Yahoo Assets LLC

Amazon Technologies, Inc.

Oath Inc.

Excalibur Ip, LLC

Avihai Mejer

Dan S Pelleg

Ido Guy

Ran Wolff

Alexander Nus

Fiana Raiber

Ali Tabaja

Yoelle Smadja Maarek

Ariel Raviv

Idan Szpektor

Lital Kuchy

Eilon Sheetrit

Yuval Nezri

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

Natural language-based question answering systems and techniques are generally described. In some examples, a natural language processing system, first natural language data. The natural language processing system may determine first slot data included in the first natural language data. A set of content items associated with the first slot data may be determined. A first machine learning model may use the first natural language data to generate prediction data associated with a first attribute among a list of attributes of the set of content items. In some examples, a first value associated with the first attribute for a first content item of the set of content items may be determined. Second natural language data may be generated based at least in part on the first value. The second natural language data may include a response to the first natural language data.

Relevant object embedding in aggregated answers to questions regarding broad set of objects

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

Disclosed are systems and methods for generating voice renderings of machine-generated electronic messages. The disclosed systems and methods provide a novel framework for organizing often fragmented machine-generated electronic messages and providing mechanisms for a virtual assistant to produce voice-renderings data extracted from electronic messages. The disclosed system may implement steps for receiving user queries via virtual assistants, extracting data from machine-generated electronic messages, converting the extracted data to purposeful organizational schemas, and generating human perceivable voice renderings based on the user queries and extracted data.

Systems and methods for voice rendering of machine-generated electronic messages

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

Briefly, embodiments disclosed herein may relate to formulating synthetic questions, such as in response to a search query, for example. Candidate synthetic questions may be presented to a user who may initiate a search at least in part by selecting one or more candidate synthetic questions, for example, in an embodiment.

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Atlit, Israel

Avihai Mejer