Machine learning
First Claim
1. A method comprising:
- receiving a set of conversations between a members of a first party type and a members 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 second party type classifiers for one or more clusters in the second set of clusters.
4 Assignments
0 Petitions
Accused Products
Abstract
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).
-
Citations
83 Claims
-
1. A method comprising:
-
receiving a set of conversations between a members of a first party type and a members 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 second party type classifiers for one or more clusters in the second set of clusters. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
-
-
18. A method comprising:
-
by machine, applying a set of classifiers to categorize initiating communications that are part of conversations that also include responsive communications; and
by machine, using the categorized initiating communication to identify common communication patterns. - View Dependent Claims (19, 20, 21, 22, 23, 24, 25)
-
-
26. A method comprising:
-
by machine, applying classifiers to identify a set of classified communications made by a member of a first party type in a conversation that also includes responsive communications made by a member of a second party type; and
by machine, determining a subject matter of each of the conversations based on the set classified communications of the member of the first party type in a conversation. - View Dependent Claims (27, 28, 29, 30, 31, 32, 33, 34, 35)
-
-
36. A computer-implemented method comprising:
-
receiving digital representations of conversations at least some of which comprise a series of communications between a person and an agent associated with a contact center; and
selecting a communication as a learning opportunity if one or more selection criteria are satisfied. - View Dependent Claims (37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58)
-
-
59. A computer-implemented method comprising:
-
receiving a digital representation of a conversation that includes a series of utterances between a caller and an agent associated with a contact center; and
after receiving digital representation, selecting the utterance for transcription based on one or more selection criteria. - View Dependent Claims (60, 61, 62, 63, 64, 65)
-
-
66. A method comprising:
-
based on an interaction between a person and a human agent associated with an automated response system in which the agent selected a response to a communication of a person from among responses proposed by the automated response system; and
selecting the communication as an example to train the automated response system. - View Dependent Claims (67, 68, 69, 70, 71)
-
-
72. A method comprising:
-
by machine, identifying a communication between a person contacting an response system and a human agent; and
modifying the automated response system to respond to similar future communications from persons contacting the system. - View Dependent Claims (73)
-
-
74. A computer-implemented method comprising:
-
adding a communication to a set of training examples for a classifier in a concept recognition engine;
generating a new classifier using the set of training examples that includes the added communication; and
disregarding the new classifier based on a performance requirement for a new classifier. - View Dependent Claims (75, 76)
-
-
77. The method comprising:
generating a set of classifiers for at least one cluster of responsive communications, the cluster being based on one or more clusters of initiating communications with which the responsive communications are associated within conversations. - View Dependent Claims (78, 79, 80, 81, 82, 83)
Specification