Abstract: Architecture that facilitates online advertising taking on characteristics of a commodities market approach to purchasing advertising space, options for ad space and a futures market for online ad space. Available advertising space is identified and aggregated, and subsets of the aggregated ad space are offered for purchase using a commodities market-based approach. The architecture facilitates revenue-sharing paradigms, coupon delivery, targeted advertising, point-of-sale transactions, inventory control, just-in-time delivery of ads, content and product/services, value-based advertising models, etc. The architecture comprises an aggregation component that aggregates advertisement space information associated with online advertisements, and a transaction component that facilitates transacting subsets of the aggregated advertisement space information to bidders as a function of supply and demand.

Abstract: The subject disclosure pertains to web searches and more particularly toward influencing resultant content to increase relevancy. The resultant content can be influenced by reconfiguring a query and/or filtering results based on user location and/or context information (e.g., user characteristics/profile, prior interaction/usage temporal, current events, and third party state/context . . . ). Furthermore, the disclosure provides for query execution on at least a subset of designated web content, for example as specified by a user. Still further yet, a localized marketing system is disclosed that provides discount offers to users that match merchant criteria including proximity. A system for actively probing populations of users with different parameters and monitoring responses can be employed to collect data for identifying the best discounts and deadlines to offer to users to achieve desired results.

Abstract: The subject disclosure pertains to scrutinizing results generated or otherwise provided by a computer. A mechanism is provided that enables users to validate computer-based information. Users can receive a validity metric associated with computer generated or provided results indicative of the veracity of such results. Validation systems and methods are disclosed to facilitate determining the veracity of results including those that employ humans (e.g., referrals, voting . . . ) and/or automated means (e.g., source analysis, data mining . . . ). The disclosure also provides a mechanism for guiding computer searches (e.g., web, Internet, intranet . . . ). Machine learning and reasoning mechanisms are employed together with a search engine to facilitate intelligent guidance of queries and results based on a query and responses to computer generated inquires.

Abstract: A system that employs an explicitly and/or implicitly trained model in order to return entity-specific computer-based search results is provided. The innovation can provide for a customized search model that focuses search in connection with achieving information that is meaningful with respect to goals of an entity. The model can be used to modify a search query in accordance with a goal of the entity or to generate the search query thereby returning meaningful and/or targeted results to the user. The system can automatically gather entity-related data thereafter determining or inferring a goal as well as training the model. Moreover, the system can selectively configure (e.g., order, rank, filter) and render results to a user based upon the model.

Abstract: Architecture is provided that facilitates user-controlled access to user profile information. A user is allowed to selectively expose (or mask) portions of his/her profile to third parties. Additionally, advertisers and/or content providers can offer incentives or enticement in response to the acceptance of which a user exposes larger portions of their profile. The architecture comprises a system that facilitates profile management utilizing a profile component that facilitates creation and storage of an electronic profile of a user, and a control component under control of the user for controlling access to the profile. Machine learning and reasoning is provided to make inferences and automate aspects thereof.

Abstract: Architecture for targeted advertising using offline user behavior information. Information relating to offline behavior can be collected from cell phones, geolocation systems, credit card information, restaurants, grocery stores, etc., and this information is aggregated and employed in connection with selecting and displaying targeted advertising to a user when online. Machine learning and reasoning can be employed to make inferences and dynamically tune advertisement processing. Offline user information can also be employed to enhance context-based searching when the user goes online. The ranking of search results and content for display can be modified as a function of offline behavior. A system is provided that facilitates online advertising based on at least offline activity using a profile component for aggregating offline behavior information of a user and generating a related user profile.

Abstract: A system that can analyze a multi-dimensional input thereafter establishing a search query based upon extracted features from the input. In a particular example, an image can be used as an input to a search mechanism. Pattern recognition and image analysis can be applied to the image thereafter establishing a search query that corresponds to features extracted from the image input. The system can also facilitate indexing multi-dimensional searchable items thereby making them available to be retrieved as results to a search query. More particularly, the system can employ text analysis, pattern and/or speech recognition mechanisms to extract features from searchable items. These extracted features can be employed to index the searchable items.

