Machine Learning for Digital Notification Interaction Metrics

ChangeBridge: Patent Apps - Networking (H04L)

Detected March 27th, 2026

Summary

The USPTO has published a patent application detailing systems and methods for using machine learning to predict user interaction metrics for digital notifications. The application, filed on October 1, 2024, aims to optimize the distribution of these notifications across computer networks.

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What changed

This document is a patent application (US20260089050A1) from the USPTO, filed on October 1, 2024, and published with a projected date of March 26, 2026. It describes a system utilizing machine learning models to generate predicted referee interaction metrics. These metrics are intended to inform a digital notification distribution policy for various tiers of referrer client devices, optimizing how and when notifications are transmitted across computer networks.

As this is a patent application, it does not impose immediate regulatory requirements or compliance deadlines on entities. However, it signals potential future technological developments in notification systems and AI-driven analytics. Companies involved in network communications, software development, and AI may wish to monitor the progress of this patent and similar applications for insights into emerging technologies and potential intellectual property landscapes.

Source document (simplified)

← USPTO Patent Applications

UTILIZING MACHINE LEARNING MODELS TO GENERATE PREDICTED REFEREE INTERACTION METRICS FOR GENERATING AND TRANSMITTING DIGITAL NOTIFICATIONS ACROSS COMPUTER NETWORKS TO REFERRER CLIENT DEVICES

Application US20260089050A1 Kind: A1 Mar 26, 2026

Inventors

Akshat Khandelwal, Jason Michael Lee, Li-Ping Chin, Andrew Robert Ratcliffe, Lin-Yu Tai, Hadi Ramezani-Dakhel

Abstract

The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning to generate predicted referee interaction metrics for building a digital notification distribution policy for tiers of referrer client devices and transmitting digital notifications to referrer client devices across computer networks. In particular, in one or more embodiments, the disclosed systems utilize a referee interaction prediction machine learning model that generate predicted referee interaction metrics indicating likelihoods of downstream interactions of referee client devices based on features of referrer client devices. The disclosed systems generate referrer client device tiers for referrer client devices based on the predicted referee interaction metrics and then utilizes an optimization model to generate a digital notification distribution policy for the tiers of the referrer client devices. Further, the disclosed systems transmit digital notifications to referrer client devices in accordance with the digital notification policy and the referrer client device tiers.