IP Library Granted Patent US 12,322,246
Granted Patent B2
US 12,322,246 · App. 17/993,284 · Granted Jun 3, 2025

Automated conversational multi-player gaming platform

Inventor: Richard Turner (Universal City, TX)
Assignee: 52 Productions Inc.
G07F17/3239G06F7/588G06F40/40G07F17/3293G10L15/26
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Quick Facts
Patent No.
US 12,322,246
App. No.
17/993,284
Granted
Jun 3, 2025
Kind
B2
Abstract

An automated conversational multi-player gaming platform is described. A machine-learning model is trained to use a natural language processor and a speech synthesizer as an automated dealer communicating with players of a game of chance. The trained machine-learning model receives requests from people to become players of a game of chance, outputs private game content associated with the game of chance to the players' private user interfaces, and outputs public game content associated with the game of chance to the players' public user interface. The trained machine-learning model receives selections of game playing options and wager values associated with the game of chance from the players via their private user interfaces, and outputs information identifying one player as a winner of the game of chance and a credit of the winner's wager value to the players' public user interface and/or the winner's private user interface.

Claims (34)

1. A system for an automated conversational multi-player gaming platform, the system comprising:

one or more processors; and

a non-transitory computer readable medium storing a plurality of instructions, which when executed, cause the one or more processors to:

train a machine-learning model to use a natural language processor and a speech synthesizer as an automated dealer communicating with players of at least one game of chance;

output, by the trained machine-learning model, private game content associated with the game of chance to private user interfaces for players of a game of chance, in response to receiving requests from people to become the players of the game of chance;

output, by the trained machine-learning model, public game content associated with the game of chance to a public user interface for the players; and

output, by the trained machine-learning model, information identifying one of the players as a winner of the game of chance and a credit of a wager value by the winner to at least one of one of the private user interfaces for the winner or the public user interface, in response to receiving selections of game playing options and wagers values associated with the game of chance by the players via the private user interfaces,

wherein outputting to one of the private user interfaces is exclusively output by speech synthesis via a natural language selected by a player, in response to an identification of a corresponding player as visually impaired, or exclusively output by text in response to an identification of a corresponding player as hearing impaired.

2. The system of claim 1 , wherein the plurality of instructions, when executed, will further cause the one or more processors to output, by the trained machine-learning model, information in natural languages about playing different games of chance, including the requested game of chance.

3. The system of claim 1 , wherein the requested game of chance comprises one of poker, blackjack, baccarat, rummy, roulette, craps, Shark Showdown, Texas Showdown, or Batty.

4. The system of claim 1 , wherein the private game content and the public game content comprise playing cards associated with a deck of 52 playing cards.

5. The system of claim 4 , wherein the automated dealer uses a random number generator to deal the playing cards to the players.

6. The system of claim 1 , wherein the plurality of instructions, when executed, will further cause the one or more processors to debit the values of the wagers by the other players of the game of chance, in response to the identification of the one of the players as the winner of the game of chance.

7. A computer-implemented method for a computer game of chance, the method comprising:

training a machine-learning model to use a natural language processor and a speech synthesizer as an automated dealer communicating with players of at least one game of chance;

outputting, by the trained machine-learning model, private game content associated with the game of chance to private user interfaces for players of a game of chance, in response to receiving requests from people to become the players of the game of chance;

outputting, by the trained machine-learning model, public game content associated with the game of chance to a public user interface for the players; and

outputting, by the trained machine-learning model, information identifying one of the players as a winner of the game of chance and a credit of a wager value by the winner to at least one of one of the private user interfaces for the winner or the public user interface, in response to receiving selections of game playing options and wagers values associated with the game of chance by the players via the private user interfaces,

wherein outputting to one of the private user interfaces is exclusively output by speech synthesis via a natural language selected by a player, in response to an identification of a corresponding player as visually impaired, or exclusively output by text in response to an identification of a corresponding player as hearing impaired.

8. The computer-implemented method of claim 7 , wherein the method further comprises outputting, by the trained machine-learning model, information in natural languages about playing different games of chance, including the requested game of chance.

9. The computer-implemented method of claim 7 , wherein the requested game of chance comprises one of poker, blackjack, baccarat, rummy, roulette, craps, Shark Showdown, Texas Showdown, or Batty.

10. The computer-implemented method of claim 7 , wherein the private game content and the public game content comprise playing cards associated with a deck of 52 playing cards.

11. The computer-implemented method of claim 10 , wherein the automated dealer uses a random number generator to deal the playing cards to the players.

12. The computer-implemented method of claim 7 , wherein the method further comprises debiting the values of the wagers by the other players of the game of chance, in response to the identification of the one of the players as the winner of the game of chance.

13. A computer program product, comprising a non-transitory computer-readable medium having a computer-readable program code embodied therein to be executed by one or more processors, the program code including instructions to:

train a machine-learning model to use a natural language processor and a speech synthesizer as an automated dealer communicating with players of at least one game of chance;

output, by the trained machine-learning model, private game content associated with the game of chance to private user interfaces for players of a game of chance, in response to receiving requests from people to become the players of the game of chance;

output, by the trained machine-learning model, public game content associated with the game of chance to a public user interface for the players; and

output, by the trained machine-learning model, information identifying one of the players as a winner of the game of chance and a credit of a wager value by the winner to at least one of one of the private user interfaces for the winner or the public user interface, in response to receiving selections of game playing options and wagers values associated with the game of chance by the players via the private user interfaces,

wherein outputting to one of the private user interfaces is exclusively output by speech synthesis via a natural language selected by a player, in response to an identification of a corresponding player as visually impaired, or exclusively output by text in response to an identification of a corresponding player as hearing impaired.

14. The computer program product of claim 13 , wherein the program code includes further instructions to output, by the trained machine-learning model, information in natural languages about playing different games of chance, including the requested game of chance.

15. The computer program product of claim 13 , wherein the requested game of chance comprises one of poker, blackjack, baccarat, rummy, roulette, craps, Shark Showdown, Texas Showdown, or Batty.

16. The computer program product of claim 13 , wherein the private game content and the public game content comprise playing cards associated with a deck of 52 playing cards, and the automated dealer uses a random number generator to deal the playing cards to the players.

17. The computer program product of claim 13 , wherein the program code includes further instructions to debit the values of the wagers by the other players of the game of chance, in response to the identification of the one of the players as the winner of the game of chance.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 2, 2022
From: TURNER, RICHARD
To: 52 PRODUCTIONS INC.
Reel/Frame 061953/0177 →
Continuity (2)
Provisional Application 63282857 · Nov 24, 2021
Related Publication 20230162563A1 · May 25, 2023
References Cited (3)
US 20100121808A1 · Kuhn · 2010 [cited by examiner]
US 20160284157A1 · Marrinson · 2016 [cited by examiner]
US 20220309936A1 · Jang · 2022 [cited by examiner]
Cited By (5)
US 12,632,913 US 12,657,643 US 12,731,201 US 12,731,202 US 12,743,735