[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85183-en":3,"doc-seo-85183-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85183,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","From Patent Expiry to Business Pathways: AI Workflows for Activating Innovation Archives","Patent databases store vast public technical knowledge, yet expired or lapsed patents are difficult to locate, interpret, and reuse at scale. The work introduces an AI-enabled framework to discover expired and near-expiry patents, extract technology trends, and translate disclosures into structured commercialization pathways, including SaaS, services, licensing, consulting, training, data products, and internal tools. Patent expiry is treated as a business signal and archival transition, combining legal status risk screening with customer need, feasibility, channel access, and market timing, implemented via semantic search, family analysis, market signals, and generative AI workflows. A proof of concept ingests a weekly CIPO archive, identifies candidates, stabilizes transparent scoring, and outputs schema-conformant review packets while highlighting legal-coverage gaps and the need for expert review.","arXiv :2607 . 10 179v 1 [ cs .IR] 11 Jul 2026  \nFrom Patent Expiry to Business Pathways: AI Workflows for Activating Innovation Archives  \nSidney Shapiro  \nDhillon School of Business, University of Lethbridge  \n[sidney. shapiro@uleth. ca](sidney. shapiro@uleth. ca)  \nMark Price  \nOpus College of Business, University of St. Thomas  \n[pric4635@stthomas. edu](pric4635@stthomas. edu)  \nAbstract  \nPatent 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 schemaconformant 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.  \nKeywords: patent analytics, business pathways, innovation archives, natural language processing, entrepreneurship, expired patents, design science  \n1 Introduction  \nPatent databases are among the largest publicly accessible archives of technical knowledge. In exchange for temporary monopoly rights, inventors disclose detailed specifications that describe methods, systems, and applications across nearly every industry. Patent disclosure becomes public well before expiry in many jurisdictions, but expiry or lapse can change whether the claimed invention remains enforceable in a particular jurisdiction. In practice, this disclosed knowledge remains difficult to discover, interpret, and operationalize at scale.  \nThe barrier is not merely access. Patent records are written in specialized legal and technical language. Legal status depends on jurisdiction-specific maintenance rules, patent families span multiple national registers, and commercial viability depends on market context that is rarely encoded in patent metadata. Entrepreneurs, university technology transfer offices, small and medium enterprises (SMEs), and corporate innovation teams therefore face a translation problem: public archives exist, but actionable business pathway maps do not.  \nThis paper proposes an AI-enabled framework for treating expired and lapsing patents as a dynamic innovation archive. We frame patent expiry not only as a legal status change but also as an archival transition event and a business pathway signal, a point at which disclosed technical knowledge may become more available for reuse, reinterpretation, and commercialization. We use the term pathway to emphasize that the central task is exploration: identifying plausible routes from disclosed knowledge to business action. These routes may include SaaS products, pro","cbCaidfnWWXUFmX4","https://ap.wps.com/l/cbCaidfnWWXUFmX4","pdf",366351,1,30,"English","en",105,"# Introduction\n## Patent systems and expiry events\n# Background\n## Data access and patent NLP context\n# Related work\n## Expired-patent opportunity research\n# System architecture\n## Core components and signals\n# Python-backed workflows\n## Semantic search and scoring\n# Implications and limitations\n## Legal uncertainty and commercialization risk\n# Conclusion","[{\"question\":\"What problem does the paper address with expired and lapsed patents?\",\"answer\":\"It targets the difficulty of discovering, interpreting, and operationalizing patent disclosures after rights expire or lapse, when actionable commercialization maps are not readily available from patent archives.\"},{\"question\":\"How does the proposed framework use AI workflows?\",\"answer\":\"It combines patent metadata, maintenance-fee records, legal-status indicators, semantic search, patent-family analysis, market signals, and generative AI workflows to translate patent disclosures into ranked business pathway hypotheses.\"},{\"question\":\"What risks does the approach explicitly account for?\",\"answer\":\"The framework represents legal uncertainty and data limitations and incorporates commercialization risk factors such as customer need, implementation feasibility, channel access, market timing, and pathway-specific 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