Computer-implemented method for unsupervised task segmentation

A computer-implemented method for unsupervised task segmentation comprising:

• receiving a stream of data of desktop-actions,
• wherein each desktop-action relates to User Interface (UI) data-handling operations of applications, and
• wherein each desktop-action is labeled with an action related integer identification (id);
• operating an unsupervised task segmentation module on the stream of data of desktop-actions to identify one or more sequences of desktop-actions,
• wherein each sequence of desktop-actions is identified as a sequence to achieve a task, and
• wherein each task is a business process that is operated by a user and the business process is appropriate for automation,
• said unsupervised task segmentation module comprising:
• (i) creating an integer sequence from the action related integer id, such that desktop-actions in the stream of data are consecutively concatenated;
• (ii) creating word embeddings of the UI data-handling operations of applications for each desktop-action based on the integer id thereof to yield a vector of embeddings;
• and
• (iii) implementing unsupervised topic-segmentation Natural Language Processing (NLP) module on the created vector of embeddings to determine one or more cutting-points in the integer sequence to yield one or more segments such that semantic similarity level of embeddings in each yielded segment is maximized, and a number of non-complete business processes is reduced,
• wherein each cutting-point of the one or more cutting-points in the integer sequence indicates an end of a first segment and a beginning of a second consecutive segment, and
• wherein the one or more segments which represent complete tasks are provided to a routine mining module to identify repetitive segments for automation thereof.