Entity

Time filter

Source Type

Haddonfield, NJ, United States

Babko-Malaya O.,BAE Systems | Pustejovsky J.,Brandeis University | Thomas P.,1790 Analytics LLC | Stromsten S.,BAE Systems | Barlos F.,BAE Systems
AAAI Fall Symposium - Technical Report | Year: 2012

There is growing interest in automating the detection of interesting new developments in science and technology. BAE Systems is pursuing ARBITER (Abductive Reasoning Based on Indicators and Topics of EmeRgence), a multi-disciplinary study and development effort to analyze fulltext and metadata for indicators of emergent technologies and scientific fields. To define these indicators, our team has applied the primary insights of actant network theory developed within the disciplines of Science and Technology Studies and the history of technology and science to create a pragmatic theory of technoscientific emergence. Specifically, this practical theory articulates emergence in terms of the robustness of actant networks. This applied actant-network theory currently guides our definition of indicators and indicator patterns for the ARBITER system, and represents a novel contribution to the discussion of emergent technologies and fields. Several elements of our theory were validated with 15 case studies and 25 example technologies. Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Source


Thomas P.,1790 Analytics LLC | Babko-Malaya O.,BAE Systems | Hunter D.,BAE Systems | Meyers A.,New York University | Verhagen M.,Brandeis University
Proceedings of ISSI 2013 - 14th International Society of Scientometrics and Informetrics Conference | Year: 2013

This paper outlines a system designed to determine whether practical applications exist for research fields, particularly emerging research fields. The system uses indicator patterns, based on features extracted from the metadata and full text of scientific papers and patents, to assess different characteristics that point to the existence of practical applications for research fields. The system may thus help determine whether a particular research field has moved beyond the early, conceptual phase towards a more applied, practical phase. It may also help to classify emerging research fields as being more 'technological' or more 'scientific' in nature. The system is tested on data from a number of research fields across a range of time periods, and the outputs are compared to responses from subject matter experts. The results suggest that the system shows promise, albeit based on a relatively small data sample, in terms of determining whether practical applications exist for given research fields. The system also shows promise in detecting the transition from absence to existence of practical applications over time, which may be of particular value in evaluating emerging technologies. © AIT Austrian Institute of Technology GmbH Vienna 2013. Source


Breitzman A.,1790 Analytics LLC | Thomas P.,1790 Analytics LLC
Research Policy | Year: 2015

Emerging technologies are of great interest to a wide range of stakeholders, but identifying such technologies is often problematic, especially given the overwhelming amount of information available to analysts and researchers on many subjects. This paper describes the Emerging Clusters Model, which uses advanced patent citation techniques to locate emerging technologies in close to real time, rather than retrospectively. The model covers multiple patent systems, and is designed to be extensible to additional systems. This paper also describes the first large scale test of the Emerging Clusters Model. This test reveals that patents in emerging clusters consistently have a significantly higher impact on subsequent technological developments than patents outside these clusters. Given that these emerging clusters are defined as soon as a given time period ends, without the aid of any forward-looking information, this suggests that the Emerging Clusters Model may be a useful tool for identifying interesting new technologies as they emerge. © 2014 Elsevier B.V. All rights reserved. Source


Breitzman A.,1790 Analytics LLC | Thomas P.,1790 Analytics LLC
Scientometrics | Year: 2015

Forward citations are widely recognized as a useful measure of the impact of patents upon subsequent technological developments. However, an inherent characteristic of forward citations is that they take time to accumulate. This makes them valuable for retrospective impact evaluations, but less helpful for prospective forecasting exercises. To overcome this, it would be desirable to have indicators that forecast future citations at the time a patent is issued. In this paper, we outline one such indicator, based on the size of the inventor teams associated with patents. We demonstrate that, on average, patents with eight or more co-inventors are cited significantly more frequently in their first 5 years than peer patents with fewer inventors. This result holds true across technologies, assignee type, citation source (examiner versus applicant), and after self-citations are accounted for. We hypothesize that inventor team size may be a reflection of the amount of resources committed by an organization to a given innovation, with more researchers attached to innovations regarded as having particular promise or value. © 2015, Akadémiai Kiadó, Budapest, Hungary. Source

Discover hidden collaborations