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:
Jul. 11, 2023

Filed:

May. 29, 2020
Applicant:

Maxar Mission Solutions Inc., Westminster, CO (US);

Inventors:

Arnold Boedihardjo, Westminster, CO (US);

Adam Estrada, Westminster, CO (US);

Andrew Jenkins, Westminster, VA (US);

Nathan Clement, Westminster, CO (US);

Alan Schoen, Westminster, CO (US);

Assignee:

MAXAR MISSION SOLUTIONS INC., Westminster, CO (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/20 (2019.01); G06N 20/00 (2019.01); G06N 5/04 (2023.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06V 10/75 (2022.01); G06V 10/764 (2022.01); G06V 10/77 (2022.01); G06V 40/16 (2022.01);
U.S. Cl.
CPC ...
G06N 20/20 (2019.01); G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06V 10/76 (2022.01); G06V 10/764 (2022.01); G06V 10/7715 (2022.01); G06V 40/172 (2022.01);
Abstract

Techniques for quantifying accuracy of a prediction model that has been trained on a data set parameterized by multiple features are provided. The model performs in accordance with a theoretical performance manifold over an intractable input space in connection with the features. A determination is made as to which of the features are strongly correlated with performance of the model. Based on the features determined to be strongly correlated with performance of the model, parameterized sub-models are created such that, in aggregate, they approximate the intractable input space. Prototype exemplars are generated for each of the created sub-models, with the prototype exemplars for each created sub-model being objects to which the model can be applied to result in a match with the respective sub-model. The accuracy of the model is quantified using the generated prototype exemplars. A recommendation engine is provided for when there are particular areas of interest.


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