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:
Jun. 10, 2025

Filed:

Dec. 20, 2022
Applicant:

Dow Global Technologies Llc, Midland, MI (US);

Inventors:

Fabio Aguirre Vargas, Lake Jackson, TX (US);

Sukrit Mukhopadhyay, Midland, MI (US);

Jerome Claracq, Ghent, BE;

Bart Rijksen, Middelburg, NL;

Valeriy V. Ginzburg, Midland, MI (US);

Paul Cookson, Cheadle, GB;

Alix Schmidt, Midland, MI (US);

Shachit Shankaran Iyer, Pearland, TX (US);

Assignee:

Dow Global Technologies LLC, Midland, MI (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G16C 20/30 (2019.01); G16C 20/70 (2019.01);
U.S. Cl.
CPC ...
G16C 20/30 (2019.02); G16C 20/70 (2019.02);
Abstract

Methods include training a machine learning module to predict one or more target product properties for a prospective chemical formulation, including (a) constructing or updating a training data set from one or more variable parameters; (b) performing feature selection on the training data set; (c) building one or more machine learning models using one or more model architectures; (d) validating the one or more machine learning models; (e) selecting at least one of the one or more machine learning models and generating prediction intervals; (g) interpreting the one or more machine learning models; and (h) determining if the one or more target product properties calculated are acceptable and deploying one or more trained machine learning models, or optimizing the one or more machine learning models by repeating steps (b) to (g). Methods also include application of trained machine learning modules to predict formulation properties from prospective data.


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