Capital One Patent for ML Recommendation Optimization

ChangeBridge: Patent Grants - Business Methods (G06Q)

Published March 31st, 2026

Detected April 1st, 2026

Summary

The USPTO granted patent US12591915B2 to Capital One Services, LLC for methods and systems optimizing machine learning models for real-time recommendations. The patent covers distributed computing improvements including load balancing techniques for recommendation systems across multiple servers. Competitors in financial services technology should review potential infringement implications.

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What changed

The USPTO issued patent US12591915B2 to Capital One Services, LLC on March 31, 2026, covering methods and systems for real-time optimization of machine learning recommendation models. The patent addresses distributed computing improvements, including load balancing between client devices and backend servers to prevent server overload and performance degradation. The patent was filed on March 19, 2021, under application number 17207554, with 17 claims covering the ML optimization techniques.

Financial services technology companies and fintech firms developing ML-based recommendation systems should review this patent to assess potential licensing needs or design-around implications. No immediate compliance deadline applies as this is a patent grant rather than a regulatory requirement. Companies should consult intellectual property counsel regarding any concerns about existing or planned recommendation systems.

What to do next

Review patent US12591915B2 claims to assess potential infringement exposure
Consult intellectual property counsel regarding licensing requirements or design alternatives
Evaluate current ML recommendation systems against the patented load balancing and optimization methods

Source document (simplified)

← USPTO Patent Grants

Methods and systems for determining recommendations based on real-time optimization of machine learning models

Grant US12591915B2 Kind: B2 Mar 31, 2026

Assignee

Capital One Services, LLC

Inventors

Cheng Jiang, Yun Zhou, Christy Purnadi, Abhishek Tewari, Seyed Hossein Zahed Zahedani, Ryan Sralla, Yasong Zhou, Brian McGill, Gavin Olson

Abstract

Methods and systems are described for improvements to the use of distributed computer networks. For example, conventional systems may rely on the distribution of network or application traffic across multiple servers and may maintain load balancers to maintain that distribution in an efficient manner. Each load balancer may sit between client devices and backend servers, receiving and then distributing incoming requests to any available server capable of fulfilling them. The load balancers may ensure that no one server is overworked based on the number of processing requests directed to that server, which could degrade performance.