INFORMATION PROCESSING SYSTEM, INFORMATION PROCESSING METHOD, AND STORAGE MEDIUM
An information processing system according to an embodiment is configured to: acquire a numerical representation and a combination ratio for each of a plurality of component objects; execute, based on a plurality of the numerical representations and a plurality of the combination ratios corresponding to the plurality of component objects, machine learning and application of the plurality of combination ratios to calculate a composite feature vector indicating features of a composite object obtained by combining the plurality of component objects; and output the composite feature vector.
1 . An information processing system comprising:
at least one processor,
wherein the at least one processor is configured to:
acquire a numerical representation and a combination ratio for each of a plurality of component objects;
execute, based on a plurality of the numerical representations and a plurality of the combination ratios corresponding to the plurality of component objects, machine learning and application of the plurality of combination ratios to calculate a composite feature vector indicating features of a composite object obtained by combining the plurality of component objects; and
output the composite feature vector.
2 . The information processing system according to claim 1 , wherein the at least one processor is configured to:
input the plurality of numerical representations into a machine learning model to calculate a feature vector of each of the plurality of component objects;
execute the application of the plurality of combination ratios in association with the machine learning model; and
input a plurality of the feature vectors reflecting the plurality of combination ratios into an aggregation function to calculate the composite feature vector.
3 . The information processing system according to claim 1 , wherein the at least one processor is configured to:
input the plurality of numerical representations into a first machine learning model to calculate a first feature vector of each of the plurality of component objects;
input a plurality of the first feature vectors into a second machine learning model to calculate a second feature vector of each of the plurality of component objects;
execute the application of the plurality of combination ratios in association with at least one machine learning model selected from the first machine learning model and the second machine learning model; and
input a plurality of the second feature vectors reflecting the plurality of combination ratios into an aggregation function to calculate the composite feature vector.
4 . The information processing system according to claim 3 ,
wherein the first machine learning model is a machine learning model which generates the first feature vector that is a fixed-length vector from the numerical representation that is unstructured data.
5 . The information processing system according to claim 2 ,
wherein the application of the plurality of combination ratios in association with the machine learning model comprises applying the plurality of combination ratios to output data of an intermediate layer of the machine learning model.
6 . The information processing system according to claim 1 , wherein the at least one processor is further configured to:
input the composite feature vector into another machine learning model to calculate a predicted value of characteristics of the composite object; and
output the predicted value.
7 . The information processing system according to claim 1 ,
wherein the component object is a material, and the composite object is a multi-component substance.
8 . The information processing system according to claim 7 ,
wherein the material is a polymer, and the multi-component substance is a polymer alloy.
9 . An information processing method executable by an information processing system including at least one processor, the method comprising:
acquiring a numerical representation and a combination ratio for each of a plurality of component objects;
executing, based on a plurality of the numerical representations and a plurality of the combination ratios corresponding to the plurality of component objects, machine learning and application of the plurality of combination ratios to calculate a composite feature vector indicating features of a composite object obtained by combining the plurality of component objects; and
outputting the composite feature vector.
10 . A non-transitory computer-readable storage medium storing an information processing program causing a computer to execute:
acquiring a numerical representation and a combination ratio for each of a plurality of component objects;
executing, based on a plurality of the numerical representations and a plurality of the combination ratios corresponding to the plurality of component objects, machine learning and application of the plurality of combination ratios to calculate a composite feature vector indicating features of a composite object obtained by combining the plurality of component objects; and
outputting the composite feature vector.