Sg Gaming, Inc.

Lnw Gaming, Inc.

Airmagnet, Inc.

Seagate Technology Incorporated

Terrin Eager

Martin S Lyons

Bryan M Kelly

Timothy Bucher

Joseph Randolph Hedrick

Scott Thomas Hilbert

John F Link

Yogendrasinh Hematji Rajput

Chen Wang

Yalun Li

Theodore Keenan

Mohan Kumar Areanalli Manju

Dong H Nguyen

Bruno Jean Masson

Ludovic Raulet

Ricardo Tomas Farrington

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

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 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

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 create a ground truth set, which is then used to train and test a neural network. When the neural network's classification of the extracted images is deemed accurate enough, the neural network may then be deployed in a casino to track the use of corresponding physical objects in real casino table game play as part of the casino table game monitoring system.

System and method for synthetic image training of a neural network associated with a casino table game monitoring system

A system including an image sensor that captures image data of a gaming table and a player area, and a tracking controller communicatively coupled to the image sensor. The tracking controller detects a player and a token set from the captured image data by applying an image neural network model to the image data to generate at least one key player data element for the player and at least one key token data element for the token set, generates a player data object representing physical characteristics of the player based on the key player data elements, links the player data object to a player identifier of the player, generates a token identifier based on the key token data elements, and links the token identifier to the player data object based on a physical relationship between the player and the token set indicated by the key data elements.

Systems and methods of automated linking of players and gaming tokens

Systems and methods are disclosed for accelerating the delivery of a data file. The data file may be uploaded to a content accelerator connected to a local area network. At some later point in time, the data file may be uploaded to a cloud storage provider based on the status of the network between the content accelerator and the cloud storage provider.

Cloud storage accelerator

In one exemplary embodiment, a process for detecting a phone includes monitoring wirelessly transmitted traffic between first and second stations. Next, the process determines whether the traffic sent in both directions between the first and second stations are close to each other in term of traffic volume. The process identifies a total frames count of a number of frames of an identical frame size transmitted, where the number of frames is greater than any number of frames of a same size. The process calculates the percentage of the count that was just collected out of the count of the total frames. The process identifies the first station as a phone if the calculated percentage is over a first threshold and the total frames count is over a second threshold.

Methods to detect wireless stations in a wireless local area network

Exemplary embodiments of methods and apparatuses to provide shielding from key cracking in wireless networks are described. In one embodiment, a first frame having a first content is identified. Determination is made whether the first frame needs to be shielded. A second frame having at least a portion of the first content is transmitted in response to determining that the first frame needs to be shielded. The first frame has data encrypted with a first encryption and the second frame has data encrypted with a second encryption.

Shielding in wireless networks

In one exemplary embodiment, voice quality of Voice over IP data in a wireless network is measured by first receiving at a detector in the wireless network a set of wireless frames transmitted between a first wireless device and a second wireless device in the wireless network in a predetermined time period. The wireless frames in the set carry Voice over Internet Protocol data. A rating value indicative of voice quality is determined based on the loss rate and burst rate that are determined only using frames received by the detection during monitoring.

Growing community of inventors

Campbell, CA, United States of America

Terrin Eager