IP Library Granted Patent US 12,272,207
Granted Patent B2
US 12,272,207 · App. 18/354,605 · Granted Apr 8, 2025

Systems and methods for slot machine game development utilizing artificial intelligence game performance data analytics systems

Inventors: David Colvin (Las Vegas, NV); Eric Colvin (Meridian, ID)
Assignee: Sierra Artificial Neural Networks
G07F17/3223G06F8/30G06N3/04G06N3/0455G06N3/088G06N20/00G07F17/32G07F17/3213G07F17/3234G07F17/3241G07F17/326G07F17/329G07F17/34G06N3/0895G07F17/3267
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,272,207
App. No.
18/354,605
Granted
Apr 8, 2025
Kind
B2
Abstract

Systems and methods for developing a game of chance. The game of chance being at least partially developed using specialized artificial intelligence (AI) game design systems or specialized artificial intelligence game design system modules or components which may include different types of machine learning or training techniques, including supervised, unsupervised, reinforced, deep learning and artificial neural networks and/or similar and may include analyzing past game performance utilizing full, partial or estimated prior game performance data for developing slot machine game math models, slot machine game mechanics and associated game programming, slot machine game art and graphics, slot machine animations, slot machine game sound effects, partial or full slot machine game development, slot machine computer code, slot machine game quality assurance diagnosis and editing, slot machine game analytics, slot machine compliance, slot machine help screens, and slot game predictive models, etc.

Claims (36)

1. A gaming method comprising:

at least partially developing executable instructions or computer readable files related to animation elements for a game of chance for a gaming machine using a animation-based artificial intelligence game design system based upon machine learning training including analyzing past game performance;

utilizing a media encoding and transcoding router to (i) change input data type to a different media format or consolidate the input data type to a specific file type and (ii) direct the changed or consolidated input data type to a specific neural network in a transformer process and select an output with a higher probability variable; and

utilizing the at least partially developed executable instructions or computer readable files to present and allow play of the game of chance for a gaming machine, the gaming machine including at least one of a monetary input device configured to receive a physical item associated with a monetary value and/or cashless wagering, a user interface, at least one processor for running the at least partially developed executable instructions or computer readable files related to the animation elements for the game of chance, a game display and memory in communication with the at least one processor.

2. The gaming method of claim 1 wherein the animation-based artificial intelligence game design system includes utilizing at least partially supervised machine learning training to at least partially develop graphical elements for a game of chance.

3. The gaming method of claim 1 wherein the animation-based artificial intelligence game design system includes at least an animation-based game design system module utilizing at least partially supervised machine learning training to at least partially develop executable instructions or computer readable files.

4. The gaming method of claim 1 wherein the animation-based artificial intelligence game design system includes at least a past game performance analytics module utilizing at least partially supervised machine learning training.

5. The gaming method of claim 1 further comprising the at least partially developing executable instructions or computer readable files related to animation elements for a game of chance for a gaming machine including developing a graphic of at least one of a game character, a reel set, game symbols, free symbols, a game background, a game logo, a game progressive box, a game credit bar, a secondary game character, or a game persistence graphic.

6. The gaming method of claim 1 wherein the animation-based artificial intelligence game design system includes utilizing at least partially unsupervised machine learning training to at least partially develop animation elements for a game of chance.

7. The gaming method of claim 1 wherein the animation-based artificial intelligence game design system includes at least a animation-based artificial intelligence game design system module utilizing at least partially unsupervised machine learning training to at least partially develop executable instructions or computer readable files.

8. The gaming method of claim 1 wherein the animation-based artificial intelligence game design system includes at least a animation-based game design system module utilizing at least partially unsupervised machine learning training to at least partially develop executable instructions or computer readable files.

9. The gaming method of claim 1 wherein the animation-based artificial intelligence game design system includes at least a past game performance analytics module utilizing at least partially unsupervised machine learning training.

10. The gaming method of claim 1 further comprising the at least partially developing executable instructions or computer readable files related to animation elements for a game of chance for a gaming machine utilizing at least partially unsupervised machine learning training and including developing a graphic of at least one of a game character, a reel set, game symbols, free symbols, a game background, a game logo, a game progressive box, a game credit bar, a secondary game character, or a game persistence graphic.

11. The gaming method of claim 1 wherein the animation-based artificial intelligence game design system includes utilizing at least partially reinforced machine learning training to at least partially develop animation elements for a game of chance.

12. The gaming method of claim 1 wherein the animation-based artificial intelligence game design system includes at least a animation-based artificial intelligence game design system module utilizing at least partially reinforced machine learning training to at least partially develop executable instructions or computer readable files.

