Intellectual Property India Publishes Patent Application for System And Method For Generating A Deterministic Narrative Learning Environment From A Source Code Repository Through Large-Language-Model-Driven Story Generation And Spatial Rendering

MUMBAI, India, July 13 -- Intellectual Property India has published a patent application (202621058994 A) filed by Arnab Biswas; and Veena Gyanchandani on May 08, 2026, for System And Method For Generating A Deterministic Narrative Learning Environment From A Source Code Repository Through Large-Language-Model-Driven Story Generation And Spatial Rendering.

Inventors include Arnab Biswas; and Veena Gyanchandani.

The application for the patent was published on July 10, 2026, under issue no. 28/2026.

Abstract: ABSTRACT SYSTEM AND METHOD FOR GENERATING A DETERMINISTIC NARRATIVE LEARNING ENVIRONMENT FROM A SOURCE CODE REPOSITORY THROUGH LARGE-LANGUAGE-MODEL-DRIVEN STORY GENERATION AND SPATIAL RENDERING The present invention relates to a system (100) and a method (200) for generating a deterministic narrative learning environment from a source code repository. The system (100) comprises a parsing module (102) that generates a language- agnostic normalized abstract syntax tree, a feature extraction module (104) that derives structural and code quality parameters and a dependency graph, a content fingerprinting module (120) that computes a cryptographic content fingerprint over a canonically-ordered serialization of abstract syntax tree node identifiers, a narrative generation engine (112) that invokes a large language model under a deterministic sampling configuration to produce a teaching narrative comprising per-node narrative elements, plain-English explanatory text, and a prerequisite-ordered learning sequence, a content-addressable narrative cache (118) keyed on the content fingerprint and flavor identifier, a biome resolver (114) that maps code health metrics to biome identifiers, a mapping engine (106) that deterministically transforms the abstract syntax tree into a spatial graph, a layout computation module (108) that generates a three- dimensional arrangement, a rendering module (110) that instantiates the arrangement as an interactive three-dimensional environment, and a teaching delivery subsystem (116) that renders the teaching narrative as in-world mission boards, briefing cards, proximity information panels, and progressive reveal cards gated by the prerequisite-ordered learning sequence. The transformation is reproducible for an unchanged input and flavor identifier. Fig. 1 will be the reference figure.

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