International Business Machines Corporation

Yahoo Assets LLC

Oath Inc.

Yahoo! Inc.

Excalibur Ip, LLC

Verizon Media Inc.

Verizon Patent and Licensing Inc.

Dan S Pelleg

Shmuel Ben-Yehuda

Yoelle Smadja Maarek

Idan Szpektor

Avihai Mejer

Ran Wolff

Ido Guy

Alexander Nus

Shirin Oskooi

Lance Warren Russell

David Carmel

Ariel Raviv

Oded Fuhrmann

Noa Avigdor-Elgrabli

Gal Lalouche

Shmuel Ur

Aviad Zlotnick

Eran Raichstein

Jeffrey Bonforte

Amir Ronen

Oleg Rokhlenko

Orna Raz-Pelleg

Yarden Nir-Buchbinder

Gideon Dror

Lisa F Spainhower

Fiana Raiber

Ali Tabaja

Yuval David Pinter

Somesh Jain

Archit Shrivastava

Samiksha Harish Kothari

Michael Edward Factor

Richard Edwin Harper

Elliot Karl Kolodner

Elad Yom-Tov

Ron Hoory

Paula Kim Ta-Shma

David Breitgand

Shai Fine

Adam Darlow

Naama Parush-Shear-Yashuv

Avichai Giat

The present teaching relates to a method, system, and programming for content personalization. A request is received from a user to obtain a content item. Information indicative of a literacy-level of the user is obtained and the content item to be provided to the user is retrieved. The content item is modified by updating information included in the content item based on the literacy-level of the user to generate an updated content item. The updated content item is provided to the user in response to the request.

Method and system for literacy adaptive content personalization

Disclosed are systems and methods for improving interactions with and between computers in electronic messaging and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The disclosed systems and methods provide systems and methods for generating electronic message filters and for using electronic message filters comprising item category filtering criteria and having an automatically-determined expiration. The discloses systems and methods filter electronic messages using the item category filtering criteria while an electronic message filter remains active as determined using the automatically-determined expiration information.

Automatic electronic message filtering method and apparatus

Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosure provides a novel, computerized framework for automatically identifying and recommending socially-engaging photos to their creators for sharing. Execution of the disclosed systems and methods turns a tedious manual chore into an automated, software-driven process. The disclosed systems and methods utilizes a novel, computerized learn-to-rank (LTR) algorithm for identifying the most engaging, socially driven photos by: (a) grouping near-duplicate photos; (b) selecting a representative photo for sharing per group; and (c) ranking of the groups by their likelihood to contain a 'shareable' photo.

Computerized system and method for automatically determining and providing digital content within an electronic communication system

The present teaching relates to a method, system, and programming for providing content. A plurality of content items and publication information related thereto are obtained. For each of the plurality of content items, one or more topics are determined in accordance with a model. The related publication information associated with each content item is analyzed to identify at least one source of a plurality of sources that published the content item. A distribution is generated of each of the plurality of content items with respect to the plurality of sources and the one or more topics of the content item, and a bias of a source with respect to publishing content is identified based on the distributions of the plurality of content items.

Method and system for content bias detection

Methods and devices for assessing the quality of user-generated content are described. In one embodiment, a method is disclosed for measuring the quality of a user-generated answer to a question by combining various factors, including question-answer surface word vector similarity, question-answer explicit semantic analysis vector similarity, answer-answer explicit sematic analysis vector similarity, query performance predictor, sentiment analysis, textual analysis of the answer, and reputation of the answerer. The method uses a learning procedure to determine the best algorithm for measuring the overall quality of the answer based on these factors.

Quality-based scoring and inhibiting of user-generated content

One or more computing devices, systems, and/or methods for generating a set of tips for an entity are provided. For example, users may create user generated content describing an entity, such as a user review for a consumer good, a location, an event, etc. Because a user may be unable to read and digest all of the user reviews for the entity, the user may merely read a few user reviews, and thus miss out on useful information. Accordingly, tip templates, indicative of how tips are linguistically/grammatically constructed, are applied to the user reviews to automatically extract a set of tips for the entity (e.g., 'make sure to bring a rain jacket'). The set of tips may be filtered to remove undesirable tips, ranked based upon usefulness, and/or diversified to remove redundant tips. In this way, a set of useful tips may be provided to the user.

Tip generation

Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. A novel, computerized framework for automatically identifying and recommending socially-engaging photos to their creators for sharing is provided. Execution of the disclosed framework turns a tedious manual chore into an automated, software-driven process. The disclosed framework utilizes a novel, computerized learn-to-rank (LTR) algorithm for identifying the most engaging, socially driven photos by: (a) grouping near-duplicate photos; (b) selecting a representative photo for sharing per group; and (c) ranking of the groups by their likelihood to contain a 'shareable' photo.

Disclosed herein is a mechanism, which may be in a community-driven system, to assist a user, or member of the community, to articulate a request, such as a question, that is likely to receive a response, e.g., an answer to a question posed, from the user community. A machine learning approach may be used to generate a model, which model may be trained using previously-posted requests, e.g., questions, and response, e.g., answers. The generated model may be used to make a prediction, e.g., to predict a potential number of answers for a given question, which prediction may also have an accompanying confidence score. A prediction generated by the model may be presented to the user as the user is providing the input, e.g., typing a question. Such prediction may be generated for a partial question, e.g., as the question is being typed, as well as a completed question.

System and method of predicting community member responsiveness

Users may engage with content that may invoke various emotions. For example, a user may find an image as inspirational, a social network post as funny, etc. Accordingly, content may be labeled with user emotion labels, specified by users that engaged with the content, to create labeled content (e.g., the image may be labeled as inspirational). Emotional transition triggers may be defined for users (e.g., 30 minutes of a user writing a school report using a word processing application). Responsive to a triggering of an emotional transition trigger, labeled content may be provided to a user (e.g., the image may be provided to the user as an inspirational break from writing the school report). In this way, content may be labeled based upon emotions that the content invokes in users, and such labeled content may be provided to users in a contextually relevant manner (e.g., a study break).

Emotionally relevant content

Growing community of inventors

Liman, Israel

Dan S Pelleg