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.
G06Q 20/34 - Payment architectures, schemes or protocols characterised by the use of specific devices using cards, e.g. integrated circuit [IC] cards or magnetic cards
2.
SYSTEMS AND METHODS FOR IMPLEMENTING MACHINE LEARNING FOR VIDEO CLIPPING
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.
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.
G06Q 20/10 - Payment architectures specially adapted for electronic funds transfer [EFT] systemsPayment architectures specially adapted for home banking systems
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
G06Q 40/02 - Banking, e.g. interest calculation or account maintenance
4.
SYSTEMS AND METHODS FOR IMPLEMENTING MACHINE LEARNING FOR VIDEO CLIPPING
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.
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.
G06F 18/2135 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
G06F 18/22 - Matching criteria, e.g. proximity measures
G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
G06T 7/70 - Determining position or orientation of objects or cameras
G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
G06T 7/80 - Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 20/40 - ScenesScene-specific elements in video content
G06V 40/20 - Movements or behaviour, e.g. gesture recognition
H04N 21/44 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs
6.
SYSTEMS AND METHODS FOR AUTOMATED TRANSFORMATION OF SPORTS DATA
A method including receiving video data including a plurality of video frames captured during a sporting occasion, wherein each frame of the plurality of video frames includes data corresponding to one or more agents. The method including receiving event data associated with the video data. The method including processing the plurality of video frames and the event data to generate imputed tracking data, wherein the imputed tracking data includes positional data and movement data for each of the one or more agents. The method including determining one or more metrics based on the imputed tracking data, wherein the one or more metrics includes at least one of a pass option, a pressure, a line detection, and a marking. The method including determining an output based on the one or more metrics for the one or more agents.
G06V 20/40 - ScenesScene-specific elements in video content
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 10/62 - Extraction of image or video features relating to a temporal dimension, e.g. time-based feature extractionPattern tracking
7.
SYSTEMS AND METHODS FOR AUTOMATED TRANSFORMATION OF SPORTS DATA
A method including receiving video data including a plurality of video frames captured during a sporting occasion, wherein each frame of the plurality of video frames includes data corresponding to one or more agents. The method including receiving event data associated with the video data. The method including processing the plurality of video frames and the event data to generate imputed tracking data, wherein the imputed tracking data includes positional data and movement data for each of the one or more agents. The method including determining one or more metrics based on the imputed tracking data, wherein the one or more metrics includes at least one of a pass option, a pressure, a line detection, and a marking. The method including determining an output based on the one or more metrics for the one or more agents.
A system interconnects multiple client devices over a network. A local group of the client devices is located at a live event and a remote group is located remote from the live event. Each client device of the local group is a potential source of a live stream which, when rendered by another client device, causes display of a vantage point of the live event. The system dynamically updates a subscription list to include an indication of any client device of the local group that is actively live streaming and remove an indication of any client device of the local group that terminated live streaming. The system can enable selective access to any live streams of any client device of the updated subscription list and disable access to any live streams of any client device removed from the updated subscription list.
A method including receiving a first data set. The method including comparing the first data set to one or more template plays. The method including determining a representative play in response to comparing the first data set to the one or more template plays. The method including receiving at least one player performance data set. The method including determining a threat score associated with the representative play based on the at least one player performance data set for each of a plurality of players associated with the first data set. The method including combining the at least one player performance data set and the threat score associated with the representative play. The method including generating a sports play graphic in response to combining the at least one player performance data set and the threat score associated with the representative play. The method including outputting the sports play graphic.
A method including receiving a first data set. The method including comparing the first data set to one or more template plays. The method including determining a representative play in response to comparing the first data set to the one or more template plays. The method including receiving at least one player performance data set. The method including determining a threat score associated with the representative play based on the at least one player performance data set for each of a plurality of players associated with the first data set. The method including combining the at least one player performance data set and the threat score associated with the representative play. The method including generating a sports play graphic in response to combining the at least one player performance data set and the threat score associated with the representative play. The method including outputting the sports play graphic.
A boundary of a highlight of audiovisual content depicting an event is identified. The audiovisual content may be a broadcast, such as a television broadcast of a sporting event. The highlight may be a segment of the audiovisual content deemed to be of particular interest. Audio data for the audiovisual content is stored, and the audio data is automatically analyzed to detect one or more audio events indicative of one or more occurrences to be included in the highlight. Each audio event may be a brief, high-energy audio burst such as the sound made by a tennis serve. A time index within the audiovisual content, before or after the audio event, may be designated as the boundary, which may be the beginning or end of the highlight.
G10L 25/51 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination
G10L 21/14 - Transforming into visible information by displaying frequency domain information
G10L 25/18 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
12.
SYSTEMS AND METHODS FOR IMPLEMENTING SPORTS TRACKING DATA
A computer implemented method for tracking one or more individuals during a sporting event, the method including: receiving, as an input, broadcast tracking data of a sporting event and labeled event data of the sporting event; performing multi-object tracking of one or more agents of the received broadcast tracking data to determine one or more vectors; inputting the labeled event data and one or more vectors into a diffusion model; and determining, using the diffusion model, one or more trajectory sequences for the one or more agents; and determining, an output, based on the one or more trajectory sequences for the one or more agents.
Systems and methods are disclosed for data object testing. One or more processors may receive a video data object (110). The one or more processors may receive one or more video specifications (132) from a database (125). The one or more processors may generate an on-demand cloud testing instance (130) having a subset of video analysis tools from a plurality of video analysis tools and computational resources determined based on the video data object (110) and the one or more video specifications (132). The one or more processors may perform, in the cloud testing instance (130), a compliance test on the video data object (110) based on the one or more video specifications (132), generate a compliance output (134) based on the compliance test, the compliance output (134) including instructions or parameters for automatic video editing of the video data object (110). The one or more processors may provide the compliance output (134) to a downstream component.
A boundary of a highlight of audiovisual content depicting an event is identified. The audiovisual content may be a broadcast, such as a television broadcast of a sporting event. The highlight may be a segment of the audiovisual content deemed to be of particular interest. Audio data for the audiovisual content is stored, and the audio data is automatically analyzed to detect one or more audio events indicative of one or more occurrences to be included in the highlight. Each audio event may be a brief, high-energy audio burst such as the sound made by a tennis serve. A time index within the audiovisual content, before or after the audio event, may be designated as the boundary, which may be the beginning or end of the highlight.
