This paper introduces a new measure of patent value – Maintenance Renewal Score (MRSc) – reflecting assignee valuing the patent by paying successive renewal fees. We generate MRSc’s for nanotechnology patents issued by the US Patent Office from 1999 through 2009, with US assignees and US inventors. Patenting increases over this period, coincident with increased US funding of nanotechnology R&D. We compare maintenance rates over the period, and against a comparison set of all 1999 USPTO grants to US inventors/assignees. We find differences in propensity to maintain the nanopatents by institution type, technological sector, and patent complexity.
- We introduce a new measure of patent quality – Maintenance Renewal Score.
- We report differences in propensity to maintain US patents.
- The US National Nanotechnology Initiative (NNI) begins in 2003.
- US Nanotechnology patenting increases from 1999 to 2009 as NNI takes place.
- 52.5% of 1999 US patents maintain to full term; 1999–2009 US nanopatents, 40.5%.
For FULL-TEXT https://doi.org/10.1016/j.wpi.2023.102178
Author(s): Alan L. Porter, Mark Markley, Richard Snead, Nils C. Newman
Organization: Search Technology, Inc.
Source: World Patent Information
Profitable companies that used data analytics have a double gain in cost reduction, demand prediction, and decision-making. However, using data analysis in non-profit organisations (NPOs) can help understand and identify more patterns of donors, volunteers, and anticipated future cash, gifts, and grants. This article presents a bibliometric study of 2673 to discover the use of data analytics in different NPOs and understand its contribution. We characterise the associations between data analysis techniques and NPOs using, Bibliometrics R tool, a co-term analysis and scientific evolutionary pathways analysis, as well as identify the research topic changes in this field throughout time. The findings revealed three key conclusions may be drawn from the findings: (1) In the sphere of NPOs, robust and conventional statistical methods-based data analysis procedures are dominantly common at all times; (2) Healthcare and public affairs are two crucial sectors that involve data analytics to support decision-making and problem-solving; (3) Artificial Intelligence (AI) based data analytics is a recently emerging trending, especially in the healthcare-related sector; however, it is still at an immature stage, and more efforts are needed to nourish its development. The research findings can leverage future research and add value to the existing literature on the subject of data analytics.
For FULL-TEXT https://doi.org/10.20517/jsegc.2022.09
Author(s): Idrees Alsolbi, Mengjia Wu, Yi Zhang, Sudhanshu Joshi, Manu Sharma, Siamak Tafavogh, Ashish Sinha, Mukesh Prasad
Organization(s): University of Technology Sydney, Commonwealth Bank Health Society
Source: Journal of Smart Environments and Green Computing
The spring up of new & emerging technologies brings a lot of innovation opportunities for society, which enables technology opportunities analysis attracts increasing attention by both industry and academia recently. This study proposes a hybrid approach which integrates topic modeling, sentiment analysis, patent mining and expert judgment to identify technological topics and the potential development opportunities. In order to illustrate how the approach is validated and optimized, and to present its potential to contribute technical intelligence for research and development management, we apply the hybrid approach to analyze a set of 9883 DII records that involved dye sensitized solar cell research. The main contributions of this study include three-fold. First, we distinguished the terms in the different parts of DII patent documents when utilizing them to recognize technical topics. Second, we utilized the terms extracted from the Advantage and the Use part to identify topics on technical problems and applications, and proposed a probability-based topic relation measurement method to identify the relationships of the technical problems and applications with the core sub-technologies. Third, we introduced both topic modeling and sentiment analysis to support technical topic analysis.
