USPTO Patent Grant US12585970B1: Scorecards, Boosted Decision Trees, Machine Learning

ChangeBridge: Patent Grants - Business Methods (G06Q)

Published March 24th, 2026

Detected March 25th, 2026

Summary

The USPTO has granted patent US12585970B1 to Experian Information Solutions, Inc. for systems and methods related to machine learning-based generation of scorecards and boosted decision trees. The patent covers techniques for creating explainable predictions using historical data and variable binning.

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What changed

The United States Patent and Trademark Office (USPTO) has granted patent US12585970B1, titled 'Systems and methods of implementing scorecards and boosted decision trees,' to Experian Information Solutions, Inc. The patent, filed on November 23, 2021, describes machine learning models that generate scorecards and boosted decision trees to facilitate explainable predictions. Key features include the automatic generation of normal and special bins for variables, with assigned score values, and the generation of a risk assessment score based on these assignments.

This patent grant is primarily an intellectual property matter and does not impose direct regulatory obligations on businesses. However, it signifies a patented innovation in the field of AI and machine learning for predictive analytics and risk assessment. Companies operating in financial services, data analytics, and technology sectors that utilize similar machine learning techniques for scorecard generation should be aware of this patent and its potential implications for their intellectual property landscape.

Source document (simplified)

← USPTO Patent Grants

Systems and methods of implementing scorecards and boosted decision trees

Grant US12585970B1 Kind: B1 Mar 24, 2026

Assignee

Experian Information Solutions, Inc.

Inventors

Honghao Shan, Liang Lin, Chi Zhang, Keming Cao, Zhe An, Shanji Xiong

Abstract

Systems and methods are described for machine learning-based generation of scorecards and boosted decision trees that facilitate explainable predictions. A scorecard machine learning model may be applied to historical records such that the model, for each of a number of variables, automatically generates (a) normal bins for normal values of the variable that fall within a valid range of values and (b) at least one special bin for special values of the variable that fall outside the valid range of values. Adjacent bins of the normal bins may be separated by a threshold value and each normal bin and each special bin may have an assigned score value. A risk assessment score may be generated based at least in part on the model identifying the assigned value score for each of the variables based on the normal or special bin to which each variable is assigned.