Breaking Out of the Ivory Tower: A Large-scale Analysis of Patent Citations to HCI Research

• URL: http://arxiv.org/abs/2301.13431v1
• Date: Tue, 31 Jan 2023 05:56:59 GMT
• Title: Breaking Out of the Ivory Tower: A Large-scale Analysis of Patent Citations to HCI Research
• Authors: Hancheng Cao, Yujie Lu, Yuting Deng, Daniel A. McFarland, Michael S. Bernstein
• Abstract summary: We perform a large-scale measurement study primarily of 70,000 patent citations to premier HCI research venues. We observe that 20.1% of papers from these venues are cited by patents -- far greater than premier venues in science overall. The time lag between a patent and its paper citations is long (10.5 years) and getting longer, suggesting that HCI research and practice may not be efficiently connected.
• Score: 13.172300323407143
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: What is the impact of human-computer interaction research on industry? While it is impossible to track all research impact pathways, the growing literature on translational research impact measurement offers patent citations as one measure of how industry recognizes and draws on research in its inventions. In this paper, we perform a large-scale measurement study primarily of 70,000 patent citations to premier HCI research venues, tracing how HCI research are cited in United States patents over the last 30 years. We observe that 20.1% of papers from these venues, including 60--80% of papers at UIST and 13% of papers in a broader dataset of SIGCHI-sponsored venues overall, are cited by patents -- far greater than premier venues in science overall (9.7%) and NLP (11%). However, the time lag between a patent and its paper citations is long (10.5 years) and getting longer, suggesting that HCI research and practice may not be efficiently connected.

Related papers

• Mapping the Increasing Use of LLMs in Scientific Papers [99.67983375899719]
  We conduct the first systematic, large-scale analysis across 950,965 papers published between January 2020 and February 2024 on the arXiv, bioRxiv, and Nature portfolio journals. Our findings reveal a steady increase in LLM usage, with the largest and fastest growth observed in Computer Science papers.
  arXiv  Detail & Related papers  (2024-04-01T17:45:15Z)
• Position: AI/ML Influencers Have a Place in the Academic Process [82.2069685579588]
  We investigate the role of social media influencers in enhancing the visibility of machine learning research. We have compiled a comprehensive dataset of over 8,000 papers, spanning tweets from December 2018 to October 2023. Our statistical and causal inference analysis reveals a significant increase in citations for papers endorsed by these influencers.
  arXiv  Detail & Related papers  (2024-01-24T20:05:49Z)
• Machine Learning to Promote Translational Research: Predicting Patent and Clinical Trial Inclusion in Dementia Research [0.0]
  Projected to impact 1.6 million people in the UK by 2040 and costing pounds25 billion annually, dementia presents a growing challenge to society. We used the Dimensions database to extract data from 43,091 UK dementia research publications between the years 1990-2023. For patent predictions, an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.84 and 77.17% accuracy; for clinical trial predictions, an AUROC of 0.81 and 75.11% accuracy.
  arXiv  Detail & Related papers  (2024-01-10T13:25:49Z)
• Analyzing the Impact of Companies on AI Research Based on Publications [1.450405446885067]
  We compare academic- and company-authored AI publications published in the last decade. We find that the citation count an individual publication receives is significantly higher when it is (co-authored) by a company.
  arXiv  Detail & Related papers  (2023-10-31T13:27:04Z)
• Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021 [73.06361680847708]
  In 1960 14% of 333 research fields were related to AI, but this increased to over half of all research fields by 1972, over 80% by 1986 and over 98% in current times. In 1960 14% of 333 research fields were related to AI (many in computer science), but this increased to over half of all research fields by 1972, over 80% by 1986 and over 98% in current times. We conclude that the context of the current surge appears different, and that interdisciplinary AI application is likely to be sustained.
  arXiv  Detail & Related papers  (2023-06-15T14:08:07Z)
• Forgotten Knowledge: Examining the Citational Amnesia in NLP [63.13508571014673]
  We show how far back in time do we tend to go to cite papers? How has that changed over time, and what factors correlate with this citational attention/amnesia? We show that around 62% of cited papers are from the immediate five years prior to publication, whereas only about 17% are more than ten years old. We show that the median age and age diversity of cited papers were steadily increasing from 1990 to 2014, but since then, the trend has reversed, and current NLP papers have an all-time low temporal citation diversity.
  arXiv  Detail & Related papers  (2023-05-29T18:30:34Z)
• AI and the future of pharmaceutical research [0.0]
  The paper argues that continued innovation in pharmaceutical AI will enable rapid development of safe and effective therapies for previously untreatable diseases. The industry already reported results such as a 10-fold reduction in drug molecule discovery times. The paper concludes that the focus on pharmaceutical AI put the industry on a trajectory towards another significant disruption: open data sharing and collaboration.
  arXiv  Detail & Related papers  (2021-06-25T17:56:36Z)
• A Measure of Research Taste [91.3755431537592]
  We present a citation-based measure that rewards both productivity and taste. The presented measure, CAP, balances the impact of publications and their quantity. We analyze the characteristics of CAP for highly-cited researchers in biology, computer science, economics, and physics.
  arXiv  Detail & Related papers  (2021-05-17T18:01:47Z)
• Two tales of science technology linkage: Patent in-text versus front-page references [8.731097761118972]
  This paper explores how the value of a patent depends on the characteristics of the scientific papers that it builds on. In-text referenced papers have a higher chance of being listed on the front-page of the same patent when they are moderately basic, less interdisciplinary, less novel, and more highly cited.
  arXiv  Detail & Related papers  (2021-03-16T09:28:28Z)
• Learnings from Frontier Development Lab and SpaceML -- AI Accelerators for NASA and ESA [57.06643156253045]
  Research with AI and ML technologies lives in a variety of settings with often asynchronous goals and timelines. We perform a case study of the Frontier Development Lab (FDL), an AI accelerator under a public-private partnership from NASA and ESA. FDL research follows principled practices that are grounded in responsible development, conduct, and dissemination of AI research.
  arXiv  Detail & Related papers  (2020-11-09T21:23:03Z)
• Interdisciplinary research and technological impact: Evidence from biomedicine [2.741266294612776]
  We study one aspect of societal benefits that is contributing to the development of patented technologies. We measure the degree of interdisciplinarity of a paper using three popular indicators, namely variety, balance, and disparity. Our work may have policy implications for interdisciplinary research and scientific and technological impact.
  arXiv  Detail & Related papers  (2020-06-27T15:21:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.