Abstract: In some embodiments, a method for incident ticket management includes receiving input data having information relating to one or more incidents, classifying each incident of the one or more incidents, escalating an incident of the one or more incidents based, at least in part, on at least one of the classification of each incident or a prioritization level associated with each incident and generating a resolution for the incident, the resolution including at least one of a solution to the incident or an assignment of the incident to a related team.

Abstract: A method for facilitating delivery of streaming content from content servers includes: receiving, from a user device at one or more computing devices remote with respect to the content servers, a first request for a first set of data segments of the content, identifying, based on information on performance parameters of the content servers, a first content server for delivering the first set, transmitting, to the user device, a first response including a list of uniform resource locators (URLs) for obtaining the first set, receiving, from a user device, a second request for a second set of data segments of the content, where the first and second requests are received within a same content playback session, identifying, based on the information, a second content server for delivering the second set, and transmitting, to the user device, a second response including a second list of URLs for obtaining the second set.

Abstract: The disclosed computer-implemented method may include accessing media segments that correspond to respective media items. At least one of the media segments may be divided into discrete video shots. The method may also include matching the discrete video shots in the media segments to corresponding video shots in the corresponding media items according to various matching factors. The method may further include generating a relative similarity score between the matched video shots in the media segments and the corresponding video shots in the media items, and training a machine learning model to automatically identify video shots in the media items according to the generated relative similarity score between matched video shots. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A method for facilitating delivery of streaming content from content servers includes: receiving, from a user device at one or more computing devices remote with respect to the content servers, a first request for a first set of data segments of the content, identifying, based on information on performance parameters of the content servers, a first content server for delivering the first set, transmitting, to the user device, a first response including a list of uniform resource locators (URLs) for obtaining the first set, receiving, from a user device, a second request for a second set of data segments of the content, where the first and second requests are received within a same content playback session, identifying, based on the information, a second content server for delivering the second set, and transmitting, to the user device, a second response including a second list of URLs for obtaining the second set.

Abstract: Methods, systems, and computer-program products for creating a model for predicting error propensity are disclosed. The method includes receiving input data including historical know your client (KYC) audit cases and associated error data, extracting KYC audit attributes from the historical KYC audit cases and associated error data, receiving analyst attributes, constructing a dataset from input variables including at least one of the KYC audit attributes or the analyst attributes, creating at least one artificial intelligence (AI) model based, at least in part, on the dataset, and selecting one of the at least one AI model for predicting error propensity for a customer due diligence process.

Abstract: Methods, systems, and computer-program products for ticket management. The methods, systems, and computer-program products include receiving input data having information relating to one or more incidents, classifying each incident of the one or more incidents, escalating an incident of the one or more incidents based, at least in part, on at least one of the classification of each incident or a prioritization level associated with each incident and generating a resolution for the incident, the resolution including at least one of a solution to the incident or an assignment of the incident to a related team.

Abstract: The disclosed computer-implemented method may include accessing media segments that correspond to respective media items. At least one of the media segments may be divided into discrete video shots. The method may also include matching the discrete video shots in the media segments to corresponding video shots in the corresponding media items according to various matching factors. The method may further include generating a relative similarity score between the matched video shots in the media segments and the corresponding video shots in the media items, and training a machine learning model to automatically identify video shots in the media items according to the generated relative similarity score between matched video shots. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A neural network system that is trained to identify one or more portions of a media title where synchronization errors are likely to be present. The neural network system is trained based on a first set of media titles where synchronization errors are present and a second set of media titles where synchronization errors are absent. The second set of media titles can be generated by introducing synchronization errors into a set of media titles that otherwise lack synchronization errors. Via training, the neural network system learns to identify specific visual features included in one or more video frames and corresponding audio features that should be played back in synchrony with the associated visual features. Accordingly, when presented with a media title that includes synchronization errors, the neural network can indicate the specific frames where synchronization errors are likely to be present.

Abstract: Dynamically re-locating transcoding processes of live content data is described herein. In an example, a computer system causes a first server to execute a first transcode process on a first portion of live stream content. A first output of executing the first transcode process includes first transcoded content. The computer system determines a transcode capacity of one or more servers. The computer system determines that transcoding the live stream content is to be moved to a second server based at least in part on the transcode capacity and a transcode optimization parameter. The computer system causes the second server to execute a second transcode process on a second portion of the live stream content. The second transcode process is equivalent to the first transcode process. A second output of executing the second transcode process includes second transcoded content.

Abstract: A first portion of a video item is transmitted using a first encoding profile. The first encoding profile is a first set of one or more first video representations of the video item having first image quality characteristics. Viewer information including at least one of a viewer quantity for the video item or a viewer playback platform distribution for the video item is receiving during transmitting the video item. A second encoding profile is determined based on the at least one of the viewer quantity or the viewer playback platform distribution. The second encoding profile is a second set of one or more second video representations of the video item having second image quality characteristics. The second image quality characteristics include a selected image quality characteristic that is not included in the first image quality characteristics. A second portion of the video item is transmitted using the second encoding profile.

