Bally Gaming, Inc.

Lnw Gaming, Inc.

Sg Gaming, Inc.

Other

Scientific Games Corporation

Martin S Lyons

Bryan M Kelly

Stephen Patton

Sean Christopher Martin

John Sommer

Joseph Randolph Hedrick

Brett Jackson

Scott Thomas Hilbert

Roderick Ang

Terrin Eager

Randy Hedrick

Rod Ang

Rolland Steil

Ronald E Gray

Yogendrasinh Hematji Rajput

Joseph Lerner

Murugeshun Arun Arumugam

John F Link

Ryan Masao Yee

William R Wadleigh

Michael Vizzo

Colin Helsen

Julia Childs

Timothy Bucher

Joseph Randy Hedrick

Ian Arrowsmith

Joseph R Hedrick

Theodore Keenan

Mohan Kumar Areanalli Manju

Amy Lynn Monette

Daniel Savage

Ray Maras

Robert McPEAK

Anthony J Baerlocher

Joel Roger Jaffe

Michael J Mitchell

David B Schultz

Kenneth Shawn Soong

John R Vallejo

Kimberly Cohn

Kiran Brahmandam

Dougal Austin

Mark S Ross

David Ponce

Prateek Kumar Baishkhiyar

Suhas Rangaswamy

Vamsi Krishna Gandu

Joanna Musick

Paulina Rogers

Theordore Keenan

David Bunch

Sathishkumar Durai

According to one aspect of the present disclosure, a gaming system with a chip tray positioned above a light-diffusion box. The chip tray has a transparent portion on its underside. A tracking controller illuminates the light-diffusion box with diffused light that shines through a transparent portion on an underside of the chip tray and illuminates the edge of one or more chips in a chip stack. An image sensor captures an image of the one or more chips in response to illumination of the light-diffusion box. The tracking controller analyzes a visible indicator on the edge of the one or more chips. The tracking controller associates the visible indicator with a denomination value for the chip stack. Further, the tracking controller computes a value of the chip stack based on the denomination value and a dimension of at least one of the one or more chips.

Chip tracking system

In one example, a gaming system for determining, via image analysis, an outcome value (e.g., a card value) of a randomizing game object (e.g., a playing card) for a game played at a gaming table and also detecting, based on the outcome value and one or more game rules, an occurrence of a winning outcome for the game. The gaming system can further determine, via image analysis, a location at a gaming table surface related to the winning outcome. The gaming system can further, in response to determining the location, render a virtual-scene overlay having an outcome indicator positioned at pixel coordinates that correspond to the location. The gaming system can further project the virtual-scene overlay at the gaming table to cause an image of the outcome indicator to appear at, and in some examples conform to a shape of, the location at the gaming table surface.

Animating gaming-table outcome indicators for detected randomizing-game-object states

A method and apparatus to automatically modify one or more presentation attributes of a gaming system. For instance, the gaming system detects, via analysis of an image data by a neural network model, an appearance of one or more features of a gaming surface. The gaming system further automatically modifies, via the neural network model in response to detecting the appearance of the one or more features, a presentation attribute associated with presentation of gaming content via a designated area of the gaming surface. The gaming system further projects, via a projection system based on the modified presentation attribute, the gaming content onto the designated area of the gaming surface.

Gaming environment tracking system calibration

According to one aspect of the present disclosure, a system comprises a processor configured to execute instructions that cause operations to detect, using sensors of one or more card-handling devices ('card-handling device(s)') in a network, one or more anomalies (“anomaly (ies)”) on one or more cards (“card(s)”) used during play of one or more games (“game(s)”). The game(s) are played at one or more gaming tables (“table(s)”) associated with the card-handling device(s). The anomaly (ies) vary from one or more previously taken images of the card(s). The operations further include, in response to detecting the anomaly (ies), determining, via analysis by a machine learning model of image data captured by one or more image sensors at the table(s), identifiers for participants that played at the table(s) for the game(s) when the anomaly (ies) was/were detected. The operations further include relating, in a memory store associated with one or more collusion-confidence scores, the identifiers.

Systems and methods for collusion detection

Disclosed are methods for augmenting information presented in association with a gaming device. The methods include controlling a camera on a mobile device using an augmented reality gaming assistance component and enabling a user to employ the camera to capture an image of a game screen display. The captured image is sent via a network to a server that determines a game paytable, a current state of the game and a current wager amount from the captured image, then determines one or more possible strategies for the game based on that data. The game paytable, current state, current wager amount and one or more possible strategies are then transmitted to the mobile device and displayed. At least one of the possible strategies may be selected at the mobile device, wherein the odds of one or more possible game outcomes for the selected strategy are displayable on the display.

Methods for augmented reality gaming

According to one aspect of the present disclosure, a gaming system is provided for tracking chips in a gaming environment. In some embodiments, an apparatus, system, etc. comprises a chip tray having one or more image sensors. The apparatus further comprises a tracking controller. The chip tray includes columns. The image sensor(s) are positioned between a least two of the columns and aligned substantially parallel to a vertical height of the columns. The tracking controller is configured, in some embodiments, to perform operations that cause the apparatus to capture, via the one or more image sensors, image data of a chip stack in at least one of the two of the columns. The tracking controller is further configured to segment the chip stack from the image data, and detect, via a machine learning model, a value of each chip in the chip stack in response analysis of chip-edge features of each gaming chip in the chip stack. The tracking controller is further configured to electronically present information associated with detection of the value of each gaming chip in the chip stack.

