Ebay, Incorporated

Adobe, Inc.

Mihir Naware

Jatin Chhugani

Jonathan Su

Neelakantan Sundaresan

Balaji Krishnamurthy

Nikaash Puri

Yaman Kumar

Hyman Chung

Kumar Mrityunjay Singh

Kunal Kumar Jain

Matthew William Rozen

Pranjal Prasoon

Somesh Singh

William Brandon George

Deepak Pai

Jiri Medlen

Nithyanand Kota

Maneesh Dewan

Sudheer Tumu

Ganesh Satish Mallya

Lauren Dest

Joshua Butikofer

Shankar Venkitachalam

Jonas Dahl

Sukriti Verma

Brandon Pack

Simra Shahid

Cheri Nola Leonard

Vinh Ngoc Khuc

Vijay Srivastava

Nimish Srivastav

Shirsh Bansal

Sandipan Karmakar

Sean Steimer

Florin Silviu Iordache

Horea Bacila

Umang Moorarka

Suman Basetty

Clinton Hansen Goudie-Nice

James Patrick O'Reilly

Abhinav Chauhan

Mihai Corlan

Timothy Chia-chi Liu

Alexander Klimetschek

Franck Dernoncourt

Trung Huu Bui

Milan Aggarwal

Seunghyun Yoon

Jayakumar Subramanian

Ganesh Karbhari Palwe

Nikitha Srikanth

Nisarg Shailesh Joshi

Seoyoung Park

Nithyakala Sainath

Roger K Brooks

Yeon Seonwoo

In implementations of systems for digital content analysis, a computing device implements an analysis system to extract a first content component and a second content component from digital content to be analyzed based on content metrics. The analysis system generates first embeddings using a first machine learning model and second embedding using a second machine learning model. The first embeddings and the second embeddings are combined as concatenated embeddings. The analysis system generates an indication of a content metric for display in a user interface using a third machine learning model based on the concatenated embeddings.

Digital content analysis

Methods, computer systems, computer-storage media, and graphical user interfaces are provided for determining user affinities by tracking historical user interactions with tagged digital content and using the user affinities in content generation applications. Accordingly, the system may track user interactions with published digital content in order to generate user interaction reports whenever a user engages with the digital content. The system may aggregate the interaction reports to generate an affinity profile for a user or audience of users. A marketer may then generate digital content for a target user or audience of users and the system may process the digital content to generate a set of tags for the digital content. Based on the set of tags, the system may then evaluate the digital content in view of the affinity profile for the target user/audience to determine similarities or differences between the digital content and the affinity profile.

Determining user affinities for content generation applications

Systems and methods for text processing are described. Embodiments of the present disclosure receive a query comprising a natural language expression; extract a plurality of mentions from the query; generate a relation vector between a pair of the plurality of mentions using a relation encoder network, wherein the relation encoder network is trained using a contrastive learning process where mention pairs from a same document are labeled as positive samples and mention pairs from different documents are labeled as negative samples; combine the plurality of mentions with the relation vector to obtain a virtual knowledge graph of the query; identify a document corresponding to the query by comparing the virtual knowledge graph of the query to a virtual knowledge graph of the document; and transmit a response to the query, wherein the response includes a reference to the document.

Virtual knowledge graph construction for zero-shot domain-specific document retrieval

Techniques for mapping size information associated with a client to target brands, garments, sizes, shapes, and styles for which there is no standardized correlation. The size information associated with a client may be generated by modeling client garments, accessing computer aided drawing (CAD) files associated with client garments, or by analyzing a history of garment purchases associated with the client. Information for target garments may be generated in a similar fashion. A system may then create a standardized scale with a set of sizes for a target, and map a client base size to that standardized size scale. Similar matching and mapping may also be done with shape and style considerations. A recommendation based on the mapping may then be communicated to the client.

Garment size mapping

Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

Content analytics as part of content creation

Techniques for generating a digital wardrobe are presented herein. A transceiver can be configured to receive a request having a garment identifier and a user identifier. Additionally, an access module can be configured to access a first garment model, access a body model of the user corresponding to the user identifier, and access a second garment model corresponding to the user identifier. Furthermore, a processor can be configured by a garment simulation module to position the body model inside the first garment model and the second garment model, and calculate simulated forces based on the positioning. Moreover, a rendering module can be configured to generate an image of the garment models draped on the body model based on the calculated simulated forces. Subsequently, a display module can be configured to cause presentation of the generated image on a display of a device.

Digital wardrobe

The present disclosure relates to systems, methods, and non-transitory computer readable media for clustering media items in a semantic space to generate theme-based folders that organize media items by content theme. In particular, the disclosed systems can access media items that are stored in an original folder structure. The disclosed systems can generate content-based tags for each media item in a collection of media items. Based on the generated tags, the disclosed systems can map the collection of media items to a semantic space and cluster the collection of media items. The disclosed systems determine themes for the clusters based on the generated tags. The disclosed systems can present a media item navigation graphical user interface comprising the collection of media items organized by themes. The disclosed system can present the media item navigation graphical user interface without altering the original folder structure.

Generating theme-based folders by clustering digital images in a semantic space

Techniques for generated and presenting images of items within user selected context images are presented herein. In an example embodiment, an access module can be configured to receive a first environment model and a first wearable item model. A simulation module coupled to the access module may process the environment model to identify placement volumes within the environment model and to place a clothed body model within the placement volume to generate a context model. A rendering module may then generate a context image from the context model. In various embodiments, the environment model used for the context, the wearable item positioned within the environment model, and rendering values used to generate context images may be changed in response to user inputs to generate new context images that are displayed to a user.

Method system and medium for generating virtual contexts from three dimensional models

Techniques for extraction of body parameters, dimensions and shape of a customer are presented herein. A model descriptive of a garment, a corresponding calibration factor and reference garment shapes can be assessed. A garment shape corresponding to the three-dimensional model can be selected from the reference garment shapes based on a comparison of the three-dimensional model with the reference garment shapes. A reference feature from the plurality of reference features may be associated with the model feature. A measurement of the reference feature may be calculated based on the association and the calibration factor. The computed measurement can be stored in a body profile associated with a user. An avatar can be generated for the user based on the body profile and be used to show or indicate fit of a garment, as well as make fit and size recommendations.

Extraction of body dimensions from planar garment photographs of fitting garments

Systems and methods for automatic generation of new listings are provided. In example embodiments, time data is received that corresponds to a destination geolocation. An indication of a purchase of an item associated with the destination geolocation is received. A return time from the destination geolocation is determined based on the time data. A new listing is automatically generated for selling the item at a time determined from the return time.

Regional item recommendations

Growing community of inventors

Redwood City, CA, United States of America

Mihir Naware