Systems and methods for adjusting data processing components for non-operational targets

ChangeBridge: Patent Grants - AI & Computing (G06N)

Published April 7th, 2026

Detected April 7th, 2026

Summary

The USPTO granted Patent US12596926B2 to Capital One Services LLC on April 7, 2026. The patent covers machine learning systems and methods for predicting when target systems will become non-operational using similarity graphs and trained ML models, with provisions for adjusting data processing components accordingly. The patent contains 20 claims and is classified under CPC groups G06N (machine learning).

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What changed

The USPTO issued Patent US12596926B2 titled 'Systems and methods for adjusting data processing components for non-operational targets' to Capital One Services, LLC. The patent describes a two-stage machine learning approach where a first model uses similarity graphs generated from training entries to predict system non-operational status, and a second model processes these predictions with inference entries to refine forecasts. The system then adjusts data processing components based on these predictions.

Patent grants confer exclusive legal rights to the assignee for the claimed invention. Competitors developing similar ML-based predictive maintenance or system failure detection technologies should review the 20 patent claims for potential infringement exposure. The patent's 20-year term from the September 2022 filing date provides Capital One with intellectual property protection for its financial technology innovations.

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Source document (simplified)

← USPTO Patent Grants

Systems and methods for adjusting data processing components for non-operational targets

Grant US12596926B2 Kind: B2 Apr 07, 2026

Assignee

Capital One Services, LLC

Inventors

Aamer Charania, Abhisek Jana, Jiankun Liu, Behrouz Saghafi Khadem

Abstract

Systems and methods for adjusting data processing components. In some aspects, the systems and methods include training a first machine learning model using a similarity graph generated based on training entries to predict whether a target system related to a node in the similarity graph will be non-operational within a future period of time, processing using the trained first machine learning model an updated similarity graph generated based on training and inference entries to predict for each node for the inference entries whether a target system related to the node will be non-operational within the future period of time, processing using a second machine learning model predictions and associated inference entries to predict that a target system related to a node for an entry will be non-operational within the future period of time, and adjusting data processing components related to the target system.