International Business Machines Corporation

Vidya Chandrashekar

Balaji Sankar Kumar

Vamshi Dondapati

Saraswathi Sailaja Perumalla

Girish Dhanakshirur

Rachana Vishwanathula

Akshat Dixit

Bindu Tank

Manu K M

Arun Sabaresh Anantha Narayanan

Arun Aravind

Deepak Rai

A method, computer program product, and computer system for network topology optimization. A graph convolutional network (GCN) model is trained using training network topology datasets as input. Each training network topology dataset includes: (i) a specified network topology and an associated optimal network topology and an optimality function value for the optimal network topology, (ii) relative weights of optimality parameters including performance, availability, and scalability, and (iii) an identification of an optimality function of the optimality parameters weighted by the relative weights. The specified network topology includes components, relationships between the components, and metadata pertaining to the components. Each output node in an output layer of the GCN model includes an optimality function value for a different candidate network topology. One of the output nodes identifies an optimum network topology relative to the specified network topology as being the candidate network topology having a highest optimality function value.

Cloud topology optimization using a graph convolutional network model

An embodiment converts, by a voice-to-text converter, voice data to text data. The embodiment extracts, by an extractor, an intent and an entity from the text data. The embodiment predicts, by a predictor, based on the intent and the entity, a sensitive information. The embodiment compares, by an intersector, the text data to the predicted sensitive information. The embodiment determines, by the intersector, responsive to the comparing, whether the text data includes the predicted sensitive information. The embodiment intersects, by the intersector, responsive to a determination that the text data includes the predicted sensitive information, the voice data.

Identification and prevention of sensitive information exposure in telephonic conversations

An embodiment includes generating an intent representative of a verb identified in application programming interface (API) test data associated with an API test case. The embodiment also generates a first entity representative of a first noun identified in the API test data and a second entity representative of a second noun identified in the API test data. The embodiment identifies the intent as being associated with a relationship between the first and second entities in the API test case. The embodiment generates a relationship model associated with the API test case, where the relationship model includes the intent as the relationship between the first entity and the second entity. The embodiment generates a conversation model of an artificial intelligence (AI) conversation agent by mapping the intent and the first and second entities to dialog nodes of the conversation model.

Generating an AI conversation model using API test data

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Bengaluru, India

Vidya Chandrashekar