Abstract: Aspects of the present disclosure relate to automated document processing systems. In particular, embodiments disclosed herein provide an architecture for seamlessly integrating code representations of electronic documents generated by various different extraction systems into a single document processing system. Certain embodiments provide for generating a code representation of an electronic document based on an entity extracted from the electronic document. The code representation includes an entity representation that comprises an index indicator appended to an identifier for the entity, which may comprise a column of items. Some embodiments provide for applying a transformation to the entity representation to produce an intermediate code representation of the electronic document, wherein the transformation comprises a format for entities or a format for entity representations.

Abstract: Aspects of the present disclosure relate to automated data extraction and prediction using machine learning models. Embodiments include extracting, by a text extraction engine, a set of data from each document of one or more documents provided to the text extraction engine. Embodiments include instructing a machine learning model, via a prompt, to identify and classify one or more protected entities contained in the set of data from each document by parsing the set of data from each document. Embodiments include instructing the machine learning model, via the prompt, to generate, for each protected entity of the one or more protected entities identified in the set of data from each document, a corresponding unprotected entity and to replace each protected entity with the corresponding unprotected entity. Embodiments include receiving an output from the machine learning model in response to the prompt and performing an action based on the output.