13. The gaming method of claim 1 wherein the animation-based artificial intelligence game design system includes at least a animation-based game design system module utilizing at least partially reinforced machine learning training to at least partially develop executable instructions or computer readable files.

14. The gaming method of claim 1 wherein the animation-based artificial intelligence game design system includes at least a past game performance analytics module utilizing at least partially reinforced machine learning training.

15. The gaming method of claim 1 further comprising the at least partially developing executable instructions or computer readable files related to animation elements for a game of chance for a gaming machine utilizing at least partially reinforced machine learning training and including developing a graphic of at least one of a game character, a reel set, game symbols, free symbols, a game background, a game logo, a game progressive box, a game credit bar, a secondary game character, or a game persistence graphic.

16. A gaming system comprising:

at least one processor running at least a portion of developed executable instructions or computer readable files related to animation elements for a game of chance for a gaming machine using an animation-based artificial intelligence game design system based upon machine learning training including analysis of past game performance;

a media encoding and transcoding router to (i) change input data type to a different media format or consolidate the input data type to a specific file type and (ii) direct the changed or consolidated input data type to a specific neural network in a transformer process and select an output with a higher probability variable; and

wherein the at least portion of developed executable instructions or computer readable files are used to present and allow play of the game of chance on a gaming machine, the gaming machine including at least one of a monetary input device configured to receive a physical item associated with a monetary value and cashless wagering, a user interface, at least one processor for running the at least partially developed executable instructions or computer readable files related to the animation elements for the game of chance for a gaming machine, a game display and memory in communication with the at least one processor.

17. The gaming system of claim 16 wherein the animation-based artificial intelligence game design system utilizes at least partially supervised machine learning training to at least partially develop animation elements for a game of chance.

18. The gaming system of claim 16 wherein the animation-based artificial intelligence game design system includes at least a animation-based game design system module utilizing at least partially supervised machine learning training to at least partially develop executable instructions or computer readable files.

19. The gaming system of claim 16 wherein the animation-based artificial intelligence game design system includes at least a past game performance analytics module utilizing at least partially supervised machine learning training.

20. The gaming system of claim 16 further comprising the at least partially developed executable instructions or computer readable files related to animation elements for a game of chance for a gaming machine utilizing at least partially supervised machine learning training and configured to develop a graphic of at least one of a game character, a reel set, game symbols, free symbols, a game background, a game logo, a game progressive box, a game credit bar, a secondary game character, or a game persistence graphic.

21. The gaming system of claim 16 wherein the animation-based artificial intelligence game design system utilizes at least partially unsupervised machine learning training to at least partially develop animation elements for a game of chance.

22. The gaming system of claim 16 wherein the animation-based artificial intelligence game design system includes at least a animation-based artificial intelligence game design system module utilizing at least partially unsupervised machine learning training to at least partially develop executable instructions or computer readable files.

23. The gaming system of claim 16 wherein the animation-based artificial intelligence game design system includes at least a animation-based game design system module utilizing at least partially unsupervised machine learning training to at least partially develop executable instructions or computer readable files.

24. The gaming system of claim 16 wherein the animation-based artificial intelligence game design system includes at least a past game performance analytics module utilizing at least partially unsupervised machine learning training.

25. The gaming system of claim 16 further comprising the at least partially developed executable instructions or computer readable files related to animation elements for a game of chance for a gaming machine utilizing at least partially unsupervised machine learning training and configured to develop a graphic of at least one of a game character, a reel set, game symbols, free symbols, a game background, a game logo, a game progressive box, a game credit bar, a secondary game character, or a game persistence graphic.

26. The gaming system of claim 16 wherein the animation-based artificial intelligence game design system utilizes at least partially reinforced machine learning training to at least partially develop animation elements for a game of chance.

27. The gaming system of claim 16 wherein the animation-based artificial intelligence game design system includes at least a animation-based artificial intelligence game design system module utilizes at least partially reinforced machine learning training to at least partially develop executable instructions or computer readable files.

28. The gaming system of claim 16 wherein the animation-based artificial intelligence game design system includes at least a animation-based game design system module utilizes at least partially reinforced machine learning training to at least partially develop executable instructions or computer readable files.

29. The gaming system of claim 16 wherein the animation-based artificial intelligence game design system includes at least a past game performance analytics module utilizing at least partially reinforced machine learning training.