G10L 25/51 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination
G10L 21/14 - Transforming into visible information by displaying frequency domain information
G10L 25/18 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
15.
SYSTEMS AND METHODS FOR DATA OBJECT COMPLIANCE TESTING
Systems and methods are disclosed for data object testing. One or more processors may receive a video data object. The one or more processors may receive one or more video specifications from a database. The one or more processors may generate an on-demand cloud testing instance having a subset of video analysis tools from a plurality of video analysis tools and computational resources determined based on the video data object and the one or more video specifications. The one or more processors may perform, in the cloud testing instance, a compliance test on the video data object based on the one or more video specifications. generate a compliance output based on the compliance test, the compliance output including instructions or parameters for automatic video editing of the video data object. The one or more processors may provide the compliance output to a downstream component.
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
H04N 21/43 - Processing of content or additional data, e.g. demultiplexing additional data from a digital video streamElementary client operations, e.g. monitoring of home network or synchronizing decoder's clockClient middleware
H04N 21/472 - End-user interface for requesting content, additional data or servicesEnd-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification or for manipulating displayed content
The disclosed technology incorporates an in-situ-trained, personalized quality of experience model that utilizes contextual information from additional sensors to input into an adaptive bit rate method for live and real-time broadcasting. By being personalized, it adjusts to individual user variations. The in-situ training enables the technology to consider contextual data, allowing it to adapt to both current and anticipated future situations of any user. One aspect of this technology involves a system designed specifically to implement the described techniques within a streaming architecture.
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
H04L 65/752 - Media network packet handling adapting media to network capabilities
H04N 21/658 - Transmission by the client directed to the server
The disclosed technology incorporates an in-situ-trained, personalized quality of experience model that utilizes contextual information from additional sensors to input into an adaptive bit rate method for live and real-time broadcasting. By being personalized, it adjusts to individual user variations. The in-situ training enables the technology to consider contextual data, allowing it to adapt to both current and anticipated future situations of any user. One aspect of this technology involves a system designed specifically to implement the described techniques within a streaming architecture.
A computing system receives historical match data associated with a plurality of tennis players. The computing system generates player rankings. The player rankings include a player ranking for each tennis player of the plurality of tennis players based on the historical match data. The computing system receives information associated with a tennis tournament. The information includes a subset of tennis players in the tournament and a seeding of each tennis player in the subset of tennis players. The computing system generates initial predictions based on the information associated with the tournament and the player rankings. The computing system identifies a trigger event that causes an update to the initial predictions. Responsive to identifying the trigger event, the computing system generates an updated predictions based on in-match data. The in-match data includes a change to a score in a match of the tournament. The computing system outputs the updated predictions.
A63B 24/00 - Electric or electronic controls for exercising apparatus of groups
A63B 71/06 - Indicating or scoring devices for games or players
A63F 13/795 - Game security or game management aspects involving player-related data, e.g. identities, accounts, preferences or play histories for finding other playersGame security or game management aspects involving player-related data, e.g. identities, accounts, preferences or play histories for building a teamGame security or game management aspects involving player-related data, e.g. identities, accounts, preferences or play histories for providing a buddy list
A63F 13/798 - Game security or game management aspects involving player-related data, e.g. identities, accounts, preferences or play histories for assessing skills or for ranking players, e.g. for generating a hall of fame
A computer implemented method for tracking one or more individuals during a sporting event, the method including: receiving, as an input, broadcast tracking data of a sporting event and labeled event data of the sporting event; performing multi-object tracking of one or more agents of the received broadcast tracking data to determine one or more vectors; inputting the labeled event data and one or more vectors into a diffusion model; and determining, using the diffusion model, one or more trajectory sequences for the one or more agents; and determining, an output, based on the one or more trajectory sequences for the one or more agents.
Systems and methods for generating trajectories for one or more players during an event include receiving broadcast footage of a sporting event, determining tracking data of one or more players in the sporting event from the broadcast footage, the tracking data including one or more vectors, receiving event data of the sporting event, and inputting the one or more vectors and event data into a multimodal model including an event encoder and a tracking decoder. A linear layer of the multimodal model may be applied to the vectors and event data to tokenize the event data and vectors. A tensor representing a sequence of the event data and tracking data may be determined. Perturbed tracking data of the sporting event and the tensor may be input into a diffusion model. The diffusion model may generate one or more trajectories for the one or more players in the sporting event.
Systems and methods for generating trajectories for one or more players during an event include receiving broadcast footage of a sporting event, determining tracking data of one or more players in the sporting event from the broadcast footage, the tracking data including one or more vectors, receiving event data of the sporting event, and inputting the one or more vectors and event data into a multimodal model including an event encoder and a tracking decoder. A linear layer of the multimodal model may be applied to the vectors and event data to tokenize the event data and vectors. A tensor representing a sequence of the event data and tracking data may be determined. Perturbed tracking data of the sporting event and the tensor may be input into a diffusion model. The diffusion model may generate one or more trajectories for the one or more players in the sporting event.
Disclosed techniques relate to utilizing tracking data for predicting player ratings. In an example, a method for utilizing tracking data to predict a player rating includes receiving broadcast data for a plurality of games in a first league, the plurality of games including a first player, generating tracking data for each of the plurality of games, the tracking data comprising coordinates of player positions and ball positions for each frame of the broadcast data, receiving play-by-play data for each of the plurality of games, the play-by-play data describing events that occur within the plurality of games, merging the tracking data and play-by-play data to generate a set of input features, and predicting, based on the set of input features, a player rating for the first player, the player rating being indicative of a predicted level of performance in a second league.
A method for generating a probability for a first action of a sporting event by implementing a feature set, the method including: obtaining an initial set of data relating to the first action of a sporting event, the initial set of data including at least a position of a first player on a surface and a position of a target area on the surface; generating, by a machine learning model, an initial projected scoring probability based on the initial set of data; generating a feature set relating to the sporting event; and modifying, by the machine learning model, the initial projected scoring probability to an updated scoring probability using the feature set.
