System and method for concurrently conducting cause-and-effect experiments on content effectiveness and adjusting content distribution to optimize business objectives
First Claim
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1. A computer-implemented method, comprising:
- conducting an experiment using experimental content to determine effectiveness of communication content; and
executing, while conducting the experiment, a machine learning routine (MLR) using MLR content to enhance an effectiveness metric, wherein;
the machine learning routine comprises a reinforcement learning routine, the reinforcement learning routine comprising one or both of an explore routine associated with explore content and an exploit routine associated with exploit content;
the experiment is conducted in accordance with a schedule comprising a plurality of time-slot samples;
for experimental content that are related to either of explore or exploit content, using a first plurality of the time-slot samples unused for conducting the experiment for the explore or exploit routine; and
for experimental content that are unrelated to either of explore or exploit content, using at least some of the experimental content and either of explore or exploit content in the same time-slot sample of the plurality of time-slot samples.
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Abstract
The present invention is directed to systems, articles, and computer-implemented methods for assessing effectiveness of communication content and optimizing content distribution to enhance business objectives. Embodiments of the present invention are directed to computer-implemented methods for a computer-implemented method, comprising conducting an experiment using experimental content to determine effectiveness of communication content and executing, while conducting the experiment, a machine learning routine (MLR) using MLR content to enhance an effectiveness metric.
88 Citations
31 Claims
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1. A computer-implemented method, comprising:
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conducting an experiment using experimental content to determine effectiveness of communication content; and executing, while conducting the experiment, a machine learning routine (MLR) using MLR content to enhance an effectiveness metric, wherein; the machine learning routine comprises a reinforcement learning routine, the reinforcement learning routine comprising one or both of an explore routine associated with explore content and an exploit routine associated with exploit content; the experiment is conducted in accordance with a schedule comprising a plurality of time-slot samples; for experimental content that are related to either of explore or exploit content, using a first plurality of the time-slot samples unused for conducting the experiment for the explore or exploit routine; and for experimental content that are unrelated to either of explore or exploit content, using at least some of the experimental content and either of explore or exploit content in the same time-slot sample of the plurality of time-slot samples. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22)
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23. A computer-implemented method, comprising:
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generating a plurality of schedules and each comprising a plurality of time periods for presenting content and collecting data indicative of content effectiveness; and using a digital signage network comprising a plurality of geographically disparate displays and the plurality of schedules for; conducting an experiment while executing a machine learning routine using one of the plurality of schedules, wherein; the machine learning routine comprises a reinforcement learning routine, the reinforcement learning routine comprising one or both of an explore routine associated with explore content and an exploit routine associated with exploit content; for experimental content that are related to either of explore or exploit content, using a first plurality of the time-periods unused for conducting the experiment for the explore or exploit routine; and for experimental content that are unrelated to either of explore or exploit content, using at least some of the experimental content and either of explore or exploit content in the same time-periods of the plurality of time-periods. - View Dependent Claims (24, 25, 26)
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27. A computer-implemented method, comprising:
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receiving a viewer visit duration (VVD) for viewers at a location where content is to be presented; generating a schedule comprising a plurality of time periods for implementing a machine learning routine (MLR) based, in part on the VVD and an effectiveness metric; executing, using a digital signage network comprising a plurality of geographically disparate displays, the machine learning routine associated with MLR content in accordance with the schedule to determine effectiveness of the MLR content; and conducting, while executing the machine learning routine, an experiment using experimental content in accordance with the schedule to determine effectiveness of communication content, wherein; the machine learning routine comprises a reinforcement learning routine, the reinforcement learning routine comprising one or both of an explore routine associated with explore content and an exploit routine associated with exploit content; for experimental content that are related to either of explore or exploit content, using a first plurality of the time periods unused for conducting the experiment for the explore or exploit routine; and for experimental content that are unrelated to either of explore or exploit content, using at least some of the experimental content and either of explore and exploit content in the same time periods of the plurality of time periods. - View Dependent Claims (28, 29, 30, 31)
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Specification