Research & Patent Innovations
Advancing the state of the art in Generative AI, Audio DSP Synthesis, and Cognitive Learning Architecture. Bridging formal research with production-grade engineering.
Research & IP Portfolio
Pioneering research-to-product engineering bridging artificial intelligence, audio synthesis, and cognitive learning models.
Currently developing the research paper and framework for automated business-rule extraction from legacy C++ systems using LLMs and static analysis.
- Developing automated business-rule extraction methodologies.
- Combining static analysis with LLM inference for high accuracy.
- Targeting legacy C++ system modernization and documentation.
Developed a proprietary architecture for mapping linguistic structures to musical frequencies using Groq AI and Web Audio API.
- Architected real-time emotion-to-raag mapping with classical Indian music constraints.
- Implemented DSP-based Web Audio oscillator synthesis with low-latency execution.
- Engineered Groq AI integration for Hindi lyric parsing and structural cadence detection.
Research on human-AI interaction models for educational accessibility, integrated into the CodeMentorAI platform.
- Investigated cognitive load reductions during interactive code debugging sessions.
- Designed context-aware dual-LLM feedback loops to prevent tutorial hell.
- Validated accessibility framework on diverse developer learning paths.
Architectural Breakdown
Detailed system designs behind the patent-filed audio engine and publication framework.
- Linguistic Parsing via Groq AI (Hindi & Sanskrit cadence analysis)
- Raag Frequency Matrix Calculation (Swar & Aaroh/Avroh mapping)
- Web Audio API Oscillator Node Synthesis with custom ADSR envelopes
- Real-time Audio Buffer streaming with <15ms latency
This patent solves the challenge of combining strict rules of Indian Classical music theory (Raag swara matrices and microtonal intervals) with generative AI text models. The system converts raw lyrical input into melodically aligned digital synthesis buffers in real-time.
- Code AST Analysis & Static Bug Detection Engine
- Evaluator LLM: Identifies cognitive gaps without giving immediate solutions
- Socratic Mentor LLM: Generates step-by-step guided prompts
- Adaptive Learning Path Generator storing vector embeddings
Our accepted research chapter proposes a novel human-computer interaction framework aimed at breaking "tutorial hell". By modulating AI feedback depth according to developer problem-solving state, the system boosts long-term comprehension and retention by up to 40%.