Docusign, Inc.

Taiwo Raphael Alabi

Shrinivas Kiran Kaza

Ashwath Mohan

Kim Cuong Phung

Mangesh Bhandarkar

Farzaneh Rajabi

Nipun Dureja

Boon Sun Song

Eric M Zenz

Michael Anthony Palazzolo

Jerome Levadoux

Roshan Satish

Varsha Sri Raghavan

Patrick Beukema

Devin Cabillo

A document management system performs optical character recognition (OCR) on a high resolution version of an image of a document, obtaining a first set of text representative of the text of the document. The document management system applies a machine-learned model on a low-resolution version of the image of the document, producing a denoised image that is of a higher resolution than that input into the machine-learned model. The document management system performs OCR on the denoised image, obtaining a second set of text representative of the text of the document. The document management system compares the first and second sets of text in the form of an objective loss function. The document management system subsequently generates a feature vector from the comparison of the sets of text and retrains the machine-learned model with the generated feature vector.

Optical character recognition system with back propagation of an objective loss function

A highlighting engine modifies a target document by identifying and highlighting a set of text passages. The highlighting engine receives a training set of data including documents that each include a set of highlighted text passages. The highlighting engine trains a machine learned model using the training set of data. The trained machine learned model, when applied to subsequent identified candidate sets of text passages within the target document, identifies the set of text passages to highlight. The highlighting engine modifies the target document with the highlighted set of text passages and provides the modified target document for display via an interface. The highlighted set of text passages enable a user to quickly read and understand the target document.

Automated document highlighting in a digital management platform

An auto-tagging engine receives a training set of data comprising documents including a set of tagged fields with each tagged field corresponding to a portion of the document. The auto-tagging engine trains a machine learned model using the training set of data. The trained machine learned model, when applied to a target document in a document management environment, identifies portions of the target document each corresponding to fields of the target document. For each field of the target document, the auto-tagging engine identifies text of the target document associated with the identified portions of the target document corresponding to fields. Natural language processing is performed on the identified text in order to identify field types for the fields. The target document is automatically modified to include a tag identifying the portion of the target document corresponding to each field and identifying a field type of the field.

Automated document tagging in a digital management platform

An electronic document system can allow users to upload a document package containing multiple individual component documents. Each component document includes a subset of a plurality of pages that are included in the document package. The electronic document system identifies a page of each component document by applying a machine learning model to the document package. The electronic document system partitions the document package into the individual component documents based on the identified pages. For each individual component document, the electronic document system identifies a document topic corresponding to the component document by applying another machine learning model. The electronic document system modifies a user interface to display each component document and corresponding document topic.

Machine learning-based document splitting and labeling in an electronic document system

A centralized document system identifies content items for presentation to a user based initially on a cold-start algorithm and subsequently based on machine-learned models. The system detects a first access by the user. The system generates a user attribute vector for the user and a content vector for each content item. The system selects a first content item based on the initial cold-start algorithm and modifies a user interface to include the first content item. The system identifies an interaction with the first content item by the user. The system detects a second access by the user. The system selects a machine-learned model based on a set of interactions by the user with displayed content items. The system applies the selected machine-learned model to the set of interactions and the user attribute vector to identify a second content item and modifies the user interface to include the second content item.

Content item selection in a digital transaction management platform

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San Jose, CA, United States of America

Taiwo Raphael Alabi