Ebay, Incorporated

Padmapriya Gudipati

Timothy Keefer

Ashmeet Singh Rekhi

Suchitra Ramesh

John Weigel

Shane Lin

Robinson Piramuthu

Mohammadhadi Kiapour

Shuai Zheng

Nicholas Whyte

Niaz Ahamed Khaja Nazimudeen

Fujun Zhong

Kenneth Crookston

Md Atiq Ul Islam

Giridharan Iyengar

Atiq Islam

Rodolfo Gonzales

Bryan Allen Plummer

Alberto Ordonez Pereira

Ravindra Surya Lanka

Justin Nicholas House

An Feng-I Chen

Zhe Sun

Kenneth Clark Crookston

Satya Krishna Suman Voleti

A system and method for obtaining relevant information for identifying items that are being listed for sale via electronic marketplace applications. More specifically, the systems and methods relate to obtaining relevant attribute values and/or information for a plurality of items at relatively the same time. In aspects, the systems and methods involve receiving an electronic file or folder having a plurality of electronic representations of each of the items. The electronic file or folder is then parsed to separate the different items for identification. Once separated, the system and methods involve sending the separate items to an identification service and retrieving the separate information for each item and returned to the requesting device.

Multiple product identification assistance in an electronic marketplace application

Embodiments of the present disclosure provide systems, methods, and computer storage media for implementing a nuanced-color search and recommendation system. Employing various techniques described herein, an improved search engine for categorizable items is provided to generate accurate color-based search results and recommendations that are relevant to a nuanced color provided as a search parameter. In various implementations, digital assets that depict categorizable items can be analyzed to characterize the depicted items with, among other things, a particular shade of a determined dominant color. In this way, categorizable items can be searched based at least in part on a selected shade of a particular dominant color to generate a primary search result. In further embodiments, other categorizable items can be generated as recommendations based on characteristics of the primary search result. That is, other items not necessarily subject of the search, can be provided as recommended items if predefined rules dictate that such items may complement the categorizable items included in the primary search result.

Nuanced-color search and recommendation system

Techniques and systems are described that leverage computer vision as part of search to expand functionality of a computing device available to a user and increase operational computational efficiency as well as efficiency in user interaction. In a first example, user interaction with items of digital content is monitored. Computer vision techniques are used to identify digital images in the digital content, objects within the digital images, and characteristics of those objects. This information is used to assign a user to a user segment of a user population which is then used to control output of subsequent digital content to the user, e.g., recommendations, digital marketing content, and so forth.

Computer vision, user segment, and missing item determination

Embodiments of the present disclosure provide systems, methods, and computer storage media for implementing a nuanced-color search and recommendation system. Employing various techniques described herein, an improved search engine for categorizable items is provided to generate accurate color-based search results and recommendations that are relevant to a nuanced color provided as a search parameter. In various implementations, digital assets that depict categorizeable items can be analyzed to characterize the depicted items with, among other things, a particular shade of a determined dominant color. In this way, categorizeable items can be searched based at least in part on a selected shade of a particular dominant color to generate a primary search result. In further embodiments, other categorizeable items can be generated as recommendations based on characteristics of the primary search result. That is, other items not necessarily subject of the search, can be provided as recommended items if predefined rules dictate that such items may complement the categorizable items included in the primary search result.

Computer vision and image characteristic search is described. The described system leverages visual search techniques by determining visual characteristics of objects depicted in images and comparing the determined characteristics to visual characteristics of other images, e.g., to identify similar visual characteristics in the other images. In some aspects, the described system performs searches that leverage a digital image as part of a search query to locate digital content of interest. In some aspects, the described system surfaces multiple user interface instrumentalities that include images of patterns, textures, or materials and that are selectable to initiate a visual search of digital content having a similar pattern, texture, or material. The described aspects also include pattern-based authentication in which the system determines authenticity of an item in an image based on a similarity of its visual characteristics to visual characteristics of known authentic items.

Computer vision and image characteristic search

Computer vision for unsuccessful queries and iterative search is described. The described system leverages visual search techniques by determining visual characteristics of objects depicted in images and describing them, e.g., using feature vectors. In some aspects, these visual characteristics are determined for search queries that are identified as not being successful. Aggregated information describing visual characteristics of images of unsuccessful search queries is used to determine common visual characteristics and objects depicted in those images. This information can be used to inform other users about unmet needs of searching users. In some aspects, these visual characteristics are used in connection with iterative image searches where users select an initial query image and then the search results are iteratively refined. Unlike conventional techniques, the described system iteratively refines the returned search results using an embedding space learned from binary attribute labels describing images.

Computer vision for unsuccessful queries and iterative search

Systems and methods for improving a search process are provided. A networked system accesses, from online sources, fashion trend information including pictures illustrating fashion trends. The networked system determines a plurality of top fashion trends, and extracts, using an image detection algorithm, images of fashion items from a picture associated with each of the top fashion trends. Using the extracted images, the networked system performs an image search for matching inventory items. The networked system then generates and stored a plurality of fashion trend user interfaces, whereby the plurality of fashion trend user interfaces each comprise selectable elements that represent the plurality of top fashion trends. At runtime, the networked system receives an indication to initiate a search for fashion by trends, and in response, accesses the plurality of stored fashion trend user interfaces. One of the plurality of stored fashion trend user interfaces is presented on a user device.

Fashion by trend user interfaces

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San Jose, CA, United States of America

Padmapriya Gudipati