Online patent databases are powerful resources for tech mining and social network analysis and, especially, identifying rising technology stars in co-inventor networks. However, it’s difficult to detect them to meet the different needs coming from various demand sides. In this paper, we present an unsupervised solution for identifying rising stars in technological fields by mining patent information. The solution integrates three distinct aspects including technology performance, sociability and innovation caliber to present the profile of inventor, meantime, we design a series of features to reflect multifaceted ‘potential’ of an inventor. All features in the profile can get weights through the Entropy weight method, furthermore, these weights can ultimately act as the instruction for detecting different types of rising technology stars. A K-Means algorithm using clustering validity metrics automatically groups the inventors into clusters according to the strength of each inventor’s profile. In addition, using the nth percentile analysis of each cluster, this paper can infer which cluster with the most potential to become which type of rising technology stars. Through an empirical analysis, we demonstrate various types of rising technology stars: (1) tech-oriented RT Stars: growth of output and impact in recent years, especially in the recent 2 years; active productivity and impact over the last 5 years; (2) social-oriented RT Stars: own an extended co-inventor network and greater potential stemming from those collaborations; (3) innovation-oriented RT Stars: Various technical fields with strong innovation capabilities. (4) All-round RT Stars: show prominent potential in at least two aspects in terms of technical performance, sociability and innovation caliber.

Historically, nanotechnology faces the same challenges of every new, emerging and science-based industry. The transfer of viable knowledge from science to market needs to be better bridged. To enhance this transfer, a methodology is designed which integrates engineering design and subject-actionobject (SAO) text mining. The methodology is used to find, select and evaluate appropriate nanotechnologies for solving industrial problems using scientific abstracts. A case study is conducted whereby engineering design methods are used to structure a technical innovation problem in the manufacturing industry. 1.2 million abstracts from nanotechnology related articles are retrieved from the Web of Science and indexed using SAOparsing of the title and abstract. The performance of this novel methodology is measured using precision and recall, an approach common to the field of information retrieval. The methodology is further evaluated on the basis of structured interviews conducted with two engineering managers. The results demonstrate that the SAO methodology is a valuable assist to innovation and design within the firm. Significant challenges which are addressed in this article-for example knowledge loss due to SAO-parsing, and also the phenomena of information stickiness.