Method and apparatus for automated quality management of communication records
Disclosed implementations use automated transcription and intent detection and an AI model to evaluate interactions between an agent and a customer within a call center environment. The evaluation flow used for manual evaluations is leveraged so that the evaluators can correct the AI evaluations when appropriate. Based on such corrections, the AI model can be retrained to accommodate specifics of the business and center—resulting in more confidence in the AI model over time.
1. A method for assessing communications between a user and an agent in a call center, the method comprising:
extracting text from a plurality of communications between a call center user and a call center agent to thereby create a communication record;
for each of the plurality of communications:
assessing the corresponding text of a communication record by applying an AI assessment model to obtain an intent assessment of one or more aspects of the communication, wherein the AI assessment model is developed by processing a set of initial training data and supplemental training data to detect intents; and
wherein the intent assessment includes a confidence score of the communication and further comprising flagging the communication record for manual quality management analysis and for making annotations if a confidence score of the intent assessment is below a threshold value.
2. The method of claim 1 , wherein the intent assessment comprises multiple fields, each field having a value selected from a corresponding set of values and wherein the confidence level is based on a confidence sub-level determined for each value of each field.
3. The method of claim 2 , wherein the fields and corresponding sets of values correspond to a human-readable form used for the manual annotation.
4. The method of claim 1 , wherein the AI assessment model considers acceptable key words or phrases in each of a plurality of categories and the annotations include key words or phrases that are to be added to a category as acceptable.
5. The method of claim 1 , where wherein the supplemental training data is added to the model based on reviewing manual corrections to previous assessments by the assessment model.
6. The method of claim 5 , wherein the supplemental data is based on manual quality analysis by a plurality of people and determining consensus between the people.
7. The method of claim 1 , wherein the manual quality management analysis and annotation is accomplished by an agent in the call center.
8. The method of claim 1 , wherein the manual quality management analysis and annotation includes a user interface displaying suggestions that have been marked for training from multiple models.
9. The method of claim 8 wherein the suggestions have been marked for training based on lack of confidence or explicit suggestion.
10. The method of claim 8 , wherein the suggestions come from a review of an unsupervised clustering model.
11. A computer system for assessing communications between a user and an agent in a call center, the system comprising:
at least one computer hardware processor; and
at least one memory device operatively coupled to the at least one computer hardware processor and having instructions stored thereon which, when executed by the at least one com put er hardware processor, cause the at least one com put er hardware processor to carry out the method of:
extracting text from a plurality of communications between a call center user and a call center agent to thereby create a communication record;
for each of the plurality of communications:
assessing the intent of corresponding text by applying an AI assessment model to obtain an intent assessment of the communication, wherein the AI assessment model is developed by processing a set of initial training data to detect intents; and
wherein the intent assessment includes a confidence score of the communication and further comprising flagging the communication record for manual quality management analysis and for making annotations if a confidence level of the assessment is below a threshold score.
12. The system of claim 11 wherein each intent assessment comprises multiple fields, each field having a value selected from a corresponding set of values and wherein the confidence level is based on a confidence sub-level determined for each value of each field.
13. The system of claim 12 , wherein the fields and corresponding sets of values correspond to a human-readable form used for the manual annotation.
14. The system of claim 11 , wherein the AI assessment model considers acceptable key words or phrases in each of a plurality of categories and the annotations include key words or phrases that are to be added to a category as acceptable.
15. The system of claim 11 , wherein supplemental training data is added to the model based on reviewing manual corrections to previous assessments by the assessment model.
16. The system of claim 15 , wherein the supplemental training data is based on manual quality analysis by a plurality of people and determining consensus between the people.
17. The system of claim 11 , wherein the manual quality management analysis and annotation is accomplished by an agent in the call center.
18. The system of claim 11 , wherein the manual quality management analysis and annotation includes a user interface displaying suggestions that have been marked for training from multiple models.
19. The system of claim 18 wherein the suggestions have been marked for training based on lack of confidence or explicit suggestion.
20. The system of claim 18 , wherein the suggestions come from a review of an unsupervised clustering model.
21. A method for assessing communications a contact center interaction, the method comprising:
receiving communication records relating to an interaction in a contact center, wherein each communication record includes text strings extracted from the corresponding
communication and wherein each call record has been designated by an AI assessment model trained to accomplish an assessment of one or more aspects of the communication records, wherein the AI assessment model is developed by processing a set of initial training data;
for each communication record:
displaying at least one of the text strings on a user interface in correspondence with at least one AI intent assessment, wherein the AI intent assessment includes a confidence score of the communication and further comprising flagging the communication record for manual quality management analysis and for making annotations if a confidence level of the assessment is below a threshold score;
receiving, from a user, an assessment of the at least one text strings relating to the AI assessment;
updating the communication record based on the assessment to create an updated communication record; and
applying the updated communication record to the AI assessment model as supplemental training data.
22. The method of claim 21 , wherein the supplemental training data is based on manual quality analysis by a plurality of people and determining consensus between the people.