2 Scientometric & Patent Horizon

Draft abstract: this chapter sets the empirical baseline for the book by summarizing the Technology Readiness Level (TRL) scaffold used throughout Parts I–III and the multi-database corpus (Scopus, Web of Science, Lens, EPO/USPTO patents) for scientometric and patent horizon scanning.

Keywords: scientometrics, bibliometrics, patent landscaping, TRL, multi-database triangulation.

2.1 Multi-Database Ingestion

Comparative coverage of Scopus, Web of Science, and Lens for SDG tracking; strengths/blind spots of each source.
Data harvesting and taxonomy alignment against the 17 SDGs (cross-ref チャプター 4).
Deduplication and record-linkage across scholarly and patent corpora.

2.2 Technology Readiness Level (TRL) Assessment Framework

TRL 1–9 scoring rubric adapted for AI-enabled hardware/software.
Worked example: applying the rubric to a semiconductor sub-domain.

2.3 Patent Landscape Analysis & Technology Mining

The International Patent Classification (IPC) and Cooperative Patent Classification (CPC) classification, citation velocity, key-assignee mapping.
Co-patenting networks as an early signal of cross-border collaboration.

2.4 Institutional Knowledge Landscapes

Comparative mapping of UNU, OECD, and ADB published knowledge bases as a baseline corpus, distinct from the commercial databases above.
Sets up the knowledge-to-policy nexus revisited in チャプター 4.

Bridge to Chapter 2

Chapter 2 turns this raw, triangulated corpus into technology trajectories using AI-assisted content analysis, natural language processing (NLP), and prompt priming, in an AI-Powered Bibliometrics Catalogue.