A SYSTEM FOR IMPLEMENTATION OF GENERATIVE AI IN COMPETITIVE PROGRAMMING

Abstract

The present invention provides a system for implementing Generative AI in competitive programming platforms, enabling the autonomous generation of diverse programming challenges using advanced AI techniques such as Generative Adversarial Networks (GANs) and Reinforcement Learning (RL). The system features a modular design with seamless integration capabilities, resource optimization strategies, and an AI-driven personalization engine that adapts challenges to user skill levels and performance. By using dynamic resource allocation and model compression techniques, the system maintains platform stability and optimizes performance. The invention further includes validation modules to ensure that the generated problems are of high quality and meet platform standards. The system enhances the learning environment, providing users with a continuous, engaging, and adaptive programming experience.

Specification

Description:[0001] The present invention relates to the field of competitive programming, computer science education, and software development. Specifically, it involves the implementation of Generative Artificial Intelligence (AI) to autonomously create programming problems with varying complexity, integrating seamlessly into existing competitive programming platforms while optimizing resources for efficiency and performance. The invention also focuses on enhancing the learning experience by using AI-driven personalization techniques to tailor challenges according to the user's progress and skill level.

Background of the Invention
[0002] Competitive programming is a popular method for learning and practicing coding skills, helping participants improve their problem-solving abilities, algorithms, and data structures. However, the current process for generating programming challenges is often manual and time-consuming, resulting in a limited and repetitive pool of problems. As a result, users encounter challenges that may not be sufficiently diverse or adaptive to their evolving skills. Existing solutions have attempted to use AI to automate problem generation, but they face several challenges, including difficulty integrating AI into platform architectures without sacrificing performance, optimizing resource usage, and maintaining the balance between stability and complexity.
[0003] There is a growing need for a system that can autonomously and efficiently generate a wide range of programming challenges while integrating with existing competitive programming platforms. The system should also adapt to the user's skill level, ensuring a more personalized learning experience. Such an invention would benefit educators, students, and professionals by enhancing the variety and quality of programming exercises available.

Objects of the invention
[0004] An object of the present invention is to implement Generative AI techniques for autonomously generating a wide range of programming challenges with varying difficulty levels and problem types.
[0005] Another object of the present invention is to expand the variety of problems available for competitive programming, ensuring a dynamic and evolving pool of challenges that can engage users over time.
[0006] Yet another object of the present invention is to integrate Generative AI seamlessly into existing platform architectures without causing significant disruptions or performance issues.
[0007] Another object of the present invention is to optimize resource usage for the AI models, maintaining system stability and performance while minimizing computational and memory overhead.
[0008] Another object of the present invention is to establish an adaptive learning environment for competitive programming, using AI-driven personalization techniques to match problems to the user's skill level and progress, fostering continuous learning and improvement.

Summary of the invention
[0009] The present invention provides a system and method for implementing Generative AI in competitive programming platforms. The system utilizes advanced Generative AI models, such as Generative Adversarial Networks (GANs) and Reinforcement Learning (RL) algorithms, to autonomously generate a diverse set of programming problems. These problems are categorized by difficulty level, topic, and expected time to solve, ensuring a wide variety for users to engage with.
[0010] The system integrates into existing competitive programming platforms through a modular API-based approach, ensuring compatibility and easy deployment. It optimizes resource usage by utilizing model compression techniques and dynamic resource allocation to maintain performance while minimizing overhead. The invention also features an AI-driven personalization engine that analyses user data, such as skill level, problem-solving patterns, and past performance, to adaptively curate challenges that match the user's learning needs. The result is an intelligent platform that enhances the user's learning experience while expanding the diversity and availability of programming problems.
[0011] 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.
[0012] 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.

Detailed description of the invention
[0013] An embodiment of this invention, illustrating its features, will now be described in detail. The words "comprising," "having," "containing," and "including," and other forms thereof are intended to be equivalent in meaning and be open-ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items.
[0014] The terms "first," "second," and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another, and the terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item.

