Patent analysis for technology development of artificial intelligence: A country-level comparative study

Abstract

Artificial intelligence (AI) plays a key role in knowledge economies, because it can be used to develop systems that think like humans, act like humans, think rationally, and act rationally (Russell & Norvig, 2010). In this study, we divide AI into four sub-technological fields: Problem reasoning and solving, machine learning, network structures, and knowledge processing systems. This study investigates three main issues related to the technology development of AI. First, the aggregate technology development of AI is examined, and the four sub-technological fields of AI are compared. Second, we employ measures of patent quantity and patent quality to demonstrate the technology development of AI in different countries. Finally, we investigate the technology positions of different countries in the four sub-technological fields of AI. By analyzing a patent and citation dataset comprised of all patents granted by the United States patent and trademark office from 1976 to 2010, we obtain empirical findings that help us understand the technology development of AI in different countries. The major contributions of this study are four measures of patent quantity (PCA, PCI, SHAI, and SHIA) and three measures of patent quality (citation ratios, CII, and TCT). These measures are helpful in understanding technological development of AI in different counties. Moreover, we use patent citation data and investigate the technology flow in AI, in order to determine the technology position of different countries in the four sub-technological fields of AI.

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Stored only when both papers are in the corpus — 77 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.