GTM: the generative topographic mapping
Neural Computation
A patent search and classification system
Proceedings of the fourth ACM conference on Digital libraries
DIVA: a visualization system for exploring document databases for technology forecasting
Computers and Industrial Engineering
Text mining techniques for patent analysis
Information Processing and Management: an International Journal
Patent surrogate extraction and evaluation in the context of patent mapping
Journal of Information Science
Visualization of patent analysis for emerging technology
Expert Systems with Applications: An International Journal
Extracting the significant-rare keywords for patent analysis
Expert Systems with Applications: An International Journal
Design patent map visualization display
Expert Systems with Applications: An International Journal
Evaluation of e-learning systems based on fuzzy clustering models and statistical tools
Expert Systems with Applications: An International Journal
Identification of trends from patents using self-organizing maps
Expert Systems with Applications: An International Journal
Analyzing interdisciplinarity of technology fusion using knowledge flows of patents
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The patent map has long been considered as a useful tool for mining latent technological information. Among others, the detection of patent vacuums, defined as unexplored areas of new technologies, deserves intensive research. However, previous studies for identifying patent vacuums on the patent map have been subjected to some limitations, stemming from the subjective and manual identification of patent vacuums. To address these limitations, this paper proposes a generative topographic mapping (GTM)-based patent map, which aims to automatically identify a patent vacuum. Since GTM is a probabilistic approach of mapping multidimensional data space onto a low-dimensional latent space and vice versa, it contributes to the automatic detection and interpretation of patent vacuums. The proposed approach consists of three stages. Firstly, text mining is executed in order to transform patent documents into keyword vectors as structured data. Secondly, the GTM is employed to develop the patent map, subsequently leading to the discovery of patent vacuums, which are expressed as blank areas in the map. Lastly, the meaning of each patent vacuum is interpreted by the inverse mapping of patent vacuums onto the original keyword vector. The case study is conducted with lithography technology-related patents. We believe the proposed approach not only saves time and effort for identifying patent vacuums, but also increases objectivity and reliability.