in hands-on learning or troubleshooting tasks.
[020] Reinforcement learning enables the AI assistant to continuously improve its performance over time. Each interaction with a user contributes to the refinement of the system's knowledge base. The reinforcement learning algorithm evaluates user feedback and adapts the teaching approach, gradually enhancing its explanations and problem-solving strategies. For example, if a user consistently struggles with a particular electrical concept, the system adjusts its explanation methodology, offering more detailed guidance or alternative approaches. This feedback loop allows the system to evolve alongside the user's learning journey.
[021] A major innovation in this invention is the integration of a dynamic knowledge graph that evolves based on new information and user feedback. The knowledge graph contains interrelated electrical engineering concepts, their definitions, applications, and links to real-world scenarios. As users interact with the system, the knowledge graph dynamically adjusts to incorporate new data, providing updated and more accurate information. This ensures that the system remains relevant in an ever-evolving field like electrical engineering, where concepts and technologies are constantly advancing.
[022] The system also features a circuit analysis engine, which is integrated with the AI assistant to automate the process of solving electrical circuit problems. When a user presents a circuit diagram, either through text description or image input, the circuit analysis engine simulates the behavior of the circuit and provides step-by-step guidance. The system's simulation capabilities allow for the calculation of critical parameters such as current, voltage, and resistance across different components, making it a valuable tool for both educational and professional use.
[023] The user profiling and progress tracking module further distinguish this invention by allowing the AI to tailor its responses based on the user's historical interactions. This profiling module records each user's learning preferences, problem-solving style, and areas of difficulty. By analyzing this data, the module can offer customized explanations that align with the user's progress, making the learning process more efficient. Additionally, the module provides visual analytics, showing users their learning trajectory, identifying strengths, and suggesting areas for improvement.
[024] A multi-modal input/output interface supports the interactive capabilities of the AI assistant. The system can accept user queries in text or voice format, and users can also upload or capture circuit diagrams for analysis. On the output side, the system generates responses through text, voice, or visual annotations on diagrams. This multi-modal approach enhances user engagement by providing diverse methods of interaction, catering to different learning preferences.
[025] The system architecture of the invention comprises a cloud-based infrastructure to handle the computational complexity of the AI models. High-performance GPU servers are used to run the NLP, computer vision, and reinforcement learning models, enabling real-time responses to user queries. The cloud infrastructure ensures scalability, making the module accessible to a global audience. Additionally, the distributed database module securely stores user data, including the knowledge graph and interaction logs, allowing for efficient retrieval and processing.
[026] Experimental validation of the system's performance has been conducted across a variety of electrical engineering tasks. In one experiment, the AI assistant was tested on its ability to interpret and analyze circuit diagrams across 50 different configurations. The system demonstrated an accuracy rate of 92% in correctly identifying circuit components and their connections. In another test, users were asked to solve a set of electrical problems using the system's guidance. The results showed a 35% reduction in problem-solving time compared to traditional methods, highlighting the system's effectiveness in enhancing productivity.
[027] The system's adaptive learning capabilities were validated through a series of user tests involving students at different proficiency levels. Novice users showed a 40% improvement in concept retention after interacting with the system, while intermediate and advanced users benefited from more efficient problem-solving techniques, particularly in areas of circuit analysis and design. These experimental results confirm the system's potential to significantly enhance learning outcomes in electrical engineering.
[028] Security and privacy are integral components of the system's design. All user data, including interaction logs and user profiles, are anonymized and encrypted before being stored in the cloud database. This ensures that user data is protected, while still allowing the system to analyze interaction trends and improve its performance over time. Moreover, the system complies with global data privacy standards, making it suitable for use in educational institutions and professional environments.
[029] The invention's versatility extends beyond educational use. It is designed to integrate seamlessly into electrical design and simulation software, allowing professional engineers to use the AI assistant for complex circuit design tasks. By automating parts of the design process and providing instant feedback, the system enhances productivity and reduces the likelihood of errors in circuit designs. Additionally, the system is equipped with an API, allowing third-party developers to integrate the AI assistant into their engineering tools.
[030] Future development plans for the system include the incorporation of virtual reality (VR) and augmented reality (AR) features for immersive learning experiences. In a VR environment, users can interact with 3D representations of electrical circuits, manipulating components and observing the effects of changes in real-time. This would provide a more engaging and hands-on learning experience, particularly beneficial for students who are new to electrical engineering concepts.
