American Express Travel Related Services Company, Inc.

Conduent Business Services, LLC

Xerox Corporation

International Business Machines Corporation

Nuance Communications, Inc.

Rural Technology & Business Incubator

Shourya Roy

Himanshu Sharad Bhatt

Nitish Sharma

Manjira Sinha

Ragunathan Mariappan

L Venkata Subramaniam

Di W Xu

Arindam Chatterjee

Salil Joshi

Chithralekha Balamurugan

Sujit Gujar

Gabriella P Fitzgerald

Chetan Lohani

Shachindra Pandey

Vinit Jain

Gunjan Panwar

Lokesh Bhatnagar

Manoj Bhokardole

Rahul Ghosh

Hironori Takeuchi

Raghuram Krishnapuram

Tetsuya Nasukawa

Sandya Srivilliputtur Mannarswamy

Jacki O'Neill

Mohammad Torkzahrani

Swatee Singh

Pratap Singh Rathore

Brian C Rosenfield

Krishnaprasad Narayanan

Arpan Somani

Priya Radhakrishnan

Pratap Singh Singh Rathore

Lakshman Chaitanya

Anirban Sen

Sachindra Joshi

Krishna Kummamuru

Gueyoung Jung

Manoj Gupta

Yves Hoppenot

Koustuv Dasgupta

Sumit Negi

Sakyajit Bhattacharya

Vaibhav Rajan

Meera Sampath

Antonietta Grasso

Pallavi Manohar

Arun Rajkumar

Krishna Prasad Chitrapura

Vijay Huddar

Julien Bourdaillet

Sharmistha Jat

Dawn Thomas

Sandipan Dandapat

Bharath Venkatesh

Shruti Chhabra

Jutta Williamowski

Sriranjani Ramakrishnan

Anuj Mahajan

Ashok Jhunjhunwala

Yu An Sun

Divya Padmanabhan

Keerthi Laal Kala

Lakshmi Vaidyanathan

Timothy Gonsalves

Pratyush Prasanna

James Michael Allen Begole

Laxminarayan Venkata Subramaniam

Sarah A Sharma

Diwakar Punjani

Ganesh Jawahar

Deepak S Padmanaban

Yue Jiao

Paridhi Jain

Christine Malczanek

Tanmoy Patra

Deepali Semwal

Rohit Bhiogade

Shreshtha Mundra

Akhil Arora

Harvey R Raymundo

Heather A Cole

Raghuveer Thirukovalluru

Karnam Saraschandra

Disclosed are various embodiments for garbage collection for object-based storage systems. A first set of objects stored by an object storage service that have been accessed within a previously defined date range is identified. Then, a second set of objects stored by the object storage service is identified based at least in part on a relationship to one or more objects in the first set of objects. Next, a third set of objects stored by the object storage service that have been created prior to a predefined date is identified. Then, a subset of objects which are members of the third set of objects and not members of the first set of objects or the second set of objects is identified. Finally, a retention action is performed on individual members of the subset of objects based at least in part on a retention policy.

Garbage collection for object-based storage systems

At least some embodiments are directed to a system to compute uniform structured summarization of customer chats. In at least some embodiments, the system may operate a processor and receive a corpus of chats between customers and customer service representatives of an enterprise. Grouping the corpus of chats into subgroup task types and then extracting chat keywords and chat related words for each subgroup task type. Generating an expandable template data structure for each subgroup task type. Processing at least one chat to extract chat utterances and chat snippets ranking the chat utterances and chat snippets. Populating the expandable template data structure based on rankings to generate a chat summary data structure.

System for uniform structured summarization of customer chats

At least some embodiments are directed to an entity matching detection system. The entity matching detection system includes a latent similarity identification machine learning model that receives one or more data records and generates a final similarity score indicative of a latent similarity between the one or more data records and a second data record. The entity matching detection system can identify lexical and semantic similarities between attribute values and can analyze and compute similarity scores for direct-linked attribute values and cross-linked attribute values extracted from different data records.

Computer-based systems for data entity matching detection based on latent similarities in large datasets and methods of use thereof

At least some embodiments are directed to a system that receives from an online portal loaded in a computing device, a user request to instantiate a server cluster in a hybrid computer network. The system authenticates and redirects the user request to a selected computer network configured in the hybrid computer network. The system instantiates the server cluster in the selected computer network causing the server cluster to initiate a microservice agent during bootup. The system sends a command to the server cluster to initiate the execution of a process and receives from the microservice agent event data associated with the process. The system inputs the event data into a trained machine learning model to determine a first execution state of the process and sends a command to change the first execution state of the process to a second execution state.

Computer-based systems for management of big data development platforms based on machine learning techniques and methods of use thereof

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

Disclosed are various approaches for providing a natural language interface for searching databases. A natural language query is parsed to identify a plurality of tokens. At least one operator is identified with a machine-learning model based at least in part on the plurality of tokens. Next, at least one attribute and at least one respective attribute value are identified with a machine-learning model based at least in part on the plurality of tokens. Then, at least one constraint is identified with a machine-learning model based at least in part on the plurality of tokens. Finally, a machine language query is generated based at least in part on the at least one operator, the constraint(s), the attribute(s), and the respective attribute value(s).

Natural language interface to databases

At least some embodiments are directed to a system to compute uniform structured summarization of customer chats. In at least some embodiments, the system may operate a processor and receive a corpus of chats between customers and customer service representatives of an enterprise. Grouping the corpus of chats into subgroup task types and then extracting chat keywords and chat related words for each subgroup task type. Generating an expandable template data structures for each subgroup task type. Processing at least one chat to extract chat utterances and chat snippets ranking the chat utterances and chat snippets. Populating the expandable template data structure based on rankings to generate a chat summary data structure.

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

At least some embodiments are directed to a system that receives from an online portal loaded in a computing device, a user request to instantiate a server cluster in a hybrid computer network. The system authenticates and redirects the user request via a proxy service to a selected computer network configured in the hybrid computer network. The system instantiates the server cluster in the selected computer network causing the server cluster to initiate a microservice agent during bootup. The system sends a command to the server cluster to initiate the execution of a process and receives from the microservice agent event data associated with the process. The system inputs the event data into a trained machine learning model to determine a first execution state of the process and sends a command to change the first execution state of the process to a second execution state.

According to embodiments illustrated herein, there is provided a system for predicting a health condition of a first patient. The system includes a document processor configured to extract one or more headings from one or more medical records of the first patient based on one or more predefined rules. The document processor is further configured to extract one or more words from one or more phrases written under each of the extracted one or more headings, wherein the one or more phrases correspond to documentation of the observation of the first patient by a medical attender. The system further includes one or more processors configured to predict the health condition of the first patient based on a count of the one or more words in historical medical records and the one or more medical records.

System and method for predicting health condition of a patient

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

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Bengaluru, India

Shourya Roy