Abstract: Provided is mechanism for guiding computer searches (e.g. web, Internet, intranet . . . ). Machine learning and reasoning mechanisms are employed together with a search engine to facilitate intelligent guidance of queries and results based on a query and responses to computer generated inquires. Also, disclosed is a mechanism that enables users to validate computer-based information. Users can receive a validity metric associated with computer generated or provided results indicative of the veracity of such results. Validation systems and methods are disclosed to facilitate determining the veracity of results including those that employ humans (e.g., referrals, voting . . . ) and/or automated means (e.g. source analysis, data mining. . . ).

Abstract: The subject disclosure pertains to web searches and more particularly toward influencing resultant content to increase relevancy. The resultant content can be influenced by reconfiguring a query and/or filtering results based on user location and/or context information (e.g., user characteristics/profile, prior interaction/usage temporal, current events, and third party state/context . . . ). Furthermore, the disclosure provides for query execution on at least a subset of designated web content, for example as specified by a user. Still further yet, a localized marketing system is disclosed that provides discount offers to users that match merchant criteria including proximity. A system for actively probing populations of users with different parameters and monitoring responses can be employed to collect data for identifying the best discounts and deadlines to offer to users to achieve desired results.

Abstract: A system that incorporates a user context into a computer-based search is provided. To establish the context, the innovation can identify information about a user state or context via a variety of sources and sensors. The state/context information can be used to filter, arrange and/or rank search results so as to facilitate converging on meaningful searches and results. Machine learning systems (implicitly and/or explicitly trained) can be employed to infer a current and/or future context related to user. An identified or inferred user context can be employed to modify an automated or user-defined search input/query. Contextual cues can be considered directly in the construction and use of context of context-sensitive retrieval algorithms that are optimized for identifying and/or ranking of informational items of potential interest or value in different contexts. As well, the context can be employed to intelligently render results of a query (e.g., user/application defined, context-modified query).

Abstract: Architecture that facilitates online advertising taking on characteristics of a commodities market approach to purchasing advertising space, options for ad space and a futures market for online ad space. Available advertising space is identified and aggregated, and subsets of the aggregated ad space are offered for purchase using a commodities market-based approach. The architecture facilitates revenue-sharing paradigms, coupon delivery, targeted advertising, point-of-sale transactions, inventory control, just-in-time delivery of ads, content and product/services, value-based advertising models, etc. The architecture comprises an aggregation component that aggregates advertisement space information associated with online advertisements, and a transaction component that facilitates transacting subsets of the aggregated advertisement space information to bidders as a function of supply and demand.

Abstract: Architecture that monitors interaction data (e.g., search queries, query results and click-through rates), and provides users with links to other users that fall into similar categories with respect to the foregoing monitored activities (e.g., providing links to individuals and groups that share common interests and/or profiles). A search engine can be interactively coupled with one or more social networks, and that maps individuals and/or groups within respective social networks to subsets of categories associated with searches. A database stores mapped information which can be continuously updated and reorganized as links within the system mapping become stronger or weaker. The architecture can comprise a social network system that includes a database for mapping search-related information to an entity of a social network, and a search component for processing a search query for search results and returning a link to an entity of a social network based on the search query.

Abstract: Architecture for presenting advertisements in realtime in retail establishments. A sensor component includes sensors for collecting information about a customer or group of customers as they move through the store. The sensors can include capability for image processing, audio processing, light sensing, velocity sensing, direction sensing, proximity sensing, face recognition, pose recognition, transaction recognition, and biometric sensing, for example. A customer component analyzes the information and generates a profile about the customer. Advertisements are selected for presentation that target the customers as they walk in proximity of a presentation system of the store. An advertisement component facilitates dynamic presentation of a targeted advertisement to the individual as a function of the profile. The customer component can infer information during analysis using machine learning and reasoning.

