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.
G06T 7/33 - Détermination des paramètres de transformation pour l'alignement des images, c.-à-d. recalage des images utilisant des procédés basés sur les caractéristiques
G06T 7/90 - Détermination de caractéristiques de couleur
5.
SYSTEMS AND METHODS FOR SPORTS TRACKING DATA COLLECTION, PROCESSING, AND CORRECTION
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 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/766 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la régression, p. ex. en projetant les caractéristiques sur des hyperplans
G06V 20/52 - Activités de surveillance ou de suivi, p. ex. pour la reconnaissance d’objets suspects
G06V 20/70 - Étiquetage du contenu de scène, p. ex. en tirant des représentations syntaxiques ou sémantiques
9.
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.
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
16.
SYSTEMS AND METHODS FOR AGENTIC OPERATIONS USING MULTIMODAL GENERATIVE MODELS FOR CRICKET
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.
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
17.
SYSTEM AND METHOD FOR INDIVIDUAL PLAYER AND TEAM SIMULATION
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 - Répétition partielle ou entière d'actions de jeu antérieures
A63F 13/573 - Simulations de propriétés, de comportement ou de déplacement d’objets dans le jeu, p. ex. calcul de l’effort supporté par un pneu dans un jeu de course automobile utilisant les trajectoires des objets du jeu, p. ex. d’une balle de golf en fonction du point d’impact
A63F 13/86 - Regarder des jeux joués par d’autres joueurs
18.
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 - Recherche de données caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
20.
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.
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
21.
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 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.
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
27.
SYSTEMS AND METHODS FOR PANORAMIC AND TACTICAL VIDEO GENERATION
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 - Création ou modification du contenu du jeu avant ou pendant l’exécution du programme de jeu, p. ex. au moyen d’outils spécialement adaptés au développement du jeu ou d’un éditeur de niveau intégré au jeu automatiquement par des dispositifs ou des serveurs de jeu, à partir de données provenant du monde réel, p. ex. les mesures en direct dans les compétitions de course réelles
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 - Création ou modification du contenu du jeu avant ou pendant l’exécution du programme de jeu, p. ex. au moyen d’outils spécialement adaptés au développement du jeu ou d’un éditeur de niveau intégré au jeu automatiquement par des dispositifs ou des serveurs de jeu, à partir de données provenant du monde réel, p. ex. les mesures en direct dans les compétitions de course réelles
A63F 13/86 - Regarder des jeux joués par d’autres joueurs
32.
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 - Recherche de données caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
33.
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 - Recherche de données caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06N 5/00 - Agencements informatiques utilisant des modèles fondés sur la connaissance
38.
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 - Recherche de données caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
39.
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 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
G06V 10/75 - Organisation de procédés de l’appariement, p. ex. comparaisons simultanées ou séquentielles des caractéristiques d’images ou de vidéosApproches-approximative-fine, p. ex. approches multi-échellesAppariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques utilisant l’analyse de contexteSélection des dictionnaires
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 40/16 - Visages humains, p. ex. parties du visage, croquis ou expressions
45.
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.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
46.
SYSTEMS AND METHODS FOR PLAYER TO TEAM ASSOCIATION BASED ON SPORTS VIDEO FEEDS
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 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur les distances des motifs d'entraînement ou de référence
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
47.
SYSTEMS AND METHODS FOR PLAYER TO TEAM ASSOCIATION BASED ON SPORTS VIDEO FEEDS
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 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
48.
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 16/3329 - Formulation de requêtes en langage naturel
G06F 16/783 - Recherche de données caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
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 - Création ou modification du contenu du jeu avant ou pendant l’exécution du programme de jeu, p. ex. au moyen d’outils spécialement adaptés au développement du jeu ou d’un éditeur de niveau intégré au jeu en s’adaptant à ou par apprentissage des actions de joueurs, p. ex. modification du niveau de compétences ou stockage de séquences de combats réussies en vue de leur réutilisation
A63F 13/798 - Aspects de sécurité ou de gestion du jeu incluant des données sur les joueurs, p. ex. leurs identités, leurs comptes, leurs préférences ou leurs historiques de jeu pour évaluer les compétences ou pour classer les joueurs, p. ex. pour créer un tableau d’honneur des joueurs
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.
