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:
Jun. 11, 2024

Filed:

Nov. 23, 2020
Applicant:

Optum Technology, Inc., Eden Prairie, MN (US);

Inventors:

Suman Roy, Bangalore, IN;

Vijay Varma Malladi, Hyderabad, IN;

Ayan Sengupta, Noida, IN;

Souparna Das, Murshidabad, IN;

Assignee:

Optum Technology, Inc., Eden Prairie, MN (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/279 (2020.01); G06N 5/02 (2023.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/279 (2020.01); G06N 5/02 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01);
Abstract

There is a need for more effective and efficient predictive natural language topic detection. This need can be addressed by, for example, solutions for performing sequential topic detection. In one example, a method includes determining a sequential topic distribution data object for the current document sequence, determining a current term-context correlation data object for the current document sequence, determining a current context-topic correlation data object for the current document sequence, determining an updated term-topic correlation data object based at least in part on the current context-topic correlation data object, determining topic modeling predictions based at least in part on the sequential topic distribution data object and the updated term-topic correlation data object, and performing prediction-based actions based at least in part on the topic modeling predictions.


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