Abstract: Aspects of the present disclosure identify social media conversational signals and deliver prospects of potential opportunities to conduct a sale in an automated fashion. Individuals, or groups of people, are identified who are in decision making mode, and the communications are presented to businesses and/or organizations to help complete the transaction. Unlike social listening platforms, which use keyword matching and sentiment analysis, in some embodiments this platform leverages machine learning (ML), natural language processing (NLP) and the Universal Human Relevance System (UHRS) to identity relevant results by classifying them into a domain specific taxonomy. These transactional events may be defined by the date and time stamp, what the potential customer is looking for, the time-frame for consideration of the purchase, and the geographic location of the individual at the time of the signal's publication. In addition, these transactional events can be customized to suit the context of a domain.

Abstract: Example methods, apparatuses, and systems (e.g., machines) are presented for a natural language classification engine or platform capable of processing configurable classification criteria in real time or near real time. While typical classification engines tend to require specific training for each domain to be classified for a subscriber, the classification engine of the present disclosure is capable of analyzing a single corpus of human communications and providing only the relevant messages or documents according to criteria generated on the fly by a subscriber. The classification engine of the present disclosure need not know beforehand what type of content is desired by the subscriber. In this way, the criteria specified by a subscriber can change dynamically, and the classification engine of the present disclosure may be capable of evaluating the criteria and then provide relevant documents or messages according to the changed criteria, without needing additional corpus training.

Abstract: Examples are presented for a classification system that utilizes multiple classification models to adapt to any desired set of raw data to be classified. The classification system may include multiple classification models stored in a model repository. A truth set of the raw data may be used to evaluate the fitness of each of the stored classification models. The models may be scored and ranked to determine which is the most appropriate to use for real time classification of the raw data. The optimal classification model may be used in a classification engine to classify the raw data in real time. This generates a classified output that may be interacted with by a user. A user interface may be used to permit feedback of the classified output to be generated. This feedback may then be transmitted to the offline system and recorded to further improve the classification models.

Abstract: Example methods, apparatuses, and systems (e.g., machines) are presented for an auction platform that determines winning bids for high value digital message content using a multi-tiered share-rank system. The auction platform allows for subscribers to bid for merely a share of a set of high value digital message content. Multiple other subscribers may also bid for the same share amount of the set of high value digital message content. The collective bids of multiple subscribers that equals the entire set of the high value digital message content is then compared against other subscribers who have formed collective bids by different sized shares.

Abstract: Systems, computer-implemented methods and computer-storage media are provided for presenting customizable advertisements to a user. Advertising is customized through the use of user-specific or user-relevant data. Items such as keywords, profile data, and application data are received and are then used to retrieve customizations. These customizations are stored by a server or group of servers that the vendor makes available to the advertising provider. Items that could further identify adaptations desirable for advertising, such as a user profile, may be sent to the vendor's server as well. The adaptations take forms that include pictures, text, graphics, links, audio, and video files. Systems that comprise an advertising server, a vendor server, and a client application used for completing the above method, as well as computer-readable media necessary at each device to accomplish the customizations, are also described.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method for registering a media for configurable advertising is described herein. Advertising policies from a publisher of the media may be received at a video advertising platform. A request from the publisher to register the media for advertising may be received at a video advertising platform. The request may have media metadata. A media manifest may be generated at the video advertising platform. The media manifest may be based on the media metadata and the advertising policies. The media manifest may be configured to be received by a media player. The media player may play the media with one or more advertisements as specified by the media manifest.

Abstract: 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.

Abstract: 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.

Abstract: Systems, methods, and computer-storage media for presenting answers are provided. The system includes a search engine to receive user queries and contexts, a query understanding component to provide results, and an answer generator to present answers to the user queries. The answers may include a link to a browser that provides a graph, table, or cluster for the results, where nodes of the graph are associated with a confidence level.

Abstract: 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.

Abstract: 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.