Abstract: The present disclosure according to at least one embodiment provides a method performed by a computing device for generating a task model based on meta-learning, including calculating a task-adaptation loss of the task model, the calculating the task-adaptation loss being based on a result of training the task model by using a training data set, updating the task model based on the task-adaptation loss, calculating a meta-optimization loss of the updated task model by using a validation data set, and further updating the updated task model based on the meta-optimization loss.

Abstract: According to aspects of the present disclosure, a change detection method for ROBOT process automation (RPA) performed by a computer device includes obtaining object information of a target object from first working environment information at a first time, and determining a comparison object detected in second working environment information at a second time as an object corresponding to the target object based on the obtained object information of the target information. Thus, even if an error occurs due to a change in a working environment, an RPA apparatus can operate normally by correcting the error by itself without the need for a user to recreate a new bot or modify the existing bot. Also, even if the position or shape of an object referred to by RPA in a screen is changed, the RPA apparatus can accurately find an object to manipulate based on surrounding information and perform RPA normally.

Abstract: According to aspects of the present disclosure, a change detection method for ROBOT process automation (RPA) performed by a computer device includes obtaining object information of a target object from first working environment information at a first time, and determining a comparison object detected in second working environment information at a second time as an object corresponding to the target object based on the obtained object information of the target information. Thus, even if an error occurs due to a change in a working environment, an RPA apparatus can operate normally by correcting the error by itself without the need for a user to recreate a new bot or modify the existing bot. Also, even if the position or shape of an object referred to by RPA in a screen is changed, the RPA apparatus can accurately find an object to manipulate based on surrounding information and perform RPA normally.