Leveraging machine learning models to implement accessibility features during gameplay

A computer-implemented method for proving an accessibility user interface for a game based upon accessibility features provided by an accessibility service, the method comprising:

• generating a user interface depicting a plurality of accessibility features, wherein the plurality of accessibility features comprise accessibility features provided by the accessibility service, and wherein the accessibility service is a service separate from the game;
• receiving a selection of a first accessibility feature provided by the accessibility service;
• sending the first accessibility feature to the accessibility service;
• and
• in response to sending the accessibility feature to the accessibility service, receiving a plurality of accessibility commands, during gameplay, from the accessibility service;
• and
• executing the plurality of accessibility commands to implement the first accessibility feature.

A computer storage medium encoding computer-executable instructions that, when executed by at least one processor, cause the at least one processor to perform a method comprising:

• receiving a selection of an accessibility feature for the game;
• based upon the accessibility feature, instantiating one or more machine learning model, wherein the one or more machine learning model is operable to generate commands to implement the accessibility feature;
• receiving current gameplay data;
• generating, using the one or more machine learning models, an accessibility command, wherein the current gameplay data is provided as an input to the one or more machine learning models;
• and
• providing the accessibility command to the game, wherein providing the accessibility command causes implementation of the accessibility feature during gameplay.