Model generation system and model generation method

Patent metadata

Field	Value
Publication number	US-2025363410-A1
Application number	US-202318690540-A
Country	US
Kind code	A1
Filing date	Feb 20, 2023
Priority date	Feb 20, 2023
Publication date	Nov 27, 2025
Grant date	—

Abstract

A model generation system includes: a source model database 12 that stores a source model; and a model generation unit 11 configured to generate the target model using the source model searched from the source model database. The model generation unit includes a database search unit configured to search for a first source model 31 including an output of the target model as an output thereof and a second source model 32 including an input of the target model as an input thereof, and a combination determination unit configured to combine, when association between an input of the first source model and an output of the second source model is available, the input of the first source model and the output of the second source model.

First claim

1 . A model generation system that generates a target model, the model generation system comprising:
a source model database that stores a source model; and a model generation unit configured to generate the target model using the source model searched from the source model database, wherein the model generation unit includes
a database search unit configured to search for a first source model including an output of the target model as an output thereof and a second source model including an input of the target model as an input thereof, and
a combination determination unit configured to combine, when association between an input of the first source model and an output of the second source model is available, the input of the first source model and the output of the second source model, and
the source model stored in the source model database includes a trained machine learning model.

2 . The model generation system according to claim 1 , wherein
the model generation unit includes a model determination unit, the model determination unit displays, on a GUI screen, a model combination diagram indicating an input node indicating the input of the target model, and the first source model and the second source model, the model combination diagram connects the input node of the target model to an input node indicating the input of the corresponding first source model or the second source model by an edge, and connects an output node indicating the output of the second source model to the input node indicating the input of the corresponding first source model by an edge, and the model determination unit corrects, on the GUI screen, a combination of the input of the target model and the input of the first source model or the second source model and a combination of the output of the second source model and the input of the first source model according to corrected connection when the connection by the edges in the model combination diagram is corrected.

3 . The model generation system according to claim 2 , wherein
the combination determination unit determines that the association is available when an item name of the input of the first source model and an item name of the output of the second source model match, when the item name of the input of the first source model and the item name of the output of the second source model are synonyms, when there is a combination history between the item name of the input of the first source model and the item name of the output of the second source model, or when it is determined that unit dimensional information of the input of the first source model and unit dimensional information of the output of the second source model are similar.

4 . The model generation system according to claim 1 , wherein
the source model stored in the source model database includes an equation or an inequality having an equal sign establishment condition, and the source model representing the equation or the inequality is defined by using one parameter of the equation or the inequality as an output and another parameter as an input.

5 . The model generation system according to claim 1 , wherein
the source model stored in the source model database includes an inequality, and the source model representing the inequality is defined by using a Boolean value indicating whether the inequality is satisfied as an output and using all parameters as an input.

6 . A model generation system that generates a target model, the model generation system comprising:
a source model database that stores a source model; a model generation unit configured to generate the target model from the source model searched from the source model database; and a machine learning unit configured to perform machine learning, wherein the model generation unit includes a model determination unit, and a database search unit configured to search for a first source model including an output of the target model as an output thereof and a second source model including an input of the target model as an input thereof, the model determination unit displays, on a GUI screen, a model combination diagram that includes an input node indicating the input of the target model and one or more search source models searched by the database search unit, and that connects corresponding input nodes or an output node of a certain search source model and an input node of another corresponding search source model by an edge, when connection of the model combination diagram is corrected to add an untrained model to the GUI screen, the model determination unit determines a combination model in which the input of the target model and a combination of the search source model and the untrained model are corrected according to the corrected connection, and the machine learning unit performs training of the combination model using learning data corresponding to the input and the output of the target model.

7 . The model generation system according to claim 6 , wherein
the machine learning unit sets a learning rate of the search source model included in the combination model to 0 in the training of the combination model.

8 . The model generation system according to claim 6 , wherein
the machine learning unit sets a standard model that is a machine learning model using the input of the target model as an input and the output of the target model as an output, and performs training of the standard model using the learning data, and the model determination unit selects one of the combination model and the standard model as the target model.

9 . The model generation system according to claim 6 , wherein
the model generation unit includes a combination determination unit, the search source model includes the first source model including the output of the target model as the output thereof, and the second source model including the input of the target model as the input thereof, the combination determination unit combines, when association between an input of the first source model and an output of the second source model is available, the input of the first source model and the output of the second source model, the model combination diagram connects the input node of the target model to an input node indicating the input of the corresponding first source model or the second source model by an edge, and connects an output node indicating the output of the second source model to the input node indicating the input of the corresponding first source model by an edge, and the model determination unit corrects, on the GUI screen, a combination of the input of the target model and the input of the first source model or the second source model and a combination of the output of the second source model and the input of the first source model according to corrected connection when the connection by the edges in the model combination diagram is corrected.

10 . The model generation system according to claim 9 , wherein
the combination determination unit determines that the association is available when an item name of an input of the certain search source model and an item name of an output of the another search source model match, when the item name of the input of the certain search source model and the item name of the output of the another search source model are synonyms, when there is a combination history between the item name of the input of the certain search source model and the item name of the output of the another search source model, or when it is determined that unit dimensional information of the input of the certain search source model and unit dimensional information of the output of the another search source model are similar.

Assignees

• Hitachi High Tech Corp

Inventors

• Nakayama Takeshi
• Ohmori Takeshi

Classifications

• G06N20/00 Primary

  Machine learning · CPC title

• G06F16/9038

  Presentation of query results · CPC title

• G06F16/9032

  Query formulation · CPC title

• G06F8/38

  for implementing user interfaces · CPC title

• G06F8/36

  Software reuse · CPC title