Kyndryl, Inc.

Mouleswara Reddy Chintakunta

Omar Odibat

Pritpal Arora

An approach is disclosed that creates a network graph that identifies computing resources as nodes. Each of the computing resources is weighted based upon the corresponding resource's attributes and usage. The approach connects a first set of nodes as directly connected and a second set of nodes as indirectly connected. Node longevity values are calculated for each node in the network graph with each of the node longevity values corresponding to one of the nodes in the network graph. The calculations are based on the direct and indirect connections between each of the nodes. The computing resources are managed based on the corresponding node longevity values.

System to infer longevity of cloud computing resource usage and rank in order of importance

A user experience theme description is obtained, along with a new user experience feature image set. The theme description and new user experience feature image set are input into a generative adversarial network (GAN). The GAN is trained to output multiple user experience designs based on the new feature image set. The multiple designs are displayed on an electronic display device that includes an eye gaze tracking system. User interface elements and their corresponding positions within a user interface are identified based on eye gaze of a user towards the electronic display device. The position and type of user interface elements are compared between a desired user interface design and a generated user interface design. Errors between the desired user interface design and the generated user interface design are input as feedback into the GAN to further enhance the results.

Automatic generation of user experience mockups using adaptive gaze tracking

Embodiments of the present invention provide an approach for identifying outliers (e.g., detecting the outliers and generating outlier explainability) in a heterogeneous system. Heterogeneous input data is received from any number of data sources having any number of data types and converted into a single predefined format. Global outliers are detected in a first pass of the data. Contextual outliers are detected in a second pass. Global and contextual outliers are then collectively grouped based on outlier type. Output data is then generated including explainability for each detected outlier.

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Allagadda, India

Mouleswara Reddy Chintakunta