Disclosed techniques relate to utilizing tracking data for predicting player ratings. In an example, a method for utilizing tracking data to predict a player rating includes receiving broadcast data for a plurality of games in a first league, the plurality of games including a first player, generating tracking data for each of the plurality of games, the tracking data comprising coordinates of player positions and ball positions for each frame of the broadcast data, receiving play-by-play data for each of the plurality of games, the play-by-play data describing events that occur within the plurality of games, merging the tracking data and play-by-play data to generate a set of input features, and predicting, based on the set of input features, a player rating for the first player, the player rating being indicative of a predicted level of performance in a second league.
According to systems and techniques disclosed herein, a method for generating an interactive user interface using artificial intelligence models may include receiving one or more streams of event data (e.g., real-time or non-live event data) comprising a plurality of visual elements (e.g., real-time or non-live visual elements). The method may further include providing the plurality of visual elements to a computer vision artificial intelligence model trained to classify the plurality of visual elements and output object identifiers and a confidence score associated with each of the object identifiers. The method may further include receiving user input from the interactive user interface displayed on a user device. The user input may include a user query associated with a first object identifier of the object identifiers. The method may further include updating the interactive user interface with one or more interactive user elements associated with the first object identifier.
Examples disclosed herein may estimate locations of players not visible in a sporting broadcast video. A prediction model may be generated based on a training data set of in-venue tracking data that includes locations of all players at all times and the corresponding broadcast tracking data that may not necessarily contain the locations of all players at all times. The prediction model may be based on an algorithmic logic (e.g., a spline regression) or machine learning model (e.g., k-nearest neighbor, deep neural network). The generated predicted model may be used to estimate the unknown locations of players in broadcast tracking based on the known locations.
According to systems and techniques disclosed herein, a method for generating an interactive user interface using artificial intelligence models may include receiving one or more streams of real-time event data comprising a plurality of real-time visual elements. The method may further include providing the plurality of real-time visual elements to a computer vision artificial intelligence model trained to classify the plurality of real-time visual elements and output one or more object identifiers and a confidence score associated with each of the one or more object identifiers. The method may further include receiving user input from the interactive user interface displayed on a user device. The user input may include a user query associated with a first object identifier of the one or more object identifiers. The method may further include updating the interactive user interface with one or more interactive user elements associated with the first object identifier.
G06F 16/783 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
28.
SYSTEM AND METHOD FOR PREDICTING FINE-GRAINED ADVERSARIAL MULTI-AGENT MOTION
A system and method for predicting multi-agent locations is disclosed herein. A computing system retrieves tracking data from a data store. The computing system generates a predictive model using a conditional variational autoencoder. The conditional variational autoencoder learns one or more paths a subset of agents of the plurality of agents are likely to take. The computing system receives tracking data from a tracking system positioned remotely in a venue hosting a candidate sporting event. The computing system identifies one or more candidate agents for which to predict locations. The computing system infers, via the predictive model, one or more locations of the one or more candidate agents. The computing system generates a graphical representation of the one or more locations of the one or more candidate agents.
A computing system receives a plurality of game files corresponding to a plurality of games across a plurality of seasons. The computing system generates a prediction model configured to generate a possession value for an event. The computing system receives a target event, in real-time or near real-time, from a tracking system monitoring a target game. The computing system generates target features for the target event based on target event data associated with the target event. The computing system generates, via the prediction model, a target possession value for the target event based on the target event data and the target features. The target possession value represents a likelihood that a team with possession will score within a following x-seconds after the target event.
A computing system receives a broadcast video stream of a game. A codec module of the computing system extracts image level features from the broadcast video stream. The codec module includes an object detection portion configured to detect players in the broadcast video stream and a subnet portion attached to the object detection portion. The subnet portion is configured to identify foreground information of the detected players. The codec module provides the image level features to a plurality of task specific modules for analysis. The plurality of task specific modules generates a plurality of outputs based on the image level features.
A computing system receives pre-match data for an upcoming match between a first player and a second player. The computing system generates, using one or more prediction models, one or more pre-match predictions based on the pre-match data. The computing system receives in-match data for the match currently in progress. The computing system generates, using the one or more prediction models, one or more live match predictions based on the in-match data.
A computing system identifies data related to a tennis match between a first player and a second player. The data includes a current match state and a current in-match performance. The computing system generates an input data set that includes the data related to the tennis match. The generating includes modifying the current match state to assume that the first player will win a next point in the tennis match. Based on the input data set, the computing system measures an importance of the next point to the first player winning the tennis match.
A system and method of predicting a team's formation on a playing surface are disclosed herein. A computing system retrieves one or more sets of event data for a plurality of events. Each set of event data corresponds to a segment of the event. A deep neural network, such as a mixture density network, learns to predict an optimal permutation of players in each segment of the event based on the one or more sets of event data. The deep neural network learns a distribution of players for each segment based on the corresponding event data and optimal permutation of players. The computing system generates a fully trained prediction model based on the learning. The computing system receives target event data corresponding to a target event. The computing system generates, via the trained prediction model, an expected position of each player based on the target event data.
Disclosed techniques relate to using one or more of golf match statistics, textual insights, predictions (e.g., team and player at the event level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.
Disclosed techniques relate to using one or more of cricket match statistics, textual insights, predictions (e.g., team and player at the event level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.
A system may receive a plurality of sports event video feeds, whereupon, the system may calibrate the plurality of sports event video feeds. The system may generate a panoramic video feed, wherein the panoramic video feed is generated by stitching together the calibrated plurality of sports event video feeds. The system may obtain tracking data for at least one asset in the sports event and generate a tactical video feed, wherein generation of the tactical video feed is based on the tracking data. The system may further balance and calibrate color data across the plurality of sports event video feeds.
A system may receive one or more data feeds for a sports event, wherein the one or more data feeds includes at least one data entry. The system may receive one or more video feeds for the sports event, wherein the one or more video feeds include event data. The system may identify a data feed error, wherein the data feed error is a difference between the at least one data entry and the event data in the one or more video feeds. The system may correct the data feed error, wherein correction of the data feed error includes altering the at least one data entry to be consistent with the event data.
