Sap Se

Adithya Kumar

Suchitra Sundararaman

Michael Stark

Evan Adkins

Jesper B Lind

Jesper Lind

Some embodiments provide a non-transitory machine-readable medium that stores a program executable by a device. The program receives a request to process an image for multiple objects. The program further uses a machine learning model to detect a plurality of objects in the image. The program also generates a plurality of images based on the plurality of objects in the image. For each image in the plurality of images, the program further converts text in the image to machine-readable text. For each image in the plurality of images, the program also uses a set of machine learning models to determine a set of values for a set of attributes. For each set of values determined for the set of attributes, the program further generates a record comprising the set of attributes and storing the set of values for the set of attributes in the record.

Detecting the same type of objects in images using machine learning models

The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes receiving receipt data associated with an entity. Policy questions associated with the entity are associated with at least one policy question answer that corresponds to a conformance or a violation of a policy selected by the entity. For each policy question, a machine learning policy model is identified for the policy question that includes, for each policy question answer, receipt data features that correspond to the policy question answer. The machine learning policy model is used to automatically determine a selected policy question answer to the policy question by comparing features of extracted tokens to respective receipt data features of the policy question answers that are included in the machine learning policy model. In response to determining that the selected policy question answer corresponds to a policy violation, an audit alert is generated.

Transaction policy audit

Some embodiments provide a non-transitory machine-readable medium that stores a program executable by a device. The program receives a request to process a file. The file includes a set of images of text. The program further converts the text in each image in the set of images into a set of machine-readable text. The program also uses a machine learning model to predict, based on the set of machine-readable text, whether the set of images of the file are images of pages that belong to a single document or images of pages that belong to different documents.

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Bellevue, WA, United States of America

Adithya Kumar