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@article{181816,
        author = {Amrutiya Urvish and Yash Singh and Annant Sharma and Rajshekhar Kumar and Dr. Chitra B. T.},
        title = {Detecting Patent Infringement in Stock Market Filings  using Natural Language Processing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {5995-5999},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181816},
        abstract = {Patent infringement in financial disclosures poses significant legal and economic risks. Companies must carefully navigate transparency requirements while protecting proprietary technologies. This paper presents an NLP-driven framework for proactively detecting potential patent infringements in stock market filings within the Indian context. We integrate detailed legal analysis, regulatory mandates, extensive discussion of infringement typologies, claim-interpretation techniques, risk quantification models, and best practices for disclosure drafting. Our approach leverages semantic similarity, Named Entity Recognition (NER), claim-chart style analysis, knowledge graphs, and explainable AI to surface high- risk disclosures. Comprehensive case studies, statistical insights on patent litigation trends in India, and guidance on mitigation strategies assist practitioners in implementing robust IP risk management in corporate compliance workflows.},
        keywords = {Patent infringement, Indian patent law, financial disclosures, NLP, semantic analysis, Named Entity Recognition, regulatory compliance, claim analysis, risk quantification.},
        month = {June},
        }