Microsoft Technology Licensing, LLC

Microsoft Corporation

Li, Li

Hongbin Gao

Xianfang Wang

Tarek Najm

Ying Li

Zheng Chen

Hua-Jun Zeng

Benyu Zhang

Viswanath Vadlamani

Li Erran Li

Arungunram Chandrasekaran Surendran

Rajeev Prasad

Phani Vaddadi

Abhinai Srivastava

Munirathnam Srikanth

Brian Burdick

Murali Vajjiravel

Wei-Ying Ma

Ramesh Manne

Rico Mariani

Lee Wang

Tara Sanathanan Prakriya

Kevin Paul Kornelson

Madhan Subhas

Paul D Clark

David Earl Heckerman

Christopher A Meek

David Maxwell Chickering

Raxit A Kagalwala

Fengping Zeng

Shuzhen Nong

Sachin Dhawan

Jian Wang

Ying-Qing Xu

Ewa Dominowska

Yin Li

Ke Tang

Jody D Biggs

Savithri N Dani

Zhifeng Yang

Siva Mohan

Mahbubul Alam Ali

Prasanth Pulavarthi

John Midgley

Yusuf I Mehdi

Dean Carignan

Xiaofeng Dai

Karl D Johnson

Degelhan Truesaw

Daniel P Boerner

Search result re-ranking is provided by employing a concept graph from a metabase. When a search query is received, a query context of the search query is analyzed to identify dominant concepts for the search query. The dominant concepts are expanded by identifying the dominant concepts within a concept graph and identifying additional concepts having a strong relationship with the dominant concepts within the concept graph. A set of search results for the search query is analyzed to determine strength of relationship of each search result to the expanded concepts. The search results are re-ranked based on the strength of relationship of each search result to the expanded concepts and the strength of relationship of each expanded concept to the dominant concepts.

Re-ranking search results based on lexical and ontological concepts

A procedural inference system is described herein that infers procedural knowledge from various data sources to help a user complete one or more tasks for which the data sources provide information. The system understands users' queries, identifies a task at hand, provides recommendations on the steps to take and the agents to use based on a knowledge base of tasks and agents, and provides the fabric to determine which different agents can work together to help the user accomplish a task. Tasks can be started on one device and completed on another seamlessly. Users are able to finish complex, multi-step tasks efficiently, without trial and error or data reentry. Thus, the procedural inference system provides a generalized framework that helps users to complete tasks using already available data and does not ask each data provider to invest in infrastructure to build dedicated task information systems.

Inferring procedural knowledge from data sources

Systems, methods, and computer-storage media for generating contextual queries are provided. The system includes a search engine to receive user queries and contexts, a query understanding component to generate a semantic representation of the query, and a data source command generator to transform the semantic representation into commands for multiple data sources. The data source command generator is connected to the query understanding component. The data source command generator selects data source commands based on lexical information associated with each data source.

Contextual queries

Systems, methods, and computer-storage media for generating lateral concepts are provided. The system includes a search engine to receive user queries, a storage to store content and its associated categories, and a lateral concept generator. The lateral concept generator is connected to both the search engine and storage. The lateral concept generator selects lateral concepts from categories associated with the content based on similarity scores for the stored content.

Generating and presenting lateral concepts

Systems, methods, and computer-storage media for generating and providing knowledge content to users utilizing a web architecture that integrates information across data silos through a common, flexible data storage schema, such as a star or snowflake schema, are provided. Data from a content graph, a user activity graph, a social graph, and temporal data as it relates to each of the content graph, the social graph and the user activity graph, is stored in a knowledge content database utilizing the star schema. In this way, data from each of these formerly disparate sources may be accessed from a common, extensible application platform utilizing ontologies and pivot table functionality, thus providing smarter, more comprehensive knowledge in response to received user queries.

Providing knowledge content to users

The claimed subject matter provides a system and/or a method that facilitates establishing a true market value for an online advertisement. An interface component can receive data related to at least one of a spot market or a future market. A dynamic pricing component can dynamically establish an ad price for an ad unit based on the spot market and the future market, the ad price reflects a market value of the ad unit in accordance with the spot market and the future market.

