MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641080671 A) filed by Dr. Paavai Anand G; and Dr. Kiruthiga Devi M on June 30, 2026, for System And Method For Contextual Semantic Disambiguation Using Quantum Superposition States For Tamil And Low-Resource Languages.
Inventors include Dr. Paavai Anand G; and Dr. Kiruthiga Devi M.
The application for the patent was published on July 03, 2026, under issue no. 27/2026.
Abstract: ABSTRACT System and Method for Contextual Semantic Disambiguation Using Quantum Superposition States for Tamil and Low- Resource Languages The present disclosure relates to a computer-implemented system and method for contextual semantic disambiguation using quantum superposition states for Tamil and other low-resource languages. The invention addresses the challenge of lexical ambiguity by representing each ambiguous word as a quantum-inspired semantic state comprising multiple candidate meanings with corresponding probability amplitudes. Unlike conventional deterministic approaches, the proposed system preserves all possible meanings simultaneously until adequate contextual information becomes available for semantic interpretation. The disclosed system comprises an input processing module, an ambiguous word identification module, a quantum state generation module, a context extraction unit, a semantic interaction engine, a probability computation module, and a quantum-inspired measurement module. Contextual linguistic features are utilized to generate measurement operators that dynamically update semantic amplitudes through semantic interactions. The updated semantic state is normalized, probability values are computed, and a quantum-inspired state collapse mechanism determines the most contextually appropriate meaning for the ambiguous lexical item with improved computational efficiency and reduced dependence on large annotated datasets. The invention provides a scalable, language- independent, and extensible semantic disambiguation framework suitable for Tamil and other low-resource languages. The proposed system enhances contextual understanding for applications including machine translation, semantic search, conversational agents, intelligent tutoring systems, legal document analysis, healthcare language processing, and multilingual artificial intelligence platforms. The invention thereby improves semantic accuracy, contextual reasoning, and natural language understanding while enabling efficient deployment across cloud, edge, and enterprise computing environments.
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