AI-DRIVEN PATENT MINING: UNVEILING INNOVATION PATTERNS THROUGH AUTOMATED KNOWLEDGE EXTRACTION
Keywords:
AI-driven Patent Mining, Innovation Patterns, Automated Knowledge Extraction, Natural Language Processing, Machine Learning, Technological Trends, Innovation Clusters, Predictive Analytics, Intellectual Property, Ethical ConsiderationsAbstract
This paper explores the transformative role of AI-driven patent mining in uncovering innovation patterns through automated knowledge extraction. By integrating advanced natural language processing (NLP) and machine learning techniques, AI systems can efficiently analyze vast patent datasets to identify technological trends, map innovation clusters, and predict future innovations. The paper discusses the methodologies involved in AI-driven patent mining, including data collection, preprocessing, and pattern recognition, and examines real-world case studies to illustrate its applications. Additionally, it addresses the limitations of current AI techniques, ethical considerations, and future research opportunities. The findings underscore the significant impact of AI on strategic R&D planning, competitive intelligence, and policy making, while also highlighting the challenges and future directions necessary to advance this field.
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Copyright (c) 2024 S. B. Vinay (Author)
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