Automatic pattern recognition in conversations
First Claim
1. A computer-implemented method, comprising:
- receiving identification information regarding a first set of representatives;
retrieving a first set of recordings of conversations associated with the first set of representatives, wherein each conversation includes at least one of multiple customers and at least one representative from the first set of representatives;
retrieving a second set of recordings of conversations associated with a second set of representatives, wherein each conversation includes at least one of the multiple customers and at least one representative from the second set of representatives;
extracting a first set of features from the first set of recordings and a second set of features from the second set of recordings to generate multiple features, wherein the multiple features indicate characteristics of any of (a) a customer of multiple customers in the corresponding conversation, (b) a representative of multiple representatives in the corresponding conversation, (c) the corresponding conversation;
generating a first pattern data by analyzing the first set of features, the first pattern data indicative a pattern of the conversation of the first set of representatives;
generating a second pattern data by analyzing the second set of features, the second pattern data indicative of a pattern of the conversation of the second set of representatives; and
generating multiple distinctive features that are distinctive between the first pattern data and the second pattern data by analyzing the first set of features and the second set of features.
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Accused Products
Abstract
A pattern recognition system (“system”) automatically determines conversation patterns that distinguish a first set of participants from a second set of participants. For example, a first set of participants can be top performing representatives and the second set of participants can be low performing representatives. The system analyzes a first set of recordings of the top performing representatives to extract a first set of features associated with the first set of recordings, and analyzes the first set of features to generate first pattern data that is indicative of a pattern of the conversation of the top performing representatives. Similarly, the system also generates second pattern data that is indicative of a pattern of the conversation of the low performing representatives. The system analyzes the first pattern data and the second pattern data to generate distinctive features that distinguish the first pattern from the second pattern.
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Citations
35 Claims
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1. A computer-implemented method, comprising:
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receiving identification information regarding a first set of representatives; retrieving a first set of recordings of conversations associated with the first set of representatives, wherein each conversation includes at least one of multiple customers and at least one representative from the first set of representatives; retrieving a second set of recordings of conversations associated with a second set of representatives, wherein each conversation includes at least one of the multiple customers and at least one representative from the second set of representatives; extracting a first set of features from the first set of recordings and a second set of features from the second set of recordings to generate multiple features, wherein the multiple features indicate characteristics of any of (a) a customer of multiple customers in the corresponding conversation, (b) a representative of multiple representatives in the corresponding conversation, (c) the corresponding conversation; generating a first pattern data by analyzing the first set of features, the first pattern data indicative a pattern of the conversation of the first set of representatives; generating a second pattern data by analyzing the second set of features, the second pattern data indicative of a pattern of the conversation of the second set of representatives; and generating multiple distinctive features that are distinctive between the first pattern data and the second pattern data by analyzing the first set of features and the second set of features. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26)
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27. A non-transitory computer-readable storage medium storing computer-readable instructions, comprising:
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instructions for extracting a first set of features from a first set of recordings and a second set of features from a second set of recordings to generate multiple features, wherein the first set of recordings include conversations of a first set of representatives, wherein the second set of recordings include conversations of a second set of representatives, wherein the multiple features indicate characteristics of any of (a) a customer of multiple customers in the corresponding conversation, (b) a representative of multiple representatives in the corresponding conversation, (c) the corresponding conversation; instructions for generating; first pattern data by analyzing the first set of features, the first pattern data indicative a pattern of the conversation of the first set of representatives with a first set of customers, and second pattern data by analyzing the second set of features, the second pattern data indicative of a pattern of the conversation of the second set of representatives with a second set of customers; and instructions for determining a correlation of features between the first pattern data and the second pattern data, wherein the correlation is indicative of a difference between a specified feature of the multiple features in the first pattern data and the second pattern data. - View Dependent Claims (28, 29, 30, 31, 32, 33, 34)
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35. A system, comprising:
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a first component that is configured to extract a first set of features from a first set of recordings and a second set of features from a second set of recordings to generate multiple features, wherein the first set of recordings include conversations of a first set of representatives, wherein the second set of recordings include conversations of a second set of representatives, wherein the multiple features indicate characteristics of any of (a) a customer of multiple customers in the corresponding conversation, (b) a representative of multiple representatives in the corresponding conversation, (c) the corresponding conversation; a second component that is configured to generate; first pattern data by analyzing the first set of features, the first pattern data indicative a pattern of the conversation of the first set of representatives with a first set of customers, and second pattern data by analyzing the second set of features, the second pattern data indicative of a pattern of the conversation of the second set of representatives with a second set of customers; and a third component that is configured to determine a correlation of features between the first pattern data and the second pattern data, wherein the correlation is indicative of a difference between a specified feature of the multiple features in the first pattern data and the second pattern data.
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Specification