A method for using machine learning to predict a success of a matchup in a sporting event, the method including accessing tracking data from a data store; identifying, from the tracking data, one or more matchups wherein each matchup includes an identification of a first player, an identification of a second player, and a success of a corresponding outcome; filtering the identified matchups to create a subset of matchups; providing the subset of matchups to a trained machine learning model; receiving, from the machine learning model, a prediction of success of the matchup; comparing the prediction of success with a measured outcome; and adjusting a ranking of the first player, a ranking of the second player, and/or the machine learning model based on the prediction.
Disclosed techniques relate to using one or more of rugby match statistics, textual insights, predictions (e.g., team and player at the match level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.
A method for using machine learning to predict a success of a matchup in a sporting event, the method including accessing tracking data from a data store; identifying, from the tracking data, one or more matchups wherein each matchup includes an identification of a first player, an identification of a second player, and a success of a corresponding outcome; filtering the identified matchups to create a subset of matchups; providing the subset of matchups to a trained machine learning model; receiving, from the machine learning model, a prediction of success of the matchup; comparing the prediction of success with a measured outcome; and adjusting a ranking of the first player, a ranking of the second player, and/or the machine learning model based on the prediction.
G06V 20/40 - ScenesScene-specific elements in video content
G06V 40/20 - Movements or behaviour, e.g. gesture recognition
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/766 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using regression, e.g. by projecting features on hyperplanes
G06V 20/52 - Surveillance or monitoring of activities, e.g. for recognising suspicious objects
G06V 20/70 - Labelling scene content, e.g. deriving syntactic or semantic representations
41.
SYSTEMS AND METHODS FOR A DECISION ENGINE FOR DETERMINING DATA-POINT RECOMMENDATIONS
A method for generating recommended user content related to a sporting event, the method including: receiving, as input, digital sports content of one or more sporting events; receiving, as input, sports event data for the one or more sporting events; receiving, as input, a set of statistical odds for the one or more sporting event; determining, using a decision engine, based on the received input digital sports content and sports event data, recommended statistical odds for one or more sporting events; determining, using the decision engine, recommended contextual content based on the determined recommended statistical odds; and outputting the recommended contextual content and recommended statistical odds to one or more users.
According to systems and techniques disclosed herein, a method for generating an interactive display may include receiving a plurality of real-time event data comprising a plurality of real-time event actions associated with a game identifier. The method may further include generating an event sequence based on the plurality of real-time event actions. The method may further include generating the interactive display including at least a graphical representation of the event sequence. The graphical representation of the event sequence may include one or more real-time event elements, and one or more interactive elements. The one or more interactive elements may be configured to cause the interactive display to update the one or more real-time event elements in response to one or more user interactions. The method may further include transmitting, to a user interface, the interactive display.
A method of generating a set of predictions associated with a possession-based sporting event using an axial transformer neural network, the method including: receiving an input tuple, including a set of tensors representing game context, team strength, player strength, live team features, live player features, game events, and a super feature; inputting the input tuple into an axial transformer neural network by inputting each tensor from the set of tensors within a corresponding initial embedding layer; concatenating the initial embedding layers to form a single tensor; applying self-attention to the single tensor; mapping output embeddings from the axial transformer layers to target layers, each of the output embeddings being of a dimension of a target metric; and generating a set of target metric predictions for each of a set of players, one or more teams, and a match, based on the output embeddings from the target layers.
Disclosed techniques relate to using one or more of football match statistics, textual insights, predictions (e.g., team and player at the match level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.
Disclosed techniques relate to using one or more of tennis match statistics, textual insights, predictions (e.g., team and player at the match level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.
Disclosed techniques relate to using one or more of baseball match statistics, textual insights, predictions (e.g., team and player at the match level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.
Disclosed techniques relate to using one or more of racing event statistics, textual insights, predictions (e.g., team and player at the event level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as event statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.
Disclosed techniques relate to using one or more of cricket match statistics, textual insights, predictions (e.g., team and player at the event level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.
A computing system retrieves historical event data for a plurality of games in a league. The historical event data includes (x,y) coordinates of players within each game and game context data. The computing system learns one or more attributes of each team in each game and each player on each team in each game. The computing system receives a request to simulate a play in a historical game. The request includes substituting a player that was in the play with a target player that was not in the play. The computing system simulates the play with the target player in place of the player based on the one or more attributes learned by the computing system. The computing system generates a graphical representation of the simulation.
A63F 13/497 - Partially or entirely replaying previous game actions
A63F 13/573 - Simulating properties, behaviour or motion of objects in the game world, e.g. computing tyre load in a car race game using trajectories of game objects, e.g. of a golf ball according to the point of impact
A63F 13/86 - Watching games played by other players
50.
SYSTEMS AND METHODS FOR AGENTIC OPERATIONS USING MULTIMODAL GENERATIVE MODELS FOR FOOTBALL
Disclosed techniques relate to using one or more of football match statistics, textual insights, predictions (e.g., team and player at the match level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.
A method of generating a set of predictions associated with position-based sporting events using an axial transformer neural network, the method including: receiving an input tuple, including a set of tensors representing game context, team strength, player strength, live team features, live player features, game events, and a super feature; inputting the input tuple into an axial transformer neural network by inputting each tensor from the set of tensors within a corresponding initial embedding layer; concatenating the initial embedding layers to form a single tensor; applying self-attention to the single tensor through axial transformer layers of the axial transformer neural network; mapping output embeddings from the axial transformer layers to target layers; and generating a set of target metric predictions for each racer, team, and overall for the position-based sporting events, based on the output embeddings from the target layers.
G06F 16/783 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
52.
SYSTEMS AND METHODS FOR A TRANSFORMER NEURAL NETWORK FOR PREDICTIONS IN STRIKING-BASED SPORTING EVENTS
A method of generating a set of predictions associated with a striking-based sporting event using an axial transformer neural network, the method including: receiving an input tuple, including a set of tensors representing game context, team strength, player strength, live team features, live player features, game events, and a super feature; inputting the input tuple into an axial transformer neural network by inputting each tensor from the set of tensors within a corresponding initial embedding layer; concatenating the initial embedding layers to form a single tensor; applying self-attention to the single tensor through axial transformer layers of the axial transformer neural network; mapping output embeddings from the axial transformer layers to target layers; and generating a set of target metric predictions for each of a set of players, one or more teams, and a match, based on the output embeddings from the target layers.