Abstract: Techniques for migrating live broadcast streams from one data center to another data center are described herein. A data center from a plurality of data centers may be determined to transfer live streaming video content from a first data center to the data center. A separate live streaming session for each live streaming session being broadcast by the first data center may be generated by communicating with an ingest service server that receives media content used to generate each separate live streaming session and the live streaming session. Each separate live streaming session may be routed to the candidate data center. A determination that each separate live streaming session of the candidate data center is synchronized with each live streaming session of the first data center may be made. An update to a mapping of IP addresses to redirect requests from the first data center to the candidate data center.

Abstract: A video streaming service may include a segmentation component that provides segment metadata that indicates starting locations for a plurality of segments of video content that are produced during transcoding of the video content. The segment metadata may be sent to a first transcode server and a second transcode server to allow alignment of segmentations between the first transcode server and the second transcode server. This may allow transcoding of the video content to be seamlessly switched from the first transcode server to the second transcode server, during transmission of the video content, without causing interruption of playback. The first transcode server and the second transcode server may also operate in parallel to produce different versions of the video content, such as versions encoded using different codecs. A video player may seamlessly switch between these different codec versions without causing interruption of playback.

Abstract: The disclosed computer-implemented method may include accessing media segments that correspond to respective media items. At least one of the media segments may be divided into discrete video shots. The method may also include matching the discrete video shots in the media segments to corresponding video shots in the corresponding media items according to various matching factors. The method may further include generating a relative similarity score between the matched video shots in the media segments and the corresponding video shots in the media items, and training a machine learning model to automatically identify video shots in the media items according to the generated relative similarity score between matched video shots. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include accessing media segments that correspond to respective media items. At least one of the media segments may be divided into discrete video shots. The method may also include matching the discrete video shots in the media segments to corresponding video shots in the corresponding media items according to various matching factors. The method may further include generating a relative similarity score between the matched video shots in the media segments and the corresponding video shots in the media items, and training a machine learning model to automatically identify video shots in the media items according to the generated relative similarity score between matched video shots. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include generating a three-dimensional (3D) feature map for a digital image using a fully convolutional network (FCN). The 3D feature map may be configured to identify features of the digital image and identify an image region for each identified feature. The method may also include generating a region composition graph that includes the identified features and image regions. The region composition graph may be configured to model mutual dependencies between features of the 3D feature map. The method may further include performing a graph convolution on the region composition graph to determine a feature aesthetic value for each node according to the weightings in the node's weighted connecting segments, and calculating a weighted average for each node's feature aesthetic value to provide a combined level of aesthetic appeal for the digital image. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A content production system and content management system are provided to produce and manage content with multiple components, such as multi-track content with any number or combination of video tracks, audio tracks, and the like. Individual tracks of the content are selectively included in, or excluded from, different versions of the content such that different versions of the multi-track content may be generated from different subsets of the individual tracks. For example, one version of content may be generated for real-time streaming and may include a first subset or combination of the multiple tracks. Another version of the content may be generated for storage and future on-demand access, and may include a second subset or combination of the multiple tracks. Selection of the particular tracks of the multi-track content to be included in each version may be based on varying types of authorization associated with the individual tracks.

Abstract: The disclosed computer-implemented method may include accessing media segments that correspond to respective media items. At least one of the media segments may be divided into discrete video shots. The method may also include matching the discrete video shots in the media segments to corresponding video shots in the corresponding media items according to various matching factors. The method may further include generating a relative similarity score between the matched video shots in the media segments and the corresponding video shots in the media items, and training a machine learning model to automatically identify video shots in the media items according to the generated relative similarity score between matched video shots. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include accessing media segments that correspond to respective media items. At least one of the media segments may be divided into discrete video shots. The method may also include matching the discrete video shots in the media segments to corresponding video shots in the corresponding media items according to various matching factors. The method may further include generating a relative similarity score between the matched video shots in the media segments and the corresponding video shots in the media items, and training a machine learning model to automatically identify video shots in the media items according to the generated relative similarity score between matched video shots. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: In various embodiments, a defective pixel detection application automatically detects defective pixels in video content. In operation, the defective pixel detection application computes a first set of pixel intensity gradients based on a first frame of video content and a first neighborhood of pixels associated with a first pixel. The defective pixel detection application also computes a second set of pixel intensity gradients based on the first frame and a second neighborhood of pixels associated with the first pixel. Subsequently, the defective pixel detection application computes a statistical distance between the first set of pixel intensity gradients and the second set of pixel intensity gradients. The defective pixel detection application then determines that the first pixel is defective based on the statistical distance.

Abstract: The disclosed computer-implemented method may include accessing media segments that correspond to respective media items. At least one of the media segments may be divided into discrete video shots. The method may also include matching the discrete video shots in the media segments to corresponding video shots in the corresponding media items according to various matching factors. The method may further include generating a relative similarity score between the matched video shots in the media segments and the corresponding video shots in the media items, and training a machine learning model to automatically identify video shots in the media items according to the generated relative similarity score between matched video shots. Various other methods, systems, and computer-readable media are also disclosed.