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US 2025/0356448 A1Published application
Information processing device
AssigneeMitsubishi Electric Corporation
InventorYoshiyuki NORIMATSU
Life of this patent
Estimating the term…
Abstract
An estimation device includes an input unit that generates supervised data including causal variables, process types, and outcome variable for each of multiple processes, and a training unit that uses the supervised data to generate a learning model by learning the outcome variables from the causal variables and the process types for each of the processes.
Claims10
An information processing device comprising:
- a processor to execute a program;
- and
- a memory to store the program which, when executed by the processor, performs processes of,
- generating supervised data including causal variables, processing types, and outcome variables for each of a plurality of processes;
- and
- generating a learning model by using the supervised data to learn the outcome variables from the causal variables and the process types for each of the processes;
- wherein
- the causal variables are at least one of attributes and purchase history of railway users, a factor affecting a sales value of railway services, action history of railway users, fare price fare increases, price reductions, and coupon amounts to promote railway use,
- the process types are fare price increases, fare price reductions, campaigns to promote railway use, and distribution of coupons to promote railway use, and
- the outcome variables are sales values of high-priced railway services.
7 more elements
An information processing device comprising:
- a processor to execute a program;
- and
- a memory to store the program which, when executed by the processor, performs processes of,
- generating, for each of the processes, supervised data including first causal variables that change with time, second causal variables that do not change with time, process types, and history information indicating a history of changes with time of the first causal variables;
- and
- generating a learning model by using the supervised data to learn, for each of the processes, a change with time of the first causal variables at a second time from the first causal variables, the second causal variables, and the process types at a first time in accordance with the history information.
3 more elements
An information processing device comprising:
- a processor to execute a program;
- and
- a memory to store the program which, when executed by the processor, performs processes of,
- estimating outcome variables by using supervised data including causal variables, process types, and the outcome variables for each of a plurality of processes and inputting the causal variables and the process types into a learning model generated by learning the outcome variables from the causal variables and the process types for each of the processes;
- and
- outputting a result of the estimation;
- wherein,
- the causal variables are at least one of attributes and purchase history of railway users, a factor affecting a sales value of railway services, action history of railway users, fare price increases, fare price reductions, and coupon amounts to promote railway use,
- the process types are fare price increases, fare price reductions, campaigns to promote railway use, and distribution of coupons to promote railway use, and
- the outcome variables are sales values of high-priced railway services.
7 more elements
An information processing device comprising:
- a processor to execute a program;
- and
- a memory to store the program which, when executed by the processor, performs processes of,
- estimating a change with time of first causal variables obtained from a combination of two or more processes selected from a plurality of processes during a certain time period, by using a learning model generated by using supervised data including history information indicating a history of change with time of the first causal variables that change with time, second causal variables that do not change with time, process types, and the first causal variables, for each of the processes, and by learning, for each of the processes, the change with time of the first causal variables at a second time from the first causal variables, the second causal variables, and the process types at a first time in accordance with the history information;
- and
- outputting a result of the estimation.
3 more elements
Dependencies are inferred from the claim text.