Capital One Services, LLC

Deepak Ramamohan

Sourav Choudhary

Manish Agarwal

Akshay Aravindakshan Thoniparambil

Joydeep Dasgupta

Aashish Bansiwala

Gaurav Kedia

Purna Prasanna Srikar Raju Regati

In some implementations, a device may obtain a data set that indicates scores for first terms based on association scores between the first terms and second terms. The association scores may be based on occurrences of the first terms in electronic documents that provide transcripts relating to telecommunication interactions, and occurrences of the second terms in electronic records that provide summaries relating to the telecommunication interactions. The device may process an electronic document, that provides a transcript relating to a new telecommunication interaction involving an agent device and a user device, to extract candidate key phrases from the transcript. The device may determine a relevance score for each candidate key phrase based on the score(s), of the data set, associated with the first term(s) in the candidate key phrase. The device may generate tag(s) for the new telecommunication interaction based candidate key phrase(s) associated with highest relevance score(s).

Tagging telecommunication interactions

The exemplary embodiments may provide a search tool that can locate customer issues in call transcripts and agent notes and can provide an accurate count of how often such issues appear in the call transcripts and agent notes. The exemplary embodiments may improve the speed with which the search of documents is performed. The exemplary embodiments rely upon a document matrix that is computed once for a given corpus of documents and a given vocabulary of the documents. The document matrix may be used across multiple queries. The exemplary embodiments also account for similar terms in processing a query. The exemplary embodiments may use a word coverage factor to improve the relevance of the search results returned by the search tool. The word coverage factor acts as a multiple factor that computes the fraction a query terms that are present in a document.

Search tool for identifying and sizing customer issues through interaction summaries and call transcripts

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.

Learning framework for processing communication session transcripts

Systems, methods, and computer program products for search platforms for unstructured interaction summaries. An application executing on a processor may receive a query comprising a term. The application may generate, based on an embedding vector and the term, an expanded query comprising a plurality of additional terms. The application may generate, based on a term frequency inverse document frequency model, a vector for the expanded query and generate an entity vector for the query. The application may generate a combined vector for the query based on the entity vector and the vector for the expanded query. The application may compute, based on the combined vector for the query and a feature matrix of a corpus, a respective cosine similarity score for a plurality of results in the corpus. The application may return one or more of the plurality of results as responsive to the query based on the similarity scores.

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Karnataka, India

Deepak Ramamohan