[031] Another possible extension of the invention is the creation of collaborative versions that support team-based learning and engineering projects. These versions would allow multiple users to interact with the system simultaneously, enabling real-time collaboration on circuit analysis and design. This feature would be particularly valuable for engineering teams working on complex projects, allowing them to quickly share insights and solutions.
[032] In conclusion, this AI-powered assistant represents a significant advancement in both electrical engineering education and professional practice. By combining NLP, computer vision, reinforcement learning, and adaptive multi-modal learning, the system provides an unparalleled tool for understanding and solving electrical engineering problems. The system's ability to tailor its responses based on user proficiency, coupled with its seamless integration of theoretical and practical knowledge, makes it a unique and powerful resource for students, educators, and professionals alike.
[033] It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-discussed embodiments may be used in combination with each other. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description.
[034] The benefits and advantages which may be provided by the present invention have been described above with regard to specific embodiments. These benefits and advantages, and any elements or limitations that may cause them to occur or to become more pronounced are not to be construed as critical, required, or essential features of any or all of the embodiments.
, Claims:1. An AI-powered electrical concept explanation and circuit analysis assistant, comprising:
a) an adaptive multi-modal learning algorithm (AMMLA) integrating natural language processing (NLP), computer vision, and reinforcement learning;
b) a natural language processing module configured to interpret complex electrical engineering queries in multiple languages and retrieve contextually tailored explanations based on user proficiency;
c) a computer vision module to analyze and interpret circuit diagrams by identifying electrical symbols, connections, and configurations, and providing real-time guidance through interactive annotations;
d) a reinforcement learning module that adapts the system's explanations and problem-solving strategies based on user interactions and feedback, enhancing the learning experience over time;
e) a dynamic knowledge graph that evolves based on new information and user interactions, contextualizing electrical concepts and applications for real-world scenarios;
f) a circuit analysis engine for automated simulation and analysis of electrical circuits, providing step-by-step guidance and real-time calculation of parameters such as current, voltage, and resistance;
g) a user profiling and progress tracking module that tailors responses based on user preferences, learning history, and problem-solving style, along with visual analytics to track learning trajectory.
2. The AI-powered electrical concept explanation and circuit analysis assistant as claimed in claim 1, wherein the natural language processing module is further configured to provide text-based and voice-based explanations in response to user queries and supports multi-modal input in the form of text, voice, and visual data.
3. The AI-powered electrical concept explanation and circuit analysis assistant as claimed in claim 1, wherein the computer vision module utilizes a neural network-based model to detect circuit elements and configurations with an accuracy rate of at least 90%, providing real-time interactive visual feedback for educational or troubleshooting purposes.
4. The AI-powered electrical concept explanation and circuit analysis assistant as claimed in claim 1, wherein the reinforcement learning module is configured to adjust its teaching methodology and explanations based on user performance, dynamically enhancing guidance for users struggling with particular concepts.
5. The AI-powered electrical concept explanation and circuit analysis assistant as claimed in claim 1, wherein the dynamic knowledge graph is connected to a distributed database that updates based on user interactions and integrates real-world electrical engineering advancements, ensuring the system's relevancy over time.
6. The AI-powered electrical concept explanation and circuit analysis assistant as claimed in claim 1, wherein the circuit analysis engine simulates electrical circuit behavior from both text descriptions and uploaded circuit diagrams, providing real-time analytical results such as current, voltage, and resistance for educational or professional use.
7. The AI-powered electrical concept explanation and circuit analysis assistant as claimed in claim 1, further includes a multi-modal input/output interface supporting text, voice, and visual input, and generating output through text responses, voice explanations, or visual annotations on diagrams.
8. The AI-powered electrical concept explanation and circuit analysis assistant as claimed in claim 1, wherein the user profiling and progress tracking module records and analyzes individual user behavior, generating personalized learning paths and suggesting areas for improvement based on historical interactions.
9. The AI-powered electrical concept explanation and circuit analysis assistant as claimed in claim 1, wherein the system is configured to integrate with external electrical design and simulation software through an API, facilitating professional use in complex circuit design tasks and providing real-time feedback.
10. The AI-powered electrical concept explanation and circuit analysis assistant as claimed in claim 1, wherein the system architecture is cloud-based, utilizing high-performance GPU servers to process NLP, computer vision, and reinforcement learning models in real-time, and ensuring scalability for global accessibility.

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