AI-Native SEP Governance: A Global Framework for Algorithmic Essentiality, Evidence Grounding, and IP Valuation of Standard-Driven Innovation
A governance framework for using AI to structure, ground and explain technical evidence in SEP analysis without transferring essentiality determinations to automated systems. The paper connects clause-level evidence, provenance, reviewability and valuation signals across standards-driven technology ecosystems.
Citations
Moorthy, A., & Krishnaiah, M. (2026). “AI-Native SEP Governance: A Global Framework for Algorithmic Essentiality, Evidence Grounding, and IP Valuation of Standard-Driven Innovation.” ITU Kaleidoscope 2026, Geneva.
Overview
Standard-essential patent analysis operates across large and evolving technical corpora, fragmented declaration records and heterogeneous evidence practices. This ITU Kaleidoscope paper proposes AI-Native SEP Governance, an evidence-centric framework in which AI supports the construction and organisation of evidence rather than making legal essentiality determinations. The architecture combines algorithmic essentiality assistance, clause-level evidence grounding, corpus and knowledge-graph construction, explainability, provenance and valuation-signal extraction. Candidate claim–standard relationships can therefore be surfaced with supporting and contrary evidence while preserving human technical and legal judgment. The paper develops the framework through examples including Wi-Fi Aware, mmWave WLAN evidence conditions and international essentiality-audit experience. An important theme is that evidentiary gaps differ by technological environment: one domain may provide strong technical proximity but limited implementation visibility, while another may exhibit semantic coherence without sufficiently structured normative traceability. The proposed governance model therefore emphasises multiple evidence pathways, reproducibility and reviewability instead of reducing SEP analysis to a single automated score or prediction.
Key Findings
AI is positioned as an essentiality-assistance and evidence-generation mechanism, not as the final decision-maker on legal essentiality.
The framework is organised around algorithmic assistance, evidence grounding and explainability, corpus-level knowledge construction, and valuation signals.
Different standards environments exhibit different forms of evidentiary opacity, requiring parallel evidence modes rather than one universal signal.
Clause-cited evidence, negative matches, provenance and reviewability are treated as governance requirements rather than optional explanations added after analysis.
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