G06F 40/58 - Utilisation de traduction automatisée, p. ex. pour recherches multilingues, pour fournir aux dispositifs clients une traduction effectuée par le serveur ou pour la traduction en temps réel
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 - Génération d'interfaces visuellesRendu de contenu ou données additionnelles
H04N 21/45 - Opérations de gestion réalisées par le client pour faciliter la réception de contenu ou l'interaction avec le contenu, ou pour l'administration des données liées à l'utilisateur final ou au dispositif client lui-même, p. ex. apprentissage des préférences d'utilisateurs pour recommander des films ou résolution de conflits d'ordonnancement
H04N 21/466 - Procédé d'apprentissage pour la gestion intelligente, p. ex. apprentissage des préférences d'utilisateurs pour recommander des films
H04N 21/472 - Interface pour utilisateurs finaux pour la requête de contenu, de données additionnelles ou de servicesInterface pour utilisateurs finaux pour l'interaction avec le contenu, p. ex. pour la réservation de contenu ou la mise en place de rappels, pour la requête de notification d'événement ou pour la transformation de contenus affichés
61.
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 - Traitement de flux vidéo élémentaires, p. ex. raccordement de flux vidéo ou transformation de graphes de scènes du flux vidéo codé impliquant des opérations de reformatage de signaux vidéo pour la distribution ou la mise en conformité avec les requêtes des utilisateurs finaux ou les exigences des dispositifs des utilisateurs finaux
H04N 21/235 - Traitement de données additionnelles, p. ex. brouillage de données additionnelles ou traitement de descripteurs de contenu
H04N 21/25 - Opérations de gestion réalisées par le serveur pour faciliter la distribution de contenu ou administrer des données liées aux utilisateurs finaux ou aux dispositifs clients, p. ex. authentification des utilisateurs finaux ou des dispositifs clients ou apprentissage des préférences des utilisateurs pour recommander des films
H04N 21/466 - Procédé d'apprentissage pour la gestion intelligente, p. ex. apprentissage des préférences d'utilisateurs pour recommander des films
66.
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.
G07F 17/32 - Appareils déclenchés par pièces de monnaie pour la location d'articlesInstallations ou services déclenchés par pièces de monnaie pour jeux, jouets, sports ou distractions
G06Q 50/34 - Mises ou paris sportifs, p. ex. paris sur Internet
67.
SYSTEMS AND METHODS FOR GENERATING SMART TRIGGERS FOR AN INTERACTIVE DISPLAY
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 8/658 - Mises à jour par incrémentMises à jour différentielles
G06F 3/0481 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p. ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comportement ou d’aspect
A computing system retrieves historical hole-by-hole data for a plurality of holes for plurality of golf tournaments for a plurality of players. The historical hole-by-hole data includes a yardage of each hole and a par associated with each hole. The computing system clusters the plurality of holes into a plurality of clusters of hole types. The computing system generates a strokes gained metric for each hole type of the hole-by-hole data for each player. The computing system adjusts the strokes gained metric for each hole type based on a field of strength metric associated with each tournament. The field of strength metric represents a strength of a player field in a given tournament. The computing system generates a ranking of the plurality of players based on the adjusted strokes gained metric.
A computing system receives a proposed bet selection for an event. The proposed bet selection includes team information and opponent information. The computing system generates a plurality of queries by analyzing the proposed bet selection. The computing system retrieves historical data related to the proposed bet selection based on the plurality of queries. The computing system analyzes the historical data to generate a plurality of insights related to the proposed bet selection. The computing system provides the historical data and the plurality of insights to a user submitting the proposed bet selection.
G07F 17/32 - Appareils déclenchés par pièces de monnaie pour la location d'articlesInstallations ou services déclenchés par pièces de monnaie pour jeux, jouets, sports ou distractions
G06Q 50/34 - Mises ou paris sportifs, p. ex. paris sur Internet
70.
SYSTEMS AND METHODS FOR GENERATING SMART TRIGGERS FOR AN INTERACTIVE DISPLAY
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.
G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
A computing system identifies broadcast video for a plurality of games in a first league. The broadcast video includes a plurality of video frames. The computing system generates tracking data for each game from the broadcast video of a corresponding game. The computing system enriches the tracking data. The enriching includes merging play-by-play data for the game with the tracking data of the corresponding game. The computing system generates padded tracking data based on the tracking data. The computing system projects player performance in a second league for each player based on the tracking data and the padded tracking data.
Examples disclosed herein may generate a refined and denoised body pose data from a video feed of a sporting event. Tracking data containing player locations may be used to determine correspondence between a location and a body pose. For example, body pose with middle of key footpoints with shortest distance from the location may be selected as a likely body pose for the location. The body pose data may be refined to estimate the length of missing limbs or limbs with unusual length ratios. The body pose data may further be filtered to filter out unwanted body poses such as body poses of spectators or noisy body poses. The refined and filtered body pose data may be used for other downstream processing such as projecting the body poses to a three dimensional play surface.
