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:
Jan. 04, 2022

Filed:

Aug. 17, 2018
Applicant:

Uncountable Inc., Sunnyvale, CA (US);

Inventors:

Jason Isaac Hirshman, San Francisco, CA (US);

Noel Hollingsworth, San Francisco, CA (US);

Will Tashman, San Francisco, CA (US);

Assignee:

Uncountable Inc., San Francisco, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 5/04 (2006.01); G06N 3/12 (2006.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01); G06N 3/08 (2006.01); G06N 7/00 (2006.01);
U.S. Cl.
CPC ...
G06N 5/04 (2013.01); G06N 3/126 (2013.01); G06N 20/10 (2019.01); G06N 3/084 (2013.01); G06N 7/005 (2013.01); G06N 20/20 (2019.01);
Abstract

Recommendations for new experiments are generated via a pipeline that includes a predictive model and a preference procedure. In one example, a definition of a development task includes experiment parameters that may be varied, the outcomes of interest and the desired goals or specifications. Existing experimental data is used by machine learning algorithms to train a predictive model. The software system generates candidate experiments and uses the trained predictive model to predict the outcomes of the candidate experiments based on their parameters. A merit function (referred to as a preference function) is calculated for the candidate experiments. The preference function is a function of the experiment parameters and/or the predicted outcomes. It may also be a function of features that are derived from these quantities. The candidate experiments are ranked based on the preference function.


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Hirshman, Jason. (2022). Artificial intelligence guided research and development (U.S. Patent No. 11216737). U.S. Patent and Trademark Office. https://idiyas.com/patent/badge/11216737

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