Abstract: An artificial intelligence (AI) learning machine is integrated into a gaming system to adaptively learn to predict when and where excessive chronological clustering of winning hits is likely to occur for a progressive jackpot (e.g., a high frequency one) within the gaming system, to preemptively prepare for such occurrences by prefunding one or more smart-awards funds to cope with such excessive chronological clusterings of hits and to use the one or more smart-awards funds to pay off not-first-in-time winners with a same jackpot award amount as is paid to the true first-in-time winner of the jackpot if such runner up winners hit their respective win conditions within a predetermined smart-awards time span after the first-in-time winner hits. This can avoid or reduce feelings of unfairness by the not-first-in-time winners especially when the latter hit just a split second after the true first-in-time winner hits the jackpot.

Abstract: An artificial intelligence (AI) learning machine is integrated into a gaming system to adaptively learn to predict when and where excessive chronological clustering of winning hits is likely to occur for a progressive jackpot (e.g., a high frequency one) within the gaming system, to preemptively prepare for such occurrences by prefunding one or more smart-awards funds to cope with such excessive chronological clusterings of hits and to use the one or more smart-awards funds to pay off not-first-in-time winners with a same jackpot award amount as is paid to the true first-in-time winner of the jackpot if such runner up winners hit their respective win conditions within a predetermined smart-awards time span after the first-in-time winner hits. This can avoid or reduce feelings of unfairness by the not-first-in-time winners especially when the latter hit just a split second after the true first-in-time winner hits the jackpot.

Abstract: An artificial intelligence (AI) learning machine is integrated into a gaming system to adaptively learn to predict when and where excessive chronological clustering of winning hits is likely to occur for a progressive jackpot (e.g., a high frequency one) within the gaming system, to preemptively prepare for such occurrences by prefunding one or more smart-awards funds to cope with such excessive chronological clusterings of hits and to use the one or more smart-awards funds to pay off not-first-in-time winners with a same jackpot award amount as is paid to the true first-in-time winner of the jackpot if such runner up winners hit their respective win conditions within a predetermined smart-awards time span after the first-in-time winner hits. This can avoid or reduce feelings of unfairness by the not-first-in-time winners especially when the latter hit just a split second after the true first-in-time winner hits the jackpot.

Abstract: An artificial intelligence (AI) learning machine is integrated into a gaming system to adaptively learn to predict when and where excessive chronological clustering of winning hits is likely to occur for a progressive jackpot (e.g., a high frequency one) within the gaming system, to preemptively prepare for such occurrences by prefunding one or more smart-awards funds to cope with such excessive chronological clusterings of hits and to use the one or more smart-awards funds to pay off not-first-in-time winners with a same jackpot award amount as is paid to the true first-in-time winner of the jackpot if such runner up winners hit their respective win conditions within a predetermined smart-awards time span after the first-in-time winner hits. This can avoid or reduce feelings of unfairness by the not-first-in-time winners especially when the latter hit just a split second after the true first-in-time winner hits the jackpot.