Kyndryl, Inc.

Visa International Service Association

Omar Odibat

Ilyas Mohamed Iyoob

Jean Xu Yu

Emrah Zarifoglu

Shikhar Kwatra

Manish Mahesh Modh

Pritpal Arora

Claudia Barcenas

Mouleswara Reddy Chintakunta

Keri Wheatley

Vinay Kumar Gajanana Hegde

Balamukundan G Iyer

Disclosed embodiments provide computer-implemented techniques for aggregating of data involving multiple domains in the format of a news bulletin or 'newsletter' format. For various situations, this format can provide a quicker and more comprehensive understanding than is achievable with a visual dashboard. In disclosed embodiments, multiple domains are specified, where each domain can include a subject or category. One category is selected as a major domain. Other categories, referred to as satellite domains are also specified. A user provides scores that indicate the semantic importance of each satellite domain. A mathematical distance between each satellite domain and the major domain is computed based on the scores. Based on the mathematical distances, percentages of articles pertaining to each domain are computed, and used in assembling customized data that is output in a news bulletin format.

Computerized data aggregation

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

According to one embodiment, a method, computer system, and computer program product for provisioning a tag schema. The embodiment may include determining data types for tag-keys within an existing ecosystem of tags. Each tag of the existing ecosystem comprises a tag-key and a tag-value. The embodiment may include performing pairwise clustering of the tags within the ecosystem. The embodiment may include identifying a main tag-key within each cluster of tags. The embodiment may include grouping the clusters into a broader category. The embodiment may include providing a tag schema recommendation for the ecosystem based on the grouped clusters.

Automatically provisioned tag schema for hybrid multicloud cost and chargeback analysis

An embodiment includes generating an input document that includes a plurality of text fields of attribute data. The embodiment also includes extracting a set of candidate features from the attribute data using a feature extraction module that evaluates the attribute data using first and second machine learning models, where the first machine learning model scores terms in the input document and the second machine learning model includes a deep learning model. The embodiment also includes calculating feature-selection values for respective features of the set of candidate features and generating tag information for a remote computing asset using a machine learning classifier that predicts the tag information based on the feature-selection values.

Methods and apparatus for feature generation using improved term frequency-inverse document frequency (TF-IDF) with deep learning for accurate cloud asset tagging

Feature generation for asset classification

A classification server perform a method for classifying an entity and identifying reason codes for the classification. The classification server can use a gradient boosting machine to build a classification model using training data. The classification model can be an ensemble of decision trees where each terminal node in the decision tree is associated with a response. The responses from each decision tree can be aggregated by the classification server in order to determine a classification for a new entity. The classification server can determine feature contribution values based on expected feature values. These feature contribution values can be associated with each of the responses in the classification model. These feature contribution values can be used to determine reason codes for the classification of the entity. As such, the classification server can perform a single traversal of the classification model to classify the entity and identify reason codes.

Identifying reason codes from gradient boosting machines

Growing community of inventors

Cedar Park, TX, United States of America

Omar Odibat