For FULL-TEXT of manuscript https://eeke-workshop.github.io/2022/submissions/EEKE2022_paper_8.pdf
Author(s): Tingting Ma, Ruiping Cheng, Hongshu Chen, XiaoZhou
Organization(s): Beijing Wuzi University, Beijing Institute of Technology, Xidian University
Source: 3rd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2022)
To what extent is military technology innovation emergent? This study answers this question by applying an emergence detection algorithm to roughly 300,000 technical terms extracted from military technology patents granted from 1980 to 2019. Emergence – instances of sudden and rapid growth of a technical term within the military patent corpus – is found to vary greatly over time. Military technology innovation during the period of 1996-2008 is found to be highly emergent. This period was found to be characterized by high organization-type diversity; non-traditional vendors, traditional defense contractors, large civilian-facing firms, and individuals generated military patents containing many novel emergent technical terms. However, in recent years, military technology innovation has exhibited markedly less emergence. The period of low emergence is characterized by reduced contributions by non-traditional vendors, defense prime contractors, and individual inventors to military patents containing emergent terms. These observations suggest that policies attempting to ensure a healthy defense innovation ecosystem should seek organization-type diversity and may benefit from employing promotion strategies targeted at distinct organization types.
Author(s): Jon Schmid
Source: Defense and Peace Economics
- A hybrid approach to extract technical intelligence for technological opportunities analysis.
- To blend topic modeling, SAO semantic analysis and machine learning.
- An optimized LDA-based topic extraction model with high accuracy.
- A case study on dye-sensitized solar cell.
With the advancement of science and the emergence of new technologies, technology opportunities analysis has attracted increasing attention from both society and academia. This study proposes a hybrid approach to integrate topic modeling, semantic SAO analysis, machine learning, and expert judgment, identifying technological topics and potential development opportunities. The systematical methodology is applied to analyze a set of 9,883 Derwent Innovation Index (DII) patents related to the dye-sensitized solar cell to present its potential contribution of technical intelligence for R&D management. Also, how the approach is validated and optimized is illustrated. The main contributions of this paper are two-fold. First, an optimized topic extraction model with high accuracy is constructed, considering both the patent classification codes and term location. Second, we integrate the topic modeling, SAO technique, and machine learning to explore semantic relationships among technological topics represented as a suite of terms. The methodology overcomes some drawbacks of the current studies. It can be used as a powerful tool for technological opportunities analysis.
Author(s): Tingting Ma, Xiao Zhou, Jia Liu, Zhenkai Lou, Zhaoting Hua, Ruitao Wang
Organization(s): Communication University of China, Beijing Wuzi University, Xidian University, Anhui University of Technology
Source: Technological Forecasting and Social Change
Artificial intelligence (AI) is emerging as a technology at the center of many political, economic, and societal debates. This paper formulates a new AI patent search strategy and applies this to provide a landscape analysis of AI innovation dynamics and technology evolution. The paper uses patent analyses, network analyses, and source path link count algorithms to examine AI spatial and temporal trends, cooperation features, cross-organization knowledge flow and technological routes. Results indicate a growing yet concentrated, non-collaborative and multi-path development and protection profile for AI patenting, with cross-organization knowledge flows based mainly on interorganizational knowledge citation links.
Full-Text available at https://doi.org/10.1371/journal.pone.0262050
Author(s): Na Liu, Philip Shapira, Xiaoxu Yue, Jiancheng Guan
Organization(s): Shandong Technology and Business University, University of Manchester, Tsinghua University, University of Chinese Academy of Sciences
Source: PLoS ONE
From the early 2010s, policymakers and firms in advanced industrial economies began introducing approaches to systemically exploit manufacturing and industrial data using the notion of cyber-physical convergence. Three innovation concepts have been especially highlighted: Smart Manufacturing, the Industrial Internet and Industrie 4.0. In parallel, academics have employed these concepts in numerous ways to advance their work. Despite this broad interest, precise definition and delineation of the cyber-physical convergence research domain have received little attention. Also missing is systematic knowledge on the interactions of these concepts with research trajectories. This paper fills these gaps by operationalising a newly constructed definition of convergence, and delineating the associated research domain into five data-centric capabilities: Monitoring, Analytics, Modelling and Simulation, Transmission and Security. A bibliometric analysis of the domain is then performed for 2010–2019. There are three findings. First, Analytics and Security have assumed strategic positions within the domain, coinciding with a “strategic turn” in policy. Second, backed by concerted policy and funding efforts, growth in Chinese scientific output has outpaced key competitors, including the U.S. and Germany. Finally, the patterns of promoting their works in terms of the three concepts differ significantly amongst U.S.-, Germany- and China-based authors, which mirrors the different policy discourses prevalent in those countries.
