从专利到期到商业路径：激活创新档案的人工智能工作流程
From Patent Expiry to Business Pathways: AI Workflows for Activating Innovation Archives
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中文总结 AI 辅助
研究旨在利用人工智能框架将专利到期转化为商业路径，结合专利元数据等多种元素构建系统架构，通过概念验证展示了其在处理专利记录上的表现，虽有不足，但证明人工智能可助力休眠技术知识的发掘与转化。
中文摘要 AI 辅助
专利数据库是最大的技术知识公共档案之一，但专利到期或失效后，很多知识难以识别、解释和再利用。本文提出一个人工智能框架，用于发现过期和即将失效的专利、识别技术趋势并将专利披露转化为商业路径。该框架将专利到期视为商业信号和档案转变，法律状态是风险筛选输入之一。文中描述了结合多种元素的系统架构。概念验证解析官方每周的CIPO ST.96档案中的记录，识别相关候选专利，测试评分模型稳定性等。评估展示了可重复摄取等，但也暴露了法律状态覆盖不完整等问题。我们认为人工智能可作为休眠技术知识的发现和转化层，但此类系统须明确呈现法律不确定性、数据局限性和商业化风险。
英文摘要
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.
发表机构
- Dhillon School of Business, University of Lethbridge（利思利商学院，利思利大学）
- Opus College of Business, University of St. Thomas（奥普斯商学院，圣托马斯大学）
机构由 AI 辅助整理，请以论文原文为准。
