Natural language classification within an automated response system
View Patent ↗An automated response system (e.g., an automated voice response system) may employ learning strategies to develop or improve automated response capabilities. Learning strategies may include using communications(e.g., utterances, text messages, etc.) of one party in a conversation (e.g., a customer service agent) to identify and categorize communications of another party in the conversation (e.g., a caller). Classifiers can be build from the categorized communications. Classifiers can be used to identify common communications patterns of a party in a conversation (e.g., an agent). Learning strategies may also include selecting communications as learning opportunities to improve automated response capabilities based on selection criteria (e.g., selection criteria chosen to ensure that the system does not learn from unreliable or insignificant examples).
1. A computer-implemented method comprising:
receiving a set of conversations between a member of a first party type and a member of a second party type, wherein each of the conversations includes a communication of a member of the first party type and a communication of a member of the second party type that is responsive to the communication of the member of the first party type;
grouping the communications of members of the first party type into a first set of clusters;
grouping the responsive communications of members of the second party type into a second set of clusters based upon the grouping of the communications of members of the first party type; and
by machine, generating a set of agent type classifiers for one or more clusters in the second set of clusters, wherein generating is a step executed by a computer processor that is a functional component of the computer, said execution being part of execution, by the computer processor, of computer-readable instructions embedded on a computer-readable storage medium.
2. The method of claim 1 wherein the communications comprise utterances.
3. The method of claim 1 wherein the communications comprise text messages.
4. The method of claim 1 wherein the communications of members of the first party type comprise communications of human customer service agents at a call center.
5. The method of claim 1 wherein the communications of members of the first party type comprises communications of software agents configured to communicate with humans who contact a call center.
6. The method of claim 1 wherein the communications of members of the second party comprise communications of humans who have contacted a call center.
7. The method of claim 1 wherein the classifiers comprise support vector machines.
8. The method of claim 1 wherein the classifiers comprise decision trees.
9. The method of claim 1 wherein communications of members of a first party type are grouped into a first set of clusters using a computer.
10. The method of claim 9 wherein grouping communications of members of a first party type into a first set of clusters comprises determining semantic features of the communications.
11. The method of claim 1 wherein grouping communications of members of the first party type into a first set of clusters is based on a meaning of the communications of members of the first party type.
12. The method of claim 1 further comprising:
by machine, generating a set of agent type classifiers for one or more clusters in the first set of clusters.
13. The method of claim 1 wherein grouping communications of members of the first party type into a first set of clusters comprises:
grouping communications corresponding to requests for information from members of the first party type into a first set of clusters.
14. The method of claim 13 wherein grouping responsive communications of members of the second party type into a second set of clusters based upon the grouping of the communications of members of the first party type comprises:
grouping communications of members of the second party type into groups corresponding to responses to the requests for information from members of the first party type.
15. The method of claim 12 wherein grouping responsive communications of members of the second party type into a second set of clusters based upon the grouping of the communications of members of the first party type comprises:
using the first agent type classifiers to classify a communication of a member of the first party type into a cluster of the first party type;
grouping a communication of a member of the second party type that is subsequent to the classified communication of the member of the first party type into a cluster of the second party type that relates to the cluster of the first party type.
16. The method of claim 15 wherein the cluster of the first party type relates to a request for information made by a member of the first party type and the cluster of the second party type relates to a response to the request for information given by a member of the second party type.
17. The method of claim 1 further comprising:
receiving a second set of conversations between members of the first party type and members of the second party type, wherein each of the conversations includes a communication of a member of the first party type and a communication of a member of the second party type that is responsive to the communication of the member of the first party type;
applying classifiers to group the communications of members of the second party type;
by machine, regenerating agent type classifiers for a cluster in the second set of clusters using data relating to the communications grouped in the clusters.