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:
Nov. 15, 2022

Filed:

Nov. 06, 2018
Applicant:

Baldur Andrew Steingrimsson, Hillsboro, OR (US);

Inventors:

Baldur Andrew Steingrimsson, Hillsboro, OR (US);

Kwan Yi, Richmond, KY (US);

Robert L Jones, Hillsboro, OR (US);

Mikalai Kisialiou, Hillsboro, OR (US);

Anand A Kulkarni, Davidson, NC (US);

Assignee:

IMAGARS LLC, Wilsonville, OR (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/04 (2006.01); G06Q 30/02 (2012.01); G06F 16/33 (2019.01); G06F 16/2458 (2019.01); G06N 3/02 (2006.01); G06F 30/27 (2020.01);
U.S. Cl.
CPC ...
G06F 30/27 (2020.01); G06F 16/2465 (2019.01); G06F 16/3334 (2019.01); G06F 16/3344 (2019.01); G06N 3/04 (2013.01); G06Q 30/0202 (2013.01);
Abstract

This invention presents a framework for applying artificial intelligence to aid with product design, mission or retail planning. The invention outlines a novel approach for applying predictive analytics to the training of a system model for product design, assimilates the definition of meta-data for design containers to that of labels for books in a library, and represents customers, requirements, components and assemblies in the form of database objects with relational dependence. Design information can be harvested, for the purpose of improving decision fidelity for new designs, by providing such database representation of the design content. Further, a retrieval model, that operates on the archived design containers, and yields results that are likely to satisfy user queries, is presented. This model, which is based on latent semantic analysis, predicts the degree of relevance between accessible design information and a query, and presents the most relevant previous design information to the user.


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