Learning based metric determination for service sessions

Patent metadata

Field	Value
Publication number	US-11272057-B1
Application number	US-202017096544-A
Country	US
Kind code	B1
Filing date	Nov 12, 2020
Priority date	Aug 22, 2017
Publication date	Mar 8, 2022
Grant date	Mar 8, 2022

Abstract

Techniques are described for generating metrics about an individual's experience. One of the method describes providing, by at least one processor, the session record as input to at least one computer-processable model that determines, based on the session record, at least one metric for the service session, the at least one model having been trained, using machine learning and based at least partly on survey data for previous service sessions, to provide the at least one metric associated with the individual's experience. The method includes associating, by at least one processor, the metric of the individual's experience with the individual. The method also includes communicating, by at least one processor, the at least one metric for presentation through a user interface of a computing device.

First claim

What is claimed is:

1. A computer-implemented method performed by at least one processor, the method comprising:
training a neural network to process a first record of communications between a first service representative (SR) and a first individual during a first service session to generate one or more predicted survey scores that rate the first service session on one or more criteria, wherein the training is conducted using training data comprising, for each of one or more previous service sessions, a respective previous record of the previous service session and a respective set of one or more actual survey scores provided by a serviced individual to rate the previous service session on the one or more criteria;
receiving, by at least one processor, a second record of communications between a second service representative (SR) and a second individual during a second service session;
processing, using the trained neural network, the second record to determine one or more new predicted survey scores that rate the second service session;
associating, by at least one processor, the one or more new predicted survey scores with the second individual; and
communicating, by at least one processor, the one or more new predicted survey scores for presentation through a user interface of a computing device.

2. The computer-implemented method of claim 1 , wherein:
the second service session is an audio call between the SR and the individual; and
the second record includes an audio record of at least a portion of the audio call.

3. The computer-implemented method of claim 1 , wherein:
the respective previous service session is an audio call; and
the respective previous record includes an audio record of at least a portion of the audio call.

4. The computer-implemented method of claim 1 , further comprising:
updating requirements of the second individual's based on a metric; and
selecting another service representative to interact with the second individual on a subsequent service session based in part on the updated requirements.

5. The computer-implemented method of claim 1 , further comprising:
receiving a request for a service session from a third individual;
identifying a fourth individual similar to the third individual; and
connecting the third individual with another service representative, where the other service representative is selected, at least in part, based on metrics associated with the fourth individual.

6. A system comprising:
at least one processor; and a data store coupled to the at least one processor having instructions stored thereon which, when executed by the at least one processor, causes the at least one processor to perform operations comprising:
training a neural network to process a first record of communications between a first service representative (SR) and a first individual during a first service session to generate one or more predicted survey scores that rate the first service session on one or more criteria, wherein the training is conducted using training data comprising, for each of one or more previous service sessions, a respective previous record of the previous service session and a respective set of one or more actual survey scores provided by a serviced individual to rate the previous service session on the one or more criteria;
receiving a second record of communications between a second service representative (SR) and a second individual during a second service session;
processing, using the trained neural network, the second record to determine one or more new predicted survey scores that rate the second service session;
associating the one or more new predicted survey scores with the second individual; and
communicating the one or more new predicted survey scores for presentation through a user interface of a computing device.

7. The system of claim 6 , wherein:
the second service session is an audio call between the SR and the individual; and
the second record includes an audio record of at least a portion of the audio call.

8. The system of claim 6 , wherein:
the respective previous service session is an audio call; and
the respective previous record includes an audio record of at least a portion of the audio call.

9. The system of claim 6 , further comprising:
updating requirements of the second individual's based on a metric; and
selecting another service representative to interact with the second individual on a subsequent service session based in part on the updated requirements.

10. The system of claim 6 , further comprising:
receiving a request for a service session from a third individual;
identifying a fourth individual similar to the third individual; and
connecting the third individual with another service representative, where the other service representative is selected, at least in part, based on metrics associated with the fourth individual.

11. A non-transitory computer readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
training a neural network to process a first record of communications between a first service representative (SR) and a first individual during a first service session to generate one or more predicted survey scores that rate the first service session on one or more criteria, wherein the training is conducted using training data comprising, for each of one or more previous service sessions, a respective previous record of the previous service session and a respective set of one or more actual survey scores provided by a serviced individual to rate the previous service session on the one or more criteria;
receiving a second record of communications between a second service representative (SR) and a second individual during a second service session;
processing, using the trained neural network, the second record to determine one or more new predicted survey scores that rate the second service session;
associating the one or more new predicted survey scores with the second individual; and
communicating the one or more new predicted survey scores for presentation through a user interface of a computing device.

12. The medium of claim 11 , wherein:
the second service session is an audio call between the SR and the individual; and
the second record includes an audio record of at least a portion of the audio call.

13. The medium of claim 11 , wherein:
the respective previous service session is an audio call; and
the respective previous record includes an audio record of at least a portion of the audio call.

14. The medium of claim 11 , further comprising:
updating requirements of the second individual's based on a metric; and
selecting another service representative to interact with the second individual on a subsequent service session based in part on the updated requirements.

15. The medium of claim 11 , further comprising:
receiving a request for a service session from a third individual;
identifying a fourth individual similar to the third individual; and
connecting the third individual with another service representative, where the other service representative is selected, at least in part, based on metrics associated with the fourth individual.

Assignees

• Usaa

Inventors

• Smith Eric J
• Teneyck Jr John Mcchesney
• Yarbrough Gregory
• Jayapalan Vijay

Classifications

• H04L65/65

  Network streaming protocols, e.g. real-time transport protocol [RTP] or real-time control protocol [RTCP] · CPC title

• H04M3/523

  with call distribution or queueing · CPC title

• H04M3/5175 Primary

  Call or contact centers supervision arrangements · CPC title

• H04L65/1069

  Session establishment or de-establishment · CPC title

• H04M2203/401

  Performance feedback · CPC title