LAUSR

In order to gain competitive advantage, technology opportunity detection in the latest and fast-growing areas has been becoming an important research issue. However, current research on technology opportunity detection is often focus on verifying the technology opportunities that have occurred, using the accumulated data from a specific field. Because of the time needed for data accumulation, these methods often have a substantial time lag and hard to early detect technology opportunities. It also leads to challenges to explore technology opportunities which still have not been covered in the current dataset. Moreover, phrase has more semantics than words but still rarely used and semantic represented in the process of technology opportunity detection. Therefore, this paper proposes a method based on analogy design and phrase semantic representation for early detection of technology opportunity. Firstly, the source field corresponding to target field for analogy design is carefully selected, thus indirectly expanding the data coverage of the target field through the data from the source field. Secondly, effect phrases in both source field and target field are automatically extracted by BiLSTM-CRF and semantic represented by representation learning, then the analogy relationships are established through topic clustering on overall data. Finally, the scores of the topics are calculated based on ODI (outcome-driven innovation) and the topics with a high score are considered as early detected technology opportunities. The proposed method is validated using analogy between 3G and 4G. In this process, 3G is used as the source and 4G patents published in the early stage are used as the target for detecting technology opportunities in 4G, and the rest 4G patents published in the later stage are used for detecting the actual evolution results of technology opportunities. The comparison results prove that every detected technology opportunity in the early stage matches one or more topics of the actual evolution results in the later stage. In addition, this paper uses analogy between 4G (source field) and 5G (target field) for technology opportunities prediction, which may provide useful and helpful results for decision making in 5G and a good example for further application in other areas. These results have proved that the proposed method is effective and useful. Simultaneously, this method is a preliminary research and still need to be further studied on other datasets with different analogy types.