公开(公告)号：US20240047018A1

公开(公告)日：2024-02-08

申请号：US18254384

申请日：2021-11-22

Applicant: Resonac Corporation

Inventor： Kyohei HANAOKA

IPC: G16C20/30 ,  G16C20/70

CPC classification number: G16C20/30 ,  G16C20/70

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.

公开(公告)号：US20240028795A1

公开(公告)日：2024-01-25

申请号：US18256450

申请日：2021-11-24

Applicant: RESONAC CORPORATION

Inventor： Kyohei HANAOKA

IPC: G06F30/27

CPC classification number: G06F30/27

Abstract: A design assistance device, includes: a data acquisition unit acquiring performance data including a design parameter group and an observation value of a characteristic item; a model construction unit constructing a prediction model for predicting the observation value as a probability distribution, on the basis of the design parameter group; an acquisition function construction unit constructing an acquisition function for each of the characteristic items; a design parameter group candidate generation unit generating a plurality of design parameter group candidates by multi-objective optimization of a plurality of acquisition functions; and a selection unit calculating a total achievement probability with respect to target values of all of the characteristic items, on the basis of the probability distribution of the observation value obtained by inputting the design parameter group candidate to the prediction model, to select at least one design parameter group candidate with the highest total achievement probability.

公开(公告)号：US20240387004A1

公开(公告)日：2024-11-21

申请号：US18556089

申请日：2022-04-21

Applicant: Resonac Corporation

Inventor： Kyohei HANAOKA

IPC: G16C20/30 ,  G16C20/50 ,  G16C20/70

Abstract: An input data generation system is a system generating input data for machine learning for predicting the properties of a material based on a plurality of raw materials having a known partial structure, and includes a processor. The processor receives the input of partial structure data for specifying the known partial structure of each of the plurality of raw materials and blending ratio data indicating a ratio of the blending of each of the plurality of raw materials, generates partial structure input data indicating the known partial structure, on the basis of the partial structure data for each of the plurality of raw materials, generates synthetic input data by reflecting the blending ratio data relevant to the plurality of raw materials on the partial structure input data and compiling the partial structure input data, and inputs the synthetic input data to a machine learning model.

公开(公告)号：US20240387005A1

公开(公告)日：2024-11-21

申请号：US18556092

申请日：2022-04-21

Applicant: Resonac Corporation

Inventor： Kyohei HANAOKA

IPC: G16C20/30 ,  G16C20/50 ,  G16C20/70

Abstract: An input data generation system is an input data generation system generating input data for machine learning for predicting the properties of a material based on a raw material having a known structure, and includes at least one processor, in which at least one processor acquires partial structure data indicating a partial structure from a database, receives at least the input of raw material structure data for specifying the structure of the raw material and blending ratio data indicating a ratio of the blending of the raw material, generates partial structure input data indicating the partial structure existing in the structure of the raw material, on the basis of the partial structure data and the raw material structure data, generates input data by reflecting the blending ratio data on the partial structure input data of the raw material, and inputs the input data to a machine learning model.

公开(公告)号：US20240211663A1

公开(公告)日：2024-06-27

申请号：US18556900

申请日：2022-04-20

Applicant: Resonac Corporation

Inventor： Kyohei HANAOKA

IPC: G06F30/27 ,  G06F111/06

CPC classification number: G06F30/27 ,  G06F2111/06

Abstract: A design aid device according to an embodiment includes a data acquisition unit configured to acquire performance data including a design parameter group and an observation value of a feature item, a model construction unit configured to construct a predictive model predicting an observation value as a probability distribution, etc., a sampling unit configured to sample a predetermined number of points of objective variable groups using each predictive model, an evaluation value calculation unit configured to convert a vector whose elements are values of respective objective variables into a scalar, thereby calculating an evaluation value of at each sampling point, an acquisition function evaluation unit configured to output an acquisition function evaluation value based on a distribution of the evaluation value at each sampling point, and a design parameter group acquisition unit configured to acquire a design parameter group by optimization of the acquisition function evaluation value.

公开(公告)号：US20240028796A1

公开(公告)日：2024-01-25

申请号：US18256452

申请日：2021-11-24

Applicant: Resonac Corporation

Inventor： Kyohei HANAOKA

IPC: G06F30/27

CPC classification number: G06F30/27

Abstract: A design assistance device, includes: a data acquisition unit acquiring performance data including a design parameter group and an observation value of a characteristic item; a model construction unit constructing a prediction model for predicting the observation value as a probability distribution, on the basis of the design parameter group; an acquisition function construction unit constructing a single target-oriented acquisition function having the design parameter group as input and an index value relevant to improvement of characteristics of all of the characteristic items as output, the single target-oriented acquisition function including a target achievement probability term including a total achievement probability calculated on the basis of each of the prediction models, in which target values of all of the characteristic items are achieved; and a design parameter group acquisition unit acquiring at least one design parameter group by optimization of the target-oriented acquisition function.