Adaptive closed loop communication system
A communication system for processing a call includes control logic and at least one machine learning model generating call classifiers from outputs of an audio signal processor and a natural language processor operated on the call. Heuristic logic transforms the call classifiers into weighted sub-metrics for the call, and aggregate normalized Gaussian logic transforms the weighted sub-metrics into a metric control that may be applied as a feedback signal to adapt the operation of the control logic. The control logic in turn may adapt the behavior of an agent, automated voice attendant, or a template utilized in a call flow.
1. A communication system for processing a call, the communication system comprising:
control logic;
at least one automated voice attendant;
at least one machine learning model generating call classifiers from outputs of an audio signal processor and a natural language processor configured to operate on the call;
heuristic logic configured to transform the call classifiers into a plurality of weighted sub-metrics for the call;
aggregate normalized Gaussian logic to transform the weighted sub-metrics into a metric control;
the metric control applied as feedback to adapt the control logic; and
the control logic adapting the automated voice attendant responsive to the feedback metric control.
2. The communication system of claim 1 , further comprising:
at least one template; and
the control logic adapting the template responsive to the feedback metric control.
3. The communication system of claim 1 , wherein the machine learning models comprise an ensemble learning model.
4. The communication system of claim 1 , wherein the control logic comprising a machine learning model.
5. The communication system of claim 4 , wherein the machine learning model of the control logic comprises an ensemble machine learning model.
6. The communication system of claim 3 , further comprising a learning function for the machine learning model of the control logic utilizing a call history and one or more of the weighted sub-metrics and the metric control.
7. A call processing method comprising:
operating at least one machine learning model to transform outputs of an audio signal processor and a natural language processor into classifiers for a call;
transforming the call classifiers into a plurality of weighted sub-metrics for the call;
applying aggregate normalized Gaussian logic to the weighted sub-metrics to generate a metric control;
applying the metric control to adapt control logic for a call flow; and
applying the metric control to adapt a behavior of an automated voice attendant of the call flow.
8. The method of claim 7 , further comprising:
applying the metric control to adapt a template utilized in the call flow.
9. The method of claim 7 , wherein the at least one machine learning model comprises an ensemble learning model.
10. The method of claim 7 , further comprising applying the metric control to adapt a machine learning model of the control logic.
11. The method of claim 10 , wherein the machine learning model of the control logic comprises an ensemble machine learning model.
12. The method of claim 9 , further comprising applying a learning function for the machine learning model of the control logic utilizing a call history and one or more of the weighted sub-metrics and the metric control.
13. A computing apparatus comprising:
a processor; and
a memory storing instructions that, when executed by the processor, configure the apparatus to:
operate at least one machine learning model to transform outputs of an audio signal processor and a natural language processor into classifiers for a call;
transform the call classifiers into a plurality of weighted sub-metrics for the call;
apply aggregate normalized Gaussian logic to the weighted sub-metrics to generate a metric control; and
apply the metric control to adapt one or more of a behavior of an automated voice attendant and a template utilized in an ongoing call flow.
14. The computing apparatus of claim 13 , wherein the at least one machine learn model comprises an ensemble learning model.
15. The computing apparatus of claim 13 , wherein the instructions further configure the apparatus to apply the metric control to adapt a machine learning model of control logic of the call flow.
16. The computing apparatus of claim 15 , wherein the machine learn model of the control logic comprises an ensemble machine learning model.
17. The computing apparatus of claim 14 , wherein the instructions further configure the apparatus to apply a learning function for the machine learning model of the control logic utilizing a call history and one or more of the weighted sub-metrics and the metric control.
18. The computing apparatus of claim 14 , wherein the weighted sub-metrics comprise rate metrics for the call.