Contact center agent coaching tool

• US 10,601,992 B2
• Filed: 04/17/2019
• Issued: 03/24/2020
• Est. Priority Date: 01/08/2014
• Status: Active Grant

• Current Assignee: CallMiner, Inc.
• Original Assignee: CallMiner, Inc.
• Inventors: Dwyer, Michael C., Strand, Erik A., Wolf, Scott R., Salinas, Frank, Gallino, Jeffrey A., Kendrick, Scott A., Xue, Shaoyu
• Primary Examiner(s): Nguyen, Phung-Hoang J
• Application Number:
  US16/386,506
• Publication Number:
  US 20190245973A1
• Time in Patent Office: 342 Days
• CPC Class Codes: G06F 21/6254 by anonymising data, e.g. decorrelating personal data from the owner's identification
• CPC Class Codes: G06F 40/279 Recognition of textual entities
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G10L 15/02 Feature extraction for speech recognition; Selection of recognition unit
• CPC Class Codes: G10L 15/1815 Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning
• CPC Class Codes: G10L 15/26 Speech to text systems G10L15/08 takes precedence
• CPC Class Codes: G10L 15/30 Distributed recognition, e.g. in client-server systems, for mobile phones or network applications
• CPC Class Codes: G10L 2015/088 Word spotting
• CPC Class Codes: G10L 25/72 for transmitting results of analysis
• CPC Class Codes: H04M 2203/301 Management of recordings
• CPC Class Codes: H04M 2203/303 Marking
• CPC Class Codes: H04M 2203/401 Performance feedback
• CPC Class Codes: H04M 2203/551 Call history recording a computer conference H04L12/1831
• CPC Class Codes: H04M 2203/6009 Personal information, e.g. profiles or personal directories being only provided to authorised persons
• 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 3/5191 interacting with the Internet

Abstract

A contact center agent coaching tool may employ systems and methods comprising receiving a communication involving a participant, analyzing the communication in at least near real-time relative to a receipt of the communication using a computer-based communications analytics facility for at least one of a language characteristic, a non-word symbol, or an acoustic characteristic, analyzing the communication in at least near real-time relative to the receipt of the communication using a contextual analysis to obtain a context, determining at least one of a category or a score associated with an aspect of the communication using the at least one language characteristic, non-word symbol, acoustic characteristic, or the context, and triggering a coaching tool to be presented to the participant based on at least one of the category or the score.

Claims

1. A method, comprising:

receiving a communication involving an agent of an enterprise and a customer;

analyzing the communication using a computer-based communications analytics facility for at least one of a language characteristic, a non-word symbol, or an acoustic characteristic;

analyzing the communication using a contextual analysis to obtain a context, wherein the contextual analysis is done by a contextual module that is structured to examine the communication for specific language characteristics, non-word symbols, or acoustic characteristics that trigger potential contextual relevance to subsequent or prior specific language characteristics, non-word symbols, or acoustic characteristics of the communication and, monitor the subsequent or prior language characteristics, non-word symbols, or acoustic characteristics of the communication for such potential contextual relevance after receiving the specific language characteristics, non-word symbols, or acoustic characteristics that trigger the potential contextual relevance to the subsequent or prior language characteristics, non-word symbols, or acoustic characteristics;

determining a score relating to a customer satisfaction or a customer dissatisfaction, and associated with an aspect of the communication using the at least one language characteristic, non-word symbol, acoustic characteristic, or the context;

determining an out of range score of a predetermined range; and

automatically triggering an automated coaching tool to be presented to the agent based on the determining of the out of range score;

wherein the out of range score is a numerical score and a color-coded score with a predetermined color, and wherein the coaching tool comprises politeness training with a re-scoring based on an effectiveness of training.

2. The method of claim 1, further comprising, enabling a second agent of the enterprise to intervene in the communication based upon the coaching tool being triggered.

3. The method of claim 1, wherein the acoustic characteristic is at least one of a stress of words, an aggregated stress of a plurality of words, an agitation, a tempo, a change in tempo, an amount of silence, a silence between words, a gain in volume or energy of the words, a tone, an overtalk, a time lag between words or symbols, a time dependency between key words or phrases, an inter-word timing, an inter-word sequencing, an inter-word distance, an inflexion of words, or a temporal pattern.

