Oracle International Corporation

Swetha Krishnakumar

Sudhakar Kalluri

Venkata Chandrashekar Duvvuri

Techniques for summarizing lists for machine learning operations are disclosed. In some embodiments, a machine learning system generates feature vectors for a set of items based on varying values among a set of feature attributes. The system further generates, based on the feature vectors a set of clusters and generates a summary vector for a list of items as a function of the distribution of the items within the set of clusters, where the summary vector has a length equal to how many clusters are in the set of clusters. Summary vectors may be generated for a plurality of examples within a training dataset. The system may use the summary vectors to train a machine learning model to estimate unknown labels for new examples.

Systems and methods for optimizing machine learning models by summarizing list characteristics based on multi-dimensional feature vectors

The present disclosure generally relates to techniques for determining a segment of a content distribution plan. More specifically, the present disclosure discloses techniques for determining one or more key-value pairs of a content distribution plan by leveraging a trained machine learning model. A plurality of electronic communications may be generated based on completed key-value pairs with a content distribution plan. The plurality of electronic communications may then be distributed to a plurality of devices within a networked environment.

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San Carlos, CA, United States of America

Swetha Krishnakumar