Abstract: A method of question answering using enhanced retrieval-augmented generation according to an embodiment includes receiving, by a computing system, a user query, pre-processing, by the computing system, the user query to determine whether the user query is associated with malicious intent, retrieving, by the computing system, relevant data from a knowledge base by using a keyword index and a semantic index in response to determining that the user query is not associated with malicious intent, prompting, by the computing system, a large language model to generate an answer to the user query based on only the relevant data retrieved from the knowledge base, and receiving, by the computing system, the answer to the user query from the large language model in response to the prompt.

Abstract: A method for mining topics discussed in conversations that includes: receiving conversation data; and using a topic mining algorithm to mine topics from the conversation data. The topic mining algorithm includes identifying candidate topics in each of the conversations. The topic mining algorithm further includes identifying the topics of the conversations by: compiling a list of the candidate topics; pruning the list of candidate topics by discarding certain of the candidate topics per a cross-conversation factor that factors usage across all conversations; and identifying the candidate topics remaining on the pruned list of candidate topics as the topics. The topic mining algorithm further includes determining topic groups by grouping the topics according to a degree of semantic similarity between the topics; and associating a list of utterances with the topic groups.

Abstract: A method for enabling efficient performance of post interaction operations in a contact center may include obtaining, by a computing system, interaction data indicative of an interaction between a client and an agent of a contact center. The method may further include producing, by the computing system, with an artificial intelligence model trained with low-rank adaptation and as a function of the obtained interaction data, post interaction data to enable the agent to efficiently proceed to a subsequent interaction associated with the contact center. The post interaction data may be indicative of a summarization of the interaction. The method may also include providing, by the computing system, the post interaction data to the agent for confirmation. Further, the method may include storing, by the computing system and in response to receipt of agent feedback, the post interaction data in a data set of the contact center for analytics.

Abstract: A method in a contact center for generating an action classifier model and use thereof in selectively initiating turn set queries of a knowledge base to assist agents in real time during ongoing conversations with customers. The method includes: generating an action classifier model; receiving classification data that classifies a first plurality of the customer actions found in training samples as belonging to a first action category for which a knowledge base search is deemed needed, and a second plurality of the customer actions as belonging to a second action category for which a knowledge base search is deemed not needed; and using the action classifier model and the received classification data to perform a query filtering routine for selectively initiating a turn set query for a present turn set occurring in an ongoing conversation between an agent and customer.

Abstract: A method for efficient identification of knowledge base data to support interactions in a contact center may include identifying a query associated with a contemporaneous interaction between a client and an agent of a contact center and performing a search of a knowledge base to determine a set of one or more relevant documents responsive to the identified query, including ranking a set of resultant documents from the knowledge base as a function of a vector-based similarity determination in which each resultant document and the identified query is represented with a corresponding embedding. Additionally, the method may include identifying, with a transformer-based artificial intelligence model, a subset of content within each relevant document as being most pertinent to the identified query and visually demarcating the identified subset of each relevant document from other content of the relevant document in a user interface as the interaction occurs.

Abstract: A method of leveraging a global confidence classifier for information retrieval in contact centers according to an embodiment includes receiving, by a computing system, a user query from a contact center client communication with a knowledge-only bot of the computing system, performing, by the computing system, feature extraction on the user query by converting query words of the user query into a numerical vector representation of the user query, identifying, by the computing system, a subset of documents most likely to be responsive to the user query, and re-ranking, by the computing system, the subset of documents most likely to be responsive to the user query based on a global confidence classifier model.

Abstract: A method for enhancing intent health of a conversational bot. The conversational bot include a machine learning model trained for natural language understanding (NLU) within a NLU domain that is defined by a collection of intents and sets of associated utterances. The method includes: retrieving the collection of intents and associated utterances; generating an utterance embedding for each of the retrieved utterances; calculating scores for utterance-level health indicators for each intent of the collection of intents; and calculating an overall intent health score for each intent of the collection of intents. The overall intent health score may be based on a weighted combination of the calculated scores for the utterance-level health indicators. The utterance-level health indicators may include an utterance in conflict indicator based on a computed semantic similarity and an utterance outlier indicator based on local density.