A computing system receives data for a game. The data includes at least one of tracking data or event data. Based on the data for the game, the computing system determines that an event has occurred within the game. Based on the determining, the computing system generates a graphic responsive to the event. The graphic includes insights related to the event. The computing system recommends an image relevant to the event based on metatags associated with the event. The computing system generates a visual element by merging the image and the graphic.
A computing system identifies broadcast video data for a game. The computing system generates tracking data for the game from the broadcast video data using computer vision techniques. The tracking data includes coordinates of players during the game. The computing system generates optical character recognition data for the game from the broadcast video data by applying one or more optical character recognition techniques to each frame of the plurality of frames to extract score and time information from a scoreboard displayed in each frame. The computing system detects a plurality of events that occurred in the game by applying one or more machine learning techniques to the tracking data. The computing system receives play-by-play data for the game. The computing system generates enriched tracking data. The generating includes merging the play-by-play data with one or more of the tracking data, the optical character recognition data, and the plurality of events.
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
H04N 21/235 - Traitement de données additionnelles, p. ex. brouillage de données additionnelles ou traitement de descripteurs de contenu
H04N 21/25 - Opérations de gestion réalisées par le serveur pour faciliter la distribution de contenu ou administrer des données liées aux utilisateurs finaux ou aux dispositifs clients, p. ex. authentification des utilisateurs finaux ou des dispositifs clients ou apprentissage des préférences des utilisateurs pour recommander des films
H04N 21/44 - Traitement de flux élémentaires vidéo, p. ex. raccordement d'un clip vidéo récupéré d'un stockage local avec un flux vidéo en entrée ou rendu de scènes selon des graphes de scène du flux vidéo codé
A computing system identifies player tracking data and event data corresponding to a match. The match includes a first team and a second team. The player tracking data includes coordinate positions of each player during the event. The event data defines events that occur during the match. The computing system divides the player tracking data into a plurality of segments based on the event information. For each segment of the plurality of segments, the computing system learns a first formation associated with a respective team in possession. For each segment of the plurality of segments, the computing system learns a second formation associated with a respective team not in possession. The computing system maps each first formation to a first class of known formation clusters. The computing system maps each second formation to a second class of known formation clusters.
A set of techniques leverages a real-time version of a media stream to gather information on its content which information is used to improve or augment the service delivery of other versions (e.g., live or video-on-demand) of the same stream. These techniques take advantage of the fact that the real-time version of a stream is sufficiently ahead, in time, from any other version to enable the gathering and use of information on the stream content.
H04N 21/466 - Procédé d'apprentissage pour la gestion intelligente, p. ex. apprentissage des préférences d'utilisateurs pour recommander des films
H04N 21/442 - Surveillance de procédés ou de ressources, p. ex. détection de la défaillance d'un dispositif d'enregistrement, surveillance de la bande passante sur la voie descendante, du nombre de visualisations d'un film, de l'espace de stockage disponible dans le disque dur interne
H04N 21/83 - Génération ou traitement de données de protection ou de description associées au contenuStructuration du contenu
H04N 21/8547 - Création de contenu impliquant des marquages temporels pour synchroniser le contenu
77.
METHODS AND SYSTEMS FOR UTILIZING LIVE EMBEDDED TRACKING DATA WITHIN A LIVE SPORTS VIDEO STREAM
A computing system receives a video stream of a game. The computing system generates tracking data corresponding to the video stream using one or more artificial intelligence models. The computing system generates interactive video data by combining the video stream of the game with the tracking data. The computing system causes a media player to render graphics corresponding to the tracking data over the video stream by sending the interactive video data to a client device executing the media player.
H04N 21/236 - Assemblage d'un flux multiplexé, p. ex. flux de transport, en combinant un flux vidéo avec d'autres contenus ou données additionnelles, p. ex. insertion d'une adresse universelle [URL] dans un flux vidéo, multiplexage de données de logiciel dans un flux vidéoRemultiplexage de flux multiplexésInsertion de bits de remplissage dans le flux multiplexé, p. ex. pour obtenir un débit constantAssemblage d'un flux élémentaire mis en paquets
A computing system receives data for a game. The data includes at least one of tracking data or event data. Based on the data for the game, the computing system determines that an event has occurred within the game. Based on the determining, the computing system generates a graphic responsive to the event. The graphic includes insights related to the event. The computing system recommends an image relevant to the event based on metatags associated with the event. The computing system generates a visual element by merging the image and the graphic.
Metadata for highlights of a video stream is extracted from card images embedded in the video stream. The highlights may be segments of a video stream, such as a broadcast of a sporting event, that are of particular interest to one or more users. Card images embedded in video frames of the video stream are identified and processed to extract text. The text characters may be recognized by applying a machine-learned model trained with a set of characters extracted from card images embedded in sports television programming contents. The training set of character vectors may be pre-processed to maximize metric distance between the training set members. The text may be interpreted to obtain the metadata. The metadata may be stored in association with the portion of the video stream. The metadata may provide information regarding the highlights, and may be presented concurrently with playback of the highlights.
