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US 12,700,479 B2Granted
Information processing system, information processing method, and storage medium
OwnerKyohei Hanaokainventor
Life of this patent
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Abstract
An information processing system according to an embodiment includes at least one processor. 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 machine learning based on a plurality of the numerical representations to calculate a plurality of regression parameters corresponding to the plurality of component objects, and apply a plurality of the combination ratios to a regression model defined by the plurality of regression parameters to calculate a predicted value indicating characteristics of a composite object obtained by combining plurality of component objects.
Claims7
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 multiplicity of mutually different polymers or monomers, wherein the numerical representation represents attributes of the multiplicity of polymers or monomers, and wherein the combination ratio corresponds to a ratio among the multiplicity of polymers or monomers;
- generate a first trained machine learning model by executing and repeating a first machine learning process comprising:
- inputting a first input vector into a first untrained machine learning model to calculate a first output value;
- calculating a first difference between the first output value and a first ground truth;
- and
- updating a first parameter of the first untrained machine learning model based on the first difference;
- input a plurality of numerical representations to the first trained machine learning model to calculate a plurality of feature vectors corresponding to the multiplicity of polymers or monomers;
- generate a second trained machine learning model by executing and repeating a second machine learning process comprising:
- inputting a second input vector into a second untrained machine learning model to calculate a second output value;
- calculating a second difference between the second output value and a second ground truth;
- and
- updating a second parameter of the second untrained machine learning model based on the second difference;
- input the plurality of feature vectors to the second trained machine learning model to calculate a plurality of regression parameters corresponding to the multiplicity of polymers or monomers;
- and
- apply a plurality of the combination ratios to a regression model defined by the plurality of regression parameters to calculate a predicted value indicating characteristics of a polymer alloy obtained by combining the multiplicity of polymers or monomers;
- change values of the plurality of combination ratios;
- recalculate the predicted value indicating the characteristics of the polymer alloy, by using the changed values of the plurality of combination ratios and the calculated plurality of regression parameters;
- output the calculated predicted value and the recalculated predicted value;
- execute processing for searching for the characteristics of the polymer alloy using the calculated predicted value and the recalculated predicted value;
- and
- cause a display device to display a result of the processing for searching.
19 more elements
An information processing method executed by an information processing system comprising at least one processor, the method comprising:
- acquiring a numerical representation and a combination ratio for each of a multiplicity of mutually different polymers or monomers, wherein the numerical representation represents attributes of the multiplicity of polymers or monomers, and wherein the combination ratio corresponds to a ratio among the multiplicity of polymers or monomers;
- generating a first trained machine learning model by executing and repeating a first machine learning process comprising:
- inputting a first input vector into a first untrained machine learning model to calculate a first output value;
- calculating a first difference between the first output value and a first ground truth;
- and
- updating a first parameter of the first untrained machine learning model based on the first difference;
- inputting a plurality of numerical representations to the first trained machine learning model to calculate a plurality of feature vectors corresponding to the multiplicity of polymers or monomers;
- generating a second trained machine learning model by executing and repeating a second machine learning process comprising:
- inputting a second input vector into a second untrained machine learning model to calculate a second output value;
- calculating a second difference between the second output value and a second ground truth;
- and
- updating a second parameter of the second untrained machine learning model based on the second difference;
- inputting the plurality of feature vectors to the second trained machine learning model to calculate a plurality of regression parameters corresponding to the multiplicity of polymers or monomers;
- and
- applying a plurality of the combination ratios to a regression model defined by the plurality of regression parameters to calculate a predicted value indicating characteristics of a polymer alloy obtained by combining the multiplicity of polymers or monomers;
- changing values of the plurality of combination ratios;
- recalculating the predicted value indicating the characteristics of the polymer alloy, by using the changed values of the plurality of combination ratios and the calculated plurality of regression parameters;
- outputting the calculated predicted value and the recalculated predicted value;
- executing processing for searching for the characteristics of the polymer alloy using the calculated predicted value and the recalculated predicted value;
- and
- cause a display device to display a result of the processing for searching.
18 more elements
A non-transitory computer-readable storage medium storing an information processing program for causing a computer to execute:
- acquiring a numerical representation and a combination ratio for each of a multiplicity of mutually different polymers or monomers, wherein the numerical representation represents attributes of the multiplicity of polymers or monomers, and wherein the combination ratio corresponds to a ratio among the multiplicity of polymers or monomers;
- generating a first trained machine learning model by executing and repeating a first machine learning process comprising:
- inputting a first input vector into a first untrained machine learning model to calculate a first output value;
- calculating a first difference between the first output value and a first ground truth;
- and
- updating a first parameter of the first untrained machine learning model based on the first difference;
- inputting a plurality of numerical representations to the first trained machine learning model to calculate a plurality of feature vectors corresponding to the multiplicity of polymers or monomers;
- generating a second trained machine learning model by executing and repeating a second machine learning process comprising:
- inputting a second input vector into a second untrained machine learning model to calculate a second output value;
- calculating a second difference between the second output value and a second ground truth;
- and
- updating a second parameter of the second untrained machine learning model based on the second difference;
- inputting the plurality of feature vectors to the second trained machine learning model to calculate a plurality of regression parameters corresponding to the multiplicity of polymers or monomers;
- and
- applying a plurality of the combination ratios to a regression model defined by the plurality of regression parameters to calculate a predicted value indicating characteristics of a polymer alloy obtained by combining the multiplicity of polymers or monomers;
- changing values of the plurality of combination ratios;
- recalculating the predicted value indicating the characteristics of the polymer alloy, by using the changed values of the plurality of combination ratios and the calculated plurality of regression parameters;
- outputting the calculated predicted value and the recalculated predicted value;
- executing processing for searching for the characteristics of the polymer alloy using the calculated predicted value and the recalculated predicted value;
- and
- cause a display device to display a result of the processing for searching.
18 more elements
Dependencies are inferred from the claim text.