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. 12, 2019

Filed:

Nov. 14, 2017
Applicant:

Sap SE, Walldorf, DE;

Inventors:

Sivakumar N, Coimbatore, IN;

Praveenkumar A K, Bangalore, IN;

Raghavendra D, Bangalore, IN;

Vijay G, Bangalore, IN;

Pratik Shenoy, Bangalore, IN;

Kishan Kumar Kedia, Birpara, IN;

Assignee:

SAP SE, Walldorf, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06K 9/62 (2006.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06K 9/32 (2006.01); G06K 9/34 (2006.01); G06K 9/46 (2006.01); G06T 7/73 (2017.01); G06N 20/00 (2019.01); G06T 7/60 (2017.01); G06N 3/02 (2006.01); G06N 5/04 (2006.01); G06Q 10/08 (2012.01);
U.S. Cl.
CPC ...
G06K 9/6256 (2013.01); G06K 9/00208 (2013.01); G06K 9/00771 (2013.01); G06K 9/00973 (2013.01); G06K 9/3241 (2013.01); G06K 9/34 (2013.01); G06K 9/4628 (2013.01); G06K 9/6204 (2013.01); G06K 9/6218 (2013.01); G06K 9/6268 (2013.01); G06K 9/6273 (2013.01); G06K 9/6284 (2013.01); G06N 3/02 (2013.01); G06N 3/04 (2013.01); G06N 3/0454 (2013.01); G06N 3/084 (2013.01); G06N 5/047 (2013.01); G06N 20/00 (2019.01); G06T 7/60 (2013.01); G06T 7/75 (2017.01); G06Q 10/087 (2013.01); G06T 2200/28 (2013.01); G06T 2207/30242 (2013.01);
Abstract

In an example, a computerized neural fabric is created by representing each pattern of learned weights of one or more machine learning algorithm-trained models specifying a specific set of products as a column in the computerized neural fabric, each pattern comprising one or more clusters representing combinations of convolutional filters, each cluster learning low level features and sending output via a vertical flow up the corresponding column to a final classification within the corresponding pattern. One or more potential lateral flows between patterns in the computerized neural fabrics is dynamically determined based on resemblance of a new product in a candidate image to the specific sets of products in each of the patterns and identifying possible mutations of the patterns based on the resemblance. Then, one of the one or more potential lateral flows is selected as a new model.


Find Patent Forward Citations

Loading…