🤖 AI Summary
This study addresses the underutilization of expired or lapsed patents, whose embedded technical knowledge often remains inaccessible due to difficulties in identification and interpretation, thereby hindering innovation transfer. The work proposes the first AI-driven framework that treats patent expiration as both a commercial opportunity and a knowledge archival milestone, integrating legal status, technical content, and market signals to generate structured business pathways—such as SaaS offerings or consulting services. The methodology combines patent metadata, fee payment records, semantic search, patent family analysis, and a locally deployed Qwen3.6 generative model to enable multidimensional risk assessment. Evaluated on the CIPO dataset, the approach successfully identified 20 high-potential patents, demonstrating the stability of the scoring model and compliance of AI-generated outputs, while also uncovering practical challenges such as insufficient legal status coverage.
📝 Abstract
Patent databases represent one of the largest public archives of technical knowledge, yet much of this knowledge remains difficult to identify, interpret, and reuse once patent rights expire or lapse. This paper proposes an AI-enabled framework for discovering expired and lapsing patents, identifying technology trends, and translating patent disclosures into business pathways. We use pathways to mean structured commercialization routes such as SaaS products, services, licensing packages, consulting playbooks, training offerings, data products, or internal process tools. The framework treats patent expiry as both a business signal and an archival transition, not primarily as a legal problem. Legal status remains important, but it is one risk-screening input alongside customer need, implementation feasibility, channel access, and market timing. We describe a system architecture that combines patent metadata, maintenance-fee records, legal-status indicators, semantic search, patent-family analysis, market signals, and generative AI workflows. A proof of concept parses all 378 records in an official weekly CIPO ST.96 archive, identifies 20 expired, lapsed, or near-expiry candidates, tests the stability of the transparent scoring model, and uses a locally hosted Qwen3.6 model to populate structured review packets. The evaluation demonstrates reproducible ingestion, stable rankings under weight perturbation, and schema-conformant model output, while also exposing incomplete legal-status coverage and the need for register and expert review. We argue that AI can function as a discovery and translation layer for dormant technical knowledge, but that such systems must explicitly represent legal uncertainty, data limitations, and commercialization risk.