[0015] The present invention provides an advanced system and method for integrating Generative AI into competitive programming platforms to autonomously generate diverse programming challenges, optimize system performance, and personalize user experiences. The invention addresses the need for scalability, flexibility, and adaptability in competitive programming systems through several innovative modules and techniques.
1.Generative AI Problem Generator
[0016] The core component of the system is the Generative AI Problem Generator, designed to autonomously create a wide range of programming problems using state-of-the-art AI techniques, such as Generative Adversarial Networks (GANs) and Reinforcement Learning (RL) algorithms.
[0017] Problem Categorization and Diversity: The generator produces problems that vary in difficulty, topic (e.g., algorithms, data structures, graph theory, dynamic programming), and complexity level. It uses a classification system that categorizes problems into beginner, intermediate, and advanced levels, ensuring that users encounter a broad spectrum of challenges.
[0018] Training Dataset and Model Adaptation: The AI models are trained on an extensive dataset of existing programming problems and solutions, allowing them to learn from and adapt to the evolving standards of competitive programming. The dataset includes various formats and constraints, ensuring the generated problems are compatible with different platforms and programming languages.
[0019] Dynamic Problem Generation: The AI models continuously learn and adapt as new data becomes available, ensuring the problems generated remain innovative, diverse, and relevant. The system can also incorporate user feedback and performance data to adjust the problem difficulty dynamically.
2. Platform Integration Module
[0020] The Platform Integration Module allows the system to seamlessly integrate the Generative AI capabilities into existing competitive programming platforms, ensuring that the generated problems are added dynamically without disrupting the platform's operation.
[0021] API-Based Architecture: The module uses an API-based architecture, providing a flexible and modular interface for communicating with the platform's backend. This allows new problems to be pushed to the platform's database automatically, minimizing manual intervention and system downtime.
[0022] Compatibility and Scalability: The module supports integration with various programming languages, compilers, and problem formats used by the platform. This ensures that the generated problems are compatible with the platform's requirements, enhancing the system's scalability and flexibility.
[0023] Independent Model Updates: The system supports independent updates of the AI models and datasets, allowing for continuous improvement of the problem generator without affecting the platform's core functionalities.
3. Resource Optimization Module
[0024] To maintain system stability and optimize performance, the invention includes a Resource Optimization Module that efficiently manages computational resources and minimizes the overhead associated with running Generative AI models.
[0025] Model Compression Techniques: The module employs techniques such as pruning, quantization, and knowledge distillation to reduce the complexity and size of AI models. Pruning removes less critical weights and connections in the neural network, while quantization reduces the precision of model parameters to decrease memory usage.
[0026] Dynamic Resource Allocation: The module monitors platform load conditions and dynamically allocates memory and processing power based on real-time usage. For example, during periods of high user activity, it prioritizes critical tasks such as problem generation and validation, while non-essential processes are queued or executed at off-peak times.
[0027] Efficient Execution Environment: The module optimizes the execution environment for running AI models, ensuring that processing tasks are distributed effectively across available hardware resources, including CPUs and GPUs. This prevents bottlenecks and ensures stable platform performance even under high workloads.
4. AI-Driven Personalization Engine
[0028] The invention incorporates an AI-Driven Personalization Engine that enhances the user experience by adapting programming challenges based on individual performance metrics and skill progression.
[0029] User Profiling and Skill Assessment: The personalization engine builds detailed user profiles by tracking various metrics, including the types of problems solved, solution accuracy, time taken to solve problems, and overall performance trends. It uses this information to assess the user's current skill level and identify areas for improvement.
[0030] Adaptive Challenge Curation: Based on the user profile, the engine curates and recommends programming challenges that match the user's skill level and learning needs. For example, if a user consistently excels at beginner-level algorithm problems, the engine may recommend intermediate-level challenges focusing on dynamic programming or graph theory.
[0031] Machine Learning for Continuous Improvement: The personalization engine employs supervised and unsupervised learning algorithms to refine its recommendation strategies. It continuously learns from user interactions and outcomes, adjusting its models to provide more accurate and effective challenge recommendations over time.
[0032] Learning Path Customization: The engine can also create personalized learning paths for users by linking related challenges and progressively increasing difficulty levels. This ensures that users receive a structured and engaging learning experience tailored to their development needs.

