Anomaly Detection in Interaction Data

• US 20140222476A1
• Filed: 02/05/2014
• Published: 08/07/2014
• Est. Priority Date: 02/06/2013
• Status: Abandoned Application

• Current Assignee: Verint Systems Limited (Verint Systems Incorporated)
• Original Assignee: Verint Systems Limited (Verint Systems Incorporated)
• Inventors: Romano, Roni
• Application Number:
  US14/173,437
• Publication Number:
  US 20140222476A1
• Time in Patent Office: Days
• US Class Current: 705/7.11
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 30/016 After-sales

Abstract

Method of automated anomaly detection includes obtaining a corpus of interaction data. Regular interaction data is identified from the corpus of interaction data with a processor. New interaction data is received. The processor compares the new interaction data to the identified regular interaction data The processor identities anomalies in the new interaction data.

Claims

1. A method of automated anomaly detection, the method comprising:

obtaining a corpus of interaction data at a computer readable medium;

identifying, with a processor, regular interaction data from the corpus;

receiving new interaction data at the processor;

comparing the new interaction data to the identified regular interaction data with the processor; and

identifying anomalies in the new interaction data with the processor.

2. The method of claim 1, wherein the interaction data is customer service interaction data.

3. The method of claim 2, wherein the customer service interaction data comprises a plurality of data attributes, each data attribute having an attribute value from a range of attribute values.

4. The method of claim 3, wherein the corpus comprises a plurality of transcriptions of customer service interactions.

5. The method of claim 3, wherein identifying the regular interaction data further comprises:

identifying a plurality of data attributes in the corpus; and

calculating a probability for each identified data attribute to occur in the interaction data of the corpus.

6. The method of claim 5, wherein the plurality of data attributes in the corpus are identified using a minimum information gain criteria.

7. The method of claim 5, wherein the probability for each identified data attribute is a distribution of the attribute values for that data attribute.

8. The method of claim 7, wherein comparing the new interaction data to the identified regular interaction data further comprises:

identifying an attribute value from the new interaction data for each of the plurality of data attributes;

comparing each identified attribute value from the new interaction data to the distribution of the attribute values for that data attribute.

9. The method of claim 3, further comprising;

evaluating each of the identified anomalies; and

categorizing each identified anomaly as either a true anomaly or an internal error.

10. The method of claim 3, wherein identifying the regular interaction data further comprises:

selecting subset of the corpus and labeling the selected subset as regular interaction data;

generating a random corpus of interaction data and labeling the interaction data of the random corpus as irregular;

analyzing the selected subset of the corpus and the random corpus to identify at least one regular interaction data pattern;

11. The method of claim 10, wherein identifying the regular interaction data further comprises identifying at least one anomaly pattern from the analysis of the selected subset of the corpus and the random corpus.

12. The method of claim 11, further comprising storing the identified at least one anomaly pattern for later application to new interaction data.

13. The method of claim 11, wherein identifying anomalies in the new interaction data with the processor further comprises:

applying, the identified at least one regular interaction data pattern and at least one anomaly pattern to the new interaction data;

determining if the new interaction data better fits the at least one regular interaction data pattern or the at least one anomaly pattern.

14. A method of automated anomaly detection, the method comprising:

obtaining a corpus of interaction data at a computer readable medium;

identifying, with a processor, regular interaction data from the corpus by identifying a plurality of attributes in the corpus of interaction data and an associated distribution of values for each of the identified plurality of attributes;

receiving, new interaction data at the processor;

identifying the plurality of attributes in the new interaction data and values for each of the identified attributes;

comparing the attribute values from the new interaction data to the associated distribution of values for each of the plurality of attributes; and

based upon the comparison, identifying anomalies in the new interaction data with processor.

15. The method of claim 14, further comprising:

calculating an error between the attribute values from the new interaction data to the associated distribution of values for each of the plurality of attributes;

comparing the calculated errors to a predetermined threshold to identify if an attribute value is an anomaly.

16. The method of claim 15, wherein if the attribute value is an anomaly, evaluating the attribute values to determine if the anomaly is an internal error or a true anomaly.

17. The method of claim 14, wherein the plurality of data attributes in the corpus are identified using a minimum information gain criteria.

18. A method of automated anomaly detection, the method comprising:

obtaining a corpus of interaction data, the interaction data comprising a plurality of attribute values at a computer readable medium;

identifying, with a processor, regular interaction data from the corpus by taking a subset of the corpus and labeling the attribute values of the subset as regular interaction data;

generating a random corpus comprising a plurality of attribute random values, and labeling the plurality of attribute random values as irregular interaction data;

deriving at least one regular attribute pattern and at least one anomaly attribute pattern from the regular interaction data and the irregular interaction data

receiving new interaction data at the processor;

comparing the new interaction data to the at least one regular attribute pattern and at least one anomaly attribute pattern; and

identifying anomalies in the new interaction data with the processor.

19. The method of claim 18, further comprising storing the derived at least one regular attribute pattern and at least one anomaly attribute pattern.

20. The method of claim 18, wherein the at least one regular attribute pattern and at least one anomaly attribute pattern are decision trees based upon minimum information gain criteria.