30. The gaming system of claim 16 further comprising the at least partially developed executable instructions or computer readable files related to animation elements for a game of chance for a gaming machine utilizing at least partially reinforced machine learning training and including developing a graphic of at least one of a game character, a reel set, game symbols, free symbols, a game background, a game logo, a game progressive box, a game credit bar, a secondary game character, or a game persistence graphic.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 30, 2024
From: COLVIN, DAVID; COLVIN, ERIC
To: SIERRA ARTIFICIAL NEURAL NETWORKS
Reel/Frame 069703/0053 →
Continuity (3)
Continuation 18354505 · Jul 18, 2023
Provisional Application 63501389 · May 10, 2023
Related Publication 20240378026A1 · Nov 14, 2024
References Cited (172)
US 6315669B1 · Okada et al. · 2001 [cited by applicant]
US 6322447B1 · Okada et al. · 2001 [cited by applicant]
US 6563503B1 · Comair et al. · 2003 [cited by applicant]
US 7137894B2 · Okada et al. · 2006 [cited by applicant]
US 7677966B2 · Kimura · 2010 [cited by applicant]
US 7695356B2 · Fujioka et al. · 2010 [cited by applicant]
US 7892097B2 · Muir et al. · 2011 [cited by applicant]
US 7976373B2 · Kroeckel et al. · 2011 [cited by applicant]
US 7976376B2 · Kroeckel et al. · 2011 [cited by applicant]
US 8033386B2 · Roseberry et al. · 2011 [cited by applicant]
US 8079903B2 · Nicely et al. · 2011 [cited by applicant]
US 8090887B2 · Ikeno et al. · 2012 [cited by applicant]
US 8162759B2 · Yamaguchi · 2012 [cited by applicant]
US 8267760B2 · Iwakiri et al. · 2012 [cited by applicant]
US 8308546B2 · Kroeckel et al. · 2012 [cited by applicant]
US 8317588B2 · Kroeckel et al. · 2012 [cited by applicant]
US 8323087B2 · Chen et al. · 2012 [cited by applicant]
US 8328610B2 · Shimura et al. · 2012 [cited by applicant]
US 8333660B2 · Uno · 2012 [cited by applicant]
US 8382582B2 · Sammon et al. · 2013 [cited by applicant]
US 8403747B2 · Tanabe et al. · 2013 [cited by applicant]
US 8403759B2 · Muir et al. · 2013 [cited by applicant]
US 8425299B2 · Kroeckel et al. · 2013 [cited by applicant]
US 8480468B2 · Kroeckel et al. · 2013 [cited by applicant]
US 8487928B2 · Yoshimura · 2013 [cited by applicant]
US 8500558B2 · Smith · 2013 [cited by applicant]
US 8517822B2 · Yamaguchi · 2013 [cited by applicant]
US 8550889B2 · Tsunashima et al. · 2013 [cited by applicant]
US 8556702B2 · Kroeckel et al. · 2013 [cited by applicant]
US 8597115B2 · Sammon et al. · 2013 [cited by applicant]
US 8616946B2 · Yanagisawa et al. · 2013 [cited by applicant]
US 8633947B2 · Kitahara · 2014 [cited by applicant]
US 8696464B2 · Smith · 2014 [cited by applicant]
US 8758117B2 · Nicely et al. · 2014 [cited by applicant]
US 8780183B2 · Ito et al. · 2014 [cited by applicant]
US 8808090B2 · Myogan · 2014 [cited by applicant]
US 8827783B2 · Sogabe · 2014 [cited by applicant]
US 8845411B2 · Chen et al. · 2014 [cited by applicant]
US 8854356B2 · Oyagi et al. · 2014 [cited by applicant]
US 8863089B2 · Rabin et al. · 2014 [cited by applicant]
US 8888597B2 · Kroeckel et al. · 2014 [cited by applicant]
US 8894486B2 · Konno et al. · 2014 [cited by applicant]
US 8899587B2 · Grauzer et al. · 2014 [cited by applicant]
US 8913064B2 · McNeely et al. · 2014 [cited by applicant]
US 8944904B2 · Grauzer et al. · 2015 [cited by applicant]
US 8992297B2 · De Waal · 2015 [cited by examiner]
US 9067133B2 · Smith · 2015 [cited by applicant]
US 9087437B2 · Kroeckel et al. · 2015 [cited by applicant]
