The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 10, 2026

Filed:

Dec. 27, 2022
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Anna Yanchenko, Groton, MA (US);

Wesley M. Gifford, Ridgefield, CT (US);

Brian Leo Quanz, Yorktown Heights, NY (US);

Nam H. Nguyen, Pleasantville, NY (US);

Pavithra Harsha, Pleasantville, NY (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 10/0631 (2023.01);
U.S. Cl.
CPC ...
G06Q 10/06313 (2013.01);
Abstract

Mechanisms are provided for performing automated and dynamic reconciliation of forecasts for hierarchical datasets. A machine learning training is executed on a dynamic reconciliation computer model engine to train the dynamic reconciliation computer model engine, based on historical data and forecast data, to learn an association of reconciliation computer models with structural changes in a hierarchical dataset. Runtime forecast data is generated based on a runtime hierarchical dataset, and the trained dynamic reconciliation computer model engine is executed on the runtime forecast data to reconcile the runtime forecast data across a hierarchy of the runtime forecast data. The trained dynamic reconciliation computer model applies different reconciliation computer models to the runtime forecast data based on structural changes in the runtime forecast data. Reconciled runtime forecast data is generated based on results of executing the trained dynamic reconciliation computer model engine on the runtime forecast data, which is then output.


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