Despite the recognized importance of business process models, many processes remain unmodeled or inadequately modeled due to the time-consuming nature of manual modeling and the lack of expertise in this field. This paper proposes a novel approach for the automated process model generation, based on object analysis. It utilizes object instances, which are represented in documents and data entities, and which activities create, read, modify and consume during process execution. In contradistinction to conventional approaches that rely on event logs, our object-based approach encompasses both automated and manual activities by analyzing these tangible artifacts such as orders, invoices, and delivery notes that are already documented. In contrast, activities frequently require supplementary tracking mechanisms. The approach extracts process knowledge by identifying object types, assigning object instances to process instances, and deriving activity types from object relationships. The resulting process models are represented as high-level Petri nets, where places represent object types, transitions represent activity types, and arcs capture control and object flow while preserving object structures. Evaluation results show that this method produces more compact models than conventional discovery algorithms and identifies activities and business rules often missed by purely temporal analyses. It enables process discovery even in environments with limited system visibility or significant manual work.