Desktop recording. For the recording of digital process data, an existing work of the August-Wilhelm Scheer Institute is used and further developed by us. Here, AI-based image recognition methods are used to recognize the respective process activities anonymously with the help of screenshots. Moreover, additional information about the process step must be captured from the screenshots via OCR and anonymized if necessary.

Process Discovery. The acquired process data from digital and analog sources must be combined and semantically enriched to enable a summary of a holistic process. In addition to taking temporal relationships into account, unsupervised learning methods from the field of clustering are to be developed and used for this purpose. Based on the available additional information of the activities (e.g. screenshots of the application, OCR from the video), these methods learn which activities have similarities and thus belong to the same business transaction. For example, different activities in an invoicing process may be linked because the same invoice number was identified in screenshots and in the video. In practice, there may also be different ways in which a process is executed by employees. Process mining methods are used to automatically analyze the different variants and create a model that describes and documents the process holistically.