Automation Anywhere, Inc.

Change Healthcare Holdings, LLC

Thomas Corcoran

Stephen Van Lare

Vibhas Gejji

Abhijit Kakhandiki

Nishit Kumar

Adrian Lam

Derek S Chan

Bruno Selva

Adam Sullivan

Nick Giannasi

Alex Londeree

Christopher Jacoby

A method includes identifying records in a database for labeling; presenting one of the records in the database to a first labeling entity; and receiving a first observation on an information source in the one of the records from the first labeling entity. The first observation has one of a plurality of observation types associated therewith. The plurality of observation types including a validation observation type in which the first observation comprises a confirmation of whether a second observation on the information source in the one of the records from another labeling entity is accurate and an edit for the second observation when the second observation is confirmed as inaccurate. The one of the records is updated in the database with the first observation on the information source in the one of the records from the first labeling entity.

Artificial intelligence labeling platform for secure data including observation validation

A computer system and computerized method that groups documents with similar image layout together. A document similarity metric based on locally connected subgraphs is employed. Region adjacency graphs are generated from word segments extracted from document images. Fuzzy attributed graph isomorphism is performed on subgraphs checking node and edge attribute similarity. Document similarity is then calculated on a normalized score between matching subgraphs of different documents. Unsupervised clustering of document layouts is performed to generate clusters of documents with similar structure.

Region adjacent subgraph isomorphism for layout clustering in document images

A method, apparatus and computer program product are provided for classifying pages of a document with a linear regression model, and a deep learning (non-linear) model utilizing a neural network. The classification of each page is determined by determining which of the linearly predicted category or the non-linearly predicted category to use, such as for transmission to an auditor. Pages of medical records generated by concatenating reports from distinct sources are classified according to both models, and embodiments determine which classification should be used. The classifications may be optionally smoothed for continuity. The classifications may be sent to auditors and used to review and audit the medical records.

Method, apparatus, and computer program product for performing machine learning on unstructured documents

A computerized method and system for adding distortions to a computer-generated image of a document stored in an image file. An original computer-generated image file is selected and is processed to generate one or more distorted image files for each original computer-generated image file by selecting one or more augmentation modules from a set of augmentation modules to form an augmentation sub-system. The original computer-generated image file is processed with the augmentation sub-system to generate an augmented image file by altering the original computer-generated image file to add distortions that simulate distortions introduced during scanning of a paper-based representation of a document represented in the original computer-generated image file.

Synthetic augmentation of document images

A computer implemented method and system for correcting error produced by Optical Character Recognition (OCR) of text contained in an image encoded document. An error model representing frequency and type of errors produced by Optical Character Recognition Engine is generated. An OCR character string generated by OCR is retrieved. A user-defined pattern of a plurality of character strings is retrieved, where each character string represents a possible correct representation of characters in the OCR character string. The OCR character string is compared to each of the above generated character strings and a 'likelihood score' is calculated based on the information from the error model. The character string with the highest 'likelihood score' is presumed to be the corrected version of the OCR character string.

Auto-correction of pattern defined strings

Image documents that have a visually perceptible geometric structure and a plurality of visually perceptible key-value pairs are grouped. The image documents are processed to generate a corresponding textually encoded document. The textually encoded documents are each assigned into one of a plurality of layout groups, wherein all textually encoded documents in a particular layout group share a visually perceptible layout that is substantially similar. Triplets are selected from the layout groups, where two documents are from the same layout group and one document is from a different layout group. The triplets are processed with a convolutional neural network to generate a trained neural network that may be used to classify documents in a production environment such that a template designed on one image document in a group permits an extraction engine to extract all relevant fields on all image documents within the group.

Deep learning based document image embeddings for layout classification and retrieval

Key/Value pairs, each comprising a keyword string and an associated value, are extracted automatically from a document image. Each document image has a plurality of pixels with each pixel having a plurality of bits. A first subset of the plurality of bits for each pixel represents information corresponding to the document image. The document image is processed to add information to a second subset of the plurality of bits for each pixel. The information added to the second subset alters the appearance of the document image in a manner that facilitates semantic recognition of textually encoded segments within the document image by a Deep Neural Network (DNN) trained to recognize images within image documents. The DNN detects groupings of text segments within detected spatial templates within the document image. The text segments are mapped to known string values to generate the keyword strings and associated values.

Automatic key/value pair extraction from document images using deep learning

A system and method of automatically learning new keywords in a document image based on context such as when a never before seen keyword exists surrounded by other key-value pairs. A machine learning based approach leverages subword embeddings and two-dimensional geometric contexts in a gradient boosted trees classifier. Keys may be composed of multi-word strings or single-word strings.

Identification of key segments in document images

An optical character recognition system employs a deep learning system that is trained to process a plurality of images within a particular domain to identify images representing text within each image and to convert the images representing text to textually encoded data. The deep learning system is trained with training data generated from a corpus of real-life text segments that are generated by a plurality of OCR modules. Each of the OCR modules produces a real-life image/text tuple, and at least some of the OCR modules produce a confidence value corresponding to each real-life image/text tuple. Each OCR module is characterized by a conversion accuracy substantially below a desired accuracy for an identified domain. Synthetically generated text segments are produced by programmatically converting text strings to a corresponding image where each text string and corresponding image form a synthetic image/text tuple.

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San Jose, CA, United States of America

Thomas Corcoran