Groupon, Inc.

Bytedance Inc.

Cars.com, LLC

Addhyan Pandey

Boris Lerner

Abhaya Parthy

Leopold Silberstein

Chirag S Patel

Zahra Ferdowsi

Angela Han

William R Page

Alexis Wells Carpenter

Sainaga Srikantham

Waigen Zhang

Michael D Heinley

Kai-Chen Cheng

Samantha Volker

David Matthew Krell

Greg Heidorn

Joao Moreira

Bree J Radloff

Sanjay Kumar Dasari

Barbara August

Seth Goldberg

Audrey Salerno

Leena Ansari

Michael N Morgan

Dennis R Sherman

An apparatus, method, and computer program product are provided to predict and model the performance of option data objects prior to the introduction of such option data objects into a network environment through the application of specific rule sets that are applied in a multi-stage arrangement on sets of extracted option data object parameters. In some example implementations, option data object parameters are extracted and parsed into sets of categorical and non-categorical parameters, which may be both quantitative and qualitative, and transmitted to one or more predictive models. Through the operation of the predictive models, the option data object parameters are transformed into a weighted value associated with the option data object that reflects a predicted performance level of the option data object and can be used when comparing multiple option data objects against each other.

Apparatus and method for option data object performance prediction and modeling

Systems, methods, and computer-readable medium storing instructions for identifying low-quality signals of an electronic listing of a listing entity are described herein. The method, system, or instructions may include ingesting text including listing information of the electronic listing and listing entity information of the listing entity; normalizing the text; parsing the text to identify a plurality of phrases; generating a set of category scores for each of at least one of the plurality of phrases; identifying, based on the on the sets of category scores, one or more low-quality signals each including a low-quality phrase and low-quality categories; and presenting an indication of the one or more low-quality signals with the electronic listing.

Systems and methods for identifying signals of low-quality listings

Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for predicting network asset requests for a future network time interval.

Method, apparatus, and computer program product for balancing network resource demand

Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for predicting system resource volumes for future network time intervals based upon predicted likelihoods of termination transactions for the future network time interval.

Method, apparatus, and computer program product for system resource volume prediction

Systems and methods for guided vehicle matching are disclosed. In order to generate vehicle recommendations for a user of an electronic vehicle listing service, a plurality of vehicle-related lifestyle options are presented to the user, and a user selection received. Additional data regarding the user's vehicle preferences, requirements, or usage may be obtained. Based upon such information, a set of example vehicles is generated and presented to the user. Each example vehicle has characteristics representing a plurality of other vehicles. Based upon user ratings or selections of at least some of the example vehicles, a plurality of vehicle recommendations for specific available vehicles are generated and presented to the user. In some embodiments, further user interaction with such recommendations is used to refine the vehicle recommendations and identify additional vehicle recommendations.

Method, system, and manufacture for personalized vehicle matching based upon user preferences

Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for classifying merchants. In one embodiment a method is provided comprising determining, based on a first value, a first prediction value that indicates a programmatically expected number of consumers that will request termination of an accepted first promotion; and determining, based on the first prediction value, a classification for the first entity, the classification specifying a likelihood that the transmittal of the first promotion to consumers will result in a number of terminations less than a pre-specified threshold.

Method, apparatus, and computer program product for merchant classification

Growing community of inventors

Chicago, IL, United States of America

Addhyan Pandey