Abstract: The present invention relates generally to the technological field of search engines. More specifically, the present invention relates to detecting statistical irregularities in data and retrieving records pertaining to these irregularities. The invention represents a system and methods of retrieving records from an indexed database which provide an improvement of the technological field of search engines and information retrieval by making the detection and retrieval of records representing statistical irregularities in the database faster, more efficient, and less computationally complex.

Abstract: A system and method of identifying occurrence of a semantic variation of a phrase in a passage by at least one processor may include calculating a phrase embedding vector, representing a semantic meaning of the phrase; extracting, from a textual representation of the passage, at least one hierarchical set of nested sequences of words; for each sequence, calculating a corresponding sequence embedding vector, representing a semantic meaning of the sequence; for one or more sequence embedding vectors, calculating a corresponding vector similarity value, representing similarity of the sequence embedding vectors to the phrase embedding vector, identifying a sequence corresponding to a maximal vector similarity value of the one or more vector similarity values; and determining the identified sequence as a semantic variation of the phrase, based on the maximal vector similarity value.

Abstract: A system and method of determining a sentiment of a participant in an interaction may include: obtaining a plurality of textual segments, each representing a portion of the interaction, and labeled according to a specific participant; inferring a language model on one or more textual segments of the plurality of textual segments, to generate respective semantic embedding vectors, each representing a semantic meaning of the respective textual segment in a semantic vector space; compiling a semantic vector set that includes (i) a target semantic embedding vector, corresponding to a target textual segment of a target participant, and (ii) one or more peripheral semantic embedding vectors, respectively corresponding to one or more peripheral textual segments of the plurality of textual segments; and inferring a composite machine-learning (ML)-based model on the semantic vector set, to classify a sentiment of the target participant, as expressed in the target textual segment.

Abstract: A method in a contact center for generating insights from conversation data derived from interactions and storing the insights in an index. The method may include: determining an insight type; based on the insight type, determining inputs including a question prompt, answer prefix, and relevant portion of the conversation data; inputting the inputs into a LLM configured to receive the inputs and generate output text answering a question contained in the question prompt pursuant to an answer form suggested by the answer prefix given content contained in the relevant portion of the conversation data; generating the output text via operation of the LLM; transforming the output text of the first insight via a sentence transformer into vector embedding representative of a semantic meaning of the output text; and storing the computed vector embedding of the first insight in the index.

Abstract: A method for generating a hierarchical taxonomy relating to an interaction aspect from conversation data. The method includes a first process for generating insights that includes: receiving conversation data for a first interaction; determining a conversation portion relevant to the interaction aspect and a question prompt and providing them as inputs to an LLM; and generating responsive output text via the LLM as the first insight. In a second process, inputs are provided to the LLM that include the insights, a first instruction to generate category names based on the insights, and a second instruction to make a category assignment for each insight. The second process further includes receiving from the LLM the generated category names and category assignments; grouping the insights by those having the same category assignment; and generating a hierarchical taxonomy according to the groupings.

Abstract: The present invention relates generally to the technological field of search engines. More specifically, the present invention relates to detecting statistical irregularities in data and retrieving records pertaining to these irregularities. The invention represents a system and methods of retrieving records from an indexed database which provide an improvement of the technological field of search engines and information retrieval by making the detection and retrieval of records representing statistical irregularities in the database faster, more efficient, and less computationally complex.

Abstract: A method for inverse text normalization of contact center communications according to an embodiment includes performing named entity recognition on text from a contact center communication to identify one or more entities in the text, normalizing each of the identified one or more entities in the text using weighted finite-state transducers, and normalizing at least one entity identified in the text using a large language model in response to determining that the at least one entity identified in the text was unable to be normalized using the weighted finite-state transducers.

Abstract: A method in a contact center for generating insights from conversation data derived from interactions and storing the insights in an index. The method may include: determining an insight type; based on the insight type, determining inputs including a question prompt, answer prefix, and relevant portion of the conversation data; inputting the inputs into a LLM configured to receive the inputs and generate output text answering a question contained in the question prompt pursuant to an answer form suggested by the answer prefix given content contained in the relevant portion of the conversation data; generating the output text via operation of the LLM; transforming the output text of the first insight via a sentence transformer into vector embedding representative of a semantic meaning of the output text; and storing the computed vector embedding of the first insight in the index.

Abstract: A system and method of identifying occurrence of a semantic variation of a phrase in a passage by at least one processor may include calculating a phrase embedding vector, representing a semantic meaning of the phrase; extracting, from a textual representation of the passage, at least one hierarchical set of nested sequences of words; for each sequence, calculating a corresponding sequence embedding vector, representing a semantic meaning of the sequence; for one or more sequence embedding vectors, calculating a corresponding vector similarity value, representing similarity of the sequence embedding vectors to the phrase embedding vector, identifying a sequence corresponding to a maximal vector similarity value of the one or more vector similarity values; and determining the identified sequence as a semantic variation of the phrase, based on the maximal vector similarity value.

Abstract: A method for generating automatic alternative text suggestions for a speech recognition engine of a contact center system according to an embodiment includes applying a word embedding model to generate a vector representation of each unique word in a contact center communication text corpus, calculating a cosine similarity of each vector representation and each other vector representation generated by the word embedding model, discarding each calculated cosine similarity result determined to be below a predefined threshold to generate a filtered set of word pairs, calculating a Levenshtein distance between words of each word pair of the filtered set of word pairs, and generating a candidate list of alternative words for a target word based on the Levenshtein distance between the words of each word pair of the filtered set of word pairs.

Abstract: A method for inverse text normalization of contact center communications according to an embodiment includes performing named entity recognition on text from a contact center communication to identify one or more entities in the text, normalizing each of the identified one or more entities in the text using weighted finite-state transducers, and normalizing at least one entity identified in the text using a large language model in response to determining that the at least one entity identified in the text was unable to be normalized using the weighted finite-state transducers.