Abstract: A chatbot receives a player query for help and based on being trained on a wide corpus of documents including gamer comments on social media sites, returns in natural human language either spoken or written, a gameplay strategy. Accordingly, a player can input a question to a model such as chatGPT to cause the model to determine an optimum mechanic (such as a weapon) for the player's current game situation and if desired return a video clip showing the mechanic and use thereof. In training, recognized objects can be input to the chatbot with ground truth description. The chatbot can access sites such as Discord, Reddit, etc. to learn what gamers are talking about.

Abstract: Systems and methods are disclosed for determining that a first end-user entity has performed a task within a computer simulation for which a non-fungible token (NFT) is to be provided, where the NFT is associated with a digital asset. Responsive to the determination, the NFT is provided to the first end-user entity so that the digital asset may be used, via the NFT, across plural different computer simulations and/or across plural different computer simulation platforms. Ownership of the NFT may also be subsequently transferred to other end-user entities for their own use across different simulations and/or platforms.

Abstract: Techniques and devices are described for building 3D drawings within a 3D scene sourced from a game engine (or other video source) without the benefit of 3D data and other metadata from the game engine itself. Accordingly, in one aspect an apparatus may include at least one processor assembly that is configured to access a parameterization of a virtual 3D space and to receive user input directed to an object represented in the virtual 3D space. The at least pone processor assembly may also be configured to, based on the user input, change the parameterization of the virtual 3D space to represent the user input within the virtual 3D space from different viewing angles.

Abstract: A machine learning (ML) model is used to identify successful outcomes in computer games based on aggregated game metadata including activity, mechanics, actors, statistics, and zones or locations. A strategy includes how a player over time used a character (actor) to employ one or more mechanics (weapons, vehicles) to execute various activities in various zones or locations in a computer game, with strategies being graded for success. Good strategies are then surfaced to subsequent players by, e.g., advising a player to seek a better location in a game, employ a different mechanic based on the game zone the player's character is in such as employ a different car or car configurations, employ a plane, a particular weapon, etc.

Abstract: Data processing/GUIs for NFT block chain data to “tell the story” of ownership or highlight “cool” aspects of a computer game-related NFT in simplified way. Machine learning (ML) may be used to boil down the complexity of data to what people need or want to understand. The displayed timeline of ownership as presented in a GUI can be interactive. Types of metadata to encapsulate in the NFT are discussed.

Abstract: A machine learning (ML) model is used to identify successful outcomes in computer games based on aggregated game metadata including activity, mechanics, actors, statistics, and zones or locations. A strategy includes how a player over time used a character (actor) to employ one or more mechanics (weapons, vehicles) to execute various activities in various zones or locations in a computer game, with strategies being graded for success. Good strategies are then surfaced to subsequent players by, e.g., advising a player to seek a better location in a game, employ a different mechanic based on the game zone the player's character is in such as employ a different car or car configurations, employ a plane, a particular weapon, etc.

Abstract: A chatbot receives a player query for help and based on being trained on a wide corpus of documents including gamer comments on social media sites, returns in natural human language either spoken or written, a gameplay strategy. Accordingly, a player can input a question to a model such as chatGPT to cause the model to determine an optimum mechanic (such as a weapon) for the player's current game situation and if desired return a video clip showing the mechanic and use thereof. In training, recognized objects can be input to the chatbot with ground truth description. The chatbot can access sites such as Discord, Reddit, etc. to learn what gamers are talking about.

Abstract: Data processing/GUIs for NFT block chain data to “tell the story” of ownership or highlight “cool” aspects of a computer game-related NFT in simplified way. Machine learning (ML) may be used to boil down the complexity of data to what people need or want to understand. The displayed timeline of ownership as presented in a GUI can be interactive. Types of metadata to encapsulate in the NFT are discussed.

Abstract: Systems and methods are disclosed for determining that a first end-user entity has performed a task within a computer simulation for which a non-fungible token (NFT) is to be provided, where the NFT is associated with a digital asset. Responsive to the determination, the NFT is provided to the first end-user entity so that the digital asset may be used, via the NFT, across plural different computer simulations and/or across plural different computer simulation platforms. Ownership of the NFT may also be subsequently transferred to other end-user entities for their own use across different simulations and/or platforms.

Abstract: Systems and methods are disclosed for determining that a first end-user entity has performed a task within a computer simulation for which a non-fungible token (NFT) is to be provided, where the NFT is associated with a digital asset. Responsive to the determination, the NFT is provided to the first end-user entity so that the digital asset may be used, via the NFT, across plural different computer simulations and/or across plural different computer simulation platforms. Ownership of the NFT may also be subsequently transferred to other end-user entities for their own use across different simulations and/or platforms.

Abstract: A machine learning (ML) model is used to identify successful outcomes in computer games based on aggregated game metadata including activity, mechanics, actors, statistics, and zones or locations. A strategy includes how a player over time used a character (actor) to employ one or more mechanics (weapons, vehicles) to execute various activities in various zones or locations in a computer game, with strategies being graded for success. Good strategies are then surfaced to subsequent players by, e.g., advising a player to seek a better location in a game, employ a different mechanic based on the game zone the player's character is in such as employ a different car or car configurations, employ a plane, a particular weapon, etc.

Abstract: Systems and methods are disclosed for determining that a first end-user entity has performed a task within a computer simulation for which a non-fungible token (NFT) is to be provided, where the NFT is associated with a digital asset. Responsive to the determination, the NFT is provided to the first end-user entity so that the digital asset may be used, via the NFT, across plural different computer simulations and/or across plural different computer simulation platforms. Ownership of the NFT may also be subsequently transferred to other end-user entities for their own use across different simulations and/or platforms.

Abstract: Data processing/GUIs for NFT block chain data to “tell the story” of ownership or highlight “cool” aspects of a computer game-related NFT in simplified way. Machine learning (ML) may be used to boil down the complexity of data to what people need or want to understand. The displayed timeline of ownership as presented in a GUI can be interactive. Types of metadata to encapsulate in the NFT are discussed.