Oracle International Corporation

Kimberly Ann Wolfe

Michael James Strutton

Wenhua Li

Chad Estes

Natalie You

Yizhou Jiang

Mehrshad Setayesh

Tara U Roberts

Timothy P Mccandless

Noah Horton

John Benjamin Nolt

Brian Culler

Steven Keith Woods

Donald Chun Wing Ho

Biju George

Patricia Pichardo

Reza Parang

Maria Fernanda Diaz-Arscott

Jeff Condit

A single tracking URL collects tracking information for multiple tracking services based on multiple tracking URL templates. A dynamic link generator generates a tracking URL configured to collect tracking information requested by each of a set of enabled tracking URL templates. The tracking information is transmitted to the tracking services associated with the enabled tracking URL templates. Additionally or alternatively, a tracking URL is configured to redirect a consumer to one of a candidate set of destination URLs. A tracking URL includes a base URL, which is resolved to an IP address of a server of a particular tracking service. Based on tracking data associated with the tracking URL and/or digital body language associated with the consumer, the particular tracking service selects one of a candidate set of destination URLs associated with the tracking URL. A destination page corresponding to the selected destination URL is presented to the consumer.

Generating tracking URLs and redirecting from tracking URLs

Techniques for using machine learning to configure social media campaigns are disclosed. A social relationship management (SRM) service performs supervised machine learning to generate a learned model, at least by: generating feature vectors based on training data including campaign configuration data and one or more campaign success metrics; and performing pattern recognition on the feature vectors to determine one or more preferred campaign configurations. The SRM service publishes messages to one or more social media platforms and receives user interaction data associated with users' interactions with the messages. The SRM service performs unsupervised machine learning to update the learned model based at least in part on the user interaction data. The SRM service receives a request to configure a social media campaign, applies data associated with the request to the learned model to determine a preferred campaign configuration, and configures the social media campaign based on the preferred campaign configuration.

Machine learning system for configuring social media campaigns

Disclosed is an approach for implementing a system, method, and computer program product for performing social marketing using a cloud-based system. The approach is capable of accessing data across multiple types of internet-based sources of social data and commentary and to perform analysis upon that data. A social marketing campaign can then be generated and implemented in an integrated manner using the system. This permits realtime reaction to trends, with rapid ability to react to opportunities in the marketplace.

Method and system for implementing a cloud-based social media marketing method and system

Growing community of inventors

Decatur, GA, United States of America

Kimberly Ann Wolfe