Abstract: The present invention relates to a system and methodology to facilitate extraction of information from a large unstructured corpora such as from the World Wide Web and/or other unstructured sources. Information in the form of answers to questions can be automatically composed from such sources via probabilistic models and cost-benefit analyses to guide resource-intensive information-extraction procedures employed by a knowledge-based question answering system. The analyses can leverage predictions of the ultimate quality of answers generated by the system provided by Bayesian or other statistical models. Such predictions, when coupled with a utility model can provide the system with the ability to make decisions about the number of queries issued to a search engine (or engines), given the cost of queries and the expected value of query results in refining an ultimate answer. Given a preference model, information extraction actions can be taken with the highest expected utility.

Abstract: Providing for summarization and analysis of audio content is described herein. By way of example, an oral conversation can be analyzed, such that points of interest within the oral conversation can be identified and file locations related to such points of interest can be marked. Points of interest can be inferred based on a level of energy, e.g., excitement, pitch, tone, pace, or the like, associated with one or more speakers. Alternatively, or in addition, speaker and/or reviewer activity can form the basis for identifying points of interest within the conversation. Moreover, a compilation of the identified points of interest and portions of the original oral conversation related thereto can be assembled. As described herein, audio content can be succinctly summarized with respect to inferred and/or indicated points of interest, to facilitate an efficient and pertinent review of such content.

Abstract: A system and method that facilitates and effectuates making an inference related to objects of interest within a context. A web projection component can generate sub graphs by projecting objects o interest onto a web graph representing a superset or partially overlapping set of objects to the objects of interest. An inference component can provide inferences relating to the quality of the objects of interest, adjustments to the context, and patterns recognized from graphical properties of the sub graphs.

Abstract: The present invention relates to a system and methodology to facilitate extraction of information from a large unstructured corpora such as from the World Wide Web and/or other unstructured sources. Information in the form of answers to questions can be automatically composed from such sources via probabilistic models and cost-benefit analyses to guide resource-intensive information-extraction procedures employed by a knowledge-based question answering system. The analyses can leverage predictions of the ultimate quality of answers generated by the system provided by Bayesian or other statistical models. Such predictions, when coupled with a utility model can provide the system with the ability to make decisions about the number of queries issued to a search engine (or engines), given the cost of queries and the expected value of query results in refining an ultimate answer. Given a preference model, information extraction actions can be taken with the highest expected utility.

Abstract: Context-based user feedback is gathered regarding searches performed on a search mechanism. The search mechanism is monitored for user behavior data regarding an interaction of a user with the search mechanism. The response data provided by the search mechanism is also monitored. Context data (describing the search) and user feedback data (the user's feedback on the search—either explicit or implicit) are determined. This can be used, for example, to evaluate a search mechanism or to check a relevance model.

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 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 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 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 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 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: 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 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 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: 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 subject disclosure pertains to anonymous network interaction. More specifically, mechanisms are provided to ensure anonymity with respect network interaction such that third parties are unable to determine the source and/or intent of communications. Accordingly, entities can anonymize all outgoing and/or incoming data packets so as to mitigate outside entities from learning about information being sought and/or provided. For example, a user or corporation can employ an anonymizer with respect to web searching so that outside entities are not able to determine what information is attempted to be accessed and by whom.

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.

Abstract: Content management architecture for a portable wireless device. Caching and fetching techniques are provided to improve content handling for portable devices such as cellular telephones and portable computers. A search component automatically performs searches as a background process, and potentially desired content is received and cached by a content storing component to be available in the future when and if needed, mitigating latency associated with slow download speeds, refresh rates, and other system and/or network impediments. Content from background search results can be trickled into the device as part of the background process so as not to burden system resources for other processes. As part of memory management, aged and/or low priority or low interest content can be selectively removed or archived to increase available cache or memory space, as well as to maintain relevant content within the device. A presentation component facilitates presentation of the pre-stored content.

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.