Systems and methods for automation discovery and analysis using action sequence segmentation

A computerized system for automation discovery using action sequence segmentation, the system comprising:

• one or more processors,
• and
• a memory including a data store of a plurality of data items describing one or more actions input to a computer;
• wherein one or more of the processors are to:
• receive an input query describing a process, the process comprising one or more actions input to a computer;
• segment a plurality of action sequences from one or more of the data items based on the query;
• and
• produce one or more automation candidates based on the sequences.

A method for automation discovery using a vector embedding model, the method comprising:

• in a computerized system comprising one or more processors, and a data store of a plurality of data items describing one or more actions input to a computer:
• receiving an input query describing one or more actions input to a computer;
• indexing, by one or more of the processors, a plurality of action sequences from one or more of the data items based on the query;
• tagging, by one or more of the processors, one or more of the sequences matching the query;
• translating, by one or more of the processors, the sequences into one or more sentences;
• training at least one neural network (NN) based on the sentences;
• calculating, by the NN, one or more embeddings for the sentences;
• for one or more pairs of sequences, each pair including one of the tagged sequences and one untagged sequence, calculating, by one or more of the processors, a similarity score based on the embeddings;
• for each pair, if the similarity score exceeds a predetermined threshold, adding, by one or more of the processors, the unlabeled sequence to a set of similar sequences;
• and
• producing, by one or more of the processors, one or more automation candidates based on one or more of the similar sequences.