American Express Travel Related Services Company, Inc.

Conduent Business Services, LLC

Xerox Corporation

Himanshu Sharad Bhatt

Shourya Roy

Di W Xu

Gabriella P Fitzgerald

Chetan Lohani

Shachindra Pandey

Gunjan Panwar

Lokesh Bhatnagar

Manoj Bhokardole

Vinit Jain

Manjira Sinha

Raghuram Krishnapuram

Arun Rajkumar

Sandipan Dandapat

Sriranjani Ramakrishnan

Ganesh Jawahar

Heather A Cole

Raksha Sharma

Harvey R Raymundo

Sarah A Sharma

Tanmoy Patra

Christine Malczanek

Shivshankar Reddy

Tamanna Agrawal

Paridhi Jain

Rohit Bhiogade

Disclosed are various embodiments for processing multiple documents in an image. First, text from each of one or more documents in an image can be identified. An orientation of each of the one or more documents can be determined based at least in part on an alignment of the text of each of the one or more documents. Additionally, using an object detection model, the one or more documents in the image can be identified based at least in part on the orientation of each of the one or more documents. Finally, the one or more documents can be separated from the image into one or more separate image files, each separate image file representing a respective document of the one or more documents.

Processing multiple documents in an image

A system provides an end-to-end solution for invoice processing which includes reading files (such as pdfs and images), extracting key relevant information from the files, organizing the relevant information in a structured template as a key-value pair, and comparing files based on the similarities between different file fields to identify potential duplicate files.

Data extraction and duplicate detection

A system provides an end-to-end solution for invoice processing which includes reading invoices (both pdfs and images), extracting key relevant information from the face of invoices, organizing the relevant information in a structured template as a key-value pair, and comparing invoices based on the similarities between different invoice fields to identify potential duplicate invoices.

In some embodiments, a version control system receives a first version of a digital object and a second version of the digital object. The version control system extracts data from the first version of the digital object, and data from the second version of the digital object. The version control system utilizes a categorization machine learning model to generate structured representations of the first digital object and a second digital object. The version control system identifies differences between the two digital objects and utilizes a natural language technique to produce pseudo-instructions representing an update request. The version control system validates differences between the two digital objects and sends an auto-validation report to a client computing device when the differences are compliant with the pseudo-instructions.

Computer-based systems configured for machine learning version control of digital objects and methods of use thereof

The disclosed embodiments illustrate a domain adaptation method for learning transferable feature representations from a source domain for a target domain. The method includes receiving real-time input data comprising labeled instances of the source domain and unlabeled instances of the target domain from a computing device. The method further includes determining source specific representation corresponding to the source domain and a common representation shared between the source domain and the target domain. Based on a positive contribution from the source specific representation and the common representation, the labeled instances of the source domain are classified. The method further includes training a generalized classifier based on a positive contribution from the common representation. The method further includes automatically performing text classification on the unlabeled instances of the target domain based on the trained generalized classifier. The result of the text classification is rendered at a user interface of the computing device.

Method and system for text classification based on learning of transferable feature representations from a source domain

The disclosed embodiments illustrate method and system of processing data by a computing device for training a target domain classifier. The method includes extracting one or more first features and one or more second features from a first target instance, associated with a target domain. The method further includes predicting a first label for the received first target instance based on the one or more first features by utilizing a trained first classifier associated with a set of labeled source instances, wherein the predicted first label is assigned to the first target instance when a first score of the predicted first label exceeds a first pre-specified threshold. Further, the method includes updating a set of labeled target instances associated with the target domain based on the labeled first target instance, wherein the updated set of labeled target instances is utilized to train the target domain classifier.

Method and system of processing data for training a target domain classifier

The disclosed embodiments illustrate a domain adaptation method for learning transferable feature representations from a source domain for a target domain. The method includes receiving input data comprising a plurality of labeled instances of the source domain and a plurality of unlabeled instances of the target domain. The method includes learning common representation shared between the source domain and the target domain, based on the plurality of labeled instances of the source domain. The method includes labeling one or more unlabeled instances in the plurality of unlabeled instances of the target domain, based on the common representation. The method includes determining a target specific representation corresponding to the target domain. The method includes training a target specific classifier based on the target specific representation and the common representation to perform text classification on remaining one or more unlabeled instances of the plurality of unlabeled instances of the target domain.

Method and system for learning transferable feature representations from a source domain for a target domain

The disclosed embodiments illustrate methods of data processing for text classification of a target domain. The method includes generating a plurality of clusters from a plurality of first text segments corresponding to a plurality of source domains, based on an association of the plurality of first text segments with a plurality of categories. The method further includes computing a similarity score of each of a plurality of second text segments corresponding to the target domain for each of the plurality of clusters. The method further includes identifying a pre-specified count of clusters from the plurality of clusters, based on the computed similarity score. Further, the method includes training a first classifier by utilizing first text segments in the identified pre-specified count of clusters, wherein the trained first classifier is utilized to automatically classify the plurality of second text segments into categories associated with the identified pre-specified count of clusters.

Method and system for data processing for text classification of a target domain

The disclosed embodiments illustrate methods of data processing for training a target domain classifier to label text segments. The method includes identifying a set of common keywords with same label from a set of source keywords and a set of target keywords. The method includes training a first classifier, based on the set of common keywords, to label a first set of target text segments. The method includes training a second classifier based on at least a subset of the labeled first set of target text segments. The method includes training a third classifier, based on the first classifier and the second classifier, to label a second set of target text segments, wherein a subset of the labeled second set of target text segments is utilized for re-training the second classifier. The method further includes determining labels of another plurality of target text segments based on the re-trained second classifier.

Method and system for training a target domain classifier to label text segments

Methods and systems for training a conversation-classification model are disclosed. A first set of conversations in a source domain and a second set of conversation in a target domain are received. Each of the first set of conversations has an associated predetermined tag. One or more features are extracted from the first set of conversations and from the second set of conversations. Based on the similarity of content in the first set of conversations and the second set of conversations, a first weight is assigned to each conversation of the first set of conversations. Further, a second weight is assigned to the one or more features of the first set of conversations based on the similarity of the one or more features of the first set of conversations and of the second set of conversations. A conversation-classification model is trained based on the first weight and the second weight.

Growing community of inventors

Nagar, India

Himanshu Sharad Bhatt