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06F 16/908 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
H04N 21/435 - Traitement de données additionnelles, p. ex. décryptage de données additionnelles ou reconstruction de logiciel à partir de modules extraits du flux de transport
H04N 21/44 - Traitement de flux élémentaires vidéo, p. ex. raccordement d'un clip vidéo récupéré d'un stockage local avec un flux vidéo en entrée ou rendu de scènes selon des graphes de scène du flux vidéo codé
H04N 21/458 - Ordonnancement de contenu pour créer un flux personnalisé, p. ex. en combinant une publicité stockée localement avec un flux d'entréeOpérations de mise à jour, p. ex. pour modules de système d'exploitation
A computing system retrieves player tracking data for a plurality of players across a plurality of events. The player tracking data includes coordinates of player positions during each event. The computing system initializes the player tracking data based on an average position of each player in the plurality of events. The computing system learns an optimal formation of player positions based on the player tracking data using a Gaussian mixture model. The computing system aligns the optimal formation of player positions to a global template by identifying a distance between each distribution in the optimal formation and each distribution in the global template to generate a learned formation template. The computing system assigns a role to each player in the learned template.
A computing system receives a request to generate one or more narrative frameworks for a worksheet. The worksheet is hosted by a third party server. The computing system interfaces with the third party server to retrieve data associated with the worksheet. The computing system infers attributes of the worksheet from the retrieved data. The attributes correspond to a type of data represented in the worksheet. The computing system generates a narrative template for the one or more narrative frameworks. The computing system generates one or more data variables for the one or more narrative frameworks based on the generated narrative template. The computing system constructs the one or more narrative frameworks by selectively retrieving data from the worksheet for each data variable of the one or more data variables in the narrative framework.
A system and method of re-identifying players in a broadcast video feed are provided herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system generates a plurality of tracks based on the plurality of video frames. Each track includes a plurality of image patches associated with at least one player. Each image patch of the plurality of image patches is a subset of the corresponding frame of the plurality of video frames. For each track, the computing system generates a gallery of image patches. A jersey number of each player is visible in each image patch of the gallery. The computing system matches, via a convolutional autoencoder, tracks across galleries. The computing system measures, via a neural network, a similarity score for each matched track and associates two tracks based on the measured similarity.
G06F 18/2135 - Extraction de caractéristiques, p. ex. en transformant l'espace des caractéristiquesSynthétisationsMappages, p. ex. procédés de sous-espace basée sur des critères d'approximation, p. ex. analyse en composantes principales
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
G06F 18/22 - Critères d'appariement, p. ex. mesures de proximité
G06F 18/2413 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur les distances des motifs d'entraînement ou de référence
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
G06T 7/80 - Analyse des images capturées pour déterminer les paramètres de caméra intrinsèques ou extrinsèques, c.-à-d. étalonnage de caméra
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
H04N 21/44 - Traitement de flux élémentaires vidéo, p. ex. raccordement d'un clip vidéo récupéré d'un stockage local avec un flux vidéo en entrée ou rendu de scènes selon des graphes de scène du flux vidéo codé
83.
SYSTEM AND METHOD FOR PLAYER REIDENTIFICATION IN BROADCAST VIDEO
A system and method of re-identifying players in a broadcast video feed are provided herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system generates a plurality of tracks based on the plurality of video frames. Each track includes a plurality of image patches associated with at least one player. Each image patch of the plurality of image patches is a subset of the corresponding frame of the plurality of video frames. For each track, the computing system generates a gallery of image patches. A jersey number of each player is visible in each image patch of the gallery. The computing system matches, via a convolutional autoencoder, tracks across galleries. The computing system measures, via a neural network, a similarity score for each matched track and associates two tracks based on the measured similarity.
G06F 18/2135 - Extraction de caractéristiques, p. ex. en transformant l'espace des caractéristiquesSynthétisationsMappages, p. ex. procédés de sous-espace basée sur des critères d'approximation, p. ex. analyse en composantes principales
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
G06F 18/22 - Critères d'appariement, p. ex. mesures de proximité
G06F 18/2413 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur les distances des motifs d'entraînement ou de référence
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
G06T 7/80 - Analyse des images capturées pour déterminer les paramètres de caméra intrinsèques ou extrinsèques, c.-à-d. étalonnage de caméra
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
H04N 21/44 - Traitement de flux élémentaires vidéo, p. ex. raccordement d'un clip vidéo récupéré d'un stockage local avec un flux vidéo en entrée ou rendu de scènes selon des graphes de scène du flux vidéo codé
84.
