Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments herein relate to a transmitter which can operate in a non-return-to-zero (NRZ) mode or a pulse amplitude modulation (PAM) mode with three or more levels. The transmitter includes a first driver which processes most significant bits and a second driver which processes least significant bits, in the PAM3 mode. In the NRZ mode, the second driver is turned off but resistances in the second driver are used to optimize impedance in the first driver. Switches can be turned on to couple in resistors in the first driver with resistors in the second driver, for pairs of driver slices. The switches are turned off in the PAM3 mode.

Abstract: 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.

Abstract: 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.

Abstract: 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 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.