Abstract: A learning procedure involves generating a non-defective product learning model by conducting machine learning using non-defective product data as teacher data, and generating a defective product learning model for each defect type by conducting machine learning for each defect type using defective product data as teacher data. A calculating procedure involves calculating the likelihood of a non-defective product from output data calculated using the non-defective product learning model to which target product data is input, and calculating the likelihood of a defective product for each defect type from output data calculated using the defective product learning model to which the target product data is input. A determining procedure involves determining that the target product data is data on a defective product having an unknown defect when the likelihood of a non-defective product and the likelihood of a defective product for each defect type satisfy a predetermined requirement.

Abstract: A learning procedure involves generating a non-defective product learning model by conducting machine learning using non-defective product data as teacher data, and generating a defective product learning model for each defect type by conducting machine learning for each defect type using defective product data as teacher data. A calculating procedure involves calculating the likelihood of a non-defective product from output data calculated using the non-defective product learning model to which target product data is input, and calculating the likelihood of a defective product for each defect type from output data calculated using the defective product learning model to which the target product data is input. A determining procedure involves determining that the target product data is data on a defective product having an unknown defect when the likelihood of a non-defective product and the likelihood of a defective product for each defect type satisfy a predetermined requirement.

Abstract: An information processing method includes: generating a first learning model by conducting machine learning using, as teacher data, a predetermined number of pieces of non-defective product data extracted from product data; determining, for each of a plurality of pieces of product data to be determined after the first learning model is generated, whether each product is non-defective or defective in accordance with the first learning model; grouping the pieces of product data determined to be defective, such that these pieces of product data are classified according to defect type; collectively associating type labels indicative of defect types with the defective product data according to defect type group; and generating a second learning model by conducting machine learning using, as teacher data, the defective product data with which the type labels are associated and the non-defective product data.