Intent-driven contact center

Abstract

The present disclosure relates generally to providing an intent-driven contact center. The contact center according to some embodiments analyzes intents to determine to which device or agent to route a communication. The analyzed intent information can also be used to formulate reports and analyze the accuracy of the identified intents with respect to the received communication.

1 . (canceled) 
     
     
         2 . A computer-implemented method, comprising:
 routing a communication, wherein the communication is associated with a pre-defined intent that defines an action corresponding to a user device, and wherein when the communication is received at a terminal device associated with an agent, a communication session is established between the terminal device and the user device to facilitate execution of the action;   generating a set of metrics during the communication session, wherein the set of metrics correspond to ongoing performance of the agent;   generating an updated set of metrics, wherein the updated set of metrics are generated by aggregating the set of metrics with other metrics corresponding to previous communication sessions associated with the pre-defined intent, and wherein the set of metrics are aggregated with the other metrics as intents are ascertained from ongoing communications;   determining a quality of an association between the communication and the pre-defined intent based on the updated set of metrics; and   updating a dashboard to provide the updated set of metrics and the determined quality of the association.   
     
     
         3 . The computer-implemented method of  claim 2 , further comprising:
 aggregating the set of metrics corresponding to the performance of the agent with other metrics corresponding to previous communications associated with the agent to generate an aggregated set of metrics; and   updating the dashboard to present a ranking of the agent against a set of other agents, wherein the agent is ranked against the set of other agents based on the aggregated set of metrics and other metrics corresponding to the set of other agents.   
     
     
         4 . The computer-implemented method of  claim 2 , further comprising:
 updating the dashboard to present messages exchanged during the communication session and the set of metrics.   
     
     
         5 . The computer-implemented method of  claim 2 , wherein the updated set of metrics include an average duration of communication sessions associated with the pre-defined intent, an intent score, intent trends, and intent durations. 
     
     
         6 . The computer-implemented method of  claim 2 , wherein the dashboard is further updated to present sentiments by intent, conversation duration by intent, number of participating agents, number of conversations per hour, and average duration of the conversations. 
     
     
         7 . The computer-implemented method of  claim 2 , wherein the dashboard is further updated to present a graphical representation of the pre-defined intent and other intents related to the pre-defined intent. 
     
     
         8 . The computer-implemented method of  claim 2 , wherein the pre-defined intent is generated using artificial intelligence and an intent model, and wherein the artificial intelligence is applied to the intent model to aggregate intent-related data and to generate corresponding intents. 
     
     
         9 . A system, comprising:
 one or more processors; and   memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to:
 route a communication, wherein the communication is associated with a pre-defined intent that defines an action corresponding to a user device, and wherein when the communication is received at a terminal device associated with an agent, a communication session is established between the terminal device and the user device to facilitate execution of the action; 
 generate a set of metrics during the communication session, wherein the set of metrics correspond to ongoing performance of the agent; 
 generate an updated set of metrics, wherein the updated set of metrics are generated by aggregating the set of metrics with other metrics corresponding to previous communication sessions associated with the pre-defined intent, and wherein the set of metrics are aggregated with the other metrics as intents are ascertained from ongoing communications; 
 determine a quality of an association between the communication and the pre-defined intent based on the updated set of metrics; and 
 update a dashboard to provide the updated set of metrics and the determined quality of the association. 
   
     
     
         10 . The system of  claim 9 , wherein the instructions further cause the system to:
 aggregate the set of metrics corresponding to the performance of the agent with other metrics corresponding to previous communications associated with the agent to generate an aggregated set of metrics; and   update the dashboard to present a ranking of the agent against a set of other agents, wherein the agent is ranked against the set of other agents based on the aggregated set of metrics and other metrics corresponding to the set of other agents.   
     
     
         11 . The system of  claim 9 , wherein the instructions further cause the system to:
 update the dashboard to present messages exchanged during the communication session and the set of metrics.   
     
     
         12 . The system of  claim 9 , wherein the updated set of metrics include an average duration of communication sessions associated with the pre-defined intent, an intent score, intent trends, and intent durations. 
     
     
         13 . The system of  claim 9 , wherein the dashboard is further updated to present sentiments by intent, conversation duration by intent, number of participating agents, number of conversations per hour, and average duration of the conversations. 
     
     
         14 . The system of  claim 9 , wherein the dashboard is further updated to present a graphical representation of the pre-defined intent and other intents related to the pre-defined intent. 
     
     
         15 . The system of  claim 9 , wherein the pre-defined intent is generated using artificial intelligence and an intent model, and wherein the artificial intelligence is applied to the intent model to aggregate intent-related data and generate corresponding intents. 
     
     
         16 . A non-transitory, computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to:
 route a communication, wherein the communication is associated with a pre-defined intent that defines an action corresponding to a user device, and wherein when the communication is received at a terminal device associated with an agent, a communication session is established between the terminal device and the user device to facilitate execution of the action;   generate a set of metrics during the communication session, wherein the set of metrics correspond to ongoing performance of the agent;   generate an updated set of metrics, wherein the updated set of metrics are generated by aggregating the set of metrics with other metrics corresponding to previous communication sessions associated with the pre-defined intent, and wherein the set of metrics are aggregated with the other metrics as intents are ascertained from ongoing communications;   determine a quality of an association between the communication and the pre-defined intent based on the updated set of metrics; and   update a dashboard to provide the updated set of metrics and the determined quality of the association.   
     
     
         17 . The non-transitory, computer-readable storage medium of  claim 16 , wherein the executable instructions further cause the computer system to:
 aggregate the set of metrics corresponding to the performance of the agent with other metrics corresponding to previous communications associated with the agent to generate an aggregated set of metrics; and   update the dashboard to present a ranking of the agent against a set of other agents, wherein the agent is ranked against the set of other agents based on the aggregated set of metrics and other metrics corresponding to the set of other agents.   
     
     
         18 . The non-transitory, computer-readable storage medium of  claim 16 , wherein the executable instructions further cause the computer system to:
 update the dashboard to present messages exchanged during the communication session and the set of metrics.   
     
     
         19 . The non-transitory, computer-readable storage medium of  claim 16 , wherein the updated set of metrics include an average duration of communication sessions associated with the pre-defined intent, an intent score, intent trends, and intent durations. 
     
     
         20 . The non-transitory, computer-readable storage medium of  claim 16 , wherein the dashboard is further updated to present sentiments by intent, conversation duration by intent, number of participating agents, number of conversations per hour, and average duration of the conversations. 
     
     
         21 . The non-transitory, computer-readable storage medium of  claim 16 , wherein the dashboard is further updated to present a graphical representation of the pre-defined intent and other intents related to the pre-defined intent. 
     
     
         22 . The non-transitory, computer-readable storage medium of  claim 16 , wherein the pre-defined intent is generated using artificial intelligence and an intent model, and wherein the artificial intelligence is applied to the intent model to aggregate intent-related data and generate corresponding intents.