System and method for an optimized, self-learning and self-organizing contact center

• US 10,586,175 B2
• Filed: 02/28/2017
• Issued: 03/10/2020
• Est. Priority Date: 05/23/2014
• Status: Active Grant

• Current Assignee: Vonage Business Limited (Telefonaktiebolaget LM Ericsson)
• Original Assignee: NewVoiceMedia Limited (Telefonaktiebolaget LM Ericsson)
• Inventors: McCord, Alan, Unitt, Ashley
• Primary Examiner(s): Hashem, Lisa
• Application Number:
  US15/445,869
• Publication Number:
  US 20170169325A1
• Time in Patent Office: 1,106 Days
• CPC Class Codes: G06F 16/3329 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 40/205 Parsing
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: H04M 3/5183 Call or contact centers with computer-telephony arrangements
• CPC Class Codes: H04M 3/5233 Operator skill based call distribution
• CPC Class Codes: H04M 3/5235 Dependent on call type or called number [DNIS]

Abstract

A system and method for an optimized, self-learning and self-organizing contact center has been developed. This system and method uses principles and tools of information theory, including the latent Dirichlet allocation which reduces information to specific predetermined topics and a distribution of topic related words to infer its hidden, generative underpinnings so to self-organize a contact center, infer its desired electronic versus human make up, and optimally route all customer requests to an electronic resource or a specific human agent best suited to respond to the request for maximal business value per interaction.

Claims

1. A self-learning and self-organizing contact center routing system comprising:

a topic-based routing module stored in a memory of and operating on a processor of a computing device; and

an interaction information optimization module stored in a memory of and operating on a processor of a computing device;

wherein the interaction information optimization module;

continuously monitors all communications into and out of the contact center;

for each incoming communication, analyzes the incoming communication using probabilistic models to identify any topics and hidden variables within the incoming communication that led a contact center customer to initiate the incoming communication;

for each outgoing communication, identifies the outgoing communication in response to a particular incoming communication;

determines an effectiveness of a response to each incoming communication by comparing the topics and hidden variables identified from the analysis of the incoming communication with a plurality of topics and hidden variables identified by similar analyses of corresponding outgoing communications for each respective incoming communication; and

ranks each human agent in the contact center based on the agent'"'"'s knowledge of, experience with, and determined effectiveness for each identified topic and hidden variable; and

wherein the topic-based routing module;

receives a text request for assistance from a user via a network;

automatically identifies a specific human agent best suited to service the request based on the rankings from the interaction information optimization module pertaining to a topic and a hidden variable derived from the text request; and

automatically routes the request directly to the specific human agent.

2. A method for self-learning and self-optimizing contact center routing, comprising the steps of:

(a) continuously monitoring, at an interaction information optimization module stored in a memory of and operating on a processor of a computing device, all communications into and out of a contact center;

(b) analyzing each incoming communication to the contact center using probabilistic models to identify any topics and hidden variables within the communication that may have led a contact center customer to initiate the incoming communication on those topics;

(c) determining an effectiveness of a response to each incoming communication by comparing information obtained from the analysis of the incoming communication with a plurality of topics and hidden variables identified by similar analyses of corresponding outgoing communications for each respective incoming communication;

(d) ranking each human agent in the contact center based on the agent'"'"'s knowledge of, experience with, and determined effectiveness for each identified topic and hidden variable;

(e) receiving, at a topic-based routing module stored in a memory of and operating on a processor of a computing device, a text request for assistance from a user via a network;

(f) automatically identifying a specific human agent best suited to service the request based on the rankings pertaining to a topic and a hidden variable derived from the text request; and

(g) automatically routing the request directly to the specific human agent best suited to service the request.