Patent application number | Description | Published |
20120317088 | Associating Search Queries and Entities - The subject disclosure is directed towards processing data to obtain associations between queries and entities. Association is modeled using a query-entity click graph, blending general query-click logs with vertical query-click logs. Smoothing techniques address the data sparsity in such graphs, including interpolation using a query synonymy model. The association models may be applied to the task of recommending products to web queries, by annotating queries with products from a large catalog and then mining query-product associations through web search session analysis. | 12-13-2012 |
20130144854 | MODELING ACTIONS FOR ENTITY-CENTRIC SEARCH - In one embodiment, a web service engine server | 06-06-2013 |
20130159219 | Predicting the Likelihood of Digital Communication Responses - Different advantageous embodiments provide for response prediction. A social element is received by a prediction mechanism. A feature set is generated for the social element. A prediction is generated using the feature set and a prediction model. | 06-20-2013 |
20130262114 | Crowdsourced, Grounded Language for Intent Modeling in Conversational Interfaces - Different advantageous embodiments provide a crowdsourcing method for modeling user intent in conversational interfaces. One or more stimuli are presented to a plurality of describers. One or more sets of describer data are captured from the plurality of describers using a data collection mechanism. The one or more sets of describer data are processed to generate one or more models. Each of the one or more models is associated with a specific stimulus from the one or more stimuli. | 10-03-2013 |
20130282704 | SEARCH SYSTEM WITH QUERY REFINEMENT - A search system that automatically generates questions to refine an underspecified query. The system may generate questions even for queries against a database that contains unstructured textual descriptions of items, allowing the system to operate on a database of items that can be constructed inexpensively. The system extracts from the unstructured text combinations of words that may serve as a set of attribute values. The system uses a classifier to filter out attribute values from the set that would generate unanswerable questions. The remaining attribute values are ranked on their ability to narrow the search results and the highest ranking attribute value is used to generate a question to the user who submitted the query. The response to the question narrows the search results, and the process can be repeated iteratively until the search results are sufficiently narrow. | 10-24-2013 |
20140172412 | ACTION BROKER - Among other things, one or more techniques and/or systems are provided for building an action catalogue, generating an action frame for an action within the action catalogue, and/or executing an action. In an example, an action may be included within the action catalogue based upon descriptive text associated with an application indicating that the application is capable of performing the action (e.g., a movie app may be capable of performing an order movie tickets action). A parameter (e.g., a movie name) and/or an execution endpoint (e.g., a uniform resource identifier used to access movie ticket ordering functionality) may be used to generate an action frame for the action. In this way, user intent to perform an action may be identified from user input (e.g., a spoken command), and the action may be performed (e.g., on behalf of the user with minimal additional user input) by using the action frame. | 06-19-2014 |
20140279730 | IDENTIFYING SALIENT ITEMS IN DOCUMENTS - A set of representations of item-page pairs of items and respective web pages that include the respective items is obtained, each representation including feature function values indicating weights associated with features of associated web pages, the features including page classification features. An annotated set of labeled training data that is annotated with salience annotation values of items for respective web pages that include the items is obtained. The salience annotation values are determined based on a soft function, by determining a first count of a total number of user queries associated with corresponding visits to the respective web pages, and determining a ratio of a second count to the first count, the second count determined as a cardinality of a subset of the corresponding visits that are associated with user queries that include the item, the subset included in the corresponding visits. Models are trained using the annotated set. | 09-18-2014 |
20150100524 | SMART SELECTION OF TEXT SPANS - A text span forming either a single word or a series of two or more words that a user intended to select is predicted. A document and a location pointer that indicates a particular location in the document are received and input to different candidate text span generation methods. A ranked list of one or more scored candidate text spans is received from each of the different candidate text span generation methods. A machine-learned ensemble model is used to re-score each of the scored candidate text spans that is received from each of the different candidate text span generation methods. The ensemble model is trained using a machine learning method and features from a dataset of true intended user text span selections. A ranked list of re-scored candidate text spans is received from the ensemble model. | 04-09-2015 |
20150100562 | CONTEXTUAL INSIGHTS AND EXPLORATION - Techniques and systems are presented for providing “contextual insights,” or information that is tailored to the context of the content a user is consuming or authoring. Given a request for information about a topic, which may be indicated by a user gesture in an application, one or more queries to search services may be formulated without requiring entry of a search query directly by a user. Moreover, techniques and systems may leverage the context of the content the user is consuming or authoring, as well as user, device, and application metadata, to construct the queries and to organize and filter the results into relevant contextual insights. | 04-09-2015 |