CUSTOMIZED GENERATION OF HIGHLIGHTS SHOW WITH NARRATIVE COMPONENT
Customized highlight shows for sporting events, entertainment events, and/or the like, having a narrative component, are generated and presented. The events can be sporting events, entertainment events, and/or the like. For example, in the context of sporting events, a determination is made as to what types of sports, teams, leagues, players, plays, and/or the like are of interest to the user. A customized highlight show is then generated and presented, containing those specific portions of the sporting events that are likely to be of interest, arranged in a manner that is likely to be entertaining and interesting to the user and that presents a cohesive narrative.
H04N 21/845 - Structuration du contenu, p. ex. décomposition du contenu en segments temporels
G11B 27/031 - Montage électronique de signaux d'information analogiques numérisés, p. ex. de signaux audio, vidéo
G11B 27/034 - Montage électronique de signaux d'information analogiques numérisés, p. ex. de signaux audio, vidéo sur disques
G11B 27/10 - IndexationAdressageMinutage ou synchronisationMesure de l'avancement d'une bande
H04N 21/231 - Opération de stockage de contenu, p. ex. mise en mémoire cache de films pour stockage à court terme, réplication de données sur plusieurs serveurs, ou établissement de priorité des données pour l'effacement
H04N 21/234 - Traitement de flux vidéo élémentaires, p. ex. raccordement de flux vidéo ou transformation de graphes de scènes du flux vidéo codé
H04N 21/2343 - Traitement de flux vidéo élémentaires, p. ex. raccordement de flux vidéo ou transformation de graphes de scènes du flux vidéo codé impliquant des opérations de reformatage de signaux vidéo pour la distribution ou la mise en conformité avec les requêtes des utilisateurs finaux ou les exigences des dispositifs des utilisateurs finaux
H04N 21/25 - Opérations de gestion réalisées par le serveur pour faciliter la distribution de contenu ou administrer des données liées aux utilisateurs finaux ou aux dispositifs clients, p. ex. authentification des utilisateurs finaux ou des dispositifs clients ou apprentissage des préférences des utilisateurs pour recommander des films
H04N 21/258 - Gestion de données liées aux clients ou aux utilisateurs finaux, p. ex. gestion des capacités des clients, préférences ou données démographiques des utilisateurs, traitement des multiples préférences des utilisateurs finaux pour générer des données collaboratives
H04N 21/2668 - Création d'un canal pour un groupe dédié d'utilisateurs finaux, p. ex. en insérant des publicités ciblées dans un flux vidéo en fonction des profils des utilisateurs finaux
H04N 21/278 - Base de données de descripteurs de contenu ou service de répertoire pour accès par les utilisateurs finaux
H04N 21/431 - Génération d'interfaces visuellesRendu de contenu ou données additionnelles
H04N 21/442 - Surveillance de procédés ou de ressources, p. ex. détection de la défaillance d'un dispositif d'enregistrement, surveillance de la bande passante sur la voie descendante, du nombre de visualisations d'un film, de l'espace de stockage disponible dans le disque dur interne
H04N 21/466 - Procédé d'apprentissage pour la gestion intelligente, p. ex. apprentissage des préférences d'utilisateurs pour recommander des films
H04N 21/658 - Transmission du client vers le serveur
H04N 21/84 - Génération ou traitement de données de description, p. ex. descripteurs de contenu
H04N 21/8549 - Création de résumés vidéo, p. ex. bande annonce
85.
SYSTEM AND METHOD FOR GENERATING DAILY-UPDATED RATING OF INDIVIDUAL PLAYER PERFORMANCE IN SPORTS
A computing system identifies a target player. The computing system generates rookie priors for the target player based on characteristics of the target player. The computing system generates time series data points for the player based on at least one of the rookie priors and historical statistics of the target player. The computing system projects a game position of the target player based on the historical statistics of the target player. The computing system projects next game projections for the target player based on at least one of the rookie priors, the time series data points, the game position, and the historical statistics of the target player. The computing system generates a contribution of the target player to a team's production based on the next game projections.
A63B 71/06 - Dispositifs indicateurs ou de marque pour jeux ou joueurs
A63B 24/00 - Commandes électriques ou électroniques pour les appareils d'exercice des groupes
G06F 18/2415 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur des modèles paramétriques ou probabilistes, p. ex. basées sur un rapport de vraisemblance ou un taux de faux positifs par rapport à un taux de faux négatifs
86.
