MUMBAI, India, April 17 -- Intellectual Property India has published a patent application (202641043222 A) filed by Sr University, Warangal, Telangana, on April 4, for 'a cognitive computing framework for reassembling silenced and fragmented histories from partition narratives.'

Inventor(s) include Shruti Nair; and Dr. Vahini Billu.

The application for the patent was published on April 17, under issue no. 16/2026.

According to the abstract released by the Intellectual Property India: "The current invention reveals a cognitive computing model aimed at recreating suppressed and fractured pasts hidden in Partition histories by using sophisticated computational practices. It is a system that combines the techniques of natural language processing (NLP), machine learning, and knowledge representation in order to analyse literary texts, oral histories, and archival documents on the Partition of the Indian subcontinent. It points to the patterns of omission, narrative gaps, and expression coded by trauma, especially in marginalized voices, women, and subaltern communities. The framework uses the models of semantic mapping, sentiment analysis, and context-aware inference to discover latent meanings and rebuild the discontinuous historical narratives into the structure of coherent narratives. Multi-layered architecture allows comparison across texts, sequencing over time, and clustering the topics, and allows one to recover the events that have been repressed or cut up. Moreover, the system has a cognitive reasoning module that emulates the human interpretive processes to contextualize memory, trauma, and identity in socio-political contexts. It offers a digital humanities research instrument that is both scalable and adaptable and allows scholars to reinterpret Partition narratives systematically, without losing narrative plurality and complexity. It also finds uses in historiography, cultural analytics, as well as in archival restoration through converting unstructured data of narrative into structured, analysable knowledge representations. The suggested model will improve the availability, deciphering, and continuity of the voices that are historically marginalized, which will help to create a more inclusive reconstruction of collective memory."

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