Abstract: The following detailed description presents a method for supporting Digital Rights Management (DRM) in real-time streaming. The proposed method attains real-time constraints by reusing the original encoded real-time stream as the carrier of the encrypted data. A system is also specified for implementing the described method on a real-time streaming architecture.

Abstract: A system and method for predicting next pitch are disclosed herein. A computing system retrieves pitch-by-pitch information for a plurality of events and game context information associated with each pitch in the pitch-by-pitch information. The computing system converts the pitch-by-pitch information and the game context information into a plurality of graph-based representation. A graph neural network learns to generate a pitch type prediction for each pitch based on the plurality of graph-based representations. The computing system generates a trained graph neural network based on the learning. The computing system receives a current graph-based representation of current pitch-by-pitch information for a current pitcher and current game context information. The computing system predicts, via the trained graph neural network, a pitch type for the next pitch to be delivered from the current pitcher.

Abstract: A system and method of generating trackable frames from a broadcast video feed are provided herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system generates a set of frames for classification using a principal component analysis model. The set of frames are a subset of the plurality of video frames. The computing system partitions each frame of the set of frames into a plurality of clusters. The computing system classifies each frame of the plurality of frames as trackable or untrackable. Trackable frames capture a unified view of the sporting event. The computing system compares each cluster to a predetermined threshold to determine whether each cluster comprises at least a threshold number of trackable frames. The computing system classifies each cluster that includes at least the threshold number of trackable frames as trackable.

Abstract: A customized highlight sequence depicting multiple events, and based on a user's personal characteristics, interests, and/or preferences, is generated and presented. The events can be sporting events, entertainment events, and/or the like. For example, in the context of sporting events, a determination is made as to what types of sports, teams, leagues, players, plays, and/or the like are of interest to the user. In at least one embodiment, the amount of time available to the user can be obtained, so that the length of the highlight sequence can be tailored to the available time. A customized highlight sequence is then generated and presented, containing those specific portions of the sporting events that are likely to be of interest, arranged in a manner that is likely to be entertaining to the user and comports with the time restrictions.

Abstract: The present embodiments relate to generating various predictive overlays over a video of a tennis match. Tracking data of a tennis match can be processed to derive insights into the tennis match. For example, the tracking data can indicate a current state of the match and a likelihood of a ball being returned to various regions of the court. This can be used to create a first overlay that can be added over a video of the tennis match. Further, player rankings based on various categories (e.g., technical, tactical, mental, physical) can be generated to provide insights into skills of each player relative to other ranked players. Additional insights, such as a forecast predicting a likelihood of winning an upcoming match or tournament can also be generated. A player card can be generated using such information to provide various aspects of a player skill onto a single output.

Abstract: A computing system retrieves tracking data from a data store. The computing system converts the tracking data into a plurality of graph-based representations. The prediction engine learns to model defensive behavior based on the plurality of graph-based representations. The computing system generates a trained prediction engine based on the learnings. The computing system receives target tracking data for a target event. The target tracking data includes a plurality of target frames. The computing system converts the target tracking data to a plurality of target graph-based representations. The computing system models, via the trained graph neural network, defensive behavior of a team in the target event based on plurality of graph-based representations.

Abstract: Systems and methods related to performing dynamic infrastructure adaptation for a real-time or near real-time video streaming environment, comprising collecting telemetry data of a user device during an observation window of an active video streaming session, predicting, based on the telemetry data, an estimated duration of the active video streaming session, classifying the active video streaming session as a long-lived session based on the estimated duration, wherein the long-lived session is greater than a session threshold, determining, by an adaptation engine and based on classifying the active video streaming session as a long-lived session and one or more operational constraints, that a mid-session infrastructure adaptation meets a predicted threshold to improve performance or reduce computational cost, and executing the mid-session infrastructure adaptation based on the determining.

Abstract: A computing system receives live event data corresponding to a live game. The live event data includes events occurring within the live game. The computing system analyzes the live event data to identify a potential error in the live event data. The computing system generates a ticket corresponding to the potential error flagged in the live event data. The computing system assigns the ticket to a first quality assurance agent to resolve. The computing system receives an indication that the ticket has been reviewed by the first quality assurance agent. the computing system provides the reviewed event data to an end user.

Abstract: Systems and methods related to a computing system for generating query responses, the computing system configured to generate a tracking data feed based on a broadcast feed of a sporting occasion, receive a query corresponding to an event in the sporting occasion, receive event data related to the sporting occasion, translate the query into one or more tokens, and, based on the tracking data feed, the event data, and the one or more tokens, determine a response to the query, wherein the response is based on a likelihood of the event occurring.

Abstract: A system and method for predicting next pitch are disclosed herein. A computing system retrieves pitch-by-pitch information for a plurality of events and game context information associated with each pitch in the pitch-by-pitch information. The computing system converts the pitch-by-pitch information and the game context information into a plurality of graph-based representation. A graph neural network learns to generate a pitch type prediction for each pitch based on the plurality of graph-based representations. The computing system generates a trained graph neural network based on the learning. The computing system receives a current graph-based representation of current pitch-by-pitch information for a current pitcher and current game context information. The computing system predicts, via the trained graph neural network, a pitch type for the next pitch to be delivered from the current pitcher.

