NLP patent examination

NLP patent examinationexamine the Impact of Firm Knowledge Source Diversity and Technology Cycle on Patent Examination Duration in Natural Language Processing FieldAs a rapidly evolving frontier of artificial intelligence, natural language processing (NLP) is characterized by fast-paced iterations driven by software innovation and knowledge recombination, making patent examination duration a critical factor in sustaining its innovation momentum. This study employs the Cox proportional hazards model to explore the influence of firm knowledge source diversity and the technology cycle on patent examination duration in the NLP domain, using 200 firms with 30524 USPTO granted patents from 2001 to 2023. The findings reveal that a 1% increase in firm knowledge source diversity leads to a 1.51% increase in patent examination duration, and a 1% increase in technology cycle leads to a 0.19% decrease in patent examination duration. The AFT model confirms the robustness of these results. These findings underscore the distinct roles of knowledge source composition and temporal distance of technological knowledge in shaping patent examination duration.Keywords: patent examination duration; firm knowledge source diversity; technology cycle; natural language processing; emerging technologies; Cox proportional hazard model