Abstract: Systems and methods for improving query creation and AQM are provided. A user interface is provided that allows an analyst to generate and test queries. After the analyst provides a query, one or more subsets of previous call center interactions are selected to test the provided query using methods such as a large language model or string vectorization. Once the subsets of interactions are selected, an LLM is used to evaluate the query against the subset. The evaluations may indicate interactions which were positive and which interactions were negative and may include quotes from the interactions that support the evaluations. The evaluations are presented to the analyst for review, and the analyst may revise the query in view of the evaluations. Once the analyst is satisfied with the performance of the query, the query may be used by the LLM to perform AQM for new interactions in the call center.

Abstract: Systems and methods for improving query creation and AQM are provided. A user interface is provided that allows an analyst to generate and test queries. After the analyst provides a query, one or more subsets of previous call center interactions are selected to test the provided query using methods such as a large language model or string vectorization. Once the subsets of interactions are selected, an LLM is used to evaluate the query against the subset. The evaluations may indicate interactions which were positive and which interactions were negative and may include quotes from the interactions that support the evaluations. The evaluations are presented to the analyst for review, and the analyst may revise the query in view of the evaluations. Once the analyst is satisfied with the performance of the query, the query may be used by the LLM to perform AQM for new interactions in the call center.

Abstract: Certain aspects of the disclosure provide methods and systems for providing resolution guidance to a help center agent are provided. One method generates a set of intent-resolution pairs based on a historical interactions, including: grouping similar intents extracted from the corpus of historical interactions in a same intent topic group of a plurality of intent topic groups, and grouping resolutions associated with the similar intents in a same resolution topic group of a plurality of resolution topic groups. Additionally, the method determines an intent of the current user utterance as a user intent. The method maps the user intent to one or more intent-resolution pairs from the set of intent-resolution pairs. Moreover, method provides, to the help center agent, one or more suggested resolutions corresponding to the one or more intent-resolution pairs. Also, the method provides, to the customer, a selected resolution from among the one or more suggested resolutions.

Abstract: A method for providing an intent expressed in a conversational interaction in narrative form includes detecting a first intent, with a first large language model, from an input comprising at least an interaction transcript and an engineered prompt; generating, with the first large language model, a first narrative for the first intent and a first confidence score corresponding to a probability that the first intent is an intention in fact expressed in the interaction transcript; determining that the first confidence score is greater than or equal to a threshold; checking that the first narrative conforms to fluency and completeness rules, when the first confidence score is greater than or equal to the threshold; and outputting the first narrative as an output detected intent, when the first confidence score is greater than or equal to the threshold and when the first narrative conforms to the fluency and completeness rules.

Abstract: Certain aspects of the present disclosure provide techniques for providing an issue resolution indication for an interaction. A method includes obtaining an interaction transcript; obtaining a resolution prompt; determining one or more issues presented to the first entity by the second entity, generating, with a first large language model based on the interaction transcript, the one or more issues, and the resolution prompt, one or more issue resolution indications; generating, for each of the one or more issue resolution indications indicating resolved status, a first narrative summarizing one or more actions implemented to resolve the one or more issues; generating, for each of the one or more issue resolution indications indicating unresolved status, a second narrative summarizing a reason the one or more issues are unresolved; and outputting the first narrative or the second narrative with each respective one of the one or more issue resolution indications.

Abstract: Systems and methods for automating quality metrics are provided. Transcripts of interactions with agents and assigned quality metrics for a variety of questions are collected for an entity. The quality metrics may relate to questions such as “Did the agent greet the customer with a friendly greeting?” and “Did the agent answer the customer's question after returning from the hold?”. For each question used by the entity to generate quality metrics, a large language model or classifier is trained for the question using the transcripts and the assigned quality metrics for the question. The transcripts may be processed to include textual information corresponding to metadata associated with the communications such as time stamps for call holds, utterances, and silent periods. The trained large language model or classifiers may then be used later to automatically assign quality metrics to current interactions for their corresponding question.

Abstract: A method for providing an intent expressed in a conversational interaction in narrative form includes detecting a first intent, with a first large language model, from an input comprising at least an interaction transcript and an engineered prompt; generating, with the first large language model, a first narrative for the first intent and a first confidence score corresponding to a probability that the first intent is an intention in fact expressed in the interaction transcript; determining that the first confidence score is greater than or equal to a threshold; checking that the first narrative conforms to fluency and completeness rules, when the first confidence score is greater than or equal to the threshold; and outputting the first narrative as an output detected intent, when the first confidence score is greater than or equal to the threshold and when the first narrative conforms to the fluency and completeness rules.