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:
Sep. 03, 2019

Filed:

Jul. 29, 2016
Applicant:

Sap SE, Walldorf, DE;

Inventors:

Judith Hoetzer, Sulzfeld, DE;

Philip Miseldine, Karlsruhe, DE;

Assignee:

SAP SE, Walldorf, DE;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
H03M 7/30 (2006.01); H04N 1/00 (2006.01); G06N 7/00 (2006.01); G06N 20/00 (2019.01); G06F 3/00 (2006.01); G06F 17/27 (2006.01); G06Q 40/08 (2012.01); G06Q 10/10 (2012.01); G06F 16/33 (2019.01); G06F 3/0484 (2013.01); G06F 17/22 (2006.01); G06F 3/0482 (2013.01); G06F 17/24 (2006.01); G06F 3/0488 (2013.01); G06F 17/21 (2006.01); G06F 3/0483 (2013.01);
U.S. Cl.
CPC ...
G06N 7/005 (2013.01); G06F 3/00 (2013.01); G06F 16/3338 (2019.01); G06F 17/277 (2013.01); G06F 17/2785 (2013.01); G06N 20/00 (2019.01); G06Q 10/10 (2013.01); G06Q 40/08 (2013.01); G06F 3/0482 (2013.01); G06F 3/0483 (2013.01); G06F 3/0484 (2013.01); G06F 3/04842 (2013.01); G06F 3/04883 (2013.01); G06F 3/04886 (2013.01); G06F 17/211 (2013.01); G06F 17/22 (2013.01); G06F 17/242 (2013.01); G06F 17/243 (2013.01); G06F 17/2765 (2013.01);
Abstract

In an example embodiment, first user input including handwriting input and non-alphanumeric symbolic input is detected. The non-alphanumeric symbolic input is input into a first machine learning model trained to output a set of possible actions corresponding to the non-alphanumeric symbolic input and a probability score assigned to each action in the set of possible actions. A combination of the action having the highest probability score and textual input from the handwriting input is input into a second machine learning model trained to select a service from a plurality of services based on the textual input and the selected action by referencing a service model corresponding to each service in the plurality of services. The combination of the textual input and the selected action is transformed into a native request for the selected service based on the service model for the selected service.


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