In the case above, the combined use of the Table_Detect and Table2CSV tools allows the extraction of molecular structures and bioactivity data from each page of the patent document. Additionally, the sequential use of the Y_Concat and X_Concat tools ensures that the data extracted in earlier steps is consolidated across two dimensions, addressing challenges related to cross-page tables and dispersed data in different tables. Hence, by using literatures as inputs, ChatMolData is capable of executing complex molecular data collection tasks and producing structured molecular data as output. To our knowledge, ChEMBL, the largest open-access database containing molecular structures and bioactivities, is manually curated and primarily sources its molecular data from publications (over 80,000 from papers and fewer than 3,000 from patents). In other words, the information in molecule-related patents has not been well collected.