Analysis and prediction of the formation of new technical phrases for inventive ideation

Despite the fast pace of technological development, the process of inventive ideation remains fuzzy. Meanwhile, improving innovation efficiency has become critical for research and development (R&D) teams because of the fierce competition. This study claimed that new technical phrases (NTPs) were important carriers of novel inventive ideas, and their formation was key to understanding and improving ideation processes. Therefore, this article proposed a methodology to analyse and predict the formation of NTPs. First, based on the recombinant search theory and link prediction, four variables in the prior co-word network of a phrase that may influence its formation were collected. Thereafter, logistic regression and a classification tree were employed on patent data to explore the effects of these variables on NTPs. Moreover, various machine learning methods were used for developing NTP prediction models, and procedures for applying the prediction models in real-world R&D settings were designed. Finally, a case study was conducted using the proposed methodology for its demonstration and validation in neural network technology. The case study revealed that all the four variables posed significant impact on the formation of NTPs, and the prediction models yielded the highest prediction accuracy of 78.6% on the test set. The proposed methodology would shed light on the ideation process in innovation theory and provide R&D teams with practical tools for generating new technical ideas.