Price determination and inventory allocation based on spot and futures markets in future site channels for online advertising

A peer-to-peer advertisement platform is provided to ubiquitously promote products or services supplied by advertisers across content-based applications executing on nodes in a peer-to-peer network. The peer-to-peer advertisement platform may include a registration component to register nodes in the peer-to-peer advertising platform, an advertisement submission component to receive advertisement data from the advertisers, and a distribution component to distribute the advertisement data to the nodes registered in the peer-to-peer advertisement platform. The peer-to-peer advertisement platform also includes a money sharing component to reward nodes based on a contribution level assigned to the node. Accordingly, the peer-to-peer advertisement platform stores the advertisement data locally at the plurality of nodes registered in the peer-to-peer advertising platform and shares a portion of the revenue generated from the advertisement data with the nodes registered in the peer-to-peer advertising platform.

Peer-to-peer advertisement platform

According to embodiments of the invention, an advertisement-generation system generates image-containing advertisements. The advertisement-generation system includes: at least one feature-selection guideline that specifies at least one recommended feature for image-containing advertisements based on advertiser inputs that specify at least one of advertisement-target-audience information, cost information, and advertiser-industry information; an image-clip library from which images are selected for inclusion in the image-containing advertisements; and at least one advertisement template that is based on the at least one feature-selection guideline; wherein the system automatically generates image-containing advertisements that contain one or more suggested colors that are automatically suggested based on one or more colors present on a web page that will host the image-containing advertisement.

Generation of contextual image-containing advertisements

Electronic documents are analyzed to identify assertions, which are inverted to generate questions that may be answered by the assertions. A document or a corpus of electronic documents may be analyzed to identify entities and relationships among entities within the text of the document(s). Assertions are identified based on the entities and relationships among the entities. Each assertion represents a fact about an entity, and a group of assertions represents a summary of the document or document corpus. The assertions are inverted to generate questions that may be answered by the assertions. The questions may be further analyzed to identify relevant concepts and topics and to cluster the questions around the concepts and topics. A combined graph may also be generated that facilitates traversal among topics, concepts, questions, assertions, document summaries, and documents.

Ontological categorization of question concepts from document summaries

A table of contents is generated for search results in response to a search query by identifying semantic concepts as topics for inclusion within the table of contents. When a search query is received, a search is performed to identify search results. The search results are compared to an ontology of topics to identify relevant topics. Additionally, search results are compared to an ontology of partial topics to identify relevant partial topics, which are named. Independent key-phrases are further generated from search results and identified as key-phrase topics. The identified topics, named partial topics, and key-phrase topics are ranked, and topics are selected for inclusion within a table of contents. A search results page is returned in response to the search query that includes search results and the generated table of contents.

Semantic table of contents for search results

Advertisements are selected for presentation on search result pages and web pages based on phrases generated from lateral concepts and topics identified for the search result pages and web pages. A search query or an indication of a web page is received for which advertisements are to be provided. Lateral concepts and topics are identified based on the search query or content of the web page. The lateral concepts and topics are used as phrases for selecting advertisements from an advertisement inventory. Selected advertisements are provided for presentation on a search results page in response to a search query or on a web page initially identified.

Semantic advertising selection from lateral concepts and topics

A system and method are disclosed for providing documents related to a search request. The search request may include a search query of one or more keywords, or the search request may be a demographic search query including one or more demographic attributes. An index containing data crawled from publisher's websites, demographic information of registered users, along with the search history of the registered users can be created. Once a search request is received, the search request can be compared to the information stored in the index, and one or more documents related to the request can be provided.

Advertising service based on content and user log mining

Systems and methods for determining the value of bids placed by content providers for placement positions on a page, e.g., a web page, rendered according to a given context, for instance, the search results listing for a particular query initiated on a search engine web site, are provided. Additionally, systems and methods are provided for determining placement of content items, e.g., advertisements and/or images, on a rendered page relative to other content items on the page based upon bid value.

Systems and methods for estimating placement positions of content items on a rendered page

Display screen with a user interface

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Kirkland, WA, United States of America

Tarek Najm