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Evaluating Technology Emergence Indicators with a 3-D Analysis

Technology emergence has become a hot topic in R&D policy and management communities. Various methods of measuring technology emergence have been developed. However, there is little literature discussing how to evaluate the results identified by different methods. In "A 3-dimensional analysis for evaluating technology emergence indicators," Liu and Porter sharpen a promising Technology Emergence Indicator (TEI) set by assessing alternative formulations on three distinct datasets: Dye-Sensitized Solar Cells, Non-Linear Programming, and Nano-Enabled Drug Delivery. These TEIs are derived from a conceptual foundation including three attributes of emergence: persistence, community, and growth that are systematically addressed through a 3-dimensional evaluation framework. Comparing TEI behavior through sensitivity analyses shows good robustness for the measures. The TEI serve to distinguish emerging R&D topics in the field under study. They can further be used to identify highly active players publishing on those topics. Importantly, results show that identified emerging terms and topics persist to a strong degree; thus, they serve to predict highly active R&D foci within the technical domain under study.

Facilitate Technology Management with Technology Evolution Pathways

Technological innovation is a dynamic process that spans the lifecycle of an idea, from scientific research to production. Within this process, there are few key innovations that significantly impact a technology's development, and the ability to identify and trace the development of these key innovations comes with a great payoff for researchers and technology managers. In "Exploring technology evolution pathways to Facilitate Technology Management: From a Technology Life Cycle Perspective", Huang et al, present a framework for identifying the technology's main evolutionary pathway of a technology. What is unique about this framework is that it introduces new indicators that reflect the connectivity and the modularity in the interior citation network to distinguish between the stages of a technology's development. The authors also show how information about a family of patents can be used to build a comprehensive patent citation network. Last, integrated approaches of main path analysis (MPA) -- namely global main path analysis and global key-route main analysis -- are applied for extracting technological trajectories at different technological stages. This approach is illustrated with Dye-Sensitized Solar Cells (DSSCs), a low-cost solar cell belonging to the group of thin film solar cells, contributing to the remarkable growth in the renewable energy industry. The results show how this approach can trace the main development trajectory of a research field and distinguish key technologies to help decision-makers manage the technological stages of their innovation processes more effectively.

Machine-Learning Drives Patent Analysis

As business intelligence professionals increasingly rely on accurate patent landscapes to inform technology forecasting, machine learning (ML) is being called on to aid in the analytical process. In the recently published "Parameter tuning Naïve Bayes for automatic patent classification", Cassidy provides an analysis of available settings for automatic patent categorization. A modified Naïve Bayes classifier assigns International Patent Classification (IPC) section codes for a selection of 7,309 patent applications from the World Patent Information (WPI) Test Collection (Lupu, 2019). Several measures of accuracy are compared for a variety of meta-parameter settings including data smoothing and acceptance threshold. The optimized model is also applied to IPC class and group codes and the results for patent categorization are compared to classification of academic literature.

Examining the evolution of corporate involvement...publications and patents

Search Technology and the Georgia Tech Science, Technology & Innovation Policy (STIP) Program are examining the evolution of corporate involvement in nanotechnology R&D. With NSF support (starting April, 2020), we are analyzing corporate research publishing and patenting since 1991, globally and in the US. Patent analysis challenges include: applying a multi-part PatStat database search, including noise exclusion, and consolidating patent applications into INPADOC fields, while distinguishing first patent filings. We are also separating corporate from other assignees and geo-coding to map US company activities. Stay tuned for enhanced tech mining tools to combine patents and research publications.

Text-Mining E-mails to Determine Administrative Burden

 VantagePoint text-mining software was used to analyze emails in a recent project published as "Robotic Bureaucracy and Administrative Burden: What Are the Effects of Universities' Computer Automated Research Grants Management Systems?" (2020). The authors "find that robotic emails have complex effects and that their utility pertains to researchers' familiarity with the systems and compliance requirements, the clarity of administrative requests, the extent and location of staff support, and the interaction of personal work habits with system requirements" and provide suggestions for improving automated research administration.

Exploring Technology Evolution Pathways to Facilitate Technology Management: From a Technology Life Cycle Perspective

The recently published article "Facilitate Technology Management: From a Technology Life Cycle Perspective" presents a framework for identifying the technology's main evolutionary pathway. What is unique about this framework is that it introduces new indicators that reflect the connectivity and the modularity in the interior citation network to distinguish between the stages of a technology's development. It also shows how information about a family of patents can be used to build a comprehensive patent citation network. Finally, integrated approaches of main path analysis (MPA)—namely global MPA and global key-route main analysis—are applied for extracting technological trajectories at different technological stages.

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