US 9095777B2 · Kondo · 2015 [cited by applicant]
US 9128293B2 · Ohta · 2015 [cited by applicant]
US 9142091B2 · Aoki et al. · 2015 [cited by applicant]
US 9147314B2 · Muir et al. · 2015 [cited by applicant]
US 9180371B2 · Tsuchiya · 2015 [cited by applicant]
US 9183700B2 · Allen et al. · 2015 [cited by applicant]
US 9220972B2 · Grauzer et al. · 2015 [cited by applicant]
US 9233302B2 · Nanba et al. · 2016 [cited by applicant]
US 9278281B2 · Ito et al. · 2016 [cited by applicant]
US 9282319B2 · Konno et al. · 2016 [cited by applicant]
US 9286764B2 · Kroeckel et al. · 2016 [cited by applicant]
US 9289682B2 · Kurita et al. · 2016 [cited by applicant]
US 9311777B2 · Anderson et al. · 2016 [cited by applicant]
US 9373215B2 · Lind et al. · 2016 [cited by applicant]
US 9387390B2 · Downs, III et al. · 2016 [cited by applicant]
US 9387399B2 · Ojima · 2016 [cited by applicant]
US 9412229B2 · Wolf · 2016 [cited by examiner]
US 9415309B2 · Bentdahl et al. · 2016 [cited by applicant]
US 9421469B2 · Tsunashima · 2016 [cited by applicant]
US 9454872B2 · Muir et al. · 2016 [cited by applicant]
US 9542807B2 · Anderson et al. · 2017 [cited by applicant]
US 9558614B2 · Lind et al. · 2017 [cited by applicant]
US 9592445B2 · Smith · 2017 [cited by applicant]
US 9616332B2 · Fujishiro et al. · 2017 [cited by applicant]
US 9640021B2 · Ansari · 2017 [cited by applicant]
US 9666031B2 · Jaffe et al. · 2017 [cited by applicant]
US 9710997B2 · Schrementi et al. · 2017 [cited by applicant]
US 9711008B2 · Kikuchi · 2017 [cited by applicant]
US 9767652B2 · Englman et al. · 2017 [cited by applicant]
US 9908034B2 · Downs et al. · 2018 [cited by applicant]
US 9931569B2 · Tanibuchi et al. · 2018 [cited by applicant]
US 9943768B2 · Izuno et al. · 2018 [cited by applicant]
US 9975047B2 · Hamano et al. · 2018 [cited by applicant]
US 9981188B2 · Tanibuchi et al. · 2018 [cited by applicant]
US 10004976B2 · Grauzer et al. · 2018 [cited by applicant]
US 10015473B2 · Ito · 2018 [cited by applicant]
US 10022617B2 · Stasson et al. · 2018 [cited by applicant]
US 10092846B2 · Sogabe et al. · 2018 [cited by applicant]
US 10186104B2 · Schrementi et al. · 2019 [cited by applicant]
US 10223864B2 · Ditton · 2019 [cited by applicant]
US 10293258B2 · Smith · 2019 [cited by applicant]
US 10300391B2 · Inukai et al. · 2019 [cited by applicant]
US 10322344B2 · Nishimura et al. · 2019 [cited by applicant]
US 10322345B2 · Takahashi et al. · 2019 [cited by applicant]
US 10347081B2 · Walker et al. · 2019 [cited by applicant]
US 10357630B2 · Kido et al. · 2019 [cited by applicant]
US 10366574B2 · Yamamori et al. · 2019 [cited by applicant]
US 10413822B2 · Katagai et al. · 2019 [cited by applicant]
US 10424524B2 · Shen et al. · 2019 [cited by applicant]
US 10427044B2 · Katagai et al. · 2019 [cited by applicant]
US 10431045B2 · Yamamori et al. · 2019 [cited by applicant]
US 10445983B1 · Melnick · 2019 [cited by examiner]
US 10467852B2 · Ansari et al. · 2019 [cited by applicant]
US 10478725B2 · Kawai et al. · 2019 [cited by applicant]
US 10478726B2 · Kawai et al. · 2019 [cited by applicant]
US 10506218B2 · Oyagi et al. · 2019 [cited by applicant]
US 10509648B2 · Rabin · 2019 [cited by applicant]
US 10518179B2 · Sogabe et al. · 2019 [cited by applicant]
US 10528247B2 · Abe · 2020 [cited by applicant]
US 10532272B2 · Bourbour et al. · 2020 [cited by applicant]
US 10549177B2 · Scheper et al. · 2020 [cited by applicant]
US 10569159B2 · Stasson et al. · 2020 [cited by applicant]
US 10576363B2 · Downs, III et al. · 2020 [cited by applicant]
