Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network

The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (i) identifies actual clusters of patents: i.e. technological branches, and (ii) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the {citation vector}, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at the beginning of 1997. These new tools of predictive analytics could support policy decision making processes in science and technology, and help formulate recommendations for action

Patent data driven innovation logic

Innovation research is conventionally conducted with creativity techniques such as TRIZ, Mind Mapping, Brainstorming, etc. (Dewulf, Baillie 1998). Patent research is typically used to research novelty or prior art, and legal studies. This thesis is at the intersection of creativity techniques, and patent data analysis. It describes how to utilise patent data for distilling Innovation Logic and conducting innovation research. Using the patent research tool PatentInspiration (© AULIVE Software NV), the 4 different stages of the Innovation Logic approach have been subjected to text analysis in patent literature. The specific text patterns were identified and documented on several case studies, with one case study across the whole thesis: the toothbrush. The opportunities and limitations of Patent Data Driven Innovation Research have been documented and discussed. This methodology has been demonstrated within a proposed structural approach to problem solving, technology marketing and innovation research. Furthermore, the potential of artificial idea generation and artificial creativity was examined and debated for the purpose of computer aided creativity. This thesis examines and confirms three claims: CLAIM 1: PROPERTIES AND FUNCTIONS CAN BE ADJECTIVES AND VERBS IN PATENT LITERATURE CLAIM 2: PATENT DATA ANALYSIS AUGMENTS THE FULL INNOVATION LOGIC PROCESS CLAIM 3: ARTIFICIAL INNOVATION METHODS CAN BE FUELED BY PATENT DATA Patent data can be text mined, acting as a global brain consisting of over 100 million invention documents. It is possible to use this existing data to reverse engineer thinking methodologies, allowing scientists and engineers to solve new problems, invent new products or processes, or find new markets for existing technologies. Patent Data Driven Innovation Logic will demonstrate a systematic innovation approach that combines the force of contemporary data mining methods on patent literature, with a structured innovation research methodology.Open Acces

Invention in Times of Global Challenges: A Text-Based Study of Remote Sensing and Global Public Goods

We study whether remote sensing (RS), a set of technologies with global reach and a variety of applications, can be considered instrumental to the provision of global public goods (GPG). We exploit text information from patent data and apply structural topic modeling to identify topics related (or relevant) to GPG provision, and trace their participation in the evolution of remote sensing technology over time. We develop a new indicator of affinity to GPG (and other themes) using meta information from our dataset. We find that, first, RS displays features of a general-purpose technology. Second, while peripheral, GPG-relevant topics are present in the RS topic space, and in some cases overlap with topics with high affinity in AI and participation of public sector actors in invention. With our analysis, we contribute to a better understanding of the interplay between the dynamics of technology and (global) political economy, a field of research yet under-explored

Technology, resources and geography in a paradigm shift: the case of Critical & Conflict Materials in ICTs

The mining of several critical raw materials – including the so-called ‘conflict minerals’ associated with armed conflict and human rights abuses – and their combination, refining and use in many new advanced electronic products, are providing an important material infrastructure to current technological progress. Relying on text analysis of USPTO patent data between 1976 and 2017, our explorative study provides a methodological and empirical starting point for exploring the technological and geographical linkages between technological paradigms and selected critical and conflict materials (CCMs). Our descriptive analysis finds evidence of a clear association between ICT technologies and CCM intensity over time, and of a striking resource-technology divide in global ICT value chains between value creating and value extracting activities across Global North and Global South and their regions. The paperintends to emphasize the need for a more critical, spatially sensitive approach to studying resource-based technological change to expose the uneven development consequences created, sustained, or mitigated by technological progress

Advanced Materials and Technologies in Nanogenerators

This reprint discusses the various applications, new materials, and evolution in the field of nanogenerators. This lays the foundation for the popularization of their broad applications in energy science, environmental protection, wearable electronics, self-powered sensors, medical science, robotics, and artificial intelligence

Critical minerals and countries' mining competitiveness:An estimate through economic complexity techniques

Minerals' criticality and countries' mining competitiveness are two dimensions that have gained relevance in the economic and policy agenda due to the key role of minerals in the energy transition. To a certain extent, these product-country dimensions can be seen as two faces of the same coin, which intertwine and simultaneously co-determine each other. Therefore, economic complexity techniques appear as a useful methodology to simultaneously estimate both dimensions.This paper employs economic complexity techniques to build an unsupervised Fitness-Criticality algorithm, that allows simultaneously estimating countries' mining competitiveness (Fitness Mining Index) and minerals' criticality (Criticality Minerals Index). Our indexes are efficient in terms of the set of information employed, and do not rely on subjective perspectives and assessments. The results of the estimates suggest that South Africa, Russia, the United States, Norway, Canada, Australia and Chile are the most competitive countries. Moreover, the Platinum Group Metals, Lithium, Silicon and Rare Earths appear as the most critical minerals. These results are consistent with other methodologies employed by different organizations that separately estimate both dimensions and derive countries’ and minerals’ rankings

Auto-generated materials database of Curie and Néel temperatures via semi-supervised relationship extraction.

Large auto-generated databases of magnetic materials properties have the potential for great utility in materials science research. This article presents an auto-generated database of 39,822 records containing chemical compounds and their associated Curie and Néel magnetic phase transition temperatures. The database was produced using natural language processing and semi-supervised quaternary relationship extraction, applied to a corpus of 68,078 chemistry and physics articles. Evaluation of the database shows an estimated overall precision of 73%. Therein, records processed with the text-mining toolkit, ChemDataExtractor, were assisted by a modified Snowball algorithm, whose original binary relationship extraction capabilities were extended to quaternary relationship extraction. Consequently, its machine learning component can now train with ≤ 500 seeds, rather than the 4,000 originally used. Data processed with the modified Snowball algorithm affords 82% precision. Database records are available in MongoDB, CSV and JSON formats which can easily be read using Python, R, Java and MatLab. This makes the database easy to query for tackling big-data materials science initiatives and provides a basis for magnetic materials discovery