Microsoft Technology Licensing, LLC

Aman Singhal

Marcelo Medeiros De Barros

Prithvishankar Srinivasan

Siddharth S Shenoy

Manish Mittal

Donald Frank Brinkman, Jr

Max Artemov

Deep Narayan Dubey

Marcelo M Debarros

Pranathi Reddy Tupakula

Shrestha Mohanty

The present disclosure relates to systems and methods for automatically associating additional content with sports games. The systems and methods obtain game schedule data for a plurality of sports games, and for each scheduled game, the systems and methods select articles published within a timeframe of the game date of the scheduled game. The systems and methods identify sports articles associated with the sports game based on entities and event phrases extracted from the sports articles matching the schedule game data. The systems and methods classify the sports articles into a plurality of clusters and use the clusters to associate the sports articles to the game schedule data and store the associated sports articles with the game schedule data.

Associating articles to corresponding events

Examples of the present disclosure describe systems and methods that provide a pipeline to generate personalized queries that are associated with and based on a user's interests determined from a user's past searches, on an Internet search engine, and/or the content the user viewed from the past searches. The suggested queries can be shown in a user interface component associated with the user interface of the search engine and before the user enters anything, such as a new Internet search. This pre-population of searches associated with a user's interests gives an opportunity to the user to try these queries without manually entering in a search string.

Methods and systems for personalized, zero-input suggestions based on semi-supervised activity clusters

A method of selectively providing different types of search engine results to different searchers is provided. A browsing history for each of a plurality of unique identities is logged. A unique identity is associated with a rich segment experience responsive to the browsing history for the unique identity satisfying correlation criteria of the rich segment experience. The rich segment experience is configured to present curated segment-specific information with other search results on a search result web page. Responsive to receiving a search query from the unique identity previously associated with the rich segment experience, the rich segment experience is presented with other search results on the search result web page. Responsive to receiving the search query from a different unique identity not previously associated with the rich segment experience, other search results are presented without the rich segment experience on the search result web page.

Selective presentation of rich experiences in search

A computing system obtains a first article about a first topic, where the first article references a plurality of entities including a person. The computing system identifies the person as a dominant entity of the first article. The computing system determines whether the first article expresses negative sentiment based upon content of the first article. In accordance with a determination that the first article does not express negative sentiment, the computing system retrieves a uniform resource locator (URL) of a webpage about a second topic that is of interest to the first person. Upon receiving a request for the first article from a computing device, the computing system causes the first article and a link to be concurrently displayed on a display, where the URL of the webpage is embedded in the link.

Computing system for auto-identification of secondary insights using reverse extraction

The present disclosure relates to systems, devices, and methods for annotating or classifying regional sports into new sports categories and generating webpages for the regional sports. The methods and systems may create new tabs or icons on the browser sports webpages with the regional sports allowing the user to select the regional sports and access a dedicated webpage for the regional sport with information for the regional sport.

Clustering approach for auto generation and classification of regional sports

Aspects of the present disclosure are directed to providing a rich content experience based on information received from unstructured content. A plurality of information items may be obtained from a plurality of data source, where each information item includes unstructured content. The plurality of information items may be provided to a trained machine learning model, where the model is trained with training data that includes information items and corresponding labeled entities for a plurality of historical events. In examples, a formatted request may be received, where the formatted request is associated with one or more labeled entities associated with the trained machine learning model. The trained machine learning model may identify multiple entities from the unstructured content based on the formatted request associated with the one or more labeled entities. In examples, each identified entity of the multiple identified entities is stored as structured content responsive to the formatted request.

Generating structured data for rich experiences from unstructured data streams

The present concepts relate to an improved personalized search engine that can generate personalized rankings of search results in view of individual user's personal preferences and interests. Information about a segment of online content is collected. Certain activities by a user are tracked, including search queries submitted by the user, search results clicked on by the user, and/or web pages browsed by the user. From these activities, the user's preferences relating the segment are inferred using the collected segment information. When the user conducts a search directed to the segment, certain search results that the user is more likely to be interested in, based on the user's preferences, are ranked higher to generate the personalized rankings.

Personalized search result rankings

System and methods for presenting users with different App download options in response to certain search queries. In aspects, when a user enters a search query that does not explicitly request an App, systems and methods described herein analyze the results of the search and determine whether the request is an implicit request for Apps. As a result, relevant Apps are identified and presented for download. Other aspects of the present disclosure relate to analyzing and identifying URLs of companies and developers of Apps. Once analyzed and identified, embodiments relate to associating the appropriate URLs with one or more Apps.

Systems and methods for App query driven results

A system and method of generating and using key-value clusters to classify and return app queries is provided. The system includes a computing device. The computing device includes: a processing device; and a computer readable data storage device storing instructions. When the instructions are executed by the processing device, the instructions are operative to provide: receiving a search query and retrieving a preliminary search result comprising at least n web results; and when the at least n web results matches the key cluster returning the key cluster and an associated value cluster as a result of the search.

Auto-generation of key-value clusters to classify implicit app queries and increase coverage for existing classified queries

A market out of multiple available geographic markets can be inferred for each application in a first subset of computer applications. The inferring can include inferring each market from text in one or more metadata fields in application metadata from an online computerized store for the applications. Computer-readable market clusters can be generated, with each of the market clusters indicating a corresponding one of the markets, and with the clusters indicating an inferred market cluster of the computer-readable market clusters for each of the applications in the first subset. In response to a received application query, generating search results can include favoring inclusion of the one or more applications that are assigned to the identified market cluster relative to one or more applications that are not assigned to the identified market cluster.

Computer application market clusters for application searching

Unique application identification (UAID) stamping for automated classification of an implicit query as an application intent query from web results for enabling improved delivery of applications as a search result is provided. An application indexing engine identifies web resources related to an app, and tags the resources with a unique application identifier associated with the app. Accordingly, an implicit query where a web resource responsive to the query is related to the app triggers classification of the query as an app-intent query, enabling the app to be provided as an app result in response to the query. The app is presented to a user, who may selectively download the app.

Automated application linking

A plurality of category application queries can be processed, including identifying an associated list of results for each of the category application queries, with each list of results listing one or more applications. A cluster of the category application queries that each have a category in common in an application category stamp can be identified. A proximity graph for the category application queries in the cluster can be generated. The proximity graph can indicate similarity scores based at least in part on numbers of applications in common for lists of results associated with the category application queries in pairs of queries in the cluster. Lists each having one or more suggested alternative category application queries for category application queries in the cluster can be generated, with the generating of the lists of one or more suggested alternative category application queries being based at least in part on the similarity scores.

Computer application query suggestions

Application metadata for computer applications can be retrieved, with the metadata corresponding to metadata for an online application store from which the applications are available. Computer-readable application clusters can be generated. Each of the application clusters can indicate that applications in the cluster are supported by an associated set of one or more platforms for the cluster. The generating of the clusters can include analyzing the application metadata, such as by performing pattern matching on natural language data. Results for application queries for applications supported by a specified computer platform can be limited to listings of applications in one or more of the clusters whose associated set of one or more platforms includes the specified platform.

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Bellevue, WA, United States of America

Aman Singhal