Emc IP Holding Company LLC

Dell Products Inc.

Venkata Chandra Sekar Rao

Abhishek Gupta

Sumit Gupta

Diwahar Sivaraman

Kartikeya Putturaya

Rashmi Sudhakar

Kalpana Razdan

Neeraj Tiwari

Peter Shipman

Velmurugan R

Narayan Kulkarni

Sham A R

Kirti Khade

Methods, apparatus, and processor-readable storage media for incorporating data into search engines using deep learning mechanisms are provided herein. An example computer-implemented method includes extracting one or more features from a search query by applying one or more machine learning algorithms to the search query; generating one or more word vectors by applying at least one deep learning technique to the one or more extracted features; mapping the one or more generated word vectors to one or more words from a corpus of data by implementing at least one deep similarity network; and outputting one or more results in response to the search query, wherein the one or more results are based at least in part on the one or more words from the corpus to which the one or more generated word vectors were mapped.

Incorporating data into search engines using deep learning mechanisms

Methods, apparatus, and processor-readable storage media for learning machine behavior related to install base information and determining event sequences based thereon are provided herein. An example computer-implemented method includes parsing data storage information based at least in part on parameters related to install base information comprising temporal parameters and event-related parameters; formatting the parsed set of data storage information into a parsed set of sequential data storage information compatible with a neural network model; training the neural network model using the parsed set of sequential data storage information and additional training parameters; predicting, by applying the trained neural network model to the parsed set of sequential data storage information, a future data unavailability event and/or a future data loss event; and outputting an alert based at least in part on the predicted future data unavailability event and/or predicted future data loss event.

Learning machine behavior related to install base information and determining event sequences based thereon

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

Technology that facilitates prediction of order-fulfillment abeyance are disclosed. Exemplary implementations may: obtain order details of an inchoate order from an orderer; predict that the inchoate order, upon submission, would be have its fulfillment held in abeyance; and in response to the abeyance prediction, disable submission of the inchoate order with the obtained order details.

Prediction of order-fulfillment abeyance

Technology that facilitates the encouragement of conversion of customers during their online journeys is disclosed. Exemplary implementations may: monitor an online journey of a customer with an entity; profile the customer to select a persona of a plurality of personas; predict a lack of conversion by the customer during the monitored online journey of the customer; and in response to the prediction, offer an optional online path forward for the customer's online journey to the customer.

Encouragement of conversion of customers during their online journeys

A system, method, and computer-readable medium for performing a payment tracking operation. Factors, that include input, features and attributes of various customers are received and processed. The processing includes imputing, deleting and converting the factors for further refinement for machine learning. The machine learning processes the factors using succeeding layers that include random forest to categorize customers, multivariate regression to determine which factors have the greatest effects on payment. In addition, a recurrent neural network approach is used to further refine the factors and provide a more accurate analysis.

System for insights on factors influencing payment

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

A path suggestion tool in a Software-Defined Networking (SDN) architecture to predict a router's future usage based on an analysis of the router's historical usage over a given period of time in the past and to recommend a routing path within the network in view of the predicted future usages of the routers/switches in the network. The path suggestion tool is an analytical, plug-and-play model usable as part of an SDN controller to provide more insights into different routing paths based on the future usage of each router. A Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model in the suggestion tool analyzes the historical usage data of a router to predict its future usage. A Deep Boltzmann Machine (DBM) model in the suggestion tool recommends a routing path within the SDN-based network upon analysis of the LSTM-RNN based predicted future usages of routers/switches in the network.

System and method to learn and prescribe network path for SDN

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

A method is used in evaluating a test subject in computing environments. A first machine learning system generates test subject features. A second machine learning system analyzes the test subject to detect distinguishing features of the test subject. A third machine learning system performs natural language processing on the test subject features to create evaluation information associated with the test subject. A test subject evaluation system provides an evaluation of the test subject based on the distinguishing features and the evaluation information.

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Bengaluru, India

Venkata Chandra Sekar Rao