US 10592390B2 · Rabin · 2020 [cited by applicant]
US 10764565B2 · Oyagi et al. · 2020 [cited by applicant]
US 10895918B2 · Bean · 2021 [cited by applicant]
US 10905960B2 · Yoneyama et al. · 2021 [cited by applicant]
US 10956833B1 · Yamane · 2021 [cited by examiner]
US 10957158B2 · Melnick et al. · 2021 [cited by applicant]
US 10970957B1 · Decasa, Jr. et al. · 2021 [cited by applicant]
US 11011015B2 · Achmueller et al. · 2021 [cited by applicant]
US 11049131B2 · Miyazaki et al. · 2021 [cited by applicant]
US 11080962B2 · Bitterlin et al. · 2021 [cited by applicant]
US 11094166B2 · Higgins et al. · 2021 [cited by applicant]
US 11151844B2 · Nelson et al. · 2021 [cited by applicant]
US 11213756B2 · Nair · 2022 [cited by examiner]
US 11354973B2 · Keilwert et al. · 2022 [cited by applicant]
US 11417171B2 · Idris et al. · 2022 [cited by applicant]
US 11430287B2 · Decasa, Jr. et al. · 2022 [cited by applicant]
US 11475472B2 · Nakai et al. · 2022 [cited by applicant]
US 11587388B2 · Idris et al. · 2023 [cited by applicant]
US 11636726B2 · Purohit et al. · 2023 [cited by applicant]
US 11670130B2 · Russ et al. · 2023 [cited by applicant]
US 11676448B2 · Johnson · 2023 [cited by applicant]
US 11676453B2 · Froy et al. · 2023 [cited by applicant]
US 11694506B2 · Wenzl · 2023 [cited by applicant]
US 20020039924A1 · Okada et al. · 2002 [cited by applicant]
US 20030148806A1 · Weiss · 2003 [cited by examiner]
US 20060287093A1 · Walker · 2006 [cited by examiner]
US 20070243928A1 · Iddings · 2007 [cited by applicant]
US 20080026844A1 · Wells · 2008 [cited by examiner]
US 20080070652A1 · Nguyen et al. · 2008 [cited by applicant]
US 20080214262A1 · Phillips et al. · 2008 [cited by applicant]
US 20090100409A1 · Toneguzzo · 2009 [cited by examiner]
US 20090215542A1 · Takahashi · 2009 [cited by applicant]
US 20090227373A1 · Yamamoto · 2009 [cited by applicant]
US 20110059801A1 · Shiigi et al. · 2011 [cited by applicant]
US 20110098114A1 · Ishida · 2011 [cited by applicant]
US 20120034981A1 · Yamaguchi · 2012 [cited by applicant]
US 20120252575A1 · Iida et al. · 2012 [cited by applicant]
US 20130184071A1 · Gadher · 2013 [cited by examiner]
US 20130203485A1 · Walker et al. · 2013 [cited by applicant]
US 20140073415A1 · Sammon et al. · 2014 [cited by applicant]
US 20140302932A1 · Hilbert · 2014 [cited by applicant]
US 20160110747A1 · Nakai et al. · 2016 [cited by applicant]
US 20160361650A1 · Terao et al. · 2016 [cited by applicant]
US 20170246541A1 · Sasaki et al. · 2017 [cited by applicant]
US 20170259177A1 · Aghdaie · 2017 [cited by examiner]
US 20170358170A1 · Ito et al. · 2017 [cited by applicant]
US 20180181375A1 · Hermet-Chavanne · 2018 [cited by examiner]
US 20190001219A1 · Sardari · 2019 [cited by examiner]
US 20190251603A1 · Jaatinen · 2019 [cited by examiner]
US 20190287208A1 · Yerli · 2019 [cited by examiner]
US 20200122040A1 · Juliani, Jr. · 2020 [cited by examiner]
US 20200183664A1 · Lee · 2020 [cited by examiner]
US 20210043031A1 · Keilwert et al. · 2021 [cited by applicant]
US 20210146254A1 · Snodgrass · 2021 [cited by examiner]
US 20210192884A1 · Idris · 2021 [cited by examiner]
US 20210338864A1 · Urban et al. · 2021 [cited by applicant]
US 20210346808A1 · Nair · 2021 [cited by examiner]
US 20220309270A1 · Small et al. · 2022 [cited by applicant]
US 20220414492A1 · Jezewski · 2022 [cited by examiner]
US 20240066411A1 · Nair · 2024 [cited by examiner]
US 20240086714A1 · Kimura · 2024 [cited by examiner]
Cited By (3)
US 1,095,573 US 1,098,169 US 1,117,240