Method and method for process optimization using generative ai
AssigneeCJ OliveNetworks Co., Ltd.
InventorsTae Hee LEE, Do Yeon KWAK, Yun Jong CHOI, Si Wook YONG, Laura Suh Young STRITAR, Hyun Woo JIN, Sun Bum YOUN
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Abstract
The present disclosure relates to a method and system for process optimization using generative AI, and more particularly, to a method and system for process optimization that allows the exploration or inference of causalities between multiple steps and variables involved in an arbitrary process through an artificial intelligence algorithm, and the derivation of hypotheses based thereon, and the simulation and verification thereof.
Claims16
A method of performing process optimization by a process optimization system including a central processing unit and a memory, the method comprising:
- (a) a process data receiving step of receiving a plurality of process data;
- (b) a causality derivation step of to deriving a causality between variables by analyzing the process data;
- (c) a causality inference step of calculating a strength of a causality between the variables by referring to the derived causality;
- (d) a simulation step of performing a simulation that changes a condition of a specific variable by referring to a causality inference result calculated in the causality inference step;
- and
- (e) a hypothesis generation step of generating at least one hypothesis for process optimization by referring to a simulation result calculated in the simulation step.
A method of performing process optimization by a process optimization system including a central processing unit and a memory, the method comprising:
- (a) a process data receiving step of receiving a plurality of process data;
- (b) a causality derivation step of to deriving a causality between variables by analyzing the process data;
- (c) a causality inference step of calculating a strength of a causality between the variables by referring to the derived causality;
- (d) a hypothesis generation step of generating at least one hypothesis for process optimization by referring to a causality inference result produced in the causality inference step;
- and
- (e) a simulation step of performing a simulation that changes a condition of a specific variable by referring to information on the hypothesis.
A process optimization system comprising a central processing unit and a memory, wherein the central processing unit executes instructions for executing a process optimization method stored in the memory, and
- wherein a method of executing the process optimization method comprises:
- (a) a process data receiving step of receiving a plurality of process data;
- (b) a causality derivation step of to deriving a causality between variables by analyzing the process data;
- (c) a causality inference step of calculating a strength of a causality between the variables by referring to the derived causality;
- (d) a simulation step of performing a simulation that changes a condition of a specific variable by referring to a causality inference result calculated in the causality inference step;
- and
- (e) a hypothesis generation step of generating at least one hypothesis for process optimization by referring to a simulation result calculated in the simulation step.
A process optimization system comprising a central processing unit and a memory, wherein the central processing unit executes instructions for executing a process optimization method stored in the memory, and
- wherein a method of executing the process optimization method comprises:
- (a) a process data receiving step of receiving a plurality of process data;
- (b) a causality derivation step of to deriving a causality between variables by analyzing the process data;
- (c) a causality inference step of calculating a strength of a causality between the variables by referring to the derived causality;
- (d) a hypothesis generation step of generating at least one hypothesis for process optimization by referring to a causality inference result produced in the causality inference step;
- and
- (e) a simulation step of performing a simulation to change a condition of a specific variable by referring to information on the hypothesis.
Dependencies are inferred from the claim text.