Kyndryl, Inc.

William G Dusch

Craig M Trim

Mary E Rudden

Shikhar Kwatra

Scott Robert Carrier

Ahmed M Nassar

MacDonald Isere

Stacey Heeley Ronaghan

Nicholas L Graham

An embodiment includes identifying metadata attached to a first node of a graph, where the metadata satisfies an ontological condition. The embodiment also includes transforming the graph such that the transforming results in the graph having a new graph structure, where the transforming of the graph comprises removing the metadata from the first node and adding a second node representative of the metadata removed from the first node to the graph such that the second node is connected to the first node by a first edge.

Metadata decomposition for graph transformation

Technology for applying explainable artificial training algorithms (XAI) to training machine learning algorithms for identifying potentially developing computer vision syndrome (CVS), CVS and/or recommended remedial action(s) that a user can perform to counter potentially developing CVS and/or existing CVS. In some embodiments, the XAI includes a Contrastive Explainability model. In some embodiments, the training performed by the XAI includes assigning weight factors respectively to CVS input parameters (for example, blink rate) based upon how strong the respective CVS input factor is correlated with development of CVS in the user.

Prevention of computer vision syndrome using explainable artificial intelligence

A machine learning model can be depicted as a graph. After identifying a familiarity level of a user, the graph of the machine learning model can be customized. The customization is based on the familiarity level of the user. The customized graph of the machine learning model is displayed to the user.

Interoperable smart AI enabled evaluation of models

Growing community of inventors

Morrisville, NC, United States of America

William G Dusch