Dell Products Inc.

Ajay Maikhuri

Dhilip S Kumar

Sujit Kumar Sahoo

Devaraj Marappa

Shibi Panikkar

Rohit Gosain

Rajesh Krishnan

Souptik Banerjee

In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, receiving audio data corresponding to a spoken utterance by a first user and determining an accent of the audio data. The method also includes, by the computing device, neutralizing the accent of the audio data to a preconfigured accent and transmitting a modified audio data in the preconfigured accent to another computing device. The modified audio data includes the spoken utterance by the first user.

Accent personalization for speakers and listeners

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 receiving a set of raw worker data records associated with one or more worker activities of a worker, each raw worker data record comprising one or more of an image, a video, information from a sensor, information from interaction with a tool, and information from interaction with a device. A first analysis, configured to analyze the set of raw worker data records for one or more indications regarding whether any worker activities include one or more undesired behaviors, is performed on the set of raw worker data records. Based on the first analysis, a second analysis, configured to categorize and document worker behavior as a function of time, is performed. Based on the second analysis, a third analysis, configured to analyze worker behavior for undesired conditions, is performed. Based on at least one of the second and third analysis, a summary of worker activity is generated.

Intuitive AI-powered worker productivity and safety

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

A method comprising: receiving, at a computing device, a first input specifying a first time window for a meeting and a first set of recipients who are invited to attend the meeting, the first set being a non-empty set; receiving, at the computing device, a second input specifying a plurality of segments within the meeting, for each of the plurality of segments, the second input specifying a respective second time window for the segment, and a respective second set of recipients who are invited to attend the segment, each second set of recipients being a subset of the first set, and each second time window being a time window that falls entirely within the first time window; and generating one or more invites based on the first and second input and transmitting the one or more invites to the recipients in the first set.

Meeting invite segmentation

An initial transaction is generated representing a forecasted demand for material needed to manufacture equipment via a supply chain. The initial transaction is added to a blockchain for access by entities in the supply chain comprising at least a subset of one or more supply entities, one or more material distribution entities, and one or more manufacturing entities. In one or more examples, the steps may be performed by an OEM blockchain node that accesses any transactions added by the various entities on the blockchain node and limits access to transactions on the blockchain by the various entities. Further, the initial transaction may be a main transaction and a transaction representing a commitment by one of the supply entities may be a sub-transaction of the main transaction, and the sub-transaction is closed in the blockchain upon the request for payment for the committed material or payment of the request.

Supply chain management using blockchain and machine learning functionalities

In a distributed ledger (DLT) network, a transaction, related to movement of a monitored entity from a first party's location to a second party's location, is monitored. The transaction is associated with a respective real-time status and with at least a first predetermined milestone. Sensor outputs from an IoT enabled sensor that is operably coupled to the monitored entity, are received while the monitored entity is located remotely from the first and second parties during its movement from the first location to the second location. Updates to a real-time status of the monitored entity, relating to progress towards a milestone, are determined based on the received sensor outputs. The transaction is updated, in real time, on the DLT network when the milestone is reached. Information is communicated relating to the update of the transaction and the first predetermined milestone, in real-time, to the first party and the second party.

Framework for real-time in-transit material ownership transfer and invoicing using distributed ledger (DLT)

Techniques are disclosed for data management in an information processing system. For example, a method comprises analyzing one or more data sources, wherein each of the one or more data sources comprise a set of metadata and usage information associated with the set of metadata. The method then determines at least one of data definitions and data classifications for the one or more sets of metadata across the one or more data sources, and stores the at least one of data definitions and data classifications for the one or more sets of metadata in a repository.

Data management using dynamic data flow and pattern matching

Methods, apparatus, and processor-readable storage media for automated resource prioritization using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining information pertaining to one or more user transactions, each user transaction involving one or more resources; obtaining information associated with the one or more resources, including resource demand-related information, resource supply-related information, and resource build-related information; prioritizing, in connection with one or more resource-related actions, the one or more user transactions and/or the one or more resources by processing, using one or more artificial intelligence techniques, at least a portion of the information pertaining to the one or more user transactions and at least a portion of the information associated with the one or more resources; and performing at least one automated action based at least in part on the prioritizing of the one or more user transactions and/or the one or more resources.

Growing community of inventors

Bengaluru, India

Ajay Maikhuri