The Application of Artificial Intelligence Technology to the Intellectual Property System

The swift evolution of artificial intelligence (AI), especially generative AI utilizing deep learning and extensive datasets, has brought substantial benefits across numerous sectors. However, this technological surge simultaneously presents intricate technical hurdles for conventional intellectual property rights protection frameworks. The unprecedented scale, speed, and unique characteristics of AI-generated content significantly strain existing manual and semi-automated IPR management systems. This paper first details the progression and functionalities of pertinent AI technologies. It then examines the pressure these capabilities exert on current IPR frameworks, highlighting technical obstacles related to verifying ownership, detecting infringement at scale, and computationally evaluating originality. We assert that overcoming these obstacles necessitates more than legal adjustments; it demands fundamentally novel engineering approaches applying AI to the IP system. Consequently, this work introduces an AI-powered engineering framework that integrates specific algorithms and applications: Natural Language Processing techniques like semantic embedding models for text analysis and comparison; Computer Vision algorithms such as perceptual hashing and deep feature extraction for image/video analysis; machine learning classifiers for filtering infringement alerts; and blockchain technology for secure timestamping and registration. This framework seeks to deliver scalable, efficient, and resilient tools for IPR registration, authentication, automated infringement surveillance, and support for originality analysis, thereby promoting technological progress while protecting creator entitlements within the dynamic digital ecosystem.