Emc IP Holding Company LLC

Ca, Inc.

Sumit Gupta

Venkata Chandra Sekar Rao

Madhusudhan Ganda

Swapnel Shrivastava

Kalpana Razdan

Narendra Dhulipalla

Ramanuja Charyulu

Abhishek Gupta

Kiran Kumar

Kiran Kumar B S

Diwahar Sivaraman

Neeraj Tiwari

Rashmi Sudhakar

Sham A R

Peter Shipman

Velmurugan R

Pradeep Kumar Kanagaraj

Kirti Khade

Methods, apparatus, and processor-readable storage media for proactively predicting large orders and providing fulfillment support related thereto are provided herein. An example computer-implemented method includes classifying, via a first set of one or more machine learning techniques, a transaction quote as a transaction quote that exceeds one or more size-related parameters or a transaction quote that does not exceed the one or more size-related parameters; determining, if the transaction quote is classified as a transaction quote that exceeds one or more size-related parameters, supportability of converting the transaction quote into a transaction order via a second set of one or more machine learning techniques; and outputting, based on the determined supportability, information pertaining to converting the transaction quote into a transaction order and fulfilling the transaction order to one or more entities associated with transaction order fulfillment.

Proactively predicting large orders and providing fulfillment support related thereto

Methods, apparatus, and processor-readable storage media for dynamically determining customer intent and related recommendations using deep learning techniques are provided herein. An example computer-implemented method includes generating a prediction as to whether a user will order a particular offering during a user browsing session by applying one or more deep learning techniques to browsing session data derived from the user browsing session; mapping the user browsing session to one or more pre-established procurement user types by comparing the browser session data to data pertaining to multiple pre-established procurement user types; determining a recommendation of one or more offerings distinct from the particular offering, wherein determining the recommendation is based at least in part on the generated prediction and the mapping of the user browsing session to one or more of the multiple pre-established procurement user types; and outputting, within the user browsing session, the recommendation to the user.

Dynamically determining customer intent and related recommendations using deep learning techniques

Methods, apparatus, and processor-readable storage media for image-based search and recommendation techniques implemented via artificial intelligence are provided herein. An example computer-implemented method includes detecting, in response to a user search query comprising an image, an object in the image by applying one or more artificial intelligence algorithms to the image; determining one or more features of the object by applying the one or more artificial intelligence algorithms to one or more portions of the image containing at least a portion of the object; identifying the detected object as a particular enterprise offering based at least in part on the one or more determined features of the object; determining one or more additional enterprise offerings based at least in part on the identified enterprise offering; outputting, to the user, information pertaining to the identified enterprise offering and information pertaining to the one or more additional enterprise offerings.

Image-based search and recommendation techniques implemented via artificial intelligence

Systems, methods and computer program products for performing software regression testing are provided. A sitemap comprising a hierarchy of nodes is displayed on a display, each node representing a block of source code for a program. An indication of a user selection of a plurality of nodes in the hierarchy is received. Responsive to receiving the indication, a lowest node of the nodes that is located at a lowest level of the hierarchy is determined and identified as a user selected node. A list of test cases to test the source code represented by the user selected node is displayed. Responsive to receiving an indication of a selection of one of the test cases in the list of test cases displayed, a message having an indication to execute the one of the test cases in the list of test cases displayed is transmitted, to a server device via a network.

Automated selection of test cases for regression testing

A method includes determining, using a processor, a predicted location of a particular mobile device at a future time based on location information received from the particular mobile device. The method also includes determining, using the processor, a predicted signal strength of the particular mobile device based on a signal catalog and the predicted location of the particular mobile device. The method further includes transmitting, using the processor, the predicted signal strength to the particular mobile device.

Providing run time signal loss insights to users

Growing community of inventors

Bengaluru, India

Sumit Gupta