Design of reliable technology valuation model with calibrated machine learning of patent indicators

TL;DR

This paper develops a calibrated machine learning framework for reliable patent-based technology valuation, emphasizing model confidence and feature importance to improve trustworthiness and accuracy in predictions.

Contribution

It introduces a novel analytical framework that calibrates ML models to provide reliable confidence levels in patent valuation, integrating patent indicators and SHAP analysis.

Findings

The calibrated ML models achieve high accuracy and reliable confidence levels.

The Pareto-front map effectively compares model performance metrics.

SHAP analysis identifies key patent indicators influencing valuation.

Abstract

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.