PERSONALIZING PREDICTION OF PERFORMANCE USING DATA AND BODY-POSE FOR ANALYSIS OF SPORTING PERFORMANCE
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 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 system and method of generating trackable frames from a broadcast video feed are provided herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system generates a set of frames for classification using a principal component analysis model. The set of frames are a subset of the plurality of video frames. The computing system partitions each frame of the set of frames into a plurality of clusters. The computing system classifies each frame of the plurality of frames as trackable or untrackable. Trackable frames capture a unified view of the sporting event. The computing system compares each cluster to a predetermined threshold to determine whether each cluster comprises at least a threshold number of trackable frames. The computing system classifies each cluster that includes at least the threshold number of trackable frames as trackable.
G06F 18/2135 - Extraction de caractéristiques, p. ex. en transformant l'espace des caractéristiquesSynthétisationsMappages, p. ex. procédés de sous-espace basée sur des critères d'approximation, p. ex. analyse en composantes principales
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
G06F 18/22 - Critères d'appariement, p. ex. mesures de proximité
G06F 18/2413 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur les distances des motifs d'entraînement ou de référence
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
G06T 7/80 - Analyse des images capturées pour déterminer les paramètres de caméra intrinsèques ou extrinsèques, c.-à-d. étalonnage de caméra
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
H04N 21/44 - Traitement de flux élémentaires vidéo, p. ex. raccordement d'un clip vidéo récupéré d'un stockage local avec un flux vidéo en entrée ou rendu de scènes selon des graphes de scène du flux vidéo codé
89.
SYSTEM AND METHOD FOR PLAYER REIDENTIFICATION IN BROADCAST VIDEO
A system and method of re-identifying players in a broadcast video feed are provided herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system generates a plurality of tracks based on the plurality of video frames. Each track includes a plurality of image patches associated with at least one player. Each image patch of the plurality of image patches is a subset of the corresponding frame of the plurality of video frames. For each track, the computing system generates a gallery of image patches. A jersey number of each player is visible in each image patch of the gallery. The computing system matches, via a convolutional autoencoder, tracks across galleries. The computing system measures, via a neural network, a similarity score for each matched track and associates two tracks based on the measured similarity.
G06F 18/2135 - Extraction de caractéristiques, p. ex. en transformant l'espace des caractéristiquesSynthétisationsMappages, p. ex. procédés de sous-espace basée sur des critères d'approximation, p. ex. analyse en composantes principales
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
G06F 18/22 - Critères d'appariement, p. ex. mesures de proximité
G06F 18/2413 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur les distances des motifs d'entraînement ou de référence
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
G06T 7/80 - Analyse des images capturées pour déterminer les paramètres de caméra intrinsèques ou extrinsèques, c.-à-d. étalonnage de caméra
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
H04N 21/44 - Traitement de flux élémentaires vidéo, p. ex. raccordement d'un clip vidéo récupéré d'un stockage local avec un flux vidéo en entrée ou rendu de scènes selon des graphes de scène du flux vidéo codé
90.
SYSTEM AND METHOD FOR CALIBRATING MOVING CAMERAS CAPTURING BROADCAST VIDEO
A system and method of calibrating moving cameras capturing a sporting event is disclosed herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system labels, via a neural network, components of a playing surface captured in each video frame. The computing system matches a subset of labeled video frames to a set of templates with various camera perspectives. The computing system fits a playing surface model to the set of labeled video frames that were matched to the set of templates. The computing system identifies camera motion in each video frame using an optical flow model. The computing system generates a homography matrix for each video frame based on the fitted playing surface model and camera motion. The computing system calibrates each camera based on the homography matrix generated for each video frame.
G06F 18/2135 - Extraction de caractéristiques, p. ex. en transformant l'espace des caractéristiquesSynthétisationsMappages, p. ex. procédés de sous-espace basée sur des critères d'approximation, p. ex. analyse en composantes principales
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
G06F 18/22 - Critères d'appariement, p. ex. mesures de proximité
G06F 18/2413 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur les distances des motifs d'entraînement ou de référence
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
G06T 7/80 - Analyse des images capturées pour déterminer les paramètres de caméra intrinsèques ou extrinsèques, c.-à-d. étalonnage de caméra
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
H04N 21/44 - Traitement de flux élémentaires vidéo, p. ex. raccordement d'un clip vidéo récupéré d'un stockage local avec un flux vidéo en entrée ou rendu de scènes selon des graphes de scène du flux vidéo codé
91.
GENERATING A CUSTOMIZED HIGHLIGHT SEQUENCE DEPICTING MULTIPLE EVENTS
A customized highlight sequence depicting multiple events, and based on a user's personal characteristics, interests, and/or preferences, is generated and presented. The events can be sporting events, entertainment events, and/or the like. For example, in the context of sporting events, a determination is made as to what types of sports, teams, leagues, players, plays, and/or the like are of interest to the user. In at least one embodiment, the amount of time available to the user can be obtained, so that the length of the highlight sequence can be tailored to the available time. A customized highlight sequence is then generated and presented, containing those specific portions of the sporting events that are likely to be of interest, arranged in a manner that is likely to be entertaining to the user and comports with the time restrictions.
