Abstract: A method and a system for counteracting model drift in a Latent Dirichlet Allocation (LDA) model by maintaining freshness and accuracy in the LDA model by leveraging LDA classification and vector algebra to detect potential degradations are provided. The method includes: receiving a first corpus that includes a first set of textual data; generating a first set of topics by applying a first LDA model to the first corpus; generating, for each topic, a corresponding vector of words with associated weights; measuring a cosine similarity of each topic with respect to a second set of topics that is associated with a second LDA model; using the measured cosine similarities to determine a topic match entropy between the first LDA model and the second LDA model; and when the topic match entropy exceeds a predetermined threshold, determining that the first LDA model has drifted and requires retraining.

Abstract: A method and a system for automating a task assignment and allocation process using an algorithmic approach that is designed for optimizing resource experience and availability in order to improve efficiency and client outcomes is provided. The method includes receiving task-related information; receiving candidate attribute information; receiving historical interaction information with respect to the client; analyzing the received information; and determining, based on a result of the analysis, a primary task assignment that includes an identification of at least one candidate as a primary responsible party for performing the task and a proposed schedule for completing the task. The analysis may be performed by applying an algorithm that uses the received information as input. The determination of the primary task assignment may be based on an output of the algorithm.

Abstract: Systems and methods for receiving a set analyzing case records by extracting case text, performing natural language processing, and allocating each case text to a topic. Topics may be clustered to identify meaningful patterns that are reflected in numerous case records. The data resulting from the analysis may be visualized on a dashboard to allow users to identify and explore these patterns.

Abstract: Systems and methods for receiving a set analyzing case records by extracting case text, performing natural language processing, and allocating each case text to a topic. Topics may be clustered to identify meaningful patterns that are reflected in numerous case records. The data resulting from the analysis may be visualized on a dashboard to allow users to identify and explore these patterns.