Abstract: Disclosed herein are aspects associated with contextual, or related, media enrichment presentation item of a media object served via the internet. A request to annotate a media object in connection with the media object's presentation is received, and a media object identifier and a profile identifier are obtained. The media object's information is retrieved using the media object identifier, and a profile is retrieved using the profile identifier. A response including one or more references to one or more media enrichment presentation items is transmitted, each reference to a media enrichment presentation item comprising information for use in retrieving the media enrichment presentation item for presentation in connection with presentation of the media object.

Abstract: Subject matter disclosed herein relates to image object recognition or retrieval.

Abstract: Disclosed herein are aspects associated with contextual, or related, media enrichment presentation item of a media object served via the internet. A request to annotate a media object in connection with the media object's presentation is received, and a media object identifier and a profile identifier are obtained. The media object's information is retrieved using the media object identifier, and a profile is retrieved using the profile identifier. A response including one or more references to one or more media enrichment presentation items is transmitted, each reference to a media enrichment presentation item comprising information for use in retrieving the media enrichment presentation item for presentation in connection with presentation of the media object.

Abstract: Subject matter disclosed herein relates to image object recognition or retrieval.

Abstract: Disclosed herein are aspects associated with contextual, or related, media enrichment presentation item of a media object served via the internet. A request to annotate a media object in connection with the media object's presentation is received, and a media object identifier and a profile identifier are obtained. The media object's information is retrieved using the media object identifier, and a profile is retrieved using the profile identifier. A response including one or more references to one or more media enrichment presentation items is transmitted, each reference to a media enrichment presentation item comprising information for use in retrieving the media enrichment presentation item for presentation in connection with presentation of the media object.

Abstract: An improved system and method for a task management library to execute map-reduce applications is provided. A map-reduce application may be operably coupled to a task manager library and a map-reduce library on a client device. The task manager library may include a wrapper application programming interface that provides application programming interfaces invoked by a wrapper to parse data input values of the map-reduce application. The task manager library may also include a configurator that extracts data and parameters of the map-reduce application from a configuration file to configure the map-reduce application for execution, a scheduler that determines an execution plan based on input and output data dependencies of mappers and reducers, a launcher that iteratively launches the mappers and reducers according to the execution plan, and a task executor that requests the map-reduce library to invoke execution of mappers on mapper servers and reducers on reducer servers.

Abstract: Methods, systems and computer program products for displaying geographical locations with the one or more annotations. In a particular embodiment, a language model is used to obtain the probability distribution of the locations over one or more annotations. Further, the system and the method utilizes the probability data obtained from the language model to determine a probability score for each location over the one or more annotations. Subsequently, one or more geographical locations are displayed on a world map, based on the probability score of the geographical locations over the one or more annotations. In one embodiment, geographical locations may be highlighted using a color code on a heat map overlaid on the world map. The color code may represent the ranking of the geographical locations based on the calculated probability score for each identified geographical location.

Abstract: Subject matter disclosed herein relates to electronic image object or logo recognition.

Abstract: Disclosed herein are aspects associated with contextual, or related, media enrichment presentation item of a media object served via the internet. A request to annotate a media object in connection with the media object's presentation is received, and a media object identifier and a profile identifier are obtained. The media object's information is retrieved using the media object identifier, and a profile is retrieved using the profile identifier. A response including one or more references to one or more media enrichment presentation items is transmitted, each reference to a media enrichment presentation item comprising information for use in retrieving the media enrichment presentation item for presentation in connection with presentation of the media object.

Abstract: Example methods, apparatuses, or articles of manufacture are disclosed that may be implemented using one or more computing devices to facilitate or otherwise support one or more processes or operations associated with ranking entity facets using user-click feedback.

Abstract: Briefly, embodiments of a method or system of predicting media content preferences are disclosed.

Abstract: Techniques are described herein for selecting representative images for video items using a trained machine learning engine. A training set is fed to a machine learning engine. The training set includes, for each image in the training set, input parameter values and an externally-generated score. Once a machine learning model has been generated based on the training set, input parameters for unscored images are fed to the trained machine learning engine. Based on the machine learning model, the trained machine learning engine generates scores for the images. To select a representative image for a particular video item, candidate images for that particular video item may be ranked based on their scores, and the candidate image with the top score may be selected as the representative image for the video item.

Abstract: Disclosed is a method and system for annotating video. A software application operating on a user computing device monitors a first display of video content transmitted over a network from a video server. The software application detects an action performed by a user of the user computing device on a first frame of the video content during display of the first frame. The action corresponds to the user identifying a portion of the displayed video frame upon which to overlay a visible marking on the first frame. The marking is associated with an object in the first frame. The software application iteratively derives a further position for the marking in a further frame of the video content, the further position associated with the location of the object in the further frame. The software application generates metadata associated with the user and transmits the metadata to a metadata sever.

Abstract: Disclosed herein are methods and systems for displaying content-related metainformation, and display of content-related tag metainformation in correspondence, and synchronized, e.g., time-based, with the content, so as to display content together with related time-tag information.

Abstract: Subject matter disclosed herein relates to electronic image object or logo recognition.

Abstract: Subject matter disclosed herein relates to image object recognition or retrieval.

Abstract: Techniques are described herein for selecting representative images for video items using a trained machine learning engine. A training set is fed to a machine learning engine. The training set includes, for each image in the training set, input parameter values and an externally-generated score. Once a machine learning model has been generated based on the training set, input parameters for unscored images are fed to the trained machine learning engine. Based on the machine learning model, the trained machine learning engine generates scores for the images. To select a representative image for a particular video item, candidate images for that particular video item may be ranked based on their scores, and the candidate image with the top score may be selected as the representative image for the video item.

Abstract: Disclosed herein are aspects associated with contextual, or related, media enrichment presentation item of a media object served via the internet. A request to annotate a media object in connection with the media object's presentation is received, and a media object identifier and a profile identifier are obtained. The media object's information is retrieved using the media object identifier, and a profile is retrieved using the profile identifier. A response including one or more references to one or more media enrichment presentation items is transmitted, each reference to a media enrichment presentation item comprising information for use in retrieving the media enrichment presentation item for presentation in connection with presentation of the media object.

Abstract: Disclosed herein is an ability to create segments of content, such as video, audio, multimedia, image, animation, interactive, etc. content, to associate metadata to each segment created, and to generate a summary of content comprising one or more segments using the metadata associated with the content segments. One or more acticons represented as icons in a user interface displayable to a user can be used to indicate a starting and/or ending point for a content segment. An acticon can represent an occurrence in the content, or a reaction of a user experiencing the content, for example.

Abstract: Techniques are described to increase the diversity or focus of image search results. A user submits an original query to search for images. A server generates a first results set by executing the original query using metadata associated with each image. The server selects, from the first results set, a specified number of results ranked highest and generates a list of terms from the metadata of each of the results selected. The terms may be only the tags of the results. The server generates an updated query using terms in the list that may be weighted based on the frequency of the term in the list or include only a specified number of the highest occurring terms in the list. The server generates a second results set by executing the updated query using metadata associated with each image. The second results set is then stored and displayed to the user.