Ul LLC

Kyle Michael Caulfield

Spencer Sharpe

Annie Ibrahim Rana

James Cogley

Saurav Acharya

Valeriy Liberman

Rashid Mehdiyev

Lisa Epstein

Michael Arnold

Tricia Sheehan

Christian Dorn Anschuetz

Surekha Durvasula

Systems and methods for dynamically generating object models corresponding to regulations. According to certain aspects, a server computer may access a regulation and automatically generate a summary of the regulation based on a specific set of sentences. The server computer may additionally determine a set of topics and named-entity attributes for text within a regulation object model, as well as a probability that a topic or attribute is applicable to the regulation. Further, the server computer may generate and enrich object models according to the various analyses and avail the enriched object models for review by entities and users of regulatory compliance services.

Technologies for dynamically creating representations for regulations

Systems and methods for classifying product feedback by an electronic device are described. According to certain aspects, an electronic device may receive consumer feedback entries associated with various products, where each entry may include an initial classification. The electronic device may analyze each entry using a machine learning model to determine a subsequent classification for the entry. When there is a mismatch between classifications, the electronic device may present information associated with the entry for review by a user, where the user may specify a final classification for the entry, and the electronic device may update the machine learning model for use in subsequent analyses.

Technologies for classifying feedback using machine learning models

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Mount Prospect, IL, United States of America

Kyle Michael Caulfield