H04N 21/8549 - Création de résumés vidéo, p. ex. bande annonce
H04N 21/231 - Opération de stockage de contenu, p. ex. mise en mémoire cache de films pour stockage à court terme, réplication de données sur plusieurs serveurs, ou établissement de priorité des données pour l'effacement
H04N 21/234 - Traitement de flux vidéo élémentaires, p. ex. raccordement de flux vidéo ou transformation de graphes de scènes du flux vidéo codé
H04N 21/2343 - Traitement de flux vidéo élémentaires, p. ex. raccordement de flux vidéo ou transformation de graphes de scènes du flux vidéo codé impliquant des opérations de reformatage de signaux vidéo pour la distribution ou la mise en conformité avec les requêtes des utilisateurs finaux ou les exigences des dispositifs des utilisateurs finaux
H04N 21/258 - Gestion de données liées aux clients ou aux utilisateurs finaux, p. ex. gestion des capacités des clients, préférences ou données démographiques des utilisateurs, traitement des multiples préférences des utilisateurs finaux pour générer des données collaboratives
H04N 21/262 - Ordonnancement de la distribution de contenus ou de données additionnelles, p. ex. envoi de données additionnelles en dehors des périodes de pointe, mise à jour de modules de logiciel, calcul de la fréquence de transmission de carrousel, retardement de la transmission de flux vidéo, génération de listes de reproduction
H04N 21/2668 - Création d'un canal pour un groupe dédié d'utilisateurs finaux, p. ex. en insérant des publicités ciblées dans un flux vidéo en fonction des profils des utilisateurs finaux
H04N 21/278 - Base de données de descripteurs de contenu ou service de répertoire pour accès par les utilisateurs finaux
H04N 21/4788 - Services additionnels, p. ex. affichage de l'identification d'un appelant téléphonique ou application d'achat communication avec d'autres utilisateurs, p. ex. discussion en ligne
A method of generating a dynamic rating for an entity and rearranging an icon associated with the entity on a graphical user interface (GUI) of a computer system. The method includes: receiving a first set of data associated with one or more parameters for the entity; calculating a plurality of ability scores based on the first set of data; generating a first rating based on each ability score of the plurality of ability scores; dynamically adjusting the plurality of ability scores based on receiving a second set of data associated with the one or more parameters for the entity; updating the first rating based on the adjusted plurality of ability scores; automatically rearranging the icon to a position above or below a current position of the icon on the GUI based on the updated first rating.
A method of generating a dynamic rating for an entity and rearranging an icon associated with the entity on a graphical user interface (GUI) of a computer system. The method includes: receiving a first set of data associated with one or more parameters for the entity; calculating a plurality of ability scores based on the first set of data; generating a first player rating based on each ability score of the plurality of ability scores; dynamically adjusting the plurality of ability scores based on receiving a second set of data associated with the one or more parameters for the entity; updating the first player rating based on the adjusted plurality of ability scores; automatically rearranging the icon to a position above or below a current position of the icon on the GUI based on the updated first player rating.
A method of generating a dynamic rating for an entity and rearranging an icon associated with the entity on a graphical user interface (GUI) of a computer system. The method includes: receiving a first set of data associated with one or more parameters for the entity; calculating a plurality of ability scores based on the first set of data; generating a first rating based on each ability score of the plurality of ability scores; dynamically adjusting the plurality of ability scores based on receiving a second set of data associated with the one or more parameters for the entity; updating the first rating based on the adjusted plurality of ability scores; automatically rearranging the icon to a position above or below a current position of the icon on the GUI based on the updated first rating.
A method of generating a dynamic rating for an entity and rearranging an icon associated with the entity on a graphical user interface (GUI) of a computer system. The method includes: receiving a first set of data associated with one or more parameters for the entity; calculating a plurality of ability scores based on the first set of data; generating a first player rating based on each ability score of the plurality of ability scores; dynamically adjusting the plurality of ability scores based on receiving a second set of data associated with the one or more parameters for the entity; updating the first player rating based on the adjusted plurality of ability scores; automatically rearranging the icon to a position above or below a current position of the icon on the GUI based on the updated first player rating.
A method of generating a multi-modal prediction is disclosed herein. A computing system retrieves event data from a data store. The event data includes information for a plurality of events across a plurality of seasons. Computing system generates a predictive model using a mixture density network, by generating an input vector from the event data learning, by the mixture density network, a plurality of values associated with a next play following each play in the event data. The mixture density network is trained to output the plurality of values near simultaneously. Computing system receives a set of event data directed to an event in a match. The set of event data includes information directed to at least playing surface position and current score. Computing system generates, via the predictive model, a plurality of values associated with a next event following the event based on the set of event data.
