US 12,333,561 B2
Anomaly detection systems and methods
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); and Zealand Cooley, Albion, MI (US)
Assigned to Verint Americas Inc., Alpharetta, GA (US)
Filed by Verint Americas Inc., Alpharetta, GA (US)
Filed on Jun. 14, 2022, as Appl. No. 17/840,279.
Prior Publication US 2023/0401591 A1, Dec. 14, 2023
Int. Cl. G06Q 30/0203 (2023.01); G06F 18/214 (2023.01); G06Q 30/0201 (2023.01)
CPC G06Q 30/0203 (2013.01) [G06F 18/214 (2023.01); G06Q 30/0201 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for detecting anomalies in actual survey score results, the method comprising:
receiving, by a preprocessing component, historic survey score data from a survey scores database;
generating, by the preprocessing component, a set of training survey score data for a given scored survey metric and a given duration from the historic survey score data, using a score formatting component, a grouping component, and a division component;
generating, by a modeling component, a survey score prediction model for the given scored survey metric and the given duration;
training the survey score prediction model to generate an expected survey score for the given scored survey metric and given duration with the set of training survey score data;
receiving, by the preprocessing component, an actual survey score result associated with a scored survey metric and a duration for a specific date;
determining, by the preprocessing component, the scored survey metric and the duration for the actual survey score result match the given scored survey metric and the given duration for the survey score prediction model;
passing, to the modeling component, a current survey score data associated with the actual survey score result to use the survey score prediction model associated with the given scored survey metric and the given duration;
generating, by the modeling component, an expected survey score for the specific date based on the current survey score data using the survey score prediction model;
receiving, by an anomaly detection component, a user-defined confidence level and a user-defined standard deviation augmentation;
generating, by the anomaly detection component, a confidence interval for the expected survey score based on the user-defined confidence level;
generating, by the anomaly detection component, a standard deviation band for the expected survey score based on a standard deviation and the user-defined standard deviation augmentation;
determining, by the anomaly detection component, the actual survey score result is an anomaly when the actual survey score result is outside the confidence interval and when the actual survey score result is outside of the standard deviation band;
generating, by the anomaly detection component, an anomaly report for the actual survey score result;
updating, by the modeling component, the survey score prediction model with a set of prediction survey score data to improve the survey score prediction model's ability to predict the expected survey score, wherein the set of survey score prediction data includes the current survey score data; and
replacing the survey score prediction model with the updated survey score prediction model.