Disclosed techniques relate to using one or more of soccer match statistics, textual insights, predictions (e.g., team and player at the match level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.
Disclosed techniques relate to using one or more of basketball match statistics, textual insights, predictions (e.g., team and player at the match level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.
Disclosed techniques relate to using one or more of rugby match statistics, textual insights, predictions (e.g., team and player at the match level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.
A method of generating a set of predictions associated with a rugby game using an axial transformer neural network, the method including: receiving an input tuple, including a set of tensors representing game context, team strength, player strength, live team features, live player features, game events, and a super feature; inputting the input tuple into an axial transformer neural network by inputting each tensor from the set of tensors within a corresponding initial embedding layer; concatenating the initial embedding layers to form a single tensor; applying self-attention to the single tensor; mapping output embeddings from the axial transformer layers to target layers, each of the output embeddings being of a dimension of a target metric; and generating a set of target metric predictions for each of a set of players, one or more teams, and a match, based on the output embeddings from the target layers.
A method of generating a set of predictions associated with position-based sporting events using an axial transformer neural network, the method including: receiving an input tuple, including a set of tensors representing game context, team strength, player strength, live team features, live player features, game events, and a super feature; inputting the input tuple into an axial transformer neural network by inputting each tensor from the set of tensors within a corresponding initial embedding layer; concatenating the initial embedding layers to form a single tensor; applying self-attention to the single tensor through axial transformer layers of the axial transformer neural network; mapping output embeddings from the axial transformer layers to target layers; and generating a set of target metric predictions for each racer, team, and overall for the position-based sporting events, based on the output embeddings from the target layers.
Disclosed techniques relate to using one or more of golf match statistics, textual insights, predictions (e.g., team and player at the event level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.
A system may receive a plurality of sports event video feeds, whereupon, the system may calibrate the plurality of sports event video feeds. The system may generate a panoramic video feed, wherein the panoramic video feed is generated by stitching together the calibrated plurality of sports event video feeds. The system may obtain tracking data for at least one asset in the sports event and generate a tactical video feed, wherein generation of the tactical video feed is based on the tracking data. The system may further balance and calibrate color data across the plurality of sports event video feeds.
A system may receive one or more data feeds for a sports event, wherein the one or more data feeds includes at least one data entry. The system may receive one or more video feeds for the sports event, wherein the one or more video feeds include event data. The system may identify a data feed error, wherein the data feed error is a difference between the at least one data entry and the event data in the one or more video feeds. The system may correct the data feed error, wherein correction of the data feed error includes altering the at least one data entry to be consistent with the event data.
A method of generating a player prediction is disclosed herein. A computing system retrieves data from a data store. The computing system generates a predictive model using an artificial neural network. The artificial neural network generates one or more personalized embeddings that include player-specific information based on historical performance. The computing system selects, from the data, one or more features related to each shot attempt captured in the data. The artificial neural network learns an outcome of each shot attempt based at least on the one or more personalized embeddings and the one or more features related to each shot attempt.
A method for generating recommended user content related to a sporting event, the method including: receiving, as input, digital sports content of one or more sporting events; receiving, as input, sports event data for the one or more sporting events; receiving, as input, a set of statistical odds for the one or more sporting event; determining, using a decision engine, based on the received input digital sports content and sports event data, recommended statistical odds for one or more sporting events; determining, using the decision engine, recommended contextual content based on the determined recommended statistical odds; and outputting the recommended contextual content and recommended statistical odds to one or more users.
A63F 13/65 - Generating or modifying game content before or while executing the game program, e.g. authoring tools specially adapted for game development or game-integrated level editor automatically by game devices or servers from real world data, e.g. measurement in live racing competition
A63F 13/85 - Providing additional services to players
63.
SYSTEMS AND METHODS FOR GENERATING AN INTERACTIVE DISPLAY FOR AN EVENT SEQUENCE
According to systems and techniques disclosed herein, a method for generating an interactive display may include receiving a plurality of real-time event data comprising a plurality of real-time event actions associated with a game identifier. The method may further include generating an event sequence based on the plurality of real-time event actions. The method may further include generating the interactive display including at least a graphical representation of the event sequence. The graphical representation of the event sequence may include one or more real-time event elements, and one or more interactive elements. The one or more interactive elements may be configured to cause the interactive display to update the one or more real-time event elements in response to one or more user interactions. The method may further include transmitting, to a user interface, the interactive display.
A63F 13/65 - Generating or modifying game content before or while executing the game program, e.g. authoring tools specially adapted for game development or game-integrated level editor automatically by game devices or servers from real world data, e.g. measurement in live racing competition
A63F 13/86 - Watching games played by other players
64.
SYSTEMS AND METHODS FOR A TRANSFORMER NEURAL NETWORK FOR PREDICTIONS IN POSSESSION-BASED SPORTING EVENTS
A method of generating a set of predictions associated with a possession-based sporting event using an axial transformer neural network, the method including: receiving an input tuple, including a set of tensors representing game context, team strength, player strength, live team features, live player features, game events, and a super feature; inputting the input tuple into an axial transformer neural network by inputting each tensor from the set of tensors within a corresponding initial embedding layer; concatenating the initial embedding layers to form a single tensor; applying self-attention to the single tensor; mapping output embeddings from the axial transformer layers to target layers, each of the output embeddings being of a dimension of a target metric; and generating a set of target metric predictions for each of a set of players, one or more teams, and a match, based on the output embeddings from the target layers.
G06F 16/783 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
65.
SYSTEMS AND METHODS FOR AGENTIC OPERATIONS USING MULTIMODAL GENERATIVE MODELS FOR SOCCER
Disclosed techniques relate to using one or more of soccer match statistics, textual insights, predictions (e.g., team and player at the match level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.
Disclosed techniques relate to using one or more of basketball match statistics, textual insights, predictions (e.g., team and player at the match level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.
Disclosed techniques relate to using one or more of tennis match statistics, textual insights, predictions (e.g., team and player at the match level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.
