Optimizing content distribution using a model

ChangeBridge: Patent Grants - AI & Computing (G06N)

Published March 31st, 2026

Detected March 31st, 2026

Summary

The USPTO granted Google LLC Patent US12591806B2 for methods and systems that optimize content distribution using machine learning models. The system trains a model on user attributes and behavioral metrics to predict content relevance and selects content for distribution based on threshold comparisons.

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What changed

The USPTO granted Patent US12591806B2 to Google LLC covering methods and systems for optimizing content presentation through machine learning. The patent describes a system that trains a model using user attribute data and behavioral proxy metrics, then predicts user retention or awareness for given content based on user attributes and content information. Content is selected for distribution when predicted metrics exceed defined thresholds. The patent includes 17 claims and CPC classifications spanning machine learning (G06N 20/00), content filtering (G06F 16/435), and advertising technology (G06Q 30/0261).

Compliance teams at technology companies developing content recommendation or personalization systems should conduct a patent clearance review to assess potential infringement exposure. Legal counsel should evaluate whether existing or planned products practice any claims in this granted patent. If infringement risk exists, companies may consider designing around the claims, seeking a license from Google, or preparing a formal opinion of non-infringement.

What to do next

Conduct patent clearance review for content recommendation or personalization systems
Have legal counsel assess infringement exposure against patent claims
Evaluate design-around options if products potentially practice the patented methods

Source document (simplified)

← USPTO Patent Grants

Optimizing content distribution using a model

Grant US12591806B2 Kind: B2 Mar 31, 2026

Assignee

Google LLC

Inventors

Scott Tadashi Davies, Kai Chen, Michael Jee-Kai Wang, Wei Jiang, Maryam Tavafi, Peter Zaimis Tipton

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing content presentation. In one aspect, a system includes a training database that stores training data including attribute information about users and corresponding proxy metrics quantifying behavior by the users following content presentation; a content database; a model generator that accesses the training data and trains a model for content distribution; and a content distribution server that receives a content request, uses the model to select content, transmits data identifying the selected content, wherein the model: obtains a set of attributes for a user associated with the request, receives information about a given content, predicts a proxy metric based on the set of attributes and the information about the content, the predicted proxy metric providing information about subject retention or awareness; and identifies the given content for distribution if the predicted proxy metrics meet a threshold.