Lu et al. reviewed the limitations of using ML methods on small datasets and the methods to deal with such small datasets, which included automatic data extraction from publications and high-throughput computations . Additionally, methods such as Bayesian optimization have been actively studied for deriving accurate solutions using small datasets. However, a limited dataset may be limited to existing solutions; therefore, increasing the amount of data is essential. Swain et al. developed ChemDataExtractor to extract chemical data from published papers , whereas Rohtagi proposed WebPlotDigitizer that helps extract plot data from figures . Additionally, Katsura et al. developed the Starrydata2 web system by extracting experimental data of several tens of thousands of thermoelectric materials . However, these methods still require significant human resources and can be overwhelmed by the significant volume of papers produced worldwide, necessitating a more efficient and automated process.