  Despite the usefulness of machine learning approaches for the early screening
of potential breakthrough technologies, their practicality is often hindered by
opaque models. To address this, we propose an interpretable machine learning
approach to predicting future citation counts from patent texts using a
patent-specific hierarchical attention network (PatentHAN) model. Central to
this approach are (1) a patent-specific pre-trained language model, capturing
the meanings of technical words in patent claims, (2) a hierarchical network
structure, enabling detailed analysis at the claim level, and (3) a claim-wise
self-attention mechanism, revealing pivotal claims during the screening
process. A case study of 35,376 pharmaceutical patents demonstrates the
effectiveness of our approach in early screening of potential breakthrough
technologies while ensuring interpretability. Furthermore, we conduct
additional analyses using different language models and claim types to examine
the robustness of the approach. It is expected that the proposed approach will
enhance expert-machine collaboration in identifying breakthrough technologies,
providing new insight derived from text mining into technological value.
