MUMBAI, India, Aug. 17 -- Intellectual Property India has published a patent application (202641096578 A) filed by Dr. Chalapaka Bhogachandra Vittal on August 10, 2026, for A System And Method For Enhancing English Communication Skills Using Ai-Based Role-Play Simulation, Multi-Parameter Performance Scoring, And Adaptive Feedback Generation.
Inventor includes Dr. Chalapaka Bhogachandra Vittal.
The application for the patent was published on August 14, 2026, under issue no. 33/2026.
Abstract: [088] The present invention relates to a system and method for enhancing English communication skills using artificial intelligence-based role-play simulation, contextual dialogue management, multi-parameter performance scoring, and adaptive feedback generation. The disclosed system comprises a learner profile management module, a role-play scenario generation engine, a conversation management engine, a multimodal response acquisition module, a communication assessment engine, a performance scoring engine, an adaptive feedback generation engine, and a learner analytics module configured to collectively provide personalized English communication training. The system dynamically generates realistic conversational scenarios based on learner proficiency and communication objectives, captures learner responses through speech, text, or other input modalities, evaluates communication performance using multiple linguistic and behavioural parameters including grammar, pronunciation, fluency, vocabulary, semantic relevance, contextual appropriateness, confidence, and conversational continuity, and generates objective performance scores together with personalized recommendations for improvement. Machine learning techniques continuously optimize scenario generation, assessment accuracy, and feedback quality through analysis of historical learner interactions. The invention provides scalable, adaptive, objective, and personalized English communication training suitable for educational institutions, corporate training, professional certification, and self-learning environments, thereby significantly improving communication competency while reducing dependence on continuous human instruction. Accompanied Drawing [FIGS. 1-2]
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