AI Patent for Ad Audience Optimization and Stage Transition

ChangeBridge: Patent Apps - Business Methods (G06Q)

Detected March 27th, 2026

Summary

The USPTO has published a new patent application, US20260087521A1, filed by Microsoft Technology Licensing, LLC. The application describes an AI-driven framework for dynamic ad audience optimization and stage transition using multi-tower machine learning models.

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What changed

This document is a publication of a patent application, not a regulatory rule or guidance. It details a method for optimizing ad audiences using AI and machine learning, specifically a multi-tower model that generates user and campaign embeddings to select audience identifiers and deliver content.

While not a direct regulatory obligation, this patent highlights advancements in AI for advertising technology. Companies in the ad tech and digital marketing space, particularly those utilizing machine learning for audience segmentation and content delivery, should be aware of such patented technologies as they may impact future product development and competitive landscapes. No immediate compliance actions are required.

Source document (simplified)

← USPTO Patent Applications

DYNAMIC AD AUDIENCE OPTIMIZATION AND STAGE TRANSITION FRAMEWORK

Application US20260087521A1 Kind: A1 Mar 26, 2026

Assignee

Microsoft Technology Licensing, LLC

Inventors

Lijun Peng, Xinruo Jing, Xueqian Tang, Yuzhang Hu, Tao Cai, Ruiqi Li, Yi Wu

Abstract

Artificial intelligence (AI) techniques for connection networking are described. A method comprises receiving an input vector by a multi-tower machine learning (ML) model for a content delivery system of a connection network system, generating a set of user embeddings from a first vector of the input vector by a first tower of the multi-tower ML model based on the activity data associated with users of the connection network system, generating a set of campaign embeddings from a second vector of the input vector by a second tower of the multi-tower ML model based on the textual description of the content delivery campaign, generate a metric based on a subset of the user embeddings and a subset of the campaign embeddings, selecting audience user identifiers based on the metric, and sending content items for the content delivery campaign to electronic devices of the audience user identifiers.