Dell Products Inc.

Sujit Kumar Sahoo

Dhilip S Kumar

Hung The Dinh

Bijan Kumar Mohanty

Devaraj Marappa

Ajay Maikhuri

Rajeshwari Kalyani

Aravinda Rajagopal Kotikanyadanam

Nikhil Pularru

Methods, apparatus, and processor-readable storage media for temporal supply-related forecasting using artificial intelligence techniques are provided herein. An example computer-implemented method includes determining one or more forecasts pertaining to supply of at least one item by processing supply-related data using one or more artificial intelligence techniques; generating, based at least in part on the one or more forecasts, one or more temporal recommendations associated with one or more orders of at least one a portion of the at least one item; and performing one or more automated actions based at least in part on the one or more temporal recommendations.

Temporal supply-related forecasting using artificial intelligence techniques

Techniques are disclosed for domain-driven intent-based feedback data analysis. For example, a method comprises obtaining a feedback data set, and classifying the feedback data set into at least one domain of a plurality of domains. The feedback data set is mapped to a domain data set corresponding to the at least one domain, and a root cause is computed for the feedback data set to enable one or more actions to be taken in response to the feedback data set based on the root cause. By way of further example, computing the root cause for the feedback data set may further comprise utilizing at least one computed intent attribute, at least one computed sentiment attribute, and the domain data set to compute a decision on the root cause for the feedback data set.

Domain-driven feedback data analysis in information processing system

Techniques are disclosed for a multi-layer micro model analytics framework for analyzing or otherwise processing data. For example, a method comprises building two or more micro models respectively for two or more stages of a given process, wherein each micro model of the two or more micro models comprises a user interaction layer and a predictive learning layer that coordinate to train the micro model based on input to the user interaction layer and data accessible by the predictive learning layer for the corresponding stage of the two or more stages of the given process. The method then assembles the two or more micro models to perform analysis for the given process. In one non-limiting example, the given process is a new product introduction process and each micro model is built and trained to perform analytics for a specific lifecycle stage of the process.

Multi-layer micro model analytics framework in information processing system

A method comprises analyzing a plurality of natural language inputs associated with at least one user, and determining a plurality of contexts for the plurality of natural language inputs based, at least in part, on the analysis. In the method, a plurality of relationships linked to the at least one user are identified based, at least in part, on the analysis, and the at least one user is classified in one or more categories based, at least in part, on the plurality of contexts and the plurality of relationships. At least one of the analyzing, determining, identifying and classifying is performed using one or more machine learning models.

Using machine learning for individual classification

Techniques are disclosed for hyper-segmented personalization using machine learning-based models in an information processing system. For example, a method obtains one or more product experience recommendation data sets respectively from one or more product entities, and one or more purchase experience recommendation data sets respectively from one or more commerce entities. The method applies a federated ensemble-based machine learning algorithm to at least one of the one or more purchase experience recommendation data sets and at least one of the one or more product experience recommendation data sets to generate a personalized model, and causes adaptation of a purchasing interface of at least one of the one or more commerce entities with respect to a given user based on the personalized model.

Hyper-segmented personalization using machine learning-based models in an information processing system

Methods, apparatus, and processor-readable storage media for automatically prioritizing supply chain-related demand using artificial intelligence techniques are provided herein. An example computer-implemented method includes processing supply-chain related data using a first set of artificial intelligence techniques trained based at least in part on historical demand availability data; processing supply-chain related data using a second set of artificial intelligence techniques trained based at least in part on historical supply availability data; processing supply-chain related data using a third set of artificial intelligence techniques trained based at least in part on historical production availability data; prioritizing multiple orders within a supply chain environment by processing, using a fourth set of artificial intelligence techniques, results from the first set of artificial intelligence techniques, the second set of artificial intelligence techniques, and the third set of artificial intelligence techniques; and performing one or more automated actions based on the prioritization of the multiple orders.

Automatically prioritizing supply chain-related demand using artificial intelligence techniques

In one aspect, an example methodology implementing the disclosed techniques includes, by a product subscription service, receiving information regarding a hardware asset being returned at an end of a subscription and predicting, using a first machine learning (ML) model, whether the hardware asset has reached EOL. The method also includes, responsive to predicting that the hardware asset has reached EOL, creating, by the product subscription service, a work order to dispatch an eco-partner. The method may further include, by the product subscription service, responsive to predicting that the hardware asset has not reached EOL, predicting, using a second ML model, one or more new subscription orders matching the hardware asset and recommending the one or more matching new subscription orders as possible fits for the hardware asset.

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Bengaluru, India

Sujit Kumar Sahoo