Information processing system, information processing method, and storage medium

Patent metadata

Field	Value
Publication number	US-2022405049-A1
Application number	US-202017774886-A
Country	US
Kind code	A1
Filing date	Nov 10, 2020
Priority date	Nov 11, 2019
Publication date	Dec 22, 2022
Grant date	—

Abstract

An information processing system according to an embodiment is configured to: acquire numerical representations and combination ratios for a plurality of component objects; acquire numerical representations for a plurality of reference objects; calculate a plurality of component feature vectors and a plurality of reference feature vectors by inputting the numerical representations of each of the plurality of component objects and the plurality of reference objects into a first machine learning model; calculate a probability vector for each of the plurality of component objects by inputting those feature vectors into a second machine learning model; and calculate a composite feature vector for a composite object obtained by combining the plurality of component objects, based on a plurality of probability vectors and a plurality of combination ratios.

First claim

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;
acquire a numerical representation for each of a plurality of reference objects;
calculate a component feature vector of each of the plurality of component objects and a reference feature vector of each of the plurality of reference objects by inputting a plurality of the numerical representations corresponding to the plurality of component objects and a plurality of the numerical representations corresponding to the plurality of reference objects into a first machine learning model;
calculate a probability vector indicating a degree of association with each of the plurality of reference objects, for each of the plurality of component objects, by inputting a plurality of the component feature vectors and a plurality of the reference feature vectors into a second machine learning model;
calculate a composite feature vector indicating a degree of association with each of the plurality of reference objects, for a composite object obtained by combining the plurality of component objects, based on a plurality of the probability vectors and a plurality of the combination ratios; and
output the composite feature vector.

2 . The information processing system according to claim 1 ,
wherein when the number of the plurality of component objects is denoted by m, the number of the plurality of reference objects is denoted by n, each of the plurality of probability vectors is n-dimensional, the plurality of probability vectors are denoted by Vp 1 , Vp 2 , . . . , Vp m , the plurality of combination ratios are denoted by r 1 , r 2 , . . . , r m , and the composite feature vector is denoted by Vc, the at least one processor is configured to calculate the composite feature vector by using following Equation (1),
Vc=r 1 ×Vp 1 +r 2 ×Vp 2 + . . . +r m ×Vp m   (1).

3 . The information processing system according to claim 1 ,
wherein the at least one processor is further configured to:
calculate a predicted value of characteristics of the composite object by inputting the composite feature vector into a third machine learning model; and
output the predicted value.

4 . The information processing system according to claim 1 ,
wherein the component object is a material, and the composite object is a multi-component substance.

5 . The information processing system according to claim 4 ,
wherein the material is a polymer, and the multi-component substance is a polymer alloy.

6 . An information processing method executed 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; acquiring a numerical representation for each of a plurality of reference objects; calculating a component feature vector of each of the plurality of component objects and a reference feature vector of each of the plurality of reference objects by inputting a plurality of the numerical representations corresponding to the plurality of component objects and a plurality of the numerical representations corresponding to the plurality of reference objects into a first machine learning model; calculating a probability vector indicating a degree of association with each of the plurality of reference objects, for each of the plurality of component objects, by inputting a plurality of the component feature vectors and a plurality of the reference feature vectors into a second machine learning model; calculating a composite feature vector indicating a degree of association with each of the plurality of reference objects, for a composite object obtained by combining the plurality of component objects, based on a plurality of the probability vectors and a plurality of the combination ratios; and outputting the composite feature vector.

7 . 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; acquiring a numerical representation for each of a plurality of reference objects; calculating a component feature vector of each of the plurality of component objects and a reference feature vector of each of the plurality of reference objects by inputting a plurality of the numerical representations corresponding to the plurality of component objects and a plurality of the numerical representations corresponding to the plurality of reference objects into a first machine learning model; calculating a probability vector indicating a degree of association with each of the plurality of reference objects, for each of the plurality of component objects, by inputting a plurality of the component feature vectors and a plurality of the reference feature vectors into a second machine learning model; calculating a composite feature vector indicating a degree of association with each of the plurality of reference objects, for a composite object obtained by combining the plurality of component objects, based on a plurality of the probability vectors and a plurality of the combination ratios; and outputting the composite feature vector.

Assignees

• Showa Denko Materials Co Ltd

Inventors

• Hanaoka Kyohei

Classifications

• G06F30/27 Primary

  using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model · CPC title

• G16C20/70

  Machine learning, data mining or chemometrics · CPC title

• G16C20/30

  Prediction of properties of chemical compounds, compositions or mixtures · CPC title

• G06F30/10

  Geometric CAD · CPC title

• G16C60/00 Primary

  Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation · CPC title