Yahoo! Inc.

Ebay, Incorporated

Fuji Xerox Co., Ltd.

Excalibur Ip, LLC

Xerox Corporation

Paypal, Inc.

Palo Alto Research Center Inc.

Oath Inc.

Other

Verizon Patent and Licensing Inc.

Elizabeth F Churchill

David Ayman Shamma

Lester D Nelson

Atish Das Sarma

M Cameron Jones

Athellina Athsani

Robinson Piramuthu

Laurent Denoue

Anurag Bhardwaj

Vignesh Jagadeesh

Wei Di

Marco De Sa

Neelakantan Sundaresan

William Noah Schilit

Michael Gilbert

Si Si

Joseph Patrick Sullivan

Joseph James O'Sullivan

Jonathan James Trevor

Lyndon Kennedy

Gyanit Singh

Catherine Claire Marshall

Timothy W Bickmore

Diana Kathryn Smetters

Sara A Bly

Linda K Cook

Judd Antin

Jonathan Helfman

Corinne Elizabeth Sherman

R Preston McAfee

Scott A Prevost

David N Snowdon

Chris Theodore Kalaboukis

Patrick Chiu

Scott Carter

Andreas Girgensohn

Benoit F Dumoulin

Gene Golovchinsky

Tohru Fuse

David Michael Hilbert

Omer Emre Velipasaoglu

Corinne Sherman Stewart

Kent Andrew Edmonds

Lawrence Gordon Tesler

Michael R Parker

Bryan Kent Ressler

Rosemary Ellen Jones

Tomas Sokoler

Joseph W Sullivan

Les Nelson

Corinne Elizabeth Sherman-Stewart

Shuo Chang

Henry Feild

Jeffrey Bardzell

Jude Yew

Elizabeth Goodman

Anthony D Thrall

Paul Murphy

Jonathan Trevor

A machine may be configured to perform image evaluation of images depicting items for sale and to provide recommendations for improving the images depicting the items to increase the sales of the items depicted in the images. For example, the machine accesses a result of a user behavior analysis. The machine receives an image of an item from a user device. The machine performs an image evaluation of the received image based on an analysis of the received image and the result of the user behavior analysis. The performing of the image evaluation may include determining a likelihood of a user engaging in a desired user behavior in relation to the received image. Then, the machine generates, based on the evaluation of the received image, an output that references the received image and indicates the likelihood of a user engaging in the desired behavior.

Image evaluation

A system, computer-readable storage medium storing at least one program, and computer-implemented method for providing recommendations based on social network sharing activity. Sharing activity relating to the sharing of the content item on a social network by a first user is accessed. Consumption information related to the consumption of the content item. A correlation between the sharing activity and the consumption information is determined. A recommendation is then generated based on the correlation.

Social sharing system

Systems and methods are presented for generating recommendations using multi-level social network analysis of user behavior. In some embodiments, the system receives a set of user interactions, from a plurality of users, performed on a set of data objects; generates a set of associations between the set of data objects; and identifies a set of data object clusters indicative of the set of associations. The system generates an organization of the set of data objects based on the set of associations and the set of data object clusters and causes presentation of a plurality of data objects of the set of data objects on a user interface of a user device based on the organization.

Methods and systems for social network based content recommendations

A machine is configured to determine fashion preferences of users and to provide item recommendations based on the fashion preferences. For example, the machine accesses an indication of a fashion style of a user. The fashion style is determined based on automatically captured data pertaining to the user. The machine identifies, based on the fashion style, one or more fashion items from an inventory of fashion items. The machine generates one or more selectable user interface elements for inclusion in a user interface. The one or more user interface elements correspond to the one or more fashion items. The machine causes generation and display of the user interface that includes the one or more selectable user interface elements. A selection of a selectable user interface element results in display of a combination of an image of a particular fashion item and an image of an item worn by the user.

Fashion preference analysis

A system comprising a computer-readable storage medium storing at least one program and a computer-implemented method for creating advertisement messages using generative grammar models is presented. A generative grammar model defining a message structure of requested message is accessed. The message structure includes a plurality of lexical slots. The generative grammar model includes a corpus of source data to populate each lexical slot in the plurality of lexical slots to generate an advertisement message for a product listing. An advertisement message is generated in accordance with the generative grammar model and the message is published. The advertisement message for the product listing is then transmitted to a client device.

Generative grammar models for effective promotion and advertising

Users are enabled to define and modify mappings between (1) gestures and (2) actions performed by one or more computing devices in response to a device detecting performance of a gesture. A generalized gesture-to-action mapping framework allows users to intuitively define and modify such mappings. In response to a device detecting the performance of a particular gesture, one or more devices may cause the execution of one or more device actions based on a set of user-defined gesture-to-action mappings.

Customizable gestures for mobile devices

Systems and methods are presented for matching a buyer and a seller on a market place system and generating calibrated user profiles. In one such system a plurality of subjective estimations of value is received. The subjective estimations of value are a measure between a predetermined minimum value and a predetermined maximum value. A user profile is generated. A plurality of user actions corresponding to the plurality of subjective estimations of value is received. The user profile is calibrated based on the plurality of user actions.

Machine generated recommendation and notification models

A system comprising a computer-readable storage medium storing at least one program and a computer-implemented method for creating messages using generative grammar models is presented. A generative grammar model defining a message structure of requested message is accessed. The message structure includes a plurality of lexical slots. The generative grammar model includes a corpus of source data to populate each lexical slot in the plurality of lexical slots, and a grammatical constraint for each lexical slot in the plurality of lexical slots. A message is generated in accordance with the generative grammar model and the message is published.

The present disclosure is descriptive of discovering structure, content, and context of a media event, e.g., a live media event, using real-time discussions that unfold through short messaging services. Generally, a sampling of short messages of a plurality of users is obtained. The sampling of short messages corresponds to a media event. A segment in the media event is identified using the sampling of short messages, and at least one term taken from the sampling of short messages is identified. The at least one term is indicative of a context of the identified segment.

Media event structure and context identification using short messages

Growing community of inventors

San Anselmo, CA, United States of America

Elizabeth F Churchill