Mapping Innovations in Artificial Intelligence through Patents: A Social Data Science Perspective

Warin, Thierry; Duc, Romain Le; Sanger, William

doi:10.1109/csci.2017.40

Cited by 5 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As such, it does not replace the expert’s analysis of the literature. On the contrary, it should be used to augment the expert’s study [ 3 ].…”

Section: Resultsmentioning

confidence: 99%

“…Reproducible research has accelerated in these past few years, with principles such as open science, open data, and open code. The use of data has been amplified by the development of new methodologies within the field of artificial intelligence (AI), allowing researchers to generate and analyze structured and unstructured data in a supervised way, as well as in a semi- or unsupervised way [ 3 ]. Data initiatives have flourished across the world, collecting firsthand data, aggregating various data sets, and developing simulation-based models.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Global Research on Coronaviruses: An R Package

Warin¹

2020

J Med Internet Res

Self Cite

View full text Add to dashboard Cite

Background In these trying times, we developed an R package about bibliographic references on coronaviruses. Working with reproducible research principles based on open science, disseminating scientific information, providing easy access to scientific production on this particular issue, and offering a rapid integration in researchers’ workflows may help save time in this race against the virus, notably in terms of public health. Objective The goal is to simplify the workflow of interested researchers, with multidisciplinary research in mind. With more than 60,500 medical bibliographic references at the time of publication, this package is among the largest about coronaviruses. Methods This package could be of interest to epidemiologists, researchers in scientometrics, biostatisticians, as well as data scientists broadly defined. This package collects references from PubMed and organizes the data in a data frame. We then built functions to sort through this collection of references. Researchers can also integrate the data into their pipeline and implement them in R within their code libraries. Results We provide a short use case in this paper based on a bibliometric analysis of the references made available by this package. Classification techniques can also be used to go through the large volume of references and allow researchers to save time on this part of their research. Network analysis can be used to filter the data set. Text mining techniques can also help researchers calculate similarity indices and help them focus on the parts of the literature that are relevant for their research. Conclusions This package aims at accelerating research on coronaviruses. Epidemiologists can integrate this package into their workflow. It is also possible to add a machine learning layer on top of this package to model the latest advances in research about coronaviruses, as we update this package daily. It is also the only one of this size, to the best of our knowledge, to be built in the R language.

show abstract

“…As such, it does not replace the expert’s analysis of the literature. On the contrary, it should be used to augment the expert’s study [ 3 ].…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Global Research on Coronaviruses: An R Package

Warin¹

2020

J Med Internet Res

Self Cite

View full text Add to dashboard Cite

show abstract

“…Warin et al [3] used social data science techniques to analyze 55,109 AI patents and 29,225 related articles. The study found that AI patents have grown exponentially over time and cover a wide range of sub-fields.…”

Section: Related Workmentioning

confidence: 99%

Natural Language Processing Patents Landscape Analysis

Al-Khalifa,

AlOmar,

AlOlyyan

2024

Data

View full text Add to dashboard Cite

Understanding NLP patents provides valuable insights into innovation trends and competitive dynamics in artificial intelligence. This study uses the Lens patent database to investigate the landscape of NLP patents. The overall patent output in the NLP field on a global scale has exhibited a rapid growth over the past decade, indicating rising research and commercial interests in applying NLP techniques. By analyzing patent assignees, technology categories, and geographic distribution, we identify leading innovators as well as research hotspots in applying NLP. The patent landscape reflects intensifying competition between technology giants and research institutions. This research aims to synthesize key patterns and developments in NLP innovation revealed through patent data analysis, highlighting implications for firms and policymakers. A detailed understanding of NLP patenting activity can inform intellectual property strategy and technology investment decisions in this burgeoning AI domain.

show abstract

“…Liu and Shong (2018) allow using machine technologiesin particular, Big Data and AIin the innovative activities of companies for increasing its effectiveness. Warin et al (2018) see mapping of innovations, which are created with application of AI and patents, as a social perspective of development of modern science.…”

Section: Literature Reviewmentioning

confidence: 99%

Labor division and advantages and limits of participation in creation of intangible assets in industry 4.0: humans versus machines

Lobova

Alekseev

Litvinova

et al. 2020

JIC

View full text Add to dashboard Cite

PurposeThe purpose of the work is to solve the set problem and to study the competition and perspectives of division of labor of humans and machines during creation of intangible assets in Industry 4.0.Design/methodology/approachThe research is performed with the help of regression and comparative analysis by building regression curves and with the help of the qualitative structural and logical analysis.FindingsThe authors perform an overview of the factors that determine the advantages and limits of participation in creation of intangible assets in Industry 4.0, determine the perspectives and compile recommendations for division of human and machine labor during creation of intangible assets in Industry 4.0.Originality/valueThe results of the performed research confirmed the general hypothesis that machine technologies allow improving the innovative, marketing and organizational and managerial activities and activities in the sphere of R&D through automatization of certain stages of the process of creation of intangible assets. The authors determine the factors that define the contribution of machine technologies in this process and their competitive advantages as compared to human intellectual capital during creation of intangible assets. These advantages prove the possibility and expedience of division of human and machine labor during creation of intangible assets.

show abstract

Mapping Innovations in Artificial Intelligence through Patents: A Social Data Science Perspective

Cited by 5 publications

References 2 publications

Global Research on Coronaviruses: An R Package

Global Research on Coronaviruses: An R Package

Natural Language Processing Patents Landscape Analysis

Labor division and advantages and limits of participation in creation of intangible assets in industry 4.0: humans versus machines

Contact Info

Product

Resources

About