Disclosed techniques relate to using one or more of baseball match statistics, textual insights, predictions (e.g., team and player at the match level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.
A method of generating a set of predictions associated with a striking-based sporting event using an axial transformer neural network, the method including: receiving an input tuple, including a set of tensors representing game context, team strength, player strength, live team features, live player features, game events, and a super feature; inputting the input tuple into an axial transformer neural network by inputting each tensor from the set of tensors within a corresponding initial embedding layer; concatenating the initial embedding layers to form a single tensor; applying self-attention to the single tensor through axial transformer layers of the axial transformer neural network; mapping output embeddings from the axial transformer layers to target layers; and generating a set of target metric predictions for each of a set of players, one or more teams, and a match, based on the output embeddings from the target layers.
G06F 16/783 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
G06N 5/00 - Computing arrangements using knowledge-based models
70.
SYSTEMS AND METHODS FOR A TRANSFORMER NEURAL NETWORK FOR PREDICTIONS IN RUGBY SPORTING EVENTS
A method of generating a set of predictions associated with a rugby game using an axial transformer neural network, the method including: receiving an input tuple, including a set of tensors representing game context, team strength, player strength, live team features, live player features, game events, and a super feature; inputting the input tuple into an axial transformer neural network by inputting each tensor from the set of tensors within a corresponding initial embedding layer; concatenating the initial embedding layers to form a single tensor; applying self-attention to the single tensor; mapping output embeddings from the axial transformer layers to target layers, each of the output embeddings being of a dimension of a target metric; and generating a set of target metric predictions for each of a set of players, one or more teams, and a match, based on the output embeddings from the target layers.
G06F 16/783 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
71.
SYSTEMS AND METHODS FOR AGENTIC OPERATIONS USING MULTIMODAL GENERATIVE MODELS FOR RACING
Disclosed techniques relate to using one or more of racing event statistics, textual insights, predictions (e.g., team and player at the event level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as event statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.
Techniques for generating textual content relating to sporting events using generative machine learning models are disclosed. For example, a machine-learning environment receives, from a client device, a request to generate textual content relating to a sporting event. The environment obtains relevant data and generates a prompt, which is provided to one or more generative machine learning models. In turn, the models output textual content relating to the event. The content may be provided to the client device.
Techniques for generating textual content relating to sporting events using generative machine learning models are disclosed. For example, a machine-learning environment receives, from a client device, a request to generate textual content relating to a sporting event. The environment obtains relevant data and generates a prompt, which is provided to one or more generative machine learning models. In turn, the models output textual content relating to the event. The content may be provided to the client device.
Techniques for method for using machine learning to predict stoppage time are disclosed. In an example, a method includes accessing, in real time, delay data from a sporting event. The delay data may be categorized by a type of delay. The method further includes generating, from the delay data, a linear regression. The method further includes providing, to a neural network, the linear regression and environmental data. The neural network is trained to predict an estimated stoppage time. The method further includes receiving, from the neural network, a predicted amount of stoppage time. The method further includes outputting the predicted amount of stoppage time.
Techniques for method for using machine learning to predict stoppage time are disclosed. In an example, a method includes accessing, in real time, delay data from a sporting event. The delay data may be categorized by a type of delay. The method further includes generating, from the delay data, a linear regression. The method further includes providing, to a neural network, the linear regression and environmental data. The neural network is trained to predict an estimated stoppage time. The method further includes receiving, from the neural network, a predicted amount of stoppage time. The method further includes outputting the predicted amount of stoppage time.
A method for identifying a player in a sports event, the method including: receiving a video feed of a sporting event; capturing, by a computing system, positional data of a player in one or more video frames of the video feed; receiving, by the computing system, team formation data for at least one team in the sporting event, wherein the team formation data comprises a player role associated with each player; determining, by the computing system, a correspondence between the positional data of a player and the team formation data; and generating, by the computing system, a player identification for the player, wherein the player identification is based on the correspondence between the positional data for the player and a player role from the team formation data.
G06V 20/40 - ScenesScene-specific elements in video content
G06T 7/70 - Determining position or orientation of objects or cameras
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
77.
SYSTEMS AND METHODS FOR RECURRENT GRAPH NEURAL NET-BASED PLAYER ROLE IDENTIFICATION
A method for identifying a player in a sports event, the method including: receiving a video feed of a sporting event; capturing, by a computing system, positional data of a player in one or more video frames of the video feed; receiving, by the computing system, team formation data for at least one team in the sporting event, wherein the team formation data comprises a player role associated with each player; determining, by the computing system, a correspondence between the positional data of a player and the team formation data; and generating, by the computing system, a player identification for the player, wherein the player identification is based on the correspondence between the positional data for the player and a player role from the team formation data.
A method for associating a player with a team in a sports event, the method including: receiving a video feed of a sporting event; identifying, based on an output of a first machine learning model, a patch of pixels corresponding to a player in a video frame of the video feed of the sporting event; determining, based on an output of a second machine learning model, a vector of the patch; retrieving gallery vectors for each team in the sporting event; determining a set of distances between the vector and each of the gallery vectors; and determining, based on a closest distance of the set of distances, a team identification for the player.
G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
G06V 20/40 - ScenesScene-specific elements in video content
79.
INCREASING SECURITY OF STREAMING MEDIA BY CONVERTING A SECURE MEDIA FORMAT INTO A STREAMING MEDIA FORMAT WITHOUT INTRODUCING LAG
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.
H04N 21/2343 - Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs involving reformatting operations of video signals for distribution or compliance with end-user requests or end-user device requirements
H04N 21/2347 - Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs involving video stream encryption
H04N 21/845 - Structuring of content, e.g. decomposing content into time segments
A method for associating a player with a team in a sports event, the method including: receiving a video feed of a sporting event; identifying, based on an output of a first machine learning model, a patch of pixels corresponding to a player in a video frame of the video feed of the sporting event; determining, based on an output of a second machine learning model, a vector of the patch; retrieving gallery vectors for each team in the sporting event; determining a set of distances between the vector and each of the gallery vectors; and determining, based on a closest distance of the set of distances, a team identification for the player.
G06V 20/40 - ScenesScene-specific elements in video content
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
81.
