Abstract: In various embodiments, a broker application automatically allocates tasks to application programming interfaces (APIs) in microservice architectures. After receiving a task from a client application, the broker application performs operation(s) on content associated with the task to compute predicted performance data for multiple APIs. The broker application then determines that a first API included in the APIs should process the first task based on the predicted performance data. The broker application transmits an API request associated with the first task to the first API for processing. After receiving a result associated with the first task from the first API, the client application performs operation(s) based on the result.

Abstract: There is provided a media content ontology system and method. The system includes a computing platform having a processor and a memory, a content genome database stored in the memory, and ontology software for execution by the processor. The ontology software is configured to map a media content asset to first and second content classifications based on respective first and second data, and to generate first and second content genome database entries associating the media content asset with other, correspondingly mapped, media content assets. In addition, the ontology software is configured to cross-index the first and second content genome database entries to enable identification of each of the first and second content classifications and the media content asset based on any one of the first or second content classifications or the media content asset.

Abstract: Embodiments provide techniques for distributing supplemental content based on content entities within video content. Embodiments include analyzing video data to identify a known content entity within two or more frames of the video data. For each of the two or more frames, a region of pixels within the respective frame is determined that corresponds to the known content entity. Embodiments further include determining supplemental content corresponding to the known content entity. A watermark is embedded at a first position within the video data, such that the watermark corresponds to an identifier associated with the determined supplemental content. Upon receiving a message specifying the identifier, embodiments include transmitting the supplement content to a client device for output together with the video data.

Abstract: Various embodiments of the invention disclosed herein provide techniques for automatically exposing 3D production assets to an editorial workstation in a content creation pipeline. A production asset management system transmits, to an editorial workstation, a first content library that includes first metadata associated with a 3D production asset that is included in an editorial cut. The first metadata allows a user to see what versions of the 3D production asset are available for incorporation into the editorial cut. The production asset management system retrieves second metadata associated with a second version of the 3D production asset from a production database. The production asset management system retrieves, based on the second metadata, the second version of the 3D production asset from the production database. The production asset management system transmits the second version of the 3D production asset to the editorial workstation for incorporation into the editorial cut.

Abstract: Various embodiments of the invention disclosed herein provide techniques for automatically displaying and providing electronic feedback about a 3D production asset. A client device executing a software application receives an asset data bundle associated with the 3D production asset. The client device generates a customized user interface based on at least one aspect of the asset data bundle. The client device displays the 3D production asset via the customized user interface. The client device receives an input associated with the 3D production asset via the customized user interface. The client device causes the input to be transmitted to at least one of a media content server and a production database.

Abstract: According to one implementation, a system for automating content annotation includes a computing platform having a hardware processor and a system memory storing an automation training software code. The hardware processor executes the automation training software code to initially train a content annotation engine using labeled content, test the content annotation engine using a first test set of content obtained from a training database, and receive corrections to a first automatically annotated content set resulting from the test. The hardware processor further executes the automation training software code to further train the content annotation engine based on the corrections, determine one or more prioritization criteria for selecting a second test set of content for testing the content annotation engine based on the statistics relating to the first automatically annotated content, and select the second test set of content from the training database based on the prioritization criteria.

Abstract: There is provided a system including a processor and a memory, storing an asset-data synthesis unit having a data translation module and a data integration module. The processor executes the asset-data synthesis unit to receive a first and a second domain specific asset description data being expressed using different descriptive vocabularies, translate the first and second domain specific asset description data to a common descriptive vocabulary to produce respective first and second common format asset description data, inferentially identify based on probabilistic inference that the first and second common format asset description data describe a same asset, integrate the first and second common format asset description data to produce an integrated asset description data describing the same asset, and generate a synthesized data describing the same asset and linking the integrated asset description data and the first and second domain specific asset description data.

Abstract: A content recommendation system includes a computing platform having a hardware processor and a system memory storing a content surfacing software code providing a user interface. The hardware processor executes the content surfacing software code to receive an initiation signal identifying a user, and, in response, identify content items as desirable content items to the user based on the user and metadata describing each of the desirable content items. In addition, the content surfacing software code generates a networked map of nodes corresponding respectively to the desirable content items, where the distances between nodes are based on the similarity of the metadata describing the desirable content items corresponding to the nodes. The content surfacing software code further outputs the networked map for display to the user via the user interface, the nodes being displayed as thumbnail images depicting the desirable content and selectable by the user.

Abstract: A media content analysis system includes a computing platform having a hardware processor and a system memory storing a content assessment software code. The hardware processor executes the content assessment software code to, for each consumer of media content, receive usage data including timecode information, advertising consumption, and behavioral information corresponding to use of the media content by the consumer, and assess an engagement level for each of multiple timecode intervals of the media content based on an aggregate of the usage data. The content assessment software code also obtains metadata describing features presented by the media content during each timecode interval, for each timecode interval concatenates the engagement level with the metadata to produce an aggregate consumer engagement profile for the media content, and outputs an engagement visualization map of the media content based on the aggregate consumer engagement profile for rendering on a display.

Abstract: One embodiment provides a monopole for a camera, including: a pole of length sufficient for two handed operation; an offset arrangement attached to an end of the pole, the offset arrangement comprising a first element connected at an angle to the end of the pole and a second element connected to the first element; the first element rotating about a first axis with respect to the end of the pole; the second element rotating about a second axis with respect to the first element; a camera mount attached to the second element, wherein the camera mount rotates about a third axis with respect to the second element; and at least one motor aligned with the first, the second or the third axis and imparting movement to the camera mount with respect to the pole in at least one degree of freedom selected from the group consisting of tilt, pan and roll. Other aspects are described and claimed.