4. The method of claim 1, wherein the language characteristic is at least one of a sentiment, a score, a text, an alternative text, a placeholder word used in redaction, a language pattern, a phoneme, a word, a phrase, a placeholder word used in redaction, or a confidence in word match.

5. The method of claim 4, wherein the language pattern is at least one of:

an order of occurrence of portions of the text, an order of occurrence of portions of the text within a time frame, a Boolean relationship of portions of the text, a specific location in the communication, or a proximity of a topic.

6. The method of claim 1, further comprising, assigning metadata representative of the communication comprising at least one of a speaker name, speaker type, communication type, an agent data, an agent grouping, a call handling location, a time and date of call, a department, a skill or queue, a pertinent IVR path data, another category or not another category, or a call length.

7. The method of claim 1, wherein the coaching tool delivers training automatically to the agent during the communication.

8. The method of claim 1, wherein the coaching tool delivers training subsequent to termination of the communication.

9. The method of claim 1, wherein the coaching tool is triggered in at least near real-time relative to a time of receipt of the communication.

10. The method of claim 1, wherein the score is an aggregation of any number of the language characteristic, the acoustic characteristic, and a category across a relevant organizational level.

11. The method of claim 10, wherein the organizational level is determined by a data relationship based on user input across available metadata.

12. The method of claim 10, wherein an authorized user can navigate up and down a defined hierarchy to view different aggregations of scores.

13. The method of claim 1, wherein an effectiveness of the coaching tool is evaluated by comparing an additional score for a communication subsequent to triggering the coaching tool with the score that triggered the coaching tool.

14. A method, comprising:

receiving a plurality of communications involving an agent and at least one customer;

analyzing each of the plurality of communications, using a computer-based communications analytics facility, for at least one of a language characteristic, a non-word symbol, or an acoustic characteristic;

analyzing each of the plurality of communications, using a contextual analysis, to obtain a context, wherein the contextual analysis is done by a contextual module that is structured to examine the communication for specific language characteristics, non-word symbols, or acoustic characteristics that trigger potential contextual relevance to subsequent or prior specific language characteristics, non-word symbols, or acoustic characteristics of the communication and, monitor the subsequent or prior language characteristics, non-word symbols, or acoustic characteristics of the communication for such potential contextual relevance after receiving the specific language characteristics, non-word symbols, or acoustic characteristics that trigger the potential contextual relevance to the subsequent or prior language characteristics, non-word symbols, or acoustic characteristics;

determining a score related to a customer satisfaction or a customer dissatisfaction, and associated with each of the plurality of communications using the at least one language characteristic, non-word symbol, acoustic characteristic, or the context;

determining an out of range score of an aggregate or a weighted aggregate of the score for the plurality of communications is outside of a predetermined range; and

automatically triggering an automated coaching tool to be presented to the agent based on the determining of the out of range score;

wherein the out of range score is a numerical score and a color-coded score with a predetermined color, and wherein the coaching tool comprises politeness training with a re-scoring based on an effectiveness of training.

15. The method of claim 14, wherein the language characteristic is at least one of a sentiment, a score, a text, an alternative text, a placeholder word used in redaction, a language pattern, a phoneme, a word, a phrase, a placeholder word used in redaction, or a confidence in word match.

16. The method of claim 15, wherein the language pattern is at least one of:

an order of occurrence of portions of the text, an order of occurrence of portions of the text within a time frame, a Boolean relationship of portions of the text, a specific location in the communication, or a proximity of a topic.

17. The method of claim 14, wherein the acoustic characteristic is at least one of a stress of words, an aggregated stress of a plurality of words, an agitation, a tempo, a change in tempo, an amount of silence, a silence between words, a gain in volume or energy of the words, a tone, an overtalk, a time lag between words or symbols, a time dependency between key words or phrases, an inter-word timing, an inter-word sequencing, an inter-word distance, an inflexion of words, or a temporal pattern.