  Machine learning (ML) has revolutionized the digital transformation of
technology valuation by predicting the value of patents with high accuracy.
However, the lack of validation regarding the reliability of these models
hinders experts from fully trusting the confidence of model predictions. To
address this issue, we propose an analytical framework for reliable technology
valuation using calibrated ML models, which provide robust confidence levels in
model predictions. We extract quantitative patent indicators that represent
various technology characteristics as input data, using the patent maintenance
period as a proxy for technology values. Multiple ML models are developed to
capture the nonlinear relationship between patent indicators and technology
value. The reliability and accuracy of these models are evaluated, presenting a
Pareto-front map where the expected calibration error, Matthews correlation
coefficient and F1-scores are compared. After identifying the best-performing
model, we apply SHapley Additive exPlanation (SHAP) analysis to pinpoint the
most significant input features by confidence bin. Through a case study, we
confirmed that the proposed approach offers a practical guideline for
developing reliable and accurate ML-based technology valuation models, with
significant implications for both academia and industry.
