Walmart Apollo, LLC

Facebook, Inc.

Vivek Kaul

Shankara Bhargava Subramanya

Christopher P Bender

Balamanohar Paluri

Arpit Sharma

Nikhil Johri

Michael Francis Zolli

Jingsi Zhang

Tara Brittany Stewart

Utkarsh Lath

An e-commerce voice representation system is provided and generally includes a server, a database, and a customer computing device. The server may generate a hierarchical ontology to semantically represent user queries generated by the customer computing device. The server may receive from the customer computing device data representing an e-commerce user query. The server may parse the e-commerce user query based on syntactic dependencies between the words in the e-commerce user query and translating those dependencies into semantic relations between the words. The received e-commerce user query may be tagged with actions, entities, and properties of the hierarchical ontology. Identified products may be linked to conceptual classes of the hierarchical ontology. Syntactic dependencies between words of the e-commerce user query may then be determined, and a semantic representation of the e-commerce user query may be generated based on the syntactic dependencies and mapping rules.

Method and apparatus for parsing and representation of digital inquiry related natural language

A system for targeting advertising comprises a processor configured to compute a similarity score between a plurality of customers in a seed set and each of a pool of in-store purchasers having no history of online purchases from the online store of an entity. Each user in the seed set has a history of online purchases from the online store of the entity and a history of in-store purchases from stores of the entity. The processor is configured for selecting a subset of the pool of in-store purchasers having similarity scores above a predetermined threshold. The processor is configured for initiating rendering of advertisements for the online store to the subset of the pool of in-store purchasers.

System and method for rendering advertisements using lookalike model

An e-commerce voice response determination system is provided and generally includes a server and a customer computing device. The server may determine, based on a user query, a plurality of product descriptions. The server may determine a similarity value between the user query and each of the product descriptions based on the similarity between each word of the user query and each word of the product descriptions. The server may determine the product description with the most similarity to the user query, and provide a response to the customer computing device based on that product description. In some embodiments, the server may determine whether the user query matches a predefined query. If the user query matches the predefined query, the server provides a response to the customer computing device based on a predefined response to the predefined query.

Method and apparatus for determining responses to digital inquiry related questions

Systems, methods, and non-transitory computer readable media configured to receive an advertisement including an image. A fraud assessment value for the advertisement can be determined. An image assessment value for the image can be determined. The fraud assessment value and a threshold value for fraud assessment can be compared. The image assessment value and a threshold value for image assessment can be compared. Fraud associated with the advertisement can be determined based on comparison of the fraud assessment value and the threshold value for fraud assessment and comparison of the image assessment value and the threshold value for image assessment.

Systems and methods for fraud detection based on image analysis

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Mountain View, CA, United States of America

Vivek Kaul