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MACHINE-LEARNING MODELS APPLIED TO INTERACTION DATA FOR FACILITATING EXPERIENCE-BASED MODIFICATIONS TO INTERFACE ELEMENTS IN ONLINE ENVIRONMENTS

  • US 20190311279A1
  • Filed: 04/06/2018
  • Published: 10/10/2019
  • Est. Priority Date: 04/06/2018
  • Status: Active Grant
First Claim
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1. A method for causing an online platform to modify an interactive computing environment based on an interface experience metric, where the method include one or more processing devices perform operations comprising:

  • applying a state prediction model to interaction data from an online platform and thereby computing probabilities of transitioning from a click state to different next states;

    identifying a current base value for the click state and subsequent base values for the different next states;

    computing value differentials between the current base value and the subsequent base values, wherein each value differential indicates a changed quality of interface experience between the click state and a next state; and

    computing the interface experience metric from a sum of the current base value and the value differentials weighted with the probabilities computed by the state prediction model; and

    transmitting the interface experience metric to the online platform, wherein the interface experience metric is usable for changing arrangements of interface elements to improve the interface experience metric.

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