Learning framework for processing communication session transcripts

Patent metadata

Field	Value
Publication number	US-2023196020-A1
Application number	US-202117554143-A
Country	US
Kind code	A1
Filing date	Dec 17, 2021
Priority date	Dec 17, 2021
Publication date	Jun 22, 2023
Grant date	—

Abstract

Learning frameworks for processing text transcripts may include receiving, by an application, a query comprising a topic. A topic model may determine a plurality of subtopics based on the topic. The application may receive, from a database based on the topic and plurality of subtopics, a plurality of text transcripts. A sentiment model may compute, for each text transcript, a respective sentiment score based on a text of the respective text transcript. The application may determine, for each text transcript, a duration of a communication session associated with the respective text transcript. The application may compute, for each text transcript, a total score based on the sentiment score and the duration of the respective text transcript. The application may return, as responsive to the query, a subset of the plurality of text transcripts having a total score that exceeds a threshold.

First claim

What is claimed is:

1 . A method, comprising:
receiving, by an application executing on a processor, a query comprising a topic; determining, by a topic model executing on the processor, a plurality of subtopics based on the topic; receiving, by the application from a database based on the topic and plurality of subtopics, a plurality of text transcripts, each transcript associated with a respective communication session; computing, by a sentiment model executing on the processor for each text transcript, a respective sentiment score based on a text of the respective text transcript; determining, by the application for each text transcript, a duration of the communication session associated with the respective text transcript; computing, by the application for each text transcript, a total score based on the sentiment score and the duration of the respective communication session; and returning, by the application as responsive to the query, a subset of the plurality of text transcripts having a total score that exceeds a threshold.

2 . The method of claim 1 , further comprising:
determining, by the application for each communication session, a first amount of time a customer engaged in conversation with an agent; computing, by the application for each text transcript, a first time score based on the first amount of time; determining, by the application for each communication session, a second amount of time a customer was on hold; and computing, by the application for each text transcript, a second time score based on the second amount of time, wherein the total score is further based on the first and second time scores.

3 . The method of claim 2 , wherein computing the total score comprises computing a sum of the sentiment score, the first time score, and the second time score.

4 . The method of claim 1 , further comprising:
extracting, by a key phrase model, a plurality of key phrases from each text transcript of the subset of the plurality of text transcripts; and outputting, by the application, the plurality of key phrases for display.

5 . The method of claim 4 , wherein the sentiment score reflects negative sentiment, further comprising:
determining, by the application based on the plurality of key phrases, a system error related to the negative sentiment; and transmitting, by the application, a notification comprising the system error.

6 . The method of claim 4 , wherein the key phrase model is trained based on a plurality of training transcripts using unsupervised training, wherein the topic model is based on the key phrase model using semi-supervised training.

7 . The method of claim 1 , wherein determining the plurality of subtopics is based on clustering the topic into a cluster and identifying the plurality of subtopics in the cluster.

8 . A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a processor, cause the processor to:
receive, by an application, a query comprising a topic; determine, by a topic model, a plurality of subtopics based on the topic; receive, by the application from a database based on the topic and plurality of subtopics, a plurality of text transcripts, each transcript associated with a respective communication session; compute, by a sentiment model for each text transcript, a respective sentiment score based on a text of the respective text transcript; determine, by the application for each text transcript, a duration of the communication session associated with the respective text transcript; compute, by the application for each text transcript, a total score based on the sentiment score and the duration of the respective text transcript; and return, by the application as responsive to the query, a subset of the plurality of text transcripts having a total score that exceeds a threshold.

9 . The computer-readable storage medium of claim 8 , wherein the instructions further cause the processor to:
determine, by the application for each communication session, a first amount of time a customer engaged in conversation with an agent; compute, by the application for each text transcript, a first time score based on the first amount of time; determine, by the application for each communication session, a second amount of time a customer was on hold; and compute, by the application for each text transcript, a second time score based on the second amount of time, wherein the total score is further based on the first and second time scores.

10 . The computer-readable storage medium of claim 9 , wherein compute the total score comprises computing a sum of the sentiment score, the first time score, and the second time score.

11 . The computer-readable storage medium of claim 8 , wherein the instructions further configure the computer to:
extract, by a key phrase model, a plurality of key phrases from each text transcript of the subset of the plurality of text transcripts; and output, by the application, the plurality of key phrases for display.

12 . The computer-readable storage medium of claim 11 , wherein the sentiment score reflects negative sentiment, wherein the instructions further cause the processor to:
determine, by the application based on the plurality of key phrases, a system error related to the negative sentiment; and transmit, by the application, a notification comprising the system error.

13 . The computer-readable storage medium of claim 11 , wherein the key phrase model is trained based on a plurality of training transcripts use unsupervised training, wherein the topic model is based on the key phrase model using semi-supervised training.

14 . The computer-readable storage medium of claim 8 , wherein determining the plurality of subtopics is based on clustering the topic into a cluster and identifying the plurality of subtopics in the cluster.

15 . A computing apparatus comprising:
a processor; and a memory storing instructions that, when executed by the processor, cause the processor to:
receive, by an application, a query comprising a topic;
determine, by a topic model, a plurality of subtopics based on the topic;
receive, by the application from a database based on the topic and plurality of subtopics, a plurality of text transcripts, each transcript associated with a respective communication session;
compute, by a sentiment model for each text transcript, a respective sentiment score based on a text of the respective text transcript;
determine, by the application for each text transcript, a duration of the communication session associated with the respective text transcript;
compute, by the application for each text transcript, a total score based on the sentiment score and the duration of the respective text transcript; and
return, by the application as responsive to the query, a subset of the plurality of text transcripts having a total score that exceeds a threshold.

16 . The computing apparatus of claim 15 , wherein the instructions further cause the processor to:
determine, by the application for each communication session, a first amount of time a customer engaged in conversation with an agent; compute, by the application for each text transcript, a first time score based on the first amount of time; determine, by the application for each communication session, a second amount of time a customer was on hold; and compute, by the application for each text transcript, a second time score based on the second amount of time, wherein the total score is further based on the firs

Assignees

• Capital One Services Llc

Inventors

• Thoniparambil Akshay Aravindakshan
• Agarwal Manish
• Kedia Gaurav
• Bansiwala Aashish
• Ramamohan Deepak
• Choudhary Sourav

Classifications

• G06F40/289 Primary

  Phrasal analysis, e.g. finite state techniques or chunking · CPC title

• G06F40/35

  Discourse or dialogue representation · CPC title

• G06F16/3344

  using natural language analysis · CPC title

• G06F16/355

  Creation or modification of classes or clusters · CPC title

• G06F40/30 Primary

  Semantic analysis · CPC title