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. 17, 2025

Filed:

Jun. 14, 2022
Applicant:

Verint Americas Inc., Alpharetta, GA (US);

Inventors:

Hamed Janani, Vancouver, CA;

Anirudh Challa, Burnaby, CA;

Hong Wang, San Mateo, CA (US);

Mohamad Al-Sharara, Westmount, CA;

José R. Benkí, Ann Arbor, MI (US);

Zealand Cooley, Albion, MI (US);

Assignee:

Verint Americas Inc., Alpharetta, GA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 30/0203 (2023.01); G06F 18/214 (2023.01); G06Q 30/0201 (2023.01);
U.S. Cl.
CPC ...
G06Q 30/0203 (2013.01); G06F 18/214 (2023.01); G06Q 30/0201 (2013.01);
Abstract

An anomaly detection system using machine learning to generate predicted survey scores for a given duration and a given metric based on historic survey score data. The system compares a predicted survey score to the actual survey score and identifies anomalous actual survey scores. The anomaly detection system trains a plurality of survey score prediction models using historic survey score data. Each survey score prediction model is based on a specific survey score metric and a specific duration. The survey score prediction models generate expected survey score results for the given duration and the given metric. Based on the user-determined filtering and tolerances, the system determines if the actual survey score result is anomalous. The system generates reports for the detected anomalies and continually updates the survey score prediction models with newly obtained actual survey results, thereby improving the anomaly detection accuracy over time.


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