Netlogic Microsystems, Inc

Google Inc.

Apple Inc.

Cypress Semiconductor Corporation

Other

Srinivasan Venkatachary

Elizabeth Caroline Cranfill

Pankaj Gupta

Steven D Baker

Nitin Kumar Gupta

Ibrahim Badr

Jayant Madhavan

Matthew K Gray

John Blitzer

Paul Haahr

Yonghui Wu

Anand P Rangarajan

Mark Birman

Michael E Flaster

Ajay Srikrishna

Trystan G Upstill

Jack Wright Menzel

Anand Shukla

Craig Michael Federighi

Andre Souza Dos Santos

Grant R Paul

Michael J Lebeau

Nathan De Vries

Ryan S Dixon

Johnnie B Manzari

Vignesh Jagadeesh

Marco Zuliani

James Nicholas Jones

Matthias Paulik

Andrew J Wright

Jagadeesan Rajamanickam

Eric H Voelkel

Neil Gareth Crane

Viktor Miladinov

Jeffrey A Brasket

Vincent M Lane

Brandon J Corey

Vinay Sharma

Xin Wang

Guillaume Borios

Diego Federici

Nicholas D Lupinetti

Corinn Herrick

Sam Oates

Aditya Shah

Yi Liu

Robert Andrew Brennan

Lingkun Chu

Bin Liu

Kellie L Albert

Mahsan Rofouei

Phiroze Parakh

Per Bjornsson

Massimiliano Ciaramita

Hadar Shemtov

Ioannis Tsochantaridis

Vishnu Priya Natchu

Abhishek Das

Kushal Tayal

Shravya R Shetty

Rochan Sankar

Pulah J Shah

Preyas Popat

Thomas Deselaers

Kayur Dushyant Patel

Ron C Santos

Yang Zhao

Matthias Dantone

Chen Ye

Aya Siblini

Ngoc Hong Pham

Francisco Alvaro Munoz

Sebastien V Marineau-mes

Xishuo Liu

Rajen Chatterjee

Amittai Axelrod

Jennifer Pon Chen

Sebastien V Marineau-Mes

Guangyu Zhong

Adam Huff Bradford

Mallika Priya Khullar

The present disclosure generally relates to methods and user interfaces for managing visual content at a computer system. In some embodiments, methods and user interfaces for managing visual content in media are described. In some embodiments, methods and user interfaces for managing visual indicators for visual content in media are described. In some embodiments, methods and user interfaces for inserting visual content in media are described. In some embodiments, methods and user interfaces for identifying visual content in media are described. In some embodiments, methods and user interfaces for translating visual content in media are described. In some embodiments, methods and user interfaces for translating visual content in media are described. In some embodiments, methods and user interfaces for managing user interface objects for visual content in media are described.

User interfaces for managing visual content in media

Techniques for providing an enhanced search to generate a feed based on a user's interests are disclosed. In some embodiments, a system/process/computer program product for providing an enhanced search to generate a feed based on a user's interests includes receiving a plurality of interests associated with a user, searching online content including one or more websites (e.g., news or other content websites, social networking sites, and/or other online content) based on the plurality of interests associated with the user, receiving a plurality of web documents (e.g., links to websites, social networking sites, and other online content) based on the search for online content, ranking the plurality of web documents based on a document score and a user signal, and generating a content feed that includes at least a subset of the plurality of web documents based on the ranking.

Enhanced search to generate a feed based on a user's interests

At least selectively utilizing a large language model (LLM) in generating a natural language (NL) based summary to be rendered in response to a query. In some implementations, in generating the NL based summary additional content is processed using the LLM. The additional content is in addition to query content of the query itself and, in generating the NL based summary, can be processed using the LLM and along with the query content—or even independent of the query content. Processing the additional content can, for example, mitigate occurrences of the NL based summary including inaccuracies and/or can mitigate occurrences of the NL based summary being over-specified and/or under-specified.

Generative summaries for search results

At least selectively utilizing a large language model (LLM) in generating a natural language (NL) based summary to be rendered in response to a query. In some implementations, in generating the NL based summary additional content is processed using the LLM. The additional content is in addition to query content of the query itself and, in generating the NL based summary, can be processed using the LLM and along with the query content&#x2014;or even independent of the query content. Processing the additional content can, for example, mitigate occurrences of the NL based summary including inaccuracies and/or can mitigate occurrences of the NL based summary being over-specified and/or under-specified.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for context scoring adjustments for candidate answer passages. In one aspect, a method includes scoring candidate answer passages. For each candidate answer passage, the system determines a heading vector that describes a path in the heading hierarchy from the root heading to the respective heading to which the candidate answer passage is subordinate; determines a context score based, at least in part, on the heading vector; and adjusts answer score of the candidate answer passage at least in part by the context score to form an adjusted answer score. The system then selects an answer passage from the candidate answer passages based on the adjusted answer scores.

Context scoring adjustments for answer passages

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for scoring candidate answer passages. In one aspect, a method includes receiving a query determined to be a question query that seeks an answer response and data identifying resources determined to be responsive to the query; for a subset of the resources: receiving candidate answer passages; determining, for each candidate answer passage, a query term match score that is a measure of similarity of the query terms to the candidate answer passage; determining, for each candidate answer passage, an answer term match score that is a measure of similarity of answer terms to the candidate answer passage; determining, for each candidate answer passage, a query dependent score based on the query term match score and the answer term match score; and generating an answer score that is a based on the query dependent score.

Scoring candidate answer passages

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating candidate answer passages. In one aspect, a method includes receiving a query determined to be a question query data identifying resources determined to be responsive to the query; for each resource in a top-ranked subset of the resources: identifying a plurality of passage units in the resource; applying a set of passage unit selection criterion to the passage units, each passage unit selection criterion specifying a condition for inclusion of a passage unit in a candidate answer passage, wherein a first subset of passage unit selection criteria applies to structured content and a second subset of passage unit selection criteria applies to unstructured content; and generating, from passage units that satisfy the set of passage unit selection criterion, a set of candidate answer passages.

Candidate answer passages

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying implicit question queries. In one aspect, a method includes receiving a query in unstructured form, comparing terms of the query to query templates, determining, based on the comparison, a match of the query terms to a first query template, wherein the first query template is not determined to be indicative of a question query, determining, based on the first query template, a second query template, and determining that the query is an implicit question query in response to the second query template being indicative of a question queries.

Implicit question query identification

Methods, systems, and apparatus, including computer program products, for generating synthetic queries using seed queries and structural similarity between documents are described. In one aspect, a method includes identifying embedded coding fragments (e.g., HTML tag) from a structured document and a seed query; generating one or more query templates, each query template corresponding to at least one coding fragment, the query template including a generative rule to be used in generating candidate synthetic queries; generating the candidate synthetic queries by applying the query templates to other documents that are hosted on the same web site as the document; identifying terms that match structure of the query templates as candidate synthetic queries; measuring a performance for each of the candidate synthetic queries; and designating as synthetic queries the candidate synthetic queries that have performance measurements exceeding a performance threshold.

Query generation using structural similarity between documents

Methods, apparatus, and computer-readable media are described herein related to keyword-based conversational searching using voice commands. A computing device can receive an audio command for performing a search query. The computing device may receive a plurality of keywords associated with a subject matter of the search query, and computing device may configure the plurality of keywords as a plurality of audio instructions receivable by the computing device to cause the computing device to perform a subsequent search query. The computing device can then provide the plurality of keywords for display, along with search results associated with the search query.

Keyword-based conversational searching using voice commands

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Sunnyvale, CA, United States of America

Srinivasan Venkatachary