Abstract: Architecture for creation and processing of reputation data for entities such as websites, users, hardware, software, documents, objects and facts. Reputation data can be utilized in connection with web-based searching such that the reputation of websites provides a metric in connection with ranking of search results as well as enhancing delivery of meaningful and accurate information to users. A computer-implemented system is provided that comprises an aggregation component for receiving and aggregating information relating to an entity (e.g., user, website, data, hardware, software), and a reputation engine that employs the aggregated information to generate reputation data therefrom. Other aspects allow for management of the data, hardware and software based on the reputation data, and access to such entities.

Abstract: Architecture is provided that facilitates user-controlled access to user profile information. A user is allowed to selectively expose (or mask) portions of his/her profile to third parties. Additionally, advertisers and/or content providers can offer incentives or enticement in response to the acceptance of which a user exposes larger portions of their profile. The architecture comprises a system that facilitates profile management utilizing a profile component that facilitates creation and storage of an electronic profile of a user, and a control component under control of the user for controlling access to the profile. Machine learning and reasoning is provided to make inferences and automate aspects thereof.

Abstract: A system that can analyze a multi-dimensional input thereafter establishing a search query based upon extracted features from the input. In a particular example, an image can be used as an input to a search mechanism. Pattern recognition and image analysis can be applied to the image thereafter establishing a search query that corresponds to features extracted from the image input. The system can also facilitate indexing multi-dimensional searchable items thereby making them available to be retrieved as results to a search query. More particularly, the system can employ text analysis, pattern and/or speech recognition mechanisms to extract features from searchable items. These extracted features can be employed to index the searchable items.

Abstract: A system that employs an explicitly and/or implicitly trained model in order to return entity-specific computer-based search results is provided. The innovation can provide for a customized search model that focuses search in connection with achieving information that is meaningful with respect to goals of an entity. The model can be used to modify a search query in accordance with a goal of the entity or to generate the search query thereby returning meaningful and/or targeted results to the user. The system can automatically gather entity-related data thereafter determining or inferring a goal as well as training the model. Moreover, the system can selectively configure (e.g., order, rank, filter) and render results to a user based upon the model.

Abstract: The innovation provides for a computer search to become an action that has direct nexus to an inferred (or determined) goal of an individual. The goal can be inferred or determined from any number of context/state factors. The innovation can query a user to determine user context and state factors by which a goal, objective or intent can be automatically established. The innovation can also utilize machine learning/reasoning to establish the goal of a user based upon historical, statistical and/or other probabilistic analysis. Still further, the innovation can monitor a user's context and state thereafter dynamically journaling and logging the criterion by which the user's objective(s) can be established. Once a goal is established, a goal-based search can be automatically conducted thereafter prompting for an action based upon a subset of the search results.

Abstract: The innovation provides for a computer search to become an action that has direct nexus to an inferred (or determined) goal of an individual. The goal can be inferred or determined from any number of context/state factors. The innovation can query a user to determine user context and state factors by which a goal, objective or intent can be automatically established. The innovation can also utilize machine learning/reasoning to establish the goal of a user based upon historical, statistical and/or other probabilistic analysis. Still further, the innovation can monitor a user's context and state thereafter dynamically journaling and logging the criterion by which the user's objective(s) can be established. Once a goal is established, a goal-based search can be automatically conducted thereafter prompting for an action based upon a subset of the search results.

Abstract: Architecture for targeted advertising using offline user behavior information. Information relating to offline behavior can be collected from cell phones, geolocation systems, credit card information, restaurants, grocery stores, etc., and this information is aggregated and employed in connection with selecting and displaying targeted advertising to a user when online. Machine learning and reasoning can be employed to make inferences and dynamically tune advertisement processing. Offline user information can also be employed to enhance context-based searching when the user goes online. The ranking of search results and content for display can be modified as a function of offline behavior. A system is provided that facilitates online advertising based on at least offline activity using a profile component for aggregating offline behavior information of a user and generating a related user profile.