Patenting patterns in Artificial Intelligence: Identifying national and international breeding grounds

Leusin, Matheus Eduardo; Günther, Jutta; Jindra, Björn; Moehrle, Martin G.

doi:10.1016/j.wpi.2020.101988

Cited by 13 publications

(8 citation statements)

References 22 publications

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“…There is no ’gold standard’ defining which approach is the most appropriate to for retrieving AI patents so different methods are utilised in literature e.g. WIPO approaches were used by Leusin et al ( 2020 ). On the other hand (Fujii & Managi, 2018 ) followed the practice of the USPTO.…”

Section: Methodsmentioning

confidence: 99%

“…A more formal definition from (Copeland, 2020 ) states that it is “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings”. AI as a concept was first created in the 1950 s but its market relevance has been recognised worldwide in the last decade, mainly due to the development of high-performance parallel computing chips and the availability of large datasets that have extended this technology’s applicability (Leusin et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…(Aristodemou & Tietze, 2018 ; Niu et al, 2016 ), or even both. Both strategies have pros and cons, and outcomes from their use could bring slightly different results (Leusin et al, 2020 ). Since the field of AI is dynamically developing the proposed keywords are quickly becoming outdated e.g set of keywords by IPO ( 2019 ) is missing many keywords characteristic of contemporary AI technologies e.g.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian inference of spatial and temporal relations in AI patents for EU countries

Rusek¹,

Kleszcz²,

Cabellos‐Aparicio³

2023

Scientometrics

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In the paper, we propose two models of Artificial Intelligence (AI) patents in European Union (EU) countries addressing spatial and temporal behaviour. In particular, the models can quantitatively describe the interaction between countries or explain the rapidly growing trends in AI patents. For spatial analysis Poisson regression is used to explain collaboration between a pair of countries measured by the number of common patents. Through Bayesian inference, we estimated the strengths of interactions between countries in the EU and the rest of the world. In particular, a significant lack of cooperation has been identified for some pairs of countries. Alternatively, an inhomogeneous Poisson process combined with the logistic curve growth accurately models the temporal behaviour by an accurate trend line. Bayesian analysis in the time domain revealed an upcoming slowdown in patenting intensity.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian inference of spatial and temporal relations in AI patents for EU countries

Rusek¹,

Kleszcz²,

Cabellos‐Aparicio³

2023

Scientometrics

View full text Add to dashboard Cite

show abstract

“…A more formal definition from Copeland (2020) states that it is "the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings". AI as a concept was first created in the 1950s but its market relevance has been recognised worldwide in the last decade, mainly due to the development of high-performance parallel computing chips and the availability of large datasets that have extended this technology's applicability Leusin, Günther, Jindra, and Moehrle (2020).…”

Section: Introductionmentioning

confidence: 99%

“…Aristodemou and Tietze (2018); Niu et al (2016), or even both. Both strategies have pros and cons, and outcomes from their use could bring slightly different results Leusin et al (2020). Since the field of AI is dynamically developing the proposed keywords are quickly becoming outdated e.g set of keywords by IPO ( 2019) is missing many keywords characteristic of contemporary AI technologies e.g.…”

Section: Introductionmentioning

confidence: 99%