Ebay, Incorporated

Wal-Mart Stores, Inc.

Walmart Apollo, LLC

Paypal, Inc.

Yitao Yao

Vadim Geshel

Angie Ruan

Sergiy Pereshyvaylo

Kam Kasravi

Maxim Drobintsev

Pei Wang

Mark Paul Palaima

Sangita Fatnani

Jayashree S Kolhatkar

Kazuo Matsumoto

Vivek Crasta

Bin Ni

Hui-Min Chen

Justin Christopher Early

Vidhya Raman

Rajasekhar Bhogi

Uday Akella

Gregory Choi

Sathishwar Pottavathini

Vijay Raghavendra

Zhiping Tang

James ZhiPing Tang

Guoyu Zhu

Thierry Neel

Jaya Kolhatkar

Alok Bhanot

Homayoun Zariv

Arnold Goldberg

In some examples, a method includes identifying a first listing and an additional listing having one or more characteristics in common, the first listing and the additional listing published on an online marketplace; receiving a request to perform an operation on the first listing and the additional listing; in response to the request, generating a batch of events based on the operation, the batch of events including a first event to be performed on the first listing and a second event to be performed on the additional listing; performing the first event on the first listing and the second event on the additional listing.

Business event processing

A method and system for identifying and labeling fraudulent store return activities includes receiving, by a server, retailer events from an online transaction system of a retailer, the retailer events comprising records of transactions between customers and the retailer, including sale, exchange and return activities across multiple stores. The retailer events are processed to build a network that associates stores, transactions, payment instruments, and customer identification over related activity sequences of transactions. Return fraud labels are generated for the retailer events representing returns based on identified fraud characteristics of the related activity sequences by: representing behavior variables extracted from activity sequences by respective signature vectors; calculating pairwise similarity between the signature vectors; identifying clusters of the signature vectors having common behavior patterns based on the calculated pairwise similarity; and labeling the identified clusters of signature vectors as non-fraudulent behavior or fraudulent behavior.

Identifying and labeling fraudulent store return activities

Systems, methods, and devices for polyglot computing may include, in a single execution, adapting at least one first instruction in a first language and at least one second instruction in the first language into a code comprising the at least one first instruction in the first language and the at least one second instruction in a second language; producing a first result in the first language based on executing the code using a dataset in a data store, wherein the producing the first result in the first language is performed in the single execution; producing a second result in the second language based on executing the code using the dataset; and adapting the second result in the second language into the second result in the first language.

Systems and methods for polyglot analysis

In one example, a system comprises one or more processors to execute a business event processing module. The business event processing module is configured to receive a request to perform an operation on a listing published by an online marketplace, identify at least one additional listing having certain characteristics in common with the listing from a plurality of published listings including the listing, and automatically perform the operation on the at least one additional listing. The system includes a last event processor to determine a performance of the operation on a last instance of the at least one additional listing.

In one example embodiment, a system comprises a processor-implemented event processor accessible over a network; a processor-implemented event producer associated with the event processor and configured to monitor an action or directive of a consumer resource and, in response to a detected action or directive, generate an event and event metadata; a processor-implemented converter associated with the event processor configured to acquire the event metadata and generate a generic event based on the acquired event metadata; and a transporter configured to distribute the generic event to a plurality of consumer resources.

A container-less JSP system is provided. An example container-less JSP system comprises a detector, a trigger module, and an invoker. The detector may be configured to detect a request initiated by a client application to invoke a JSP template. The request is a protocol-neutral Java&#x2122; interface. The trigger module may be configured to trigger the protocol-neutral Java&#x2122; interface to invoke the JSP template. The invoker may be configured to invoke the JSP template.

Container-less JSP template

In one example, an apparatus comprises processors to execute a business event processing module. The module is configured to receive a request to perform an operation on a first listing in a set of listings and identify, in the set of listings, additional listings having certain characteristics in common with the first listing. The operation is automatically performed on the additional listings and a performance of the operation on a last instance of the additional listings is determined by a last event processor.

A real-time predictive intelligence platform comprises: receiving from a user through a meta API definitions for predictive intelligence (PI) artifacts that describe a domain of an online transaction system for least one business entity, each of the PI artifacts including types, component modules and behavior bundles; exposing an entity API based on the PI artifacts for receiving entity events from the online transaction system comprising records of interactions and transactions between customers and the online transaction system; responsive to receiving an entity event through the entity API, executing the component modules and behavior bundles to analyze relationships found between past entity events and metrics associated with the past entity events, and computing a probabilistic prediction and/or a score, which is then returned to the online transaction system in real-time; and processing entity event replicas using modified versions of the PI artifacts for experimentation.

Real-time predictive intelligence platform

In one example embodiment, a system comprises an event processing module accessible over a network; an event producer module associated with the event processing module and configured to monitor an action or directive of a consumer resource and, in response to a detected action or directive, generate an event and event metadata. The system further includes a conversion module associated with the event processing module and configured to acquire the event metadata and generate a generic event based on the acquired event metadata, and a transport module configured to distribute the generic event to a plurality of consumer resources.

Techniques for business event processing are presented. Methods and apparatuses disclosed herein may operate receive a request to perform an operation on a first listing previously listed by a marketplace participant at an online market location; to identify at least one additional listing having certain characteristics in common with the listing from a plurality of previously listed second 1istings including the first listing; and to automatically to perform the operation on the at least one additional listing.

A browser emulator is provided. An example browser emulator system includes a communications module to receive requests for web pages from a browser application, a document object model (DOM) interceptor to intercept an initial document object model (DOM) provided by the web application, an activator to activate a browser emulator, and a DOM manipulator to generate a modified DOM. A marked-up document that corresponds to the modified DOM includes an event interceptor that replaces JavaScript code associated with the original DOM provided by the web application.

Browser emulator system

Techniques for business event processing are presented. Methods and apparatuses disclosed herein may operate to receiving a request to perform an operation on a listing previously published by an online marketplace; to identify at least one additional listing having certain characteristics in common with the listing from a plurality of previously published listings including the listing; and to automatically perform the operation on the at least one additional listing. The operation may include performing an inventory check on the at least one additional listing.

Electronic payment card money laundering detection includes receiving real-time payment card transaction data from ingress channels and an egress channels of at least one payment card system through a first API; generating transactional profiles for each of at least payment cards, the ingress channel, the egress channels, and funding sources of the payment cards; in response to receiving transaction data for a current payment card transaction, evaluating the transaction data using a predictive algorithm that compares the transaction data to the transactional profiles to calculate a probabilistic money laundering score for the current transaction; evaluating the probabilistic money laundering score and current transaction data based on a set of rules to generate a suspicious activity report that recommends whether to approve or report the current transaction; and transmitting the suspicious activity report back to the payment card system and transmitting the suspicious activity report to an identified regulatory body.

Multi-channel data driven, real-time anti-money laundering system for electronic payment cards

Exemplary embodiments for detecting electronic payment card fraud include receiving real-time payment card transaction data from ingress channels and an egress channels of at least one payment card system through a first application programming interface (API); generating transactional profiles for each of at least payment cards, the ingress channel, the egress channels, and funding sources of the payment cards; in response to receiving transaction data for a current payment card transaction, evaluating the transaction data using a predictive algorithm that compare the transaction data to the transactional profiles to calculate a probabilistic fraud score for the current transaction; evaluating the probabilistic fraud score and the current transaction data based on a set of rules to generate a recommendation to approve, decline or review the current transaction; and transmitting the recommendation back to the payment card system via a second API.

Growing community of inventors

Saratoga, CA, United States of America

Yitao Yao