Information processing device, information processing method, and computer program product for estimating parameter of model for specified time parameter

An information processing method implemented in an information processing device, comprising:

• receiving input of a plurality of pieces of input data obtained during K time periods, K being an integer equal to or greater than two, the plurality of pieces of input data including one or more explanatory variables and one or more objective variables, the one or more explanatory variables and the one or more objective variables being variables related to a manufacturing process performed by a manufacturing plant that includes a semiconductor factory and a chemical plant;
• creating K first models, each of the K first models being a model that receives input of the one or more explanatory variables and outputs the one or more objective variables, each of the K first models being created for a corresponding period among the K time periods, by machine learning using a plurality of pieces of input data obtained during the corresponding period;
• creating a second model that receives input of a first time parameter and outputs a parameter of a third model that receives input of the one or more explanatory variables and outputs the one or more objective variables, the second model being created by machine learning using a plurality of parameters of the created K first models;
• and
• estimating the parameter of the third model by inputting a specified second time parameter to the created second model.

A computer program product comprising a non-transitory computer-readable medium including instructions stored thereon, the instructions causing a computer to execute:

• receiving input of a plurality of pieces of input data obtained during K time periods, K being an integer equal to or greater than two, the plurality of pieces of input data including one or more explanatory variables and one or more objective variables, the one or more explanatory variables and the one or more objective variables being variables related to a manufacturing process performed by a manufacturing plant that includes a semiconductor factory and a chemical plant;
• creating K first models, each of the K first models being a model that receives input of the one or more explanatory variables and outputs the one or more objective variables, each of the K first models being created for a corresponding period among the K time periods, by machine learning using a plurality of pieces of input data obtained during the corresponding period;
• creating a second model that receives input of a first time parameter and outputs a parameter of a third model that receives input of the one or more explanatory variables and outputs the one or more objective variables, the second model being created by machine learning using a plurality of parameters of the created K first models, and
• estimating the parameter of the third model by inputting a specified second time parameter to the created second model.