CALL FLOW AND DISCOURSE ANALYSIS

• US 20160154782A1
• Filed: 11/25/2015
• Published: 06/02/2016
• Est. Priority Date: 10/31/2013
• Status: Active Grant

• Current Assignee: Verint Systems Incorporated
• Original Assignee: Verint Systems Limited (Verint Systems Incorporated)
• Inventors: Romano, Roni, Fehr, Rahm
• Granted Patent: US 9,542,382 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 40/211 Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: H04M 2203/2038 Call context notifications
• CPC Class Codes: H04M 3/2218 Call detail recording
• CPC Class Codes: H04M 3/42221 Conversation recording systems at the subscriber's set H04M1/656
• CPC Class Codes: H04M 3/5175 Call or contact centers supervision arrangements
• CPC Class Codes: H04M 3/5183 Call or contact centers with computer-telephony arrangements

Abstract

The disclosed solution uses machine learning-based methods to improve the knowledge extraction process in a specific domain or business environment. By formulizing a specific company'"'"'s internal knowledge and terminology, the ontology programming accounts for linguistic meaning to surface relevant and important content for analysis. Based on the self-training mechanism developed by the inventors, the ontology programming automatically trains itself to understand the business environment by processing and analyzing a defined corpus of communication data. For example, the disclosed ontology programming adapts to the language used in a specific domain, including linguistic patterns and properties, such as word order, relationships between terms, and syntactical variations. The disclosed system and method further relates to leveraging the ontology to assess a dataset and conduct a funnel analysis to identify patterns, or sequences of events, in the dataset.

Claims

1. A method of automated discourse analysis of interaction transcriptions, the method comprising;

receiving, by at least one processor, a plurality of interaction transcriptions;

for each respective interaction transcript of the plurality of interaction transcriptions;

identifying, by the at least one processor, a plurality of meaning units within the respective interaction transcription;

classifying, by the at least one processor, the plurality of meaning units into dialog acts, the dialog acts include at least the following categories;

social, information, request, response and repetition, the repetition category being used to identify a repetition of a immediately previous dialog act when one party to a given interaction repeats a meaning unit previously spoken by another party to the given interaction; and

identifying, by the at least one processor, durations between the dialog acts;

selecting, by the at least one processor, a subset of the plurality of interaction transcriptions based on the durations;

extracting, by the at least one processor, syntactic patterns from the selected subset; and

outing, by the at least one processor, the extracted patterns as a visual presentation on a graphical display.

2. The method of claim 1, further comprising selecting, by the at least one processor, the subset with long call duration.

3. The method of claim 1, further comprising selecting, by the at least one processor, the subset with the longest identified durations between dialog acts.

4. The method of claim 2, wherein the extracted patterns include an identification of the agent or group of customer service agents represented in the selected subset with long call duration.

5. The method of claim 2, wherein the extracted patterns include an identification of a reason for call length of the selected subset with long call duration and an indication of which particular topics, events or dialog acts are most likely to result in abnormally long calls.

6. The method of claim 3, wherein the extracted patterns include an identification of the agent or group of customer service agents represented in the selected subset with the longest identified durations between dialog acts.

7. The method of claim 3, wherein the extracted patterns include an identification of a reason for call length of the selected subset with the longest identified durations between dialog acts and an identification of which dialog acts are most likely to result in abnormally long calls.

8. The method of claim 1, further comprising selecting, by the at least one processor, the subset that resulted in a sales outcome.

9. The method of claim 1, wherein the extracted patterns include an identification of how a call results in an upsell start.

10. The method of claim 1, further comprising selecting, by the at least one processor, the subset that resulted in a cancelation outcome.

11. The method of claim 10, wherein the extracted patterns include an identification of what is the call flow of a call that results in a cancelation.

12. The method of claim 1, further comprising selecting, by the at least one processor, the subset by processing the plurality of interaction transcriptions to identify a triggering theme and then creating the subset around the identified triggering theme.

13. The method of claim 12, wherein the subset is created as a particular number of words before and after the trigger theme appearance.

14. The method of claim 12, wherein the subset is created as a particular number of back and forth interactions between an agent and a customer.

15. A non-transitory computer-readable medium having instructions that when executed by a hardware processor, cause the processor to perform a method comprising;

receiving a plurality of interaction transcriptions;

for each respective interaction transcript of the plurality of interaction transcriptions;

identifying a plurality of meaning units within the respective interaction transcription;

classifying the plurality of meaning units into dialog acts, the dialog acts include at least the following categories;

social, information, request, response and repetition, the repetition category being used to identify a repetition of a immediately previous dialog act when one party to a given interaction repeats a meaning unit previously spoken by another party to the given interaction; and

identifying durations between the dialog acts;

selecting a subset of the plurality of interaction transcriptions based on the durations;

extracting syntactic patterns from the selected subset; and

outing the extracted patterns as a visual presentation on a graphical display.

16. The non-transitory computer-readable medium of claim 15, wherein the method further comprises selecting the subset with long call duration.

17. The non-transitory computer-readable medium of claim 15, wherein the method further comprises selecting the subset with the longest identified durations between dialog acts.

18. The non-transitory computer-readable medium of claim 15, wherein the method further comprises selecting the subset that resulted in a sales outcome.

19. The non-transitory computer-readable medium of claim 15, wherein the method further comprises selecting the subset that resulted in a cancelation outcome.

20. The non-transitory computer-readable medium of claim 15, wherein the method further comprises selecting the subset by processing the plurality of interaction transcriptions to identify a triggering theme and then creating the subset around the identified triggering theme.