SYSTEM AND METHODS FOR INTEGRATING SPORTS DATA AND MACHINE LEARNING TECHNIQUES TO GENERATE RESPONSES TO USER QUERIES
A method for generating multi-modal response to a query using a generative machine learning model, the method including: receiving, from a client device, a query data object related to a sporting event; providing the query data object and a first prompt to a machine learning system; receiving, from the machine learning system, a function, from a set of functions, associated with the query data object; receiving, from the machine learning system, an output format; providing a data source mapped to the function, the query data object, and a second prompt to the machine learning system, receiving, from the machine learning system, a response to the query data object, wherein the response is formatted based on the output format; and outputting the response to one or more users.
G06F 16/783 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
A method for generating multi-modal response to a query using a generative machine learning model, the method including: receiving, from a client device, a query data object related to a sporting event; providing the query data object and a first prompt to a machine learning system; receiving, from the machine learning system, a function, from a set of functions, associated with the query data object; receiving, from the machine learning system, an output format; providing a data source mapped to the function, the query data object, and a second prompt to the machine learning system, receiving, from the machine learning system, a response to the query data object, wherein the response is formatted based on the output format; and outputting the response to one or more users.
Disclosed techniques relate to using machine learning for metric extraction of sports players in generating player content cards. In an example, a method for generating an interactive player ratings card may include receiving a plurality of event data comprising a plurality of real-time and historical player data. The method may further include extracting a plurality of player metric data associated with the plurality of event data. The method may further include aggregating the plurality of player metric data to determine one or more player ratings. The method may further include generating the interactive player ratings card including the one or more player ratings. The method may further include transmitting the interactive player ratings card to a user device.
A63F 13/67 - Generating or modifying game content before or while executing the game program, e.g. authoring tools specially adapted for game development or game-integrated level editor adaptively or by learning from player actions, e.g. skill level adjustment or by storing successful combat sequences for re-use
A63F 13/798 - Game security or game management aspects involving player-related data, e.g. identities, accounts, preferences or play histories for assessing skills or for ranking players, e.g. for generating a hall of fame
Disclosed techniques relate to using machine learning for sports applications. In an example, a method for generating textual summaries using one or more generative machine learning models is disclosed. The method can include receiving, from a client device, a request for a summary of a sporting event. The method can include accessing, from a database, one or more database records including sports related data that is associated with the sporting event. The method can include formulating, from the database records, a machine learning model prompt. The method can include providing the machine learning model prompt to the one or more generative machine learning models. The method can include receiving, from the one or more generative machine learning models, a textual summary of the sporting event. The method can include outputting the textual summary to the client device.
A method for extracting and processing audio data may include receiving one or more packets of multimedia content. The one or more packets of multimedia content may comprise audio data. The method may further include extracting the audio data from the one or more packets of multimedia content. The audio data may comprise verbal speech in a first language. The method may further include converting the audio data into first text data in the first language based on the verbal speech in the first language. The method may further include providing the first text data to a generative machine-learning model. The generative machine-learning model may have been trained to translate the first text data in the first language to a second language and generate second text data in the second language. The method may further include transmitting, to a user interface, the second text data in the second language.
Disclosed techniques relate to using machine learning for metric extraction of sports players in generating player content cards. In an example, a method for generating an interactive player ratings card may include receiving a plurality of event data comprising a plurality of real-time and historical player data. The method may further include extracting a plurality of player metric data associated with the plurality of event data. The method may further include aggregating the plurality of player metric data to determine one or more player ratings. The method may further include generating the interactive player ratings card including the one or more player ratings. The method may further include transmitting the interactive player ratings card to a user device.
Disclosed techniques relate to using machine learning for sports applications. In an example, a method for generating textual summaries using one or more generative machine learning models is disclosed. The method can include receiving, from a client device, a request for a summary of a sporting event. The method can include accessing, from a database, one or more database records including sports related data that is associated with the sporting event. The method can include formulating, from the database records, a machine learning model prompt. The method can include providing the machine learning model prompt to the one or more generative machine learning models. The method can include receiving, from the one or more generative machine learning models, a textual summary of the sporting event. The method can include outputting the textual summary to the client device.
A method for extracting and processing audio data may include receiving one or more packets of multimedia content. The one or more packets of multimedia content may comprise audio data. The method may further include extracting the audio data from the one or more packets of multimedia content. The audio data may comprise verbal speech in a first language. The method may further include converting the audio data into first text data in the first language based on the verbal speech in the first language. The method may further include providing the first text data to a generative machine-learning model. The generative machine-learning model may have been trained to translate the first text data in the first language to a second language and generate second text data in the second language. The method may further include transmitting, to a user interface, the second text data in the second language.
A method for delivering interactive video to a user is disclosed herein. A computing system identifies video contents corresponding to a sporting event. The video contents include a plurality of video frames. The computing system annotates each video frame of the plurality of video frames to uniquely identify the video frame and contents contained therein. The computing system receives, from a plurality of prediction models, a plurality of data inputs related to agents and actions captured in each video frame of the plurality of video frames. The computing system generates a plurality of data frames based on the plurality of data inputs. The computing system associates each data frame with a respective video frame using the annotations. The computing system causes a user device to present the interactive video to the user by instructing the user device to merge the plurality of data frames with the plurality of video frames.
Disclosed techniques relate to using machine learning for sports applications. In an example, a method for generating sports tracking data using multimodal generative models may include receiving one or more inputs by a user. The input may be related to a description. The method may further include extracting metadata items relating to the description. The method may further include mapping the metadata items to at least one or more event streams. The method may further include receiving content items relating to the event streams. The event streams contain content items that are outputted by a multimodal sports learning language model (LLM). The method may further include transmitting the content items to a user device for display.
Disclosed techniques relate to using machine learning for sports applications. In an example, a method for generating sports tracking data using multimodal generative models may include receiving one or more inputs by a user. The input may be related to a description. The method may further include extracting metadata items relating to the description. The method may further include mapping the metadata items to at least one or more event streams. The method may further include receiving content items relating to the event streams. The event streams contain content items that are outputted by a multimodal sports learning language model (LLM). The method may further include transmitting the content items to a user device for display.
