Abstract: A system and method for operating a game of chance. A gaming system includes one or more game displays, a user interface, at least one processor running executable instructions related to a game of chance and memory in communication with said at least one processor. The game of chance including a two-part game wherein at least one part of the two-part games includes a video reel game and includes the generation of random sub-events wherein two or more of said random sub-events are required to trigger another game part including a bonus game, a secondary game, a free game round, a “hold and spin” round, or other game event. The plurality of sub-events required to trigger another game part serve to reduce the frequency of significant player “cold streaks” during game play. The game of chance including randomized independent expanding reels with dynamically changing graphical elements.

Abstract: Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Abstract: Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Abstract: In one or more embodiments, one or more systems, processes, and/or methods may receive, via a network, a multiple positions corresponding to multiple physical locations of a consumer device, may receive, via the network, a position of a physical location of a financial transaction device, and may receive a request for a financial transaction, and may determine a transaction authorization based on a path indicated by the multiple positions and the position of the physical location of the financial transaction device. For example, a path and/or track may be established via periodical reports of the position information from the consumer device, which may provide and/or indicate an authenticity metric and/or a validity metric to a current position attribute when the current position attribute is utilized in a financial transaction.

Abstract: Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Abstract: Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Abstract: Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Abstract: Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Abstract: Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Abstract: Aspects of the disclosure relate to a system to monitor devices for unauthorized access of an external account. A computing platform may receive, via a communication interface, and from a first user device, metadata comprising first user device identification information and accessed account information. The computing platform may determine, based on the first user device identification information, whether the first user device is one of a plurality of internal user devices. The computing platform may identify, based on the accessed account information, an action indicating the first user device is accessing an account. The computing platform may determine, based on the action and the accessed account information, whether the action is authorized. The computing platform may receive information indicating unauthorized use of the account by the first user device. The computing platform may transmit the information indicating unauthorized use of the account by the first user device.

Abstract: Aspects of the disclosure relate to controlling device data collectors using omni-collection techniques. A computing platform may receive configuration settings associated with a first collector and a second collector. Subsequently, the computing platform may generate one or more configuration commands for a native collector based on the configuration settings. The computing platform may send, to a client interface computing platform associated with the native collector, the one or more configuration commands generated for the native collector. Thereafter, the computing platform may receive state information collected by the native collector based on the one or more configuration commands generated for the native collector.

Abstract: In one or more embodiments, one or more systems, processes, and/or methods may receive, via a network, a multiple positions corresponding to multiple physical locations of a consumer device, may receive, via the network, a position of a physical location of a financial transaction device, and may receive a request for a financial transaction, and may determine a transaction authorization based on a path indicated by the multiple positions and the position of the physical location of the financial transaction device. For example, a path and/or track may be established via periodical reports of the position information from the consumer device, which may provide and/or indicate an authenticity metric and/or a validity metric to a current position attribute when the current position attribute is utilized in a financial transaction.

Abstract: Aspects of the disclosure relate to a system to monitor devices for unauthorized access of an external account. A computing platform may receive, via a communication interface, and from a first user device, metadata comprising first user device identification information and accessed account information. The computing platform may determine, based on the first user device identification information, whether the first user device is one of a plurality of internal user devices. The computing platform may identify, based on the accessed account information, an action indicating the first user device is accessing an account. The computing platform may determine, based on the action and the accessed account information, whether the action is authorized. The computing platform may receive information indicating unauthorized use of the account by the first user device. The computing platform may transmit the information indicating unauthorized use of the account by the first user device.

Abstract: Aspects of the disclosure relate to controlling device data collectors using omni-collection techniques. A computing platform may receive configuration settings associated with a first collector and a second collector. Subsequently, the computing platform may generate one or more configuration commands for a native collector based on the configuration settings. The computing platform may send, to a client interface computing platform associated with the native collector, the one or more configuration commands generated for the native collector. Thereafter, the computing platform may receive state information collected by the native collector based on the one or more configuration commands generated for the native collector.

Abstract: A gaming system is disclosed which comprises a plurality of display positions disposed in a display area, a symbol selector arranged to select a plurality of symbols for display at respective display positions, and an outcome evaluator arranged to determine whether the selected symbols correspond to a winning outcome with reference to at least one of a plurality of defined win lines. Each of the defined win lines comprises at least one display position, and at least some of the win lines comprise differing numbers of display positions. A corresponding method of gaming is also disclosed.

Abstract: A gaming system is disclosed which comprises a plurality of display positions disposed in a display area, a symbol selector arranged to select a plurality of symbols for display at respective display positions, and an outcome evaluator arranged to determine whether the selected symbols correspond to a winning outcome with reference to at least one of a plurality of defined win lines. Each of the defined win lines comprises at least one display position, and at least some of the win lines comprise differing numbers of display positions. A corresponding method of gaming is also disclosed.

Abstract: A gaming system is disclosed which comprises a plurality of display positions disposed in a display area, a symbol selector arranged to select a plurality of symbols for display at respective display positions, and an outcome evaluator arranged to determine whether the selected symbols correspond to a winning outcome with reference to at least one of a plurality of defined win lines. Each of the defined win lines comprises at least one display position, and at least some of the win lines comprise differing numbers of display positions. A corresponding method of gaming is also disclosed.

Abstract: A gaming system is disclosed which comprises a selector arranged to select a plurality of symbols from a set of symbols for display in a display area at a plurality of display positions, an outcome evaluator arranged to evaluate a game outcome based on the selected symbols and to identify a scatter outcome when a plurality of special symbols are disposed in the display area at any display position, and a prize allocator. The prize allocator is arranged to allocate a prize to a player when a scatter outcome exists and to determine the prize awarded to a player using the plurality of special symbols. A corresponding method is also disclosed.

Abstract: A gaming system is disclosed which comprises a plurality of display positions disposed in a display area, a symbol selector arranged to select a plurality of symbols for display at respective display positions, and an outcome evaluator arranged to determine whether the selected symbols correspond to a winning outcome with reference to at least one of a plurality of defined win lines. Each of the defined win lines comprises at least one display position, and at least some of the win lines comprise differing numbers of display positions. A corresponding method of gaming is also disclosed.

Abstract: A gaming system is disclosed which comprises a plurality of display positions disposed in a display area, a symbol selector arranged to select a plurality of symbols for display at respective display positions, and an outcome evaluator arranged to determine whether the selected symbols correspond to a winning outcome with reference to at least one of a plurality of defined win lines. Each of the defined win lines comprises at least one display position, and at least some of the win lines comprise differing numbers of display positions. A corresponding method of gaming is also disclosed.