20240013617. MACHINE-LEARNING BASED MESSAGING AND EFFECTIVENESS DETERMINATION IN GAMING SYSTEMS simplified abstract (LNW Gaming, Inc.)

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MACHINE-LEARNING BASED MESSAGING AND EFFECTIVENESS DETERMINATION IN GAMING SYSTEMS

Organization Name

LNW Gaming, Inc.

Inventor(s)

Christopher P. Arbogast of Reno NV (US)

Robert Thomas Davis of Reno NV (US)

Bradley Lindberg of Reno NV (US)

Sandeep Mohanadasan of Kerala (IN)

Rajesh Subramanian of Tamil Nadu (IN)

MACHINE-LEARNING BASED MESSAGING AND EFFECTIVENESS DETERMINATION IN GAMING SYSTEMS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240013617 titled 'MACHINE-LEARNING BASED MESSAGING AND EFFECTIVENESS DETERMINATION IN GAMING SYSTEMS

Simplified Explanation

The patent application describes a system and method for evaluating the effectiveness of messages related to a game feature in a gaming environment. Here is a simplified explanation of the abstract:

  • The system uses machine-learning analysis of images of a gaming environment to determine the effectiveness of messages.
  • Messages related to a game feature are presented at a gaming table during an evaluation period.
  • The system analyzes the images of the gaming table using machine learning models to detect gaming activity associated with the game feature.
  • By comparing message data to gaming activity data, the system determines a statistical correlation between the presentation of messages and the gaming activity.
  • Based on this statistical correlation, the system computes a message effectiveness score for the messages in relation to the game feature.

Potential Applications:

  • This technology can be used in the gaming industry to evaluate the impact of messages on gaming activity.
  • It can help game developers and marketers optimize their messaging strategies by identifying which messages are most effective in driving gaming activity.

Problems Solved:

  • The technology solves the problem of determining the effectiveness of messages in a gaming environment.
  • It provides a data-driven approach to evaluate the impact of messages on gaming activity, allowing for more informed decision-making.

Benefits:

  • Game developers and marketers can improve their messaging strategies by understanding which messages are most effective.
  • The system provides a quantitative measure of message effectiveness, allowing for objective evaluation and comparison.
  • By optimizing messaging strategies, game developers and marketers can potentially increase user engagement and revenue.


Original Abstract Submitted

systems and methods are provided for determining an effectiveness of one of more messages based on machine-learning analysis of images of a gaming environment. for example, a gaming system presents, via an output device at a gaming table during an evaluation period, messages related to a game feature. the game feature is available at one or more participant stations at the gaming table. the system further detects, for the evaluation period based on analysis of the images of the gaming table by one or more machine learning models, gaming activity associated with the game feature. the system further determines, in response to comparison of message data to gaming activity data, a statistical correlation between presentation of the messages and the gaming activity. furthermore, the system computes, based on the statistical correlation, a message effectiveness score for one or more of the messages in relation to the game feature.

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