MUMBAI, India, Feb. 13 -- Intellectual Property India has published a patent application (202641008112 A) filed by Karpagam Academy Of Higher Education; Karpagam Institute Of Technology; Sasti Kumar; and Sowkanchana R, Coimbatore, Tamil Nadu, on Jan. 28, for 'language translation system using neural network.'
Inventor(s) include Sasti Kumar; and Sowkanchana R.
The application for the patent was published on Feb. 13, under issue no. 07/2026.
According to the abstract released by the Intellectual Property India: "Language translation plays a vital role in bridging communication gaps across diverse linguistic communities, enabling seamless global interaction in business, education, healthcare, and digital communication platforms. Traditional rule-based and statistical machine translation approaches have shown limitations in capturing complex linguistic relationships, idiomatic expressions, and long-range dependencies, resulting in translations that often lack fluency and contextual accuracy. To overcome these challenges, this invention proposes an advanced neural network-based language translation system leveraging modern deep learning methodologies. The proposed system integrates sequence-to-sequence (Seq2Seq) architectures with attention mechanisms and transformer-based models, including BERT, GPT, and related variants, to generate highly accurate and context-aware translations. The model is trained on large-scale parallel corpora consisting of multiple language pairs, enabling it to learn syntactic patterns, semantic structures, and context-dependent linguistic variations. A preprocessing module performs tokenization, subword segmentation, alignment, and noise filtering to ensure high-quality input representation, while an encoder-decoder framework captures comprehensive contextual features. Attention mechanisms allow the model to selectively focus on relevant components of the input sequence, significantly improving translation coherence and reducing semantic ambiguity. The inclusion of transformer architectures enhances the system's ability to process long sentences and complex grammatical structures in parallel, resulting in improved computational efficiency and scalability. The system further incorporates evaluation metrics such as BLEU and METEOR to measure translation performance and guide model optimization. Designed for real-time deployment, the invention supports applications across cloud platforms, mobile interfaces, and edge computing devices, enabling instant translation capabilities in multilingual environments. Additionally, the framework supports domain-specific customization for technical, legal, medical, and conversational contexts, ensuring accurate specialized translations. Overall, this invention provides a robust, scalable, and intelligent language translation system that surpasses traditional methods in accuracy, fluency, and adaptability, contributing significantly to the advancement of automated multilingual communication technologies."
Disclaimer: Curated by HT Syndication.