Abstract: A method for authoring a conversational bot including: receiving conversation data; receiving seed intent data that comprises seed intents having a seed intent label and sample intent-bearing utterances; using an intent mining algorithm to mine the conversation data to determine new utterances to associate with the seed intent; augmenting the seed intent data to include the mined new utterances associated with the seed intents; and uploading the augmented seed intent data into the conversation bot. The intent mining algorithm may include: identifying intent-bearing utterances; identifying candidate intents; for each of the seed intents, identifying seed intent alternatives from the sample intent-bearing utterances; associating the intent-bearing utterances from the conversation data with the seed intents via determining a degree of semantic similarity between the candidate intents of the intent-bearing utterances and the seed intent alternatives.

Abstract: A method for mining topics discussed in conversations that includes: receiving conversation data; and using a topic mining algorithm to mine topics from the conversation data. The topic mining algorithm includes identifying candidate topics in each of the conversations. The topic mining algorithm further includes identifying the topics of the conversations by: compiling a list of the candidate topics; pruning the list of candidate topics by discarding certain of the candidate topics per a cross-conversation factor that factors usage across all conversations; and identifying the candidate topics remaining on the pruned list of candidate topics as the topics. The topic mining algorithm further includes determining topic groups by grouping the topics according to a degree of semantic similarity between the topics; and associating a list of utterances with the topic groups.

Abstract: A method of leveraging a global confidence classifier for information retrieval in contact centers according to an embodiment includes receiving, by a computing system, a user query from a contact center client communication with a knowledge-only bot of the computing system, performing, by the computing system, feature extraction on the user query by converting query words of the user query into a numerical vector representation of the user query, identifying, by the computing system, a subset of documents most likely to be responsive to the user query, and re-ranking, by the computing system, the subset of documents most likely to be responsive to the user query based on a global confidence classifier model.

Abstract: A method for intent mining that includes: receiving conversation data; using an intent mining algorithm to automatically mine intents from the conversation data; and uploading the mined intents into the conversational bot. The intent mining algorithm may include: analyzing utterances of the conversation data to identify intent-bearing utterances; analyzing the identified intent-bearing utterances to identify candidate intents; selecting salient intents from the candidate intents; grouping the selected salient intents into salient intent groups in accordance with a degree of semantic similarity; for each of the salient intent groups, selecting one of the salient intents as the intent label and designating the others as the intent alternatives; and associating the intent-bearing utterances with the salient intent groups via determining a degree of semantic similarity between the candidate intents present in the intent-bearing utterance and the intent alternatives within each group.

Abstract: A method for authoring a conversational bot including: receiving conversation data; receiving seed intent data that comprises seed intents having a seed intent label and sample intent-bearing utterances; using an intent mining algorithm to mine the conversation data to determine new utterances to associate with the seed intent; augmenting the seed intent data to include the mined new utterances associated with the seed intents; and uploading the augmented seed intent data into the conversation bot. The intent mining algorithm may include: identifying intent-bearing utterances; identifying candidate intents; for each of the seed intents, identifying seed intent alternatives from the sample intent-bearing utterances; associating the intent-bearing utterances from the conversation data with the seed intents via determining a degree of semantic similarity between the candidate intents of the intent-bearing utterances and the seed intent alternatives.

Abstract: A method for intent mining that includes: receiving conversation data; using an intent mining algorithm to automatically mine intents from the conversation data; and uploading the mined intents into the conversational bot. The intent mining algorithm may include: analyzing utterances of the conversation data to identify intent-bearing utterances; analyzing the identified intent-bearing utterances to identify candidate intents; selecting salient intents from the candidate intents; grouping the selected salient intents into salient intent groups in accordance with a degree of semantic similarity; for each of the salient intent groups, selecting one of the salient intents as the intent label and designating the others as the intent alternatives; and associating the intent-bearing utterances with the salient intent groups via determining a degree of semantic similarity between the candidate intents present in the intent-bearing utterance and the intent alternatives within each group.