Groupon, Inc.

The Yes Platform

Bytedance Inc.

Bloomreach Inc.

Amit Aggarwal

David Thacker

Kevin Chang

Sean O'Brien

Michalis Potamias

Ruslan Gilfanov

Anthony Caliendo

Xiaolei Li

Navin Agarwal

David Hoover

Paul Andre Gauthier

Andrey Zaytsev

Judy Yi-Chun Hsieh

Julie Bornstein

Lianghao Chen

Ashutosh Garg

Jeffrey Alan Holden

Mohit Jain

Suneel K Gupta

Praveen K Patnala

Manojit Sarkar

James Grunewald

Shao-Chuan Wang

Debbie Ayano Limongan

Jorge Rodriguez

Xinyi Zhou

Preetam Amancharla

Dan Nawara

Prateek Gupta

Gurashish Singh Brar

Kannan Nitin Sharma

Omar Eduardo Fernández

Systems and methods are presented for managing the cadence (e.g., frequency or rate) that electronic promotion correspondence is sent to a consumer. A system may access a target cadence indicator associated with a consumer that indicates of a target rate for sending electronic promotion correspondence to the consumer. The system may also determine an actual cadence indicator for the consumer over a predetermined period of time and analyze a potential electronic promotion correspondence for sending to the consumer. The system determines whether to send the electronic correspondence to the consumer based on the target cadence indicator, the actual cadence indicator, and the analysis of the electronic promotion correspondence.

Cadence management system for consumer promotions

In a promotion offering system, a promotion offering may be presented to a consumer on more than one occasion. The previous presentation of the promotion to the consumer may affect a subsequent presentation of the same promotion to the same consumer. The present invention provides an apparatus and method for analyzing the effect a previous presentation of a promotion may have on a consumer when the promotion is presented to the consumer at a subsequent time.

Reimpression promotion system

A computer system extracts product data from a website and correlates product records from multiple sources to one another as corresponding to the same product. A website is crawled efficiently by rendering webpages using a virtual browser that ignores blacklisted elements, extracts data from objects without rendering, and suppressing retrieval of remote resources. Data is extracted according to engine control statements including a selector and extractor. A website may be crawled repeatedly and changes in extracted data may be detected and flagged. Engine control statements may be automatically changed in response to detecting a change in the configuration of the website. Images of product records may be correlated with one another by first comparing text of the product records and selecting images for comparison based on composition. Images are compared using a machine learning model. Images determined to be similar may be presented to a human for a correlation decision.

Data correlation system and method

Example order management systems and methods are described. In one implementation, a catalog ingestion system receives data associated with multiple products offered by multiple brands. A commerce management system receives a customer order from a customer. The customer order includes at least a portion of the multiple products offered by the multiple brands. The commerce management system splits the customer order into multiple brand orders such that each brand order is associated with products in the customer order from a particular brand. An integration platform fulfills the brand orders by submitting the brand orders to the associated brands.

Order management systems and methods

A promotion offering system and method is disclosed. The promotion offering system and method selects consumers for a test promotion that has one or more attributes, and is configured to generate test data for multiple consumer groupings. The promotion offering system and method may use the test data in analyzing whether to send a promotion to a consumer. For example, the promotion offering system and method may use one algorithm to generate a list of ranked promotions, and may use the test data in order to adjust the list of ranked promotions (such as replacing a highest ranked promotion with another promotion).

Pre-feature promotion system

A user preference hierarchy is determined from user response to images. Images may be tagged using machine learning models trained to determine values for images. Products are clustered according to product vectors. Images of products within a cluster are clustered according to composition and groups of images are selected from image clusters for soliciting feedback regarding user preference for products of a cluster. Feedback is used to train a user preference model to estimate affinity for a product vector. A user may provide feedback regarding a price point and products are weighted according to a distribution about the price point. The distribution may be asymmetrical according to direction of movement of the price point. Filters may be dynamically defined and presented to a user based on popularity and frequency of occurrence of attribute-value pairs of search results and based on feedback regarding the search results.

Dynamic filter recommendations

Electronic correspondence that includes one or more promotions may be generated for presenting to a consumer. In order to determine whether to present the electronic correspondence to the consumer, the promotions included in the electronic correspondences may be analyzed in terms of a probability the consumer will accept the promotions, a relevance level between the promotions and attributes of the consumer, a relevance level between the promotions and the consumer, a relevance level between the promotions and a set of goals or rules, among other similar terms. After the analysis, a determination may be made whether to send the electronic correspondence to the consumer. Similarly, the analysis may compare multiple electronic correspondences, and determine, based on the comparison, which of the multiple electronic correspondences to send to the consumer.

Inbox management system

Systems and methods are presented for managing electronic promotion correspondence sent to consumers. A system may manage electronic promotion correspondence sent on a per-consumer basis. The system may access multiple electronic promotion correspondences generated for a particular consumer, select an electronic promotion correspondence from among the multiple electronic promotion correspondences, and determine to send the electronic promotion correspondence to the consumer according to any number of factors. The system may determine a target time to send the first electronic promotion correspondence to the consumer and selected communication channel to send the electronic promotion correspondence through.

Images are tagged with values in an image data hierarchy that is most subjective at its top level and least subjective at its bottom level, such as a hierarchy including style, type, and features for clothing. A user preference hierarchy is determined from user response to images that are tagged. Tagged images may be generated by processing them with machine learning models trained to determine values for images. Product records including images and other data are analyzed to generate attribute vectors that are encoded to generate product vectors. Products are clustered according to their product vectors. Images of products within a cluster are clustered according to composition and groups of images are selected from image clusters for soliciting feedback regarding user preference for products of a cluster. Feedback is used to train a user preference model to estimate user affinity for a product having a given product vector.

Cluster and image-based feedback system

A promotion program analytical system and method is disclosed. The promotion program analytical system and method selects a promotion program to offer to a consumer. Selection of the promotion program to present to the consumer includes determining a probability that the consumer will accept the promotion program. The probability of acceptance may be determined based on past performance data of similar promotion programs, and also past performance data on the promotion program itself when it is available.

Dynamic promotion analytics

A deal program life cycle system and method is disclosed. The deal program life cycle may oversee the issuance of deals from a deal program to consumers over the life cycle of the deal program. One or more aspects of the deal program may change during different periods of the life cycle of the deal program. For example, the deal program may include deal features, a number of units for deals, and relevance features for the deal program. The deal features, number of units of deals, and/or relevance features for the deal program may change during the different periods of the life cycle.

Deal program life cycle

A sales enhancement system and method is disclosed. The sales enhancement system is configured to use one or more deal program collections, which are groupings or compilations of deal programs. The sales enhancement system manages deal programs in the deal program collections at various stages of use including: associating a deal program with multiple deal program collections; determining the number of deals to assign to the different deal programs; using triggers to select which deal program collections to access; transmitting an offer for a deal; and processing acceptances of the offers.

Sales enhancement system

A promotion system for determining a deficiency in promotion data and correcting for the deficiency is disclosed. Issuing offers from a promotion program results in promotion data being generated. The promotion data may be analyzed to determine an acceptance rate of the offers. The promotion system may compare whether the acceptance rate is above a predetermined threshold, but has a confidence level that is less than a confidence rate threshold. In that event, the promotion system may issue additional offers in order to increase the confidence level associated with the acceptance rate by a predetermined amount.

Growing community of inventors

Sunnyvale, CA, United States of America

Amit Aggarwal