Ebay, Incorporated

Paypal, Inc.

Harshal Ulhas Deo

Kavita Ganesan

Braden Christopher Ericson

Neelakantan Sundaresan

Prasanna Vinjamuri

Arvind Naidu

Monica Dhanaraj

Lakshman Shyam Sundar Maddali

Monica Dhanarj

Brian Matthew Carnes

Aspects of the present disclosure involve systems, methods, devices, and the like for tokenizing offers. The current disclosure presents a system and method that can present a tokenized offer for saving and retrieving from a digital wallet. The tokenized offer may be presented on a client site for selection by a user interested in taking advantage of the promotion without having to leave the current site. The current disclosure also presents a system that can retrieve the tokenized offer saved for use with a purchase. The tokenized offer may be automatically applied and on display at the merchant site and/or retrieved from the digital wallet.

System and method for tokenizing offers

A method for merchant-side tracking of customer selections includes receiving, via a user list script on a merchant webpage, a user selection of a product representation displayed on a UI of a user device. The method includes updating a user list with the user selection, the user list associated with a generic user representation. The method includes determining, based on a type of identifier associated with a user of the user device, to use a generic user representation with the user list. The method includes selecting, based on the generic user representation, an anonymous user flow type for tracking the user as an anonymous user of the merchant webpage. The anonymous user flow type associates the user device with the user list independently of associating the user with the merchant.

Method, non-transitory machine-readable medium, and system for merchant-side integrated shopping cart recovery

In one embodiment, a system and method is illustrated including receiving a feedback request identifying a particular user, retrieving a feedback entry in response to the feedback request, the feedback entry containing a first term, building a scoring model based, in part, upon a term frequency count denoting a frequency with which the first term appears in a searchable data structure, mapping the first term to a graphical illustration based upon a second term associated with the graphical illustration such that the graphical illustration may be used to represent the second term, and generating a feedback page containing the first term and the graphical illustration. The method may include assigning a value to the first term so as to identify the first term, assigning the first term to the searchable data structure, and extracting the first term from the searchable data structure based, in part, upon an extraction rule.

Visualization of reputation ratings

Systems and methods for providing a website tracking system include a system provider device that detects a first interaction between a user and a web browser executing on the user device while the web browser displays a first merchant website, the first interaction determined by a first downloaded code provided by the system provider device. In some embodiments, a second interaction between the user and the web browser is detected while the web browser displays a second merchant website, the second interaction determined by a second downloaded code provided by the system provider. Based on the first interaction and the second interaction, one or more entries of a system provider database is updated. The system provider device thereafter causes to be displayed, on a graphical user interface (GUI) of the user device, the one or more entries.

Website tracking system

Merchant-side integrated shopping cart recovery

Embodiments of computer-implemented methods and systems for activity-based recommendations are described. One example embodiment includes receiving data indicating historical activities of a user community, the historical activities including historical activities of the target user, selecting a reference group of users from the user community based on analysis of the historical activities of the target user, receiving generally current time activities of the reference group of users, the generally current time activities including those activities that have occurred within a defined time window, and recommending items to the target user based on the generally current time activities of the reference group of users.

Methods and systems for activity-based recommendations

A method for near real time patterns identification, in one example embodiment, comprises receiving a data stream containing information associated with a transaction and participants of the transaction and receiving an Artificial Intelligence (AI) algorithm trained to score data in the data stream. The method may further comprise receiving metadata associated with the historical information, comparing the data stream to the metadata by measuring differences between variables included in the historical metadata and the data stream. The method may further comprise modifying the data stream to suit the AI algorithm when the differences between variables are below predetermined threshold values and retraining the AI algorithm based on the data stream when the differences between the variables are greater than the predetermined threshold values. The method may further comprise feeding the data stream to the AI algorithm to classify the variables in the data stream.

Growing community of inventors

San Jose, CA, United States of America

Harshal Ulhas Deo