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US 2018/0121766 A1Published application
Enhanced human/machine workforce management using reinforcement learning
AssigneeNewVoiceMedia, Ltd.
InventorsAlan McCord, Ashley Unitt
Life of this patent
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Abstract
A system and method for enhanced human/machine workforce management using reinforcement learning, comprising a reinforcement learning server that produces a partially-observable Markov chain model, and an optimization server that uses the partially-observable Markov chain model to select work items and assign them to contact center resources.
Claims8
A system for enhanced human/machine workforce management using reinforcement learning comprising:
- a reinforcement learning server comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a computing device and configured to:
- receive a plurality of historical data from a contact center;
- form a partially-observable Markov chain model based at least in part on at least a portion of the historical data;
- provide the partially-observable Markov chain model to an optimization server;
- an optimization server comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a computing device and configured to:
- receive a partially-observable Markov chain model from a reinforcement learning server;
- select a plurality of work tasks based at least in part on the partially-observable Markov chain model;
- select a plurality of contact center resources;
- assign each of the selected work tasks to at least one of the plurality of contact center resources;
- record and analyze a plurality of observations based on each selected resource's performance of each work task assigned to it;
- provide the observations to the reinforcement learning server;
- a retrain and design server comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a computing device and configured to:
- observe and analyze a plurality of historical data from a contact center;
- provide at least a portion of the historical data to a reinforcement learning server;
- define a plurality of reward values to direct the operation of the reinforcement learning server;
- and
- design and train a Markov decision process model based at least in part on the partially- observable Markov chain model, using at least a portion of the defined reward values.
14 more elements
A method for enhanced human/machine workforce management using reinforcement learning, comprising the steps of:
- receiving, at a retrain and design server comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a computing device, a plurality of historical data from a contact center;
- defining a plurality of reward values to direct the operation of a reinforcement learning server;
- providing at least a portion of the historical data to a reinforcement learning server for use in a partially-observable Markov chain model;
- forming, using a reinforcement learning server, a partially-observable Markov chain model based at least in part on the historical data;
- selecting, using an optimization server, a plurality of work tasks based at least in part on the partially-observable Markov chain model;
- selecting a plurality of contact center resources;
- assigning each of the selected work tasks to at least one of the plurality of contact center resources;
- training a Markov decision process model based at least in part on the partially-observable Markov chain model, using at least a portion of the defined reward values.
5 more elements
Dependencies are inferred from the claim text.