METHODS AND APPARATUS FOR REAL-TIME INTERACTION ANALYSIS IN CALL CENTERS

• US 20110307257A1
• Filed: 06/10/2010
• Published: 12/15/2011
• Est. Priority Date: 06/10/2010
• Status: Active Grant

• Current Assignee: Nice Ltd
• Original Assignee: Nice Systems Limited (Nice Ltd)
• Inventors: PEREG, Oren, WASSERBLAT, Moshe, LUBOWICH, Yuval, LAPERDON, Ronen, SHAPIRA, Dori, FEIGIN, Vladislav, FOX-KAHANA, Oz
• Granted Patent: US 9,015,046 B2
• Time in Patent Office: Days
• US Class Current: 704/251
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 30/01 Customer relationship services
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G10L 15/08 Speech classification or search
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: G10L 2015/081 Search algorithms, e.g. Baum-Welch or Viterbi
• CPC Class Codes: G10L 2015/085 Methods for reducing search complexity, pruning

Abstract

A method and system for indicating in real time that an interaction is associated with a problem or issue, comprising: receiving a segment of an interaction in which a representative of the organization participates; extracting a feature from the segment; extracting a global feature associated with the interaction; aggregating the feature and the global feature; and classifying the segment or the interaction in association with the problem or issue by applying a model to the feature and the global feature. The method and system may also use features extracted from earlier segments within the interaction. The method and system can also evaluate the model based on features extracted from training interactions and manual tagging assigned to the interactions or segments thereof.

Claims

1. A method for indicating in real time that an interaction in which a representative of an organization participates is associated with a problem or issue, comprising:

receiving a segment of the interaction;

extracting a feature from the segment;

extracting a global feature associated with the interaction;

aggregating the feature and the global feature; and

classifying the segment or the interaction in association with the problem or issue by applying a model to the feature and the global feature.

2. The method of claim 1 further comprising determining an action to be taken when the classification indicated that the segment or interaction are associated with the problem or issue.

3. The method of claim 1 further comprising:

receiving a training corpus;

framing an interaction of the test corpus into segments;

extracting features from the segments; and

evaluating the model based on the features.

4. The method of claim 1 wherein the feature relates to the segment and is selected from the group consisting of:

a spotted word;

a feature or indication associated with emotion;

an emotion score that represents the probability of emotion to exist within the segment;

a talk analysis parameters;

an agent burst position;

a customer burst position;

a number of bursts;

agent talk percentage;

customer talk percentage;

a silence duration;

a word extracted by a speech to text engine;

number of holds;

hold duration;

hold position;

number of transfers;

part of speech tagging or stemming;

segment position within the interaction in absolute time, segment position within the interaction in number of words;

speaker side within the segment; and

average duration of silence between words in the segment.

5. The method of claim 1 wherein the global feature is selected from the group consisting of:

average agent activity within the interaction;

average customer activity within the interaction;

average silence;

number of bursts per participant side;

total burst duration per participant side;

an emotion detection feature;

total probability score for an emotion within the interaction;

number of emotion bursts;

total emotion burst duration;

a stemmed word, a stemmed keyphrase;

number of word repetitions; and

number of keyphrase repetitions.

6. The method of claim 1 wherein the aggregation step aggregates a feature related to a second segment preceding the segment, the feature selected from the group consisting of:

a spotted word;

a feature or indication associated with emotion;

an emotion score that represents the probability of emotion to exist within the segment;

a talk analysis parameters;

an agent burst position;

a customer burst position;

a number of bursts;

agent talk percentage;

customer talk percentage;

a silence duration;

a word extracted by a speech to text engine;

number of holds;

hold duration;

hold position;

number of transfers;

part of speech tagging or stemming;

segment position within the interaction in absolute time, segment position within the interaction in number of words;

speaker side within the segment; and

average duration of silence between words in the segment.

7. The method of claim 2 wherein the action is selected from the group consisting of:

popping a message on a display device used by a person;

generating an alert;

routing the interaction;

conferencing the interaction; and

adding data to a statistical storage.

8. The method of claim 1 wherein the problem or issues is selected from the group consisting of:

customer dissatisfaction;

churn prediction;

sales assistance; and

fraud detection.

9. The method of claim 1 wherein the feature is a word spoken in the segment or interaction, the method comprising:

generating a phoneme graph from the segment or interaction, and

flushing a part of the graph so as to enable for searching a word within the graph.

10. The method of claim 1 wherein the segment of the interaction overlaps with a previous or following segment of the interaction.

11. The method of claim 9 further comprising fast search of the part of the graph.

12. A system for indicating in real time that an interaction in which a representative of an organization participates is associated with a problem or issue, comprising:

an extraction component for extracting a feature from a segment of an interaction in which a representative of the organization participates;

an interaction feature extraction component for extracting a global feature from the interaction; and

a classification component for determining by applying a model to the feature and the global feature whether a problem or issue associated are presented by the segment or interaction.

13. The apparatus of claim 12 further comprising an action determination component for determining an action to be taken after determining that the problem or issue associated are presented by the segment or interaction.

14. The apparatus of claim 12 further comprising an application component for taking an action upon determining that the problem or issue associated are presented by the segment or interaction.

15. The apparatus of claim 12 further comprising a model evaluation component for evaluating the model used by the classification component.

16. The apparatus of claim 15 further comprising a tagging component for assigning tags associated with the problem or issue to interactions or segments.

17. The apparatus of claim 12 wherein the feature relates to the segment and is selected from the group consisting of:

a spotted word;

a feature or indication associated with emotion;

an emotion score that represents the probability of emotion to exist within the segment;

a talk analysis parameters;

an agent burst position;

a customer burst position;

a number of bursts;

agent talk percentage;

customer talk percentage;

a silence duration;

a word extracted by a speech to text engine;

number of holds;

hold duration;

hold position;

number of transfers;

part of speech tagging or stemming;

segment position within the interaction in absolute time, segment position within the interaction in number of words;

speaker side within the segment; and

average duration of silence between words in the segment.

18. The apparatus of claim 12 wherein the global feature is selected from the group consisting of:

average agent activity within the interaction;

average customer activity within the interaction;

average silence;

number of bursts per participant side;

total burst duration per participant side;

an emotion detection feature;

total probability score for an emotion within the interaction;

number of emotion bursts;

total emotion burst duration;

a stemmed word, a stemmed keyphrase;

number of word repetitions; and

number of keyphrase repetitions.

19. The apparatus of claim 12 wherein the classification component uses also a feature extracted from a second segment within the interaction, the feature selected from the group consisting of:

a spotted word;

a feature or indication associated with emotion;

an emotion score that represents the probability of emotion to exist within the segment;

a talk analysis parameters;

an agent burst position;

a customer burst position;

a number of bursts;

agent talk percentage;

customer talk percentage;

a silence duration;

a word extracted by a speech to text engine;

number of holds;

hold duration;

hold position;

number of transfers;

part of speech tagging or stemming;

segment position within the interaction in absolute time, segment position within the interaction in number of words;

speaker side within the segment; and

average duration of silence between words in the segment.

20. A computer readable storage medium containing a set of instructions for a general purpose computer, the set of instructions comprising:

receiving a segment of an interaction in which a representative of the organization participates;

extracting a feature from the segment;

extracting a global feature associated with the interaction;

aggregating the feature and the global feature; and

classifying the segment or the interaction in association with the problem or issue by applying a model to the feature and the global feature.