According to systems and techniques disclosed herein, a plurality of real-time event data including a plurality of real-time event actions of a player may be received. One or more event actions associated with a unique identifier may be updated with the plurality of real-time event actions. A unique index may be generated based on a plurality of weights applied to the one or more event actions associated with the unique identifier. The unique index may be generated in real-time as the plurality of real-time event data is received. An interactive display may be generated including at least a graphical representation of the one or more event actions associated with the unique identifier, a graphical representation of the plurality of weights applied to the one or more event actions, and the unique index. The interactive display may be generated in real-time as the plurality of real-time event data is received.
A method may include receiving data for a game, the data comprising at least one of tracking data or event data. The method may include determining an occurrence of a trigger event within the game based on the data for the game. The method may include providing the data for the game and the occurrence of the trigger event to a first machine learning (ML) model, where the first ML model is trained to generate a graphic based on the data for the game and the occurrence of the trigger event. The method may include receiving, from the first ML model, the graphic, and generating a visual element including the graphic for presentation within a user interface. The visual element may be configured to include an interactive element or be positioned adjacent to the interactive element within the user interface.
H04N 21/431 - Generation of visual interfacesContent or additional data rendering
H04N 21/45 - Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies or resolving scheduling conflicts
H04N 21/466 - Learning process for intelligent management, e.g. learning user preferences for recommending movies
H04N 21/472 - End-user interface for requesting content, additional data or servicesEnd-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification or for manipulating displayed content
94.
SYSTEMS AND METHODS FOR GENERATING SPORTS MEDIA CONTENT FOR AN INTERACTIVE DISPLAY
A method may include receiving data for a game, the data comprising at least tracking data or event data. The method may include determining an occurrence of a trigger event within the game based on the data for the game, and providing the data for the game and the trigger event to a machine learning (ML) model. The ML model may be trained to generate a graphic based on the data for the game and the occurrence of the trigger event. The method may include receiving, from the ML model, the graphic based on the data for the game and the occurrence of the trigger event; and generating, using a template, a visual element including the graphic for presentation within a user interface. The visual element may be associated with a marker, the marker representing a recommended position for an interactive element to be presented within the user interface.
Techniques described herein relate to a computer-implemented method for generating a smart overlay in an interactive display. The method may include receiving a plurality of real-time event data comprising a plurality of real-time event actions, receiving a plurality of user data comprising a plurality of user actions, capturing one or more real-time user interactions with the interactive display, generating a unique relevancy threshold using the plurality of real-time event actions, the plurality of user actions, and the one or more real-time user interactions, as the plurality of real-time event data and the one or more real-time user interactions are received, generating, in real-time, at least one unique smart overlay that may have a relevancy that exceeds the unique relevancy threshold, and updating, in real-time, the interactive display with the at least one unique smart overlay.
A method may include receiving data for a game, the data comprising at least one of tracking data or event data. The method may include determining an occurrence of a trigger event within the game based on the data for the game. The method may include providing the data for the game and the occurrence of the trigger event to a first machine learning (ML) model, where the first ML model is trained to generate a graphic based on the data for the game and the occurrence of the trigger event. The method may include receiving, from the first ML model, the graphic, and generating a visual element including the graphic for presentation within a user interface. The visual element may be configured to include an interactive element or be positioned adjacent to the interactive element within the user interface.
A method may include receiving data for a game, the data comprising at least tracking data or event data. The method may include determining an occurrence of a trigger event within the game based on the data for the game, and providing the data for the game and the trigger event to a machine learning (ML) model. The ML model may be trained to generate a graphic based on the data for the game and the occurrence of the trigger event. The method may include receiving, from the ML model, the graphic based on the data for the game and the occurrence of the trigger event; and generating, using a template, a visual element including the graphic for presentation within a user interface. The visual element may be associated with a marker, the marker representing a recommended position for an interactive element to be presented within the user interface.
Techniques described herein relate to a computer-implemented method for generating a smart overlay in an interactive display. The method may include receiving a plurality of real-time event data comprising a plurality of real-time event actions, receiving a plurality of user data comprising a plurality of user actions, capturing one or more real-time user interactions with the interactive display, generating a unique relevancy threshold using the plurality of real-time event actions, the plurality of user actions, and the one or more real-time user interactions, as the plurality of real-time event data and the one or more real-time user interactions are received, generating, in real-time, at least one unique smart overlay that may have a relevancy that exceeds the unique relevancy threshold, and updating, in real-time, the interactive display with the at least one unique smart overlay.
H04N 21/2343 - Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs involving reformatting operations of video signals for distribution or compliance with end-user requests or end-user device requirements
H04N 21/235 - Processing of additional data, e.g. scrambling of additional data or processing content descriptors
H04N 21/25 - Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication or learning user preferences for recommending movies
H04N 21/466 - Learning process for intelligent management, e.g. learning user preferences for recommending movies
99.
SYSTEMS AND METHODS FOR GENERATING AN INTERACTIVE DISPLAY FOR PLAYER INDEXING
According to systems and techniques disclosed herein, a plurality of real-time event data including a plurality of real-time event actions of a player may be received. One or more event actions associated with a unique identifier may be updated with the plurality of real-time event actions. A unique index may be generated based on a plurality of weights applied to the one or more event actions associated with the unique identifier. The unique index may be generated in real-time as the plurality of real-time event data is received. An interactive display may be generated including at least a graphical representation of the one or more event actions associated with the unique identifier, a graphical representation of the plurality of weights applied to the one or more event actions, and the unique index. The interactive display may be generated in real-time as the plurality of real-time event data is received.
Techniques described herein relate to a computer-implemented method for generating smart triggers in an interactive display. The method may include receiving a plurality of real-time event data comprising a plurality of real-time event actions, receiving a plurality of user data comprising a plurality of user actions, capturing one or more real-time user interactions with the interactive display, generating a unique relevancy threshold using the plurality of real-time event actions, the plurality of user actions, and the one or more real-time user interactions, as the plurality of real-time event data and the one or more real-time user interactions are received, generating, in real-time, at least one unique smart trigger that may have a relevancy that exceeds the unique relevancy threshold, and updating, in real-time, the interactive display with the at least one unique smart trigger.
G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
