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:
Oct. 31, 2023

Filed:

Sep. 21, 2020
Applicant:

Evolv Technology Solutions, Inc., San Francisco, CA (US);

Inventors:

Risto Miikkulainen, Stanford, CA (US);

Neil Iscoe, Austin, TX (US);

Assignee:

Evolv Technology Solutions, Inc., San Francisco, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/04 (2023.01); G06F 16/26 (2019.01); G06F 16/23 (2019.01); G06F 16/958 (2019.01); G06Q 30/02 (2023.01); G06F 40/143 (2020.01); G06N 3/086 (2023.01); G06F 3/0484 (2022.01); G06F 11/36 (2006.01); G06N 3/126 (2023.01); G06F 9/451 (2018.01); G06F 8/36 (2018.01); G06N 3/06 (2006.01);
U.S. Cl.
CPC ...
G06N 3/04 (2013.01); G06F 3/0484 (2013.01); G06F 11/3664 (2013.01); G06F 16/2379 (2019.01); G06F 16/26 (2019.01); G06F 16/958 (2019.01); G06F 40/143 (2020.01); G06N 3/086 (2013.01); G06N 3/126 (2013.01); G06Q 30/02 (2013.01); G06F 8/36 (2013.01); G06F 9/451 (2018.02); G06N 3/06 (2013.01);
Abstract

Roughly described, the technology disclosed provides a so-called machine-learned conversion optimization (MLCO) system that uses artificial neural networks and evolutionary computations to efficiently identify most successful webpage designs in a search space without testing all possible webpage designs in the search space. The search space is defined based on webpage designs provided by marketers. Neural networks are represented as genomes. Neural networks map user attributes from live user traffic to different dimensions and dimension values of output funnels that are presented to the users in real time. The genomes are subjected to evolutionary operations like initialization, testing, competition, and procreation to identify parent genomes that perform well and offspring genomes that are likely to perform well.


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