USPTO Patent Granted for CDF Estimation System

ChangeBridge: Patent Grants - AI & Computing (G06N)

Published March 24th, 2026

Detected March 25th, 2026

Summary

The USPTO has granted a patent (US12585957B2) to the Royal Bank of Canada for a system and method to efficiently estimate a Cumulative Distribution Function (CDF) using trained neural networks. The patent covers a computer-implemented process for calculating probabilities and generating control commands based on future event estimations.

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What changed

The United States Patent and Trademark Office (USPTO) has issued patent US12585957B2 to the Royal Bank of Canada. This patent details a computer-implemented system and method for estimating a Cumulative Distribution Function (CDF) of future events. The technology utilizes trained neural networks to compute flux estimations through defined volumes in base and target spaces, ultimately generating a CDF estimate, calculating the probability of a future target event, and producing a control command.

This is a grant of intellectual property and does not impose new regulatory obligations on entities. However, companies developing or utilizing AI and machine learning for predictive analytics, risk assessment, or control systems may find this patent relevant to their technology landscape. Compliance officers should note this as a development in AI patenting, particularly within the financial services sector, though no immediate compliance actions are required.

Source document (simplified)

← USPTO Patent Grants

System and method for efficient estimation of cumulative distribution function

Grant US12585957B2 Kind: B2 Mar 24, 2026

Assignee

ROYAL BANK OF CANADA

Inventors

Chandramouli Shama Sastry, Alexander Radomir Branislav Radovic, Marcus Anthony Brubaker, Andreas Steffen Michael Lehrmann

Abstract

A computer-implemented system and method for estimating a Cumulative Distribution Function (CDF) are provided. The method includes: receive input data representing a volume V of a target space indicating a future target event; compute, using the trained neural network, an estimation of a first flux through a boundary of the volume V; compute, using the trained neural network, an estimation of a second flux through a boundary of a volume W of a base space based on the estimation of the first flux through the boundary of the volume V; generate, using the trained neural network, an estimation of a CDF for the volume V based on the second flux through the boundary of the volume W; compute a probability for the future target event based on the estimated CDF for the volume V; and generate a control command based on the probability for the future target event.