Soci, Inc.

Zeetogroup, LLC

Matthew William Maybeno

Alo Sarv

Afif Khoury

Herbert Johnson

Marcie Gately

Alexander James Egan

Laura D Gonzalez

Stephan Goss

Matt Ferguson

This document describes the collection, generation, distribution, and management of online web content. The devices, systems, and methods described herein can be used to collect and generate online web content and communications. Specifically, the disclosed devices and systems may be employed to produce one or more marketing and/or advertising campaigns, as well as for monitoring, managing, defining the efficiency, effectiveness, and workability of the campaign with respect to generating user engagement, thereby accurately determining the cost benefits of the campaign. The analytic results provided may then be used to guide the generation of original web content, such as for the purposes of enhancing customer or follower experience, driving business, and for driving advertising campaigns. Alternatively, web content that is in the public domain, and determined to perform well, can be reproduced, referenced, or otherwise referred to, in the context of promoting or presenting the user's web content.

Systems, devices, and methods for dynamically generating, distributing, and managing online communications

Systems and methods disclosed herein relate to utility based selection of inquiries in a streaming data pipeline. An indication of user identification data and a data source may be received by a server in communication with a distributed streaming data pipeline. This pipeline may be in communication with asynchronous event processing services. Those event processing services may determine an inquiry rule that includes an inquiry threshold. This rule may be based on the data source and retrieval of information related to that data source from memory in a database server. The rule may prompt a comparison of an inquiry count to an inquiry threshold. If the count is below the threshold, an inquiry may be sent to a user based in part upon expected utility that is itself based upon several indications of expected utility calculated by an event processing service.

Utility based inquiry selection in a streaming data pipeline

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San Diego, CA, United States of America

Matthew William Maybeno