Disclosed are a system and method for training a neural network associated with a casino table game monitoring system. Synthetic images of objects extracted from a virtual table game environment are used to train a machine learning model, which is deployed to a casino table game monitoring system to monitor one or more physical objects relative to a physical gaming table in a physical table game environment.

System and method for synthetic image training of a machine learning model associated with a casino table game monitoring system

A system that analyzes an image of a gaming area, identifies an object depicted in the image and generates a virtual mask that conforms to a shape of the object. The system further maps the virtual mask to locations in a virtual scene that correspond to pixels in the image that correspond to the outer shape of the object. The system renders content in the virtual scene relative to a boundary of the mask based on the relevance of the physical object to the game state. The system instructs a projector to project the rendered image in relation to the physical object according to the content rendered relative to the mask.

Gaming state object tracking

Methods and systems of monitoring at least one feature associated with a patron in a gaming establishment. Such methods and systems may include detecting at least one physical feature associated with the patron and/or motions of one or more players participating in a game.

Systems and devices for identification of a feature associated with a user in a gaming establishment and related methods

A method or system to, in response to automatic shuffling of a set of cards by a shuffler, detect a first anomaly of a first card of high value that was used during a round of play for a first card game in which a first player participated at a gaming table. The method or system is further to, in response to analysis of shuffler data, detect a relationship between the first anomaly and a second anomaly. The second anomaly is associated with a second card of high value that was used during a round of play for a second card game in which a second player participated. The system or method is further to, in response to detection of the relationship between the first anomaly and the second anomaly, relate via a collusion-confidence score a first identifier for the first player with a second identifier for the second player.

A method and apparatus for a chip tracking system. A chip tray is positioned above a light-diffusion box. The chip tray has a transparent portion for a column of the chip tray One or more image sensors are positioned with a viewing perspective of chips through a transparent portion of an underside of the chip tray A tracking controller is configured to illuminate the light-diffusion box with diffused light that shines through the transparent portion of the chip tray and illuminates the edge of one or more chips in a chip stack visible via the transparent portion. The one or more image sensors capture an image of one or more chips in the column in response to illumination of the light-diffusion box. The tracking controller analyzes, via a neural network model, a color pattern on the edge of the one or more chips. The tracking controller associates the color pattern on the edge of the one or more chips with a denomination value for the chip stack. The tracking controller determines, using a range imaging device, a height of the chip stack. Further, the tracking controller computes a monetary value of the chip stack based on the denomination value, the height of the chip stack, and a known edge thickness of one of the one or more chips.

According to one aspect of the present disclosure, a system is provided for controlling a network game. In some instances, a processor of the system selects a winning card value of an undealt playing card for the network game. The network game spans a plurality of gaming tables having a deck of cards used for individual card games separate from the network game. A shuffler at each table shuffles the deck of cards for the individual card games. The system further detects, in response to analyzing image data at each of the gaming tables by a machine learning model, that a playing card, having the winning card value, is dealt. The system further determines, by a machine learning model, a participant to whom the playing card was dealt. The system further electronically validates a win for the network game with an electronic account for the participant.

Controlling a network card game

Disclosed are methods for augmenting progressive meter information presented in association with a gaming device. The methods include controlling a camera on a mobile device using an augmented reality gaming assistance component and enabling a user to employ the camera to capture an image of a game screen display, the image including one or more progressive meters associated with a respective progressive award. The captured image is sent via a network to a server that, for each of one or more progressive meters, determines a location of the progressive meters within the captured image, determines a value for each associated progressive award based on the captured image at each respective location, and determines a progressive rating for the associated progressive award based on the value. Content based on the one or more progressive ratings is received at the mobile device and displayed to the user via a display.

A gaming system that receives a frame of image data captured by a camera at a gaming table, generates a set of images from portions of the frame of image data, and determines whether the set of images meets an input requirement of a neural network model. If the set of images does not meet the input requirement, the gaming system modifies, by an incremental amount, an image property of a subset from the set of images until the set of images meets the input requirement. When the set of images meets the input requirement, the gaming system transmits the set of images as a unit (e.g., as a composite of the set of images) to the neural network model for concurrent analysis.

Gaming environment tracking optimization

A gaming terminal includes an input device, an image sensor, and logic circuitry. The logic circuitry detects user input received at the input device and that is associated with a restricted action, receives, via the image sensor, image data that corresponds to the user input, applies at least one neural network model to the image data to classify pixels of the image data as representing human characteristics including at least one face and at least one pose model, compares, based at least partially on pixel coordinates of (i) the human characteristics within the received image data and (ii) a user input zone within the image data, each of the pose models to the user input zone and the faces, and permits, in response to one of the pose models matching (i) a face of the at least one face and (ii) the user input zone, the restricted action.

Gaming systems and methods using image analysis authentication

Growing community of inventors

Henderson, NV, United States of America

Martin S Lyons