Ebay, Incorporated

Ramesh Periyathambi

Lakshimi Duraivenkatesh

Tomer Lancewicki

Selcuk Kopru

Vineet Bindal

Naga Sita Raghuram Nimishakavi Venkata

Deepti Bhosale

Sai Vipin Siripurapu

Andrew C Lin

Manojkumar Rangasamy Kannadasan

Senthil Kumar Padmanabhan

Auston McReynolds

Jean-David Ruvini

Sanjika Hewavitharana

Kandakumar Doraisamy

Ryan Reeves

Srikanth Rentachintala

Kishore Kumar Mohan

Luther S Boorn

Baohao Liao

Daniel Stein

Sivan Elkis

Simon Alexander Becker

Saral Bharathi Sukumar Jeyaseelan

A counterfeit item detection system detects counterfeit items during an item listing processes provided by an online marketplace. The system enhances the ability of the online marketplace to identify and reject potential counterfeit items. The system comprises a trained counterfeit item detection model that is configured to receive an image and identify whether the image includes a counterfeit item. The model is trained using a data set of training images. An image of the data set is taken from a video related to the time based on identifying that the context of text associated with the video relates to counterfeit items. The text can be determined from the video's audio, and the image is obtained at a time in the video where the text corresponds to a counterfeit item context.

Counterfeit item detection system

Technologies described reduce the amount of time between requesting content and receiving the requested content. In some embodiments, a client provides a search query. A search result page is generated and, prior to returning the search result page to the client, some or all of the search results are asynchronously pre-fetched and cached. The search query can include custom parameters that are also included in the pre-fetch requests. Web addresses in the search results page can be modified to indicate whether a prefetch operation was initiated. Once a user activates one of the search results, the responding web server uses the cached data while generating a response.

Asynchronous predictive caching of content listed in search results

A system for assisting users in listing items for sale in an electronic marketplace is disclosed. A video is received from a user device associated with a user, the video including a video stream depicting a plurality of items to be listed for sale in the electronic marketplace. Respective images depicting respective items among the plurality of items are obtained from the video stream, and respective attributes of the respective items among the plurality of items are extracted from the video. Respective listings for sale of the respective items are generated based at least in part on the respective attributes of the respective items among the plurality of items, and the respective listings for sale of the respective items are displayed to the user.

Systems, method, and computer storage medium for creating listing for items for sale in an electronic marketplace based on video analysis

Systems and methods for processing webpage calls via multiple module responses are described. A system may receive a first call and a second call for module data associated with a plurality of webpage modules for presentation of a webpage at a client device; categorize the plurality of webpage modules according to historical data associated with the client device; responsive to the first call, transmit to the client device a first response comprising a first webpage module corresponding to a first category; and responsive to the second call, transmit to the client device a second response comprising a second webpage module corresponding to a second category, where the first webpage module is transmitted in the first response and the second webpage module is transmitted in the second response based on the categorization.

Multi request asynchronous delegation for enhanced performance

A method of training a machine learning model to determine an item margin is provided. The method includes monitoring a first value for a first item having attributes and monitoring a first value for a second type of item having attributes where an attribute of the first attributes is the same as an attribute of the second attributes. The method also includes determining a first margin based on the first values. The first attributes, the second attributes, and the first margin are input as training data for the machine learning model where the machine learning model is trained with the training data. The monitoring operations for the first item and the second item are repeated to obtain a second value for the first and second items. Furthermore, the trained machine learning model is applied to the second values to determine a second margin.

Forward contracts in e-commerce

Different action user-interface components in a comparison view are described. Initially, a selection is received to display a comparison view via a user interface of a listing platform. Multiple listings of the listing platform are selected for inclusion in the comparison view. A comparison view system determines which action of a plurality of actions, used by the listing platform, to associate with each of the listings. A display device displays the multiple listings concurrently in a comparison view via a user interface of the listing platform and also displays an action user-interface component (e.g., a button) in each of the plurality of listings. The action user-interface component is selectable to initiate the action associated with the respective listing. In accordance with the described techniques, the action user-interface component displayed in at least two of the multiple listings is selectable to initiate different actions in relation to the respective listing.

Different action user-interface components in a comparison view

Systems and methods for processing webpage calls via multiple module responses are described. A system may receive, from a client device, a first call for module data associated with a set of webpage modules for presentation in a webpage. The system may subsequently transmit, to the client device based on receiving the first call, a first response including first module data associated with a first subset of the set of webpage modules. The first response may additionally include a token identifying the webpage. The server may additionally transmit, to the client device based on transmitting the first response, a second response including the token identifying the webpage and second module data associated with a second subset of the set of webpage modules that differs from the first subset of the set of webpage modules.

Methods for determining which image of a set of images to present in a search results page for a product are described. Components of a server system may receive a set of images for a set of items associated with a product. Components of the server system may perform image ranking to rank the set of images to identify a representative image of the set of images for the product, based on a user interaction metric of each image of the set of images. The components of the server system may then receive, from a user device, a search query that may be mapped to the product, and the component of the server system may transmit, to the user device, the search results page that includes at least one item of the set of items and the representative image based on the interaction metric of the representative image.

Method, system, and medium for network and speed enhancement for distributing unified images via a computer network

Technologies are disclosed herein for distributing information. The disclosed technologies determine an application element configured to receive information that is updated at a variable rate, the information pertaining to an object. Feature data is received that is associated with the object and data associated with use of the application element. The feature data includes a time horizon for the object and supplemental information associated with the object. Based on the feature data and the data associated with use of the application element, a first rate is predicted for sending the information about the object to the application element. The information is sent to the application element at the first rate.

Adaptive timing prediction for updating information

A first test case identifier that indicates a first test case is received. The first test case is indicative of testing one or more features of an application associated with the electronic marketplace. The first test case identifier is compared to a plurality of attributes. The plurality of attributes are associated with one or more listings that describe one or more items for sale in an electronic marketplace. Based at least in part on the comparing, it is determined that a first set of attributes, of the plurality of attributes, are test data candidates to be used as input to the first test case. Based at least in part on the determining, the first test case is caused to be run using at least one of the first set of attributes as test data for input.

Mapping of test cases to test data for computer software testing

A counterfeit item detection system detects counterfeit items during an item listing processes provided by an online marketplace. The system enhances the ability of the online marketplace to identify and reject potential counterfeit items. The system collects item data in various formats. The item data is analyzed using speech-to-text software and natural language processing to determine data elements representing items, item features, and language context. Questions are generated using the items and item features, and stored for each item. Answers to the questions have associated counterfeit indication weights. The weights are modified and used to rank the questions as feedback is received about counterfeit items. The ranking determines future question selection, allowing the best questions to continually be identified and provided. The counterfeit item detection system also trains a neural network to detect counterfeit items in images, where the training images are obtained from videos related to the item.

Growing community of inventors

San Ramon, CA, United States of America

Ramesh Periyathambi