Abstract: There is disclosed a method of training an RPA robot to use a GUI. The method comprises capturing video of the GUI as an operator uses the GUI to carry out a process; capturing a sequence of events triggered as the operator uses the GUI to carry out said process; and analyzing said video and said sequence of events to thereby generate a workflow. The workflow, when executed by an RPA robot, causes the RPA robot to carry out said process using the GUI.

Abstract: A compatibility score generator implementing a neural network is trained for assessing compatibility of items. Elements of a feature vector representing each item and of a compatibility data structure indicating items considered compatible are retrieved. The neural network is trained using training data corresponding to the items and indicating compatibility between pairs of items. The compatibility data structure is modified by removing indications that items of a pair of items are compatible. An encoding function generating encoded representations for the items based on the compatibility data structure is evaluated. Encoded representations are provided to a decoder that learns a likelihood that the indication had been removed when modified. The neural network and the decoder are optimized based on a loss function that reflects the decoder's ability to correctly determine whether the indication had been removed. The encoded representations generate a compatibility score for at least two items of interest.

Abstract: A compatibility score generator implementing a neural network is trained for assessing compatibility of items. Elements of a feature vector representing each item and of a compatibility data structure indicating items considered compatible are retrieved. The neural network is trained using training data corresponding to the items and indicating compatibility between pairs of items. The compatibility data structure is modified by removing indications that items of a pair of items are compatible. An encoding function generating encoded representations for the items based on the compatibility data structure is evaluated. Encoded representations are provided to a decoder that learns a likelihood that the indication had been removed when modified. The neural network and the decoder are optimized based on a loss function that reflects the decoder's ability to correctly determine whether the indication had been removed. The encoded representations generate a compatibility score for at least two items of interest.