Method and apparatus for improving goal-directed textual conversations between agents and customers

Patent metadata

Field	Value
Publication number	US-10038786-B2
Application number	US-201514638894-A
Country	US
Kind code	B2
Filing date	Mar 4, 2015
Priority date	Mar 5, 2014
Publication date	Jul 31, 2018
Grant date	Jul 31, 2018

Abstract

In accordance with an example embodiment a computer-implemented method and an apparatus for predicting and tracking of mood changes in textual conversations are provided. The method includes determining, by a processor, one or more mood metrics in each of two or more chat stages of a real-time textual conversation between an agent and a customer. Changes in the one or more mood metrics across the two or more chat stages of the real-time textual conversation are tracked by the processor. Further, the method includes determining, by the processor, at least one action associated with the real-time textual conversation based on the changes in the one or more mood metrics.

First claim

The invention claimed is:

1. A computer-implemented method, comprising:
determining, by a processor, one or more mood metrics in each of two or more chat stages of a real-time textual conversation between an agent and a customer, wherein said determining the one or more mood metrics for a chat stage of the real-time textual conversation, by the processor, further comprises determining an overall mood for the chat stage based on a polarity based approach by:
assigning polarity labels to features present in the chat stage;
assigning polarity strength scores for the polarity labels assigned to the features present in the chat stage;
calculating weighted polarity scores for the features based on aggregation of the polarity labels and the polarity strength scores to determine the overall mood for the chat stage; and
determining the overall mood, by the processor, based on a subjectivity-based approach by removing terms classified as objective from the real-time textual conversation prior to assigning the polarity labels and the polarity strength scores;

tracking, by the processor, changes in the one or more mood metrics across the two or more chat stages of the real-time textual conversation between the agent and the customer; and
determining, by the processor, at least one action associated with the real-time textual conversation based on the changes in the one or more mood metrics;
performing, by the processor, the at least one action associated with the real-time textual conversation, wherein performing the at least one action comprises any of:
displaying, by the processor, information associated with the at least one action to a supervisor monitoring the real-time textual conversation and providing, by the processor, the information associated with the at least one action to the agent engaged in the real-time textual conversation based on an input received from the supervisor so as to enable the agent to perform the at least one action thereby causing a target outcome of the real-time textual conversation;
monitoring an agent engagement score associated with the two or more chat stages of the real-time textual conversation;
storing the real-time textual conversation with a timestamp of the real-time textual conversation; and
displaying one or more textual recommendations to the agent so as to enable the agent to use the one or more textual recommendations in the real-time textual conversation to thereby cause a target outcome of the real-time textual conversation.

2. The method of claim 1 , further comprising:
predicting, by the processor, the two or more chat stages from among a plurality of chat stages associated with the real-time textual conversation for determining the one or more mood metrics in each of the two or more chat stages.

3. The method of claim 1 , further comprising:
displaying in a user interface a visual representation of the one or more mood metrics determined in each of the two or more chat stages for tracking the changes in the one or mood metrics.

4. The method of claim 3 , wherein the visual representation of the one or mood metrics comprises representation of textual labels, the textual labels comprising a neutral sentiment, a positive sentiment and a negative sentiment.

5. The method of claim 3 , wherein the visual representation of the one or mood metrics comprises one or more color coded representations in each of the two or more chat stages.

6. The method of claim 1 , wherein said determining the one or more mood metrics comprises determining a customer engagement score based on at least one of a customer sentiment parameter, a customer response time, and a frequency of use of emoticons in the real-time textual conversation.

7. The method of claim 1 , wherein said determining the one or more mood metrics comprises determining an agent engagement score based on at least one of an agent response time, an agent chat concurrency, and a parameter associated with agent's adherence to a recommended response template.

8. The method of claim 1 , wherein determination of the at least one action is performed further based on historical information associated with one or more completed textual conversations.

9. The method of claim 1 , wherein determination of the at least one action is performed further based on a statistical analysis of one or more completed textual conversations.

10. The method of claim 1 , further comprising:
accessing, by the processor, a real-time voice based conversation between the agent and the customer; and
converting, by the processor, the real-time voice based conversation to the real-time textual conversation.

11. The method of claim 1 , wherein said determining the one or more mood metrics for a chat stage further comprises determining an overall mood for the chat stage based on a supervised text classification approach.

12. An apparatus, comprising:
at least one processor; and
a storage module having stored therein machine executable instructions, that when executed by the at least one processor, cause the apparatus to:
determine one or more mood metrics in each of two or more chat stages of a real-time textual conversation between an agent and a customer, wherein said determining the one or more mood metrics for a chat stage of the real-time textual conversation, by the processor, further comprises determining an overall mood for the chat stage based on a polarity based approach by:
assigning polarity labels to features present in the chat stage;
assigning polarity strength scores for the polarity labels assigned to the features present in the chat stage;
calculating weighted polarity scores for the features based on aggregation of the polarity labels and the polarity strength scores to determine the overall mood for the chat stage; and
determining the overall mood based on a subjectivity-based approach by removing terms classified as objective from the real-time textual conversation prior to assigning the polarity labels and the polarity strength scores;

track changes in the one or more mood metrics across the two or more chat stages of the real-time textual conversation between the agent and the customer; and
determine at least one action associated with the real-time textual conversation based on the changes in the one or more mood metrics; and
an input-output (I/O) module; wherein the at least one processor is further configured to cause the apparatus to perform the at least one action by enabling the I/O module to any of:
displaying one or more textual recommendations to the agent so as to enable the agent to use the one or more textual recommendations in the real-time textual conversation to thereby cause a target outcome of the real-time textual conversation;
displaying information associated with the at least one action to a supervisor monitoring the real-time textual conversation and causing the I/O module to provide the information associated with the at least one action to the agent engaged in the real-time textual conversation based on an input received from the supervisor so as to enable the agent to perform the at least one action thereby causing a target outcome of the real-time textual conversation;
monitoring an agent engagement score associated with the two or more chat stages of the real-time textual conversation;
storing the real-time textual conversation with a timestamp of the real-time textual conversation; and
displaying one or more textual recommendations to the agent so as to enable the agent to use the one or more textual recommendations in the real-time textual conversation to thereby cause a target outcome of the real-time textual conversation.

Assignees

• 24/7 Customer Inc
• [24]7 Ai Inc

Inventors

• Kannan Pallipuram V
• Vijayaraghavan Ravi
• Sri R Mathangi
• Seth Suchana

Classifications

• H04L51/04

  Real-time or near real-time messaging, e.g. instant messaging [IM] · CPC title

• H04M3/42365

  Presence services providing information on the willingness to communicate or the ability to communicate in terms of media capability or network connectivity · CPC title

• H04L12/1827

  Network arrangements for conference optimisation or adaptation · CPC title

• H04M2203/357

  Autocues for dialog assistance · CPC title

• H04M3/5183 Primary

  Call or contact centers with computer-telephony arrangements · CPC title