Bill.com, Inc.

Bill Operations, LLC

Flowcast, Inc.

Eitan Anzenberg

Bei Zhang

Sangammohan Harimohan Singh

James Patrick McGuire

Kenneth So

Corby Campbell

Darin Dooley

Amol Jayant Thatte

Xin Geng Kelly

Jay Daniel Dunbar

Craig Jon Pickett

Derek Chan

Derrick Jacob Hathaway

Dalton Purnell

Particular embodiments receive a plurality of values associated with a document. One or more keywords associated with the document are also received. A first score is generated for each value, of the plurality of values. The generation of the first score excluding sending, over a computer network, a first request to one or more remote computing devices to generate the first score. Based at least in part on the generating of the first score, each value, of the plurality of values is ranked. The ranking of each value excluding sending, over the computer network, a second request to the one or more remote computing devices to rank each value. Based on the ranking, at least one value, of the plurality of values, is selected. The selecting being indicative that the at least one value is a candidate to be a constituent of the one or more keywords. Based on the ranking, an indicator indicating that the at least one value is the candidate to be the constituent of the one or more keywords is presented.

Native automatic field extraction at a user device

The accuracy of existing machine learning models, software technologies, and computers are improved by estimating whether a particular page belongs to a same document as another page or whether the page belongs to a different document. Such document distinguishing can be based on deriving relationship information between a first feature vector representing the page and a second feature vector representing the other page. This also improves the user experience and model building experience, among other things.

Document distinguishing based on page sequence learning

Image data having text associated with a plurality of text-field types is received, the image data including target image data and context image data. The target image data including target text associated with a text-field type. The context image data providing a context for the target image data. A trained neural network that is constrained to a set of characters for the text-field type is applied to the image data. The trained neural network identifies the target text of the text-field type using a vector embedding that is based on learned patterns for recognizing the context provided by the context image data. One or more predicted characters are provided for the target text of the text-field type in response to identifying the target text using the trained neural network.

Text recognition for a neural network

Methods and systems for assessing performance and risk in financing supply chain are disclosed. The method includes accessing a behavioral data of an entity. The behavioral data comprises at least one of a primary data and a secondary data. The primary data is associated with one or more assets of the entity and comprises historical transaction data of the entity with financial institutions. The secondary data is sourced from an external system and comprises at least a credit information and a business transaction information of the entity. The method also includes generating a unified model for the entity based on the behavioral data of the entity. The method includes predicting a plurality of risk metrics based on the unified model using one or more machine learning models, and determining a credit rating of the entity based on the plurality of risk metrics and a confidence measure.

Methods and systems for assessing performance and risk in financing supply chain

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Emeryville, CA, United States of America

Eitan Anzenberg