Intellectual Property India Publishes Patent Application for 'NLP-Driven Analysis Of Magical Realism, Diasporic Identity, And Symbolism In The Mistress Of Spices' Filed by Konerulakshmaih Education Foundation

MUMBAI, India, Jan. 2 -- Intellectual Property India has published a patent application (202541123796 A) filed by Konerulakshmaih Education Foundation, Guntur, Andhra Pradesh, on Dec. 9, 2025, for 'nlp-driven analysis of magical realism, diasporic identity, and symbolism in the mistress of spices.'

Inventor(s) include Ms. P. Venkataramana; and Dr. K. K. Sunalini.

The application for the patent was published on Jan. 2, under issue no. 01/2026.

According to the abstract released by the Intellectual Property India: "The invention, "NLP-Driven Analysis of Magical Realism, Diasporic Identity, and Symbolism in The Mistress of Spices", pertains to a systematic framework and methodological system designed to analyze, interpret, and enhance literary studies through the application of Natural Language Processing (NLP) tools. This innovation introduces a research-backed approach that employs computational methods to examine narrative structures, linguistic patterns, and symbolic representations in literary texts, specifically targeting The Mistress of Spices by Chitra Banerjee Divakaruni. The system incorporates modules for text preprocessing (using NLTK), syntactic and semantic analysis (using SpaCy), and sentiment assessment (using VADER), enabling detailed mapping of character voice, emotional tone, and cultural symbolism. Conducted on the full narrative text, the system captures and interprets both quantitative and qualitative features, highlighting how spices correlate with emotional states and how syntactic structures reveal shifts in perspective and agency. Core components include character-specific sentiment tracking, motif-symbol correlation, and syntactic pattern identification, producing actionable insights for literary interpretation. The proposed framework facilitates enhanced literary analysis, allowing scholars, educators, and digital humanists to integrate data-driven observations with traditional interpretive critique. By offering a scalable, computationally grounded methodology, this innovation empowers interdisciplinary approaches, providing a replicable model for combining NLP and postcolonial literary scholarship, and promoting future-oriented, technology-enhanced textual analysis."

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