Abstract: In various embodiments, a broker application automatically allocates tasks to application programming interfaces (APIs) in microservice architectures. After receiving a task from a client application, the broker application performs operation(s) on content associated with the task to compute predicted performance data for multiple APIs. The broker application then determines that a first API included in the APIs should process the first task based on the predicted performance data. The broker application transmits an API request associated with the first task to the first API for processing. After receiving a result associated with the first task from the first API, the client application performs operation(s) based on the result.. Advantageously, because the broker application automatically allocates the first task to the first API based on the content, time and resource inefficiencies are reduced compared to prior art approaches that indiscriminately allocate tasks to APIs.

Abstract: Systems, methods, and articles of manufacture to perform an operation comprising processing, by a machine learning (ML) algorithm and a ML model, a plurality of images in a first dataset, wherein the ML model was generated based on a plurality of images in a training dataset, receiving user input reviewing a respective set of tags applied to each image in the first data set as a result of the processing, identifying, based on a first confusion matrix generated based on the user input and the sets of tags applied to the images in the first data set, a first labeling error in the training dataset, determining a type of the first labeling error based on a second confusion matrix, and modifying the training dataset based on the determined type of the first labeling error.

Abstract: According to one implementation, a content classification system includes a computing platform having a hardware processor and a system memory storing a video asset classification software code. The hardware processor executes the video asset classification software code to receive video clips depicting video assets and each including images and annotation metadata, and to preliminarily classify the images with one or more of the video assets to produce image clusters. The hardware processor further executes the video asset classification software code to identify key features data corresponding respectively to each image cluster, to segregate the image clusters into image super-clusters based on the key feature data, and to uniquely identify each of at least some of the image super-clusters with one of the video assets.

Abstract: According to one implementation, a media content annotation system includes a computing platform having a hardware processor and a system memory storing a software code. The hardware processor executes the software code to receive a first version of media content and a second version of the media content altered with respect to the first version, and to map each of multiple segments of the first version of the media content to a corresponding one segment of the second version of the media content. The software code further aligns each of the segments of the first version of the media content with its corresponding one segment of the second version of the media content, and utilizes metadata associated with each of at least some of the segments of the first version of the media content to annotate its corresponding one segment of the second version of the media content.

Abstract: There is provided a system including a non-transitory memory storing a media library including an ordered plurality of media contents, each including a plurality of attribute tags, and a hardware processor configured to provide a user interface for display on a user device for navigating the media contents, receive a user input from the user device for playing one or more of the ordered media contents based on a first attribute tag of the plurality of attribute tags, stream a first portion of a first media content to the user device, based on the first attribute tag of the media content selected by the user input, and stream a second portion of a second media content to the user device following the first portion of the first media content, based on the first attribute tag of the media content selected by the user input.

Abstract: According to one implementation, a media content annotation system includes a computing platform including a hardware processor and a system memory storing a model-driven annotation software code. The hardware processor executes the model-driven annotation software code to receive media content for annotation, identify a data model corresponding to the media content, and determine a workflow for annotating the media content based on the data model, the workflow including multiple tasks. The hardware processor further executes the model-driven annotation software code to identify one or more annotation contributors for performing the tasks included in the workflow, distribute the tasks to the one or more annotation contributors, receive inputs from the one or more contributors responsive to at least some of the tasks, and generate an annotation for the media content based on the inputs.

Abstract: Embodiments provide techniques for distributing supplemental content based on content entities within video content. Embodiments include analyzing video data to identify a known content entity within two or more frames of the video data. For each of the two or more frames, a region of pixels within the respective frame is determined that corresponds to the known content entity. Embodiments further include determining supplemental content corresponding to the known content entity. A watermark is embedded at a first position within the video data, such that the watermark corresponds to an identifier associated with the determined supplemental content. Upon receiving a message specifying the identifier, embodiments include transmitting the supplement content to a client device for output together with the video data.

Abstract: A media content tagging system includes a computing platform having a hardware processor, and a system memory storing a tag selector software code configured to receive media content having segments, each segment including multiple content elements each associated with metadata tags having respective pre-computed confidence scores. For each content element, the tag selector software code assigns each of the metadata tags to at least one tag group, determines a confidence score for each tag group based on the pre-computed confidence scores of its assigned metadata tags, discards tag groups having less than a minimum number of assigned metadata tags, and filters the reduced number of tag groups based on the second confidence score to identify a further reduced number of tag groups. The tag selector software code then selects at least one representative tag group for a segment from among the further reduced number of tag groups.

Abstract: Embodiments provide techniques for distributing supplemental content based on content entities within video content. Embodiments include receiving video data containing an embedded watermark at a first position within the video data. The embedded watermark is detected at the first position within the video data. Embodiments also include transmitting, to a remote content server, a message specifying a time stamp corresponding to the first position within the video data. In response to transmitting the message, supplemental content corresponding to a content entity depicted within the video content at the first position within the video content is received from the remote content server. Embodiments also include outputting the video data for display together with at least an indication of the supplemental content.

Abstract: Embodiments provide techniques for distributing supplemental content based on content entities within video content. Embodiments include analyzing video data to identify a known content entity within two or more frames of the video data. For each of the two or more frames, a region of pixels within the respective frame is determined that corresponds to the known content entity. Embodiments further include determining supplemental content corresponding to the known content entity. A watermark is embedded at a first position within the video data, such that the watermark corresponds to an identifier associated with the determined supplemental content. Upon receiving a message specifying the identifier, embodiments include transmitting the supplement content to a client device for output together with the video data.