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This study proposes an integrated framework combining hierarchical Latent Dirichlet Allocation (LDA) modeling with multiphase technology lifecycle theory, analyzing 113,449 Derwent patent abstracts (2008\u20132022) across three dimensions: technological novelty, functional applications, and competitive advantages. By segmenting innovation stages via logistic growth curve modeling and optimizing topic extraction through perplexity validation, we constructed dynamic technology roadmaps to decode latent evolutionary patterns in AI-powered programmable manipulators (B25J classification) within an innovation trajectory. Key findings revealed: (1) a progressive transition from electromechanical actuation to sensor-integrated architectures, evidenced by 58% compound annual growth in embedded sensing patents; (2) application expansion from industrial automation (72% early stage patents) to precision medical operations, with surgical robotics growing 34% annually since 2018; and (3) continuous advancements in adaptive control algorithms, showing 2.7\u00d7 growth in reinforcement learning implementations. The methodology integrates quantitative topic modeling (via pyLDAvis visualization and cosine similarity analysis) with qualitative lifecycle theory, addressing the limitations of conventional technology analysis methods by reconciling semantic granularity with temporal dynamics. The results identify core innovation trajectories\u2014precision control, intelligent detection, and medical robotics\u2014while highlighting emerging opportunities in autonomous navigation and human\u2013robot collaboration. This framework provides empirically grounded strategic intelligence for R&amp;D prioritization, cross-industry investment, and policy formulation in Industry 4.0.<\/jats:p>","DOI":"10.3390\/informatics12030069","type":"journal-article","created":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T13:44:19Z","timestamp":1752241459000},"page":"69","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Patent-Based Technology Roadmap for AI-Powered Manipulators: An Evolutionary Analysis of the B25J Classification"],"prefix":"10.3390","volume":"12","author":[{"given":"Yujia","family":"Zhai","sequence":"first","affiliation":[{"name":"School of Management, Tianjin Normal University, Tianjin 300387, China"}]},{"given":"Zehao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Management, Tianjin Normal University, Tianjin 300387, China"}]},{"given":"Rui","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Management, Tianjin Normal University, Tianjin 300387, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4579-9795","authenticated-orcid":false,"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Special Intelligent Equipment Safety Measurement and Control, Fujian Special Equipment Inspection and Research Institute, Fuzhou 350008, China"}]},{"given":"Gengfeng","family":"Zheng","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Special Intelligent Equipment Safety Measurement and Control, Fujian Special Equipment Inspection and Research Institute, Fuzhou 350008, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40309-013-0015-4","article-title":"Foresight 2.0-Definition, Overview & Evaluation","volume":"1","author":"Schatzmann","year":"2013","journal-title":"Eur. 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