Abstract: Systems and methods for processing images, including receiving, by a computing system, an image for processing, the image comprising at least a player of interest, processing, by the computing system, the image according to an image processing workflow, generating a first copy of the image and naming the first copy of the image based on a first identifier of the at least two identifiers associated with the image, generating a second copy of the image and naming the second copy of the image based on a second identifier of the at least two identifiers associated with the image, saving the first copy of the image to a first database dedicated to identifiers of a first type associated with the first identifier, and saving the second copy of the image to a second database dedicated to identifiers of a second type associated with the second identifier.

Abstract: A customized highlight sequence depicting multiple events, and based on a user's personal characteristics, interests, and/or preferences, is generated and presented. The events can be sporting events, entertainment events, and/or the like. For example, in the context of sporting events, a determination is made as to what types of sports, teams, leagues, players, plays, and/or the like are of interest to the user. In at least one embodiment, the amount of time available to the user can be obtained, so that the length of the highlight sequence can be tailored to the available time. A customized highlight sequence is then generated and presented, containing those specific portions of the sporting events that are likely to be of interest, arranged in a manner that is likely to be entertaining to the user and comports with the time restrictions.

Abstract: A method of generating a dynamic rating for an entity and rearranging an icon associated with the entity on a graphical user interface (GUI) of a computer system. The method includes: receiving a first set of data associated with one or more parameters for the entity; calculating a plurality of ability scores based on the first set of data; generating a first rating based on each ability score of the plurality of ability scores; dynamically adjusting the plurality of ability scores based on receiving a second set of data associated with the one or more parameters for the entity; updating the first rating based on the adjusted plurality of ability scores; automatically rearranging the icon to a position above or below a current position of the icon on the GUI based on the updated first rating.

Abstract: Customized highlight shows for sporting events, entertainment events, and/or the like, having a narrative component, are generated and presented. The events can be sporting events, entertainment events, and/or the like. For example, in the context of sporting events, a determination is made as to what types of sports, teams, leagues, players, plays, and/or the like are of interest to the user. A customized highlight show is then generated and presented, containing those specific portions of the sporting events that are likely to be of interest, arranged in a manner that is likely to be entertaining and interesting to the user and that presents a cohesive narrative.

Abstract: The following detailed description presents a method for supporting Digital Rights Management (DRM) in real-time streaming. The proposed method attains real-time constraints by reusing the original encoded real-time stream as the carrier of the encrypted data. A system is also specified for implementing the described method on a real-time streaming architecture.

Abstract: A method for generating video clips of a sporting occasion by implementing a machine learning model, the method including: receiving a video feed of a sporting occasion; receiving a plurality of event data objects related to the sporting occasion, each of the event data objects indicating an action in the sporting occasion and including corresponding metadata and timestamps; determining, based on the plurality of event data objects, that a trigger event occurred, the trigger event being a predefined or dynamically determined action in the sporting occasion; determining, using a machine learning model, a qualifier associated with the trigger event; and generating a video clip of the trigger event from the video of the sporting occasion, the video clip being generated based on the determined qualifier.

Abstract: A virtual system includes a memory for storing instructions and processors configured to execute operations. The operations include receiving a request from a user in a closed-loop system for an online transaction, wherein the transaction includes transferring a fund from a closed-loop account to a virtual account. The operations include generating the virtual account and a virtual identifier associated with the virtual account, wherein the virtual account is further associated with a primary identifier. The operations include blocking the primary identifier for the transaction to prevent the primary identifier's use for the fund transfer. The operations include processing the transaction by transferring the fund using the virtual identifier, wherein the expense using the virtual identifier is lower than using the primary identifier.

Abstract: A system and method of generating a player tracking prediction are described herein. A computing system retrieves a broadcast video feed for a sporting event. The computing system segments the broadcast video feed into a unified view. The computing system generates a plurality of data sets based on the plurality of trackable frames. The computing system calibrates a camera associated with each trackable frame based on the body pose information. The computing system generates a plurality of sets of short tracklets based on the plurality of trackable frames and the body pose information. The computing system connects each set of short tracklets by generating a motion field vector for each player in the plurality of trackable frames. The computing system predicts a future motion of a player based on the player's motion field vector using a neural network.

Abstract: A method of generating an outcome for a sporting event is disclosed herein. A computing system retrieves tracking data from a data store. The computing system generates a predictive model using a deep neural network. The one or more neural networks of the deep neural network generates one or more embeddings comprising team-specific information and agent-specific information based on the tracking data. The computing system selects, from the tracking data, one or more features related to a current context of the sporting event. The computing system learns, by the deep neural network, one or more likely outcomes of one or more sporting events. The computing system receives a pre-match lineup for the sporting event. The computing system generates, via the predictive model, a likely outcome of the sporting event based on historical information of each agent for the home team, each agent for the away team, and team-specific features.

Abstract: A computing system receives an image for processing. The image includes at least a player of interest. The computing system executes an image processing workflow for automatically processing the image in accordance with standards set by an organization associated with the computing system. The executing includes removing, via a background module, background pixels from the image. The executing further includes, after removing the background pixels, resizing, via a resizing module, the image to a set size defined by the organization. The executing further includes, after resizing the image, renaming, via a renaming module, the image to a name format defined by the organization. After executing the image processing workflow, the computing system saves the image to a database for retrieval. The removing, the resizing, and the renaming are performed without user intervention.