5. Problem Validation and Testing Module
[0033] To ensure the quality and feasibility of generated programming problems, the invention includes a Problem Validation and Testing Module.
[0034] Automated Validation Algorithms: This module uses automated testing algorithms to validate the correctness of each problem generated by the system. The validation process involves generating multiple test cases for each problem and verifying that the AI-generated solution matches the expected output.
[0035] Constraint Verification: The module checks that the problems meet specific platform constraints, such as time complexity limits, memory usage restrictions, and input/output requirements. This ensures that the problems are not only correct but also feasible for competitive programming environments.
[0036] Problem Quality Assessment: The module assesses the quality of each generated problem using various metrics, such as novelty (ensuring the problem is not too similar to existing problems), difficulty balance (verifying the challenge level), and clarity (checking the problem statement for ambiguity). Problems that fail any validation step are automatically flagged for review or regeneration.
6. System Architecture and Communication Flow
[0037] The invention uses a microservices architecture, where each module operates independently but communicates with others through defined APIs. This architecture enhances system scalability and modularity, allowing components to be updated, replaced, or scaled without affecting other parts of the system.
[0038] Data Flow: The data flow starts with the Generative AI Problem Generator creating a problem and sending it to the Problem Validation and Testing Module. If the problem passes validation, it is sent to the Platform Integration Module, which then pushes it to the competitive programming platform. Simultaneously, the AI-Driven Personalization Engine updates user profiles and curates challenges based on new data.
[0039] Monitoring and Feedback Loop: The system includes a monitoring mechanism that tracks user interactions, problem performance, and platform stability. This data is fed back into the AI models and optimization modules to continually refine the problem generation and personalization processes. Additionally, the platform administrators can access a dashboard to monitor the system's health and update models as necessary.
7. Adaptation for Different Competitive Programming Platforms
[0040] The invention supports adaptation for various competitive programming environments, including online learning platforms, coding boot camps, and professional developer competitions.
[0041] Customization Interface: The system provides a customization interface for administrators to define specific parameters, such as the difficulty distribution, types of challenges, and integration details. This ensures that the AI system is tailored to the specific requirements of each platform.
[0042] Multi-Language Support: The system can generate problems compatible with multiple programming languages, such as Python, Java, C++, and JavaScript, ensuring that users can solve challenges in their preferred language.
[0043] Cross-Platform Functionality: The modular design and API-based integration approach make it easy to deploy the system across different platforms, including web-based interfaces, mobile applications, and desktop environments.
[0044] This comprehensive approach ensures that the invention enhances the competitive programming experience by providing an autonomous, diverse, and adaptive system capable of scaling with user needs while maintaining platform stability and performance. The combination of AI-driven problem generation, resource optimization, and personalized learning paths positions the system as a versatile solution for improving programming education and engagement.
[0045] The foregoing descriptions of specific embodiments of the present invention have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present invention to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present invention, and its practical application to thereby enable others skilled in the art to best utilize the present invention and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omission and substitutions of equivalents are contemplated as circumstance may suggest or render expedient, but such are intended to cover the application or implementation without departing from the spirit or scope of the claims of the present invention.
, Claims:1. A system for implementation of generative AI in competitive programming comprising:
a generative AI problem generator for creating diverse programming problems;
a platform Integration Module for seamlessly integrating the problem generator into existing competitive programming platforms via API;
a resource Optimization Module that reduces computational overhead using model compression techniques; and
an AI-driven personalization engine for adapting problems to users based on their performance metrics.

2. The system as claimed in claim 1, wherein the Generative AI Problem Generator utilizes Generative Adversarial Networks (GANs) and Reinforcement Learning (RL) algorithms to generate problems with different complexity levels, problem types, and estimated solving times.

3. The system as claimed in claim 1, wherein the platform integration module is configured to update the AI models and problem datasets independently, ensuring platform stability and minimizing downtime.


4. The system as claimed in claim 1, wherein the resource optimization module applies dynamic resource allocation techniques to manage memory and processing power based on platform load conditions.

5. The system as claimed in claim 1, wherein the AI-Driven personalization engine analyses user data such as problem-solving speed, accuracy, and performance history to curate and recommend suitable challenges.

6. The system as claimed in claim 1, wherein the generative AI problem generator is trained on an extensive dataset of existing competitive programming problems to ensure the novelty and diversity of the generated challenges.

7. The system as claimed in claim 1, further comprising a problem validation and testing module that validates the correctness and feasibility of generated problems using automated testing algorithms.

8. The system as claimed in claim 1, wherein the platform integration Module supports compatibility with various programming languages and problem formats used on the competitive programming platform.

9. The system as claimed in claim 1, wherein the resource optimization module monitors the usage patterns of the ai models and dynamically adjusts the model's parameters to optimize performance and stability.

10. The system as claimed in claim 1, wherein the AI-Driven personalization engine uses machine learning algorithms to continuously improve its recommendation accuracy based on evolving user behaviour and skill development.

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