Oracle International Corporation

Michael Edward Pearmain

Tara U Roberts

Jonathan Stanesby

Kevin Yordy

Karen Sutthakorn

Janet Barnes

David John Dewsnip

Zengguang Wang

Embodiments relate to configuring artificial-intelligence (AI) decision nodes throughout a communication decision tree. The decision nodes can support successive iteration of AI models to dynamically define iteration data that corresponds to a trajectory through the tree.

Methods and systems for configuring communication decision trees based on connected positionable elements on canvas

Techniques are described for training and evaluating a proximal factorization machine engine. In one or more embodiments, the engine receives a set of training data that identifies a set of actions taken by a plurality of users with respect to a plurality of items. The engine generates, for a prediction model, (a) a first set of model parameters representing relationships between features of the plurality of users and the set of actions, and (b) a second set of model parameters representing interactions between different features of the plurality of users and the plurality of items. For each respective item in a plurality of items, the engine computes a probabilistic score based on the model parameters. The engine selects and presents a subset of items based on the probabilistic scores.

Proximal factorization machine interface engine

Embodiments relate to de-obfuscated data to process at one or more artificial-intelligence (AI) decision nodes throughout a communication decision tree. Through processing directed by the communication decision tree, additional de-obfuscated data is collected and securely transmitted in de-obfuscated or re-obfuscated form to enrich underlying data structures.

Machine-learning-based processing of de-obfuscated data for data enrichment

Directed trajectories through communication decision tree using iterative artificial intelligence

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Guildford, United Kingdom

Michael Edward Pearmain