Microsoft Technology Licensing, LLC

Other

Parthasarathy Govindarajen

Arun Sacheti

Daniel Marantz

Aaron Chun Win Yuen

Priya Vaidyanathan

Mark Blelock Atherton

Nikita Astrakhantsev

Aaron C Yuen

Yanfeng Sun

Karim Hasham

Avinash Vemuluru

Kuansan Wang

Jun Liu

Marcelo Medeiros De Barros

Kun Wu

Gaurang Prajapati

Xiaodong Fan

Yu-ting Kuo

Diane Shambaugh

Achraf Chalabi

Malik Mehdi Pradhan

Surendra Ulabala

Yandong Guo

Nektarios Ioannides

Ravi Theja Yada

Liang Tang

Dong Han Wang

Hua Ding

Soohoon Cho

Darrin E Eide

Gautam Vishwas Vaidya

James Brian Lawson

Wallace Earl Greathouse

Aaron Chun-Win Yuen

Sumit Chatterjee

Yucan Zhang

Anjali Muralidhar

Alexandre Michelis

Bartosz L Rakowski

Alexander Jack Sunell

Alexandra Victoria Foote

Elena Usinskiene

Qianyue You

Souvick Sarkar

Adam Jeffrey Curtis

Jeffrey Roger Devries

Guarang P Prajapati

Sharada Chandrasekaran

Yin-Cheng Ting

Aditya Khandelwal

Yogesh Ajit Vaidya

Nan Wu

Deepak Santhanam

A computing system includes a processor and memory storing instructions that, when executed by the processor, cause the processor to perform acts. The acts include receiving a query provided by a user. The acts additionally include determining that the query is related to a comparison between entities. Based upon the determining that the query is related to the comparison between entities, the computing system generates a prompt that is to be input to a generative language model, where the prompt includes:) an instruction for the generative language model to generate a table based upon the query; and) attribute values for entities identified by a search system based upon the query. The acts also include providing the prompt as input to the generative language model, where the generative language model generates a table based upon the prompt, and further where the table includes the attribute values for the entities.

Computer-implemented table construction

An image selector selects images for visual similarity in some aspects and visual distinction in other aspects. A user submits a query to a search engine. Images identified by the search engine as relevant to the query are clustered into clusters having at least one similar attribute. Selection mechanisms organize images selected from each cluster based on a similarity metric into image rows. A selection metric is calculated for each image row and a row to be displayed is selected based on the selection metric. In some embodiments, images representative of a subset of image rows are displayed to a user and responsive to selection of a representative image, the corresponding image row is displayed.

Automatic image selection for visual consistency

The present disclosure relates to systems, devices, and methods for a visual exploration of search engine results. The systems, devices, and methods may present, in response to receiving the search engine results for a query to perform a task, a search result webpage with a cascading waterfall layout that includes a plurality of images from a plurality of webpages included in the search results. The search result webpage may also present, adjacent to the plurality of images, structured data corresponding to a selected image from the plurality of images where the structured data includes details from the webpage to complete the task.

Image reranking and presentation for visual exploration

This document relates to using input images to selectively trigger skills. For example, the input images can be analyzed using a machine learning model, which can output image metadata characterizing content of the input images. Different skills can be selectively triggered based on the image metadata. For example, a given skill can register to be triggered when the image metadata matches one or more triggering conditions specified by that skill.

Image-based skill triggering

The technology described herein provides an efficient mechanism for quickly analyzing huge amounts of media content to find media content (hereafter 'content' or 'media content') that is relevant to a user. The technology analyzes features of a curator to classify curators by interest and/or find curators with similar content recommendations. The curator data can be used to make curator recommendations to users based on the user's interests. The technology described herein collects curator data from multiple content sites and analyzes the data to identify curators that recommend similar content on different content sites.

Recommending a content curator

Systems, methods, computer storage media, and user interfaces are provided for non-committal intent preview, disambiguation, and refinement of a search. A search prefix comprising one or more characters associated with an unexecuted search query is received. One or more intent suggestions are suggested to a user. For each of the one or more intent suggestions, one or more entity identifications associated with each of the one or more intent suggestions are received. Metadata corresponding to at least one entity associated with the one or more entity identifications is retrieved from an entity data store. Without retrieving search results for the unexecuted search query, an aggregated intent preview based on the retrieved metadata corresponding to the at least one entity is provided.

Search intent preview, disambiguation, and refinement

A user query may be evaluated to provide a result set. In case the results do not reflect the user's intent, the device may provide recourse options for adjusting the query in a manner that yields more desirable results, e.g., a suggestion at the top of the result set for a different spelling, or recommendations at the end of the results set for additional query techniques that may yield more accurate results. However, such presentation of recourse options may clutter the user interface and/or go unnoticed by the user. Instead, an adjusted query may be identified with an interpreted probability of reflecting the intent of the query. An adjustment option describing the adjusted query may be inserted into the result set, between a higher-probability first result and a lower-probability second result. Selection of the adjustment option may cause the adjusted query to be evaluated on behalf of the user.

Providing query recourse with embedded query adjustment options

Among other things, one or more techniques and/or systems are provided for displaying a related content view within a search interface. That is, a search interface, such as a search application, may provide search results that are relevant to a query submitted through the search interface. For example, a main search engine results view comprising one or more search results of the query may be presented within the search interface. Responsive to a semantic zoom operation (e.g., a touch gesture), the search interface may be transitioned from the main search engine results view to a related content view comprising related content that corresponds to the query and/or a (e.g., supplemental) search result for the query. In this way, the user may explore supplemental content, such as query suggestions, images, entity descriptions/profiles, videos, and/or other content, that may be related to a query submitted by the user.

Semantic zoom for related content

A query processing system (QPS) for generating query suggestions to a user by matching the user's input query against a set of query-detection grammars defined by respective rule modules, and with respect to a structured knowledge base. In response to the matching operation, the QPS may generate a set of synthetic query suggestions to the user, representing refinements of the user's input query. The QPS can also present a refinement tool that conveys refinement options to the user. The QPS may also present one or more preview items to the user upon the user's selection of one of the synthetic query suggestions. Each synthetic query suggestion and each preview item is guaranteed to having a matching counterpart information item in the structured knowledge base.

User interface mechanisms for query refinement

Systems, methods and computer-readable storage media are provided for building long queries utilizing query suggestions and query suggestion entities. Upon receiving at least a portion of a search query, a query suggestion is provided. Upon receiving a selection of the query suggestion, the search query is updated. Prior to executing the updated search query, a user is permitted to edit the updated search query. Query suggestions may be presented in association with query suggestion entity representations. A query suggestion entity is a separate and distinct entity that may initiate performance of an action upon selection. A query suggestion entity representation may comprise various features and information associated with the search query.

Building long search queries

Growing community of inventors

Bothell, WA, United States of America

Parthasarathy Govindarajen