A system and method of re-identifying players in a broadcast video feed are provided herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system generates a plurality of tracks based on the plurality of video frames. Each track includes a plurality of image patches associated with at least one player. Each image patch of the plurality of image patches is a subset of the corresponding frame of the plurality of video frames. For each track, the computing system generates a gallery of image patches. A jersey number of each player is visible in each image patch of the gallery. The computing system matches, via a convolutional autoencoder, tracks across galleries. The computing system measures, via a neural network, a similarity score for each matched track and associates two tracks based on the measured similarity.
G06F 18/2135 - Extraction de caractéristiques, p. ex. en transformant l'espace des caractéristiquesSynthétisationsMappages, p. ex. procédés de sous-espace basée sur des critères d'approximation, p. ex. analyse en composantes principales
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
G06F 18/22 - Critères d'appariement, p. ex. mesures de proximité
G06F 18/2413 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur les distances des motifs d'entraînement ou de référence
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
G06T 7/80 - Analyse des images capturées pour déterminer les paramètres de caméra intrinsèques ou extrinsèques, c.-à-d. étalonnage de caméra
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
H04N 21/44 - Traitement de flux élémentaires vidéo, p. ex. raccordement d'un clip vidéo récupéré d'un stockage local avec un flux vidéo en entrée ou rendu de scènes selon des graphes de scène du flux vidéo codé
98.
VIDEO PROCESSING FOR ENABLING SPORTS HIGHLIGHTS GENERATION
One or more highlights of a video stream may be identified. The highlights may be segments of a video stream, such as a broadcast of a sporting event, that are of particular interest to one or more users. According to one method, at least a portion of the video stream may be stored. The portion of the video stream may be compared with templates of a template database to identify the one or more highlights. Each highlight may be a subset of the video stream that is deemed likely to match the one or more templates. The highlights, an identifier that identifies each of the highlights within the video stream, and/or metadata pertaining particularly to the one or more highlights may be stored to facilitate playback of the highlights for the users.
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06F 16/908 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
H04N 21/435 - Traitement de données additionnelles, p. ex. décryptage de données additionnelles ou reconstruction de logiciel à partir de modules extraits du flux de transport
H04N 21/44 - Traitement de flux élémentaires vidéo, p. ex. raccordement d'un clip vidéo récupéré d'un stockage local avec un flux vidéo en entrée ou rendu de scènes selon des graphes de scène du flux vidéo codé
H04N 21/458 - Ordonnancement de contenu pour créer un flux personnalisé, p. ex. en combinant une publicité stockée localement avec un flux d'entréeOpérations de mise à jour, p. ex. pour modules de système d'exploitation
Metadata for one or more highlights of a video stream may be extracted from one or more card images embedded in the video stream. The highlights may be segments of the video stream, such as a broadcast of a sporting event, that are of particular interest. According to one method, video frames of the video stream are stored. One or more information cards embedded in a decoded video frame may be detected by analyzing one or more predetermined video frame regions. Image segmentation, edge detection, and/or closed contour identification may then be performed on identified video frame region(s). Further processing may include obtaining a minimum rectangular perimeter area enclosing all remaining segments, which may then be further processed to determine precise boundaries of information card(s). The card image(s) may be analyzed to obtain metadata, which may be stored in association with at least one of the video frames.
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06F 16/908 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
H04N 21/435 - Traitement de données additionnelles, p. ex. décryptage de données additionnelles ou reconstruction de logiciel à partir de modules extraits du flux de transport
H04N 21/44 - Traitement de flux élémentaires vidéo, p. ex. raccordement d'un clip vidéo récupéré d'un stockage local avec un flux vidéo en entrée ou rendu de scènes selon des graphes de scène du flux vidéo codé
H04N 21/458 - Ordonnancement de contenu pour créer un flux personnalisé, p. ex. en combinant une publicité stockée localement avec un flux d'entréeOpérations de mise à jour, p. ex. pour modules de système d'exploitation
A system and method for generating a play prediction for a team is disclosed herein. A computing system retrieves trajectory data for a plurality of plays from a data store. The computing system generates a predictive model using a variational autoencoder and a neural network by generating one or more input data sets, learning, by the variational autoencoder, to generate a plurality of variants for each play of the plurality of plays, and learning, by the neural network, a team style corresponding to each play of the plurality of plays. The computing system receives trajectory data corresponding to a target play. The predictive model generates a likelihood of a target team executing the target play by determining a number of target variants that correspond to a target team identity of the target team.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