Author(s): Tausif Bordoloi, Philip Shapira, Paul Mativeng
Organization(s): The University of Manchester, University of Johannesburg
Source: Technological Forecasting and Social Change
This research uses link prediction and structural-entropy methods to predict scientific breakthrough topics. Temporal changes in the structural entropy of a knowledge network can be used to identify potential breakthrough topics. This has been done by tracking and monitoring a network’s critical transition points, also known as tipping points. The moment at which a significant change in the structural entropy of a knowledge network occurs may denote the points in time when breakthrough topics emerge. The method was validated by domain experts and was demonstrated to be a feasible tool for identifying scientific breakthroughs early. This method can play a role in identifying scientific breakthroughs and could aid in realizing forward-looking predictions to provide support for policy formulation and direct scientific research. Notes on methodology: First, text data were imported into Clarivate’s Derwent Data Analyzer, and the multi-word list in the field of “combined keywords + phrase” was selected as the field-of-topic term. The list in the “combined keywords + phrase” field was extracted from titles…
Identifying a scientific breakthrough early and helping to establish forward-looking predictions.
Depicting the non-linear characteristics of complex knowledge networks through structural changes.
Regarding the knowledge network as a complex system from a holistic perspective.
Observing the incubation mechanism of emergent scientific breakthroughs from a dynamic evolutionary perspective.
Author(s): Haiyun Xu, Rui Luo, Jos Winnink, Chao Wang, Ehsan Elahi
Organization(s): Shandong University of Technology, Jiangsu Academy of Agricultural Sciences, Leiden University
Source: Information Processing & Management
Over the past two decades, the discovery of CRISPR-Cas immune systems and the repurposing of their effector nucleases as biotechnological tools have revolutionized genome editing. The corresponding work has been captured by 90,000 authors representing 7,600 affiliations in 126 countries, who have published more than 19,000 papers spanning medicine, agriculture, and biotechnology. Here, we use tech mining and an integrated bibliometric and networks framework to investigate the CRISPR literature over three time periods. The analysis identified seminal papers, leading authors, influential journals, and rising applications and topics interconnected through collaborative networks. A core set of foundational topics gave rise to diverging avenues of research and applications, reflecting a bona fide disruptive emerging technology. This analysis illustrates how bibliometrics can identify key factors, decipher rising trends, and untangle emerging applications and technologies that dynamically shape a morphing field, and provides insights into the trajectory of genome editing
For FULL-TEXT go to https://www.liebertpub.com/doi/10.1089/crispr.2021.0001?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub++0pubmed
Author(s): Ying Huang, Yi Zhang, Mengjia Wu, Alan Porter, and Rodolphe Barrangou
Organization(s): Wuhan University, University of Technology Sydney, North Carolina State University
Source: The CRISPR Journal
Technological developments in nanomaterials can be tracked using patent indicators. However, the traditional International Patent Classification indicators cannot be considered conclusive, since nanotechnology is not easily defined as a field of research as well as there are different types of nanomaterials not well delineated into hierarchical codes. Therefore, text mining approaches can be used to enhance patent analysis and provide insightful trends to support research and development, competitive intelligence, and policy making. This study aims at proposing a method to classify nanomaterials into main types and mapping technological developments using an advanced text mining-based method to compile patent indicators. Patent records were provided by Derwent Innovations Index database, which indexes an enhanced bibliographic data of patents filed worldwide. A comparison between the IPC indicators and those developed here by text mining is presented. We concluded that the proposed method provides useful outcomes for decision-making, technological forecasting, and material selection process.
Author(s): Douglas Henrique Milanez, Leandro Innocentini Lopes de Faria, Daniel Rodrigo Leiva
Organization(s): Federal University of São Carlos
Source: Journal of Nanoparticle Research