AITRIZ: Toward an AI-Era Extension of Classical TRIZ Through Multi-Domain Patent Analysis

Classical TRIZ remains one of the most influential frameworks for inventive problem solving, yet contemporary AI systems increasingly generate contradiction classes that appear only partially captured by the original catalog of principles. This paper presents evidence for an AI-era extension framework, referred to here as AITRIZ, derived from structured patent analysis across multiple domains and validation rounds. The research is based on analysis of over 305 patents across more than nine technology domains, with inter-rater reliability reaching kappa=1.00 on rubric-scored validation sets in Medical AI and FinTech AI. Three principles appear especially significant as candidate non-reducible additions relative to classical principle combinations: epistemic transparency, guided emergence, and adversarial resilience. The paper contributes a structured contradiction taxonomy for AI-era systems, evidence from multi-domain patent analysis, a reproducible validation trajectory, and a conceptual extension framework for TRIZ in the context of modern AI, safety, trust, temporality, and multi-agent systems.