Systems, Methods and Media for Automatic Prioritizer
Exemplary embodiments include a reinforcement learning model configured at a given point in time to receive digital data about a state of a user at the given point in time, receive digital data about an environment at the given point in time, receive digital data about a campaign at the given point in time, optimize total expected future number of positive rewards at the given point in time, and to execute an action at the given point in time. The state of the user at the given point in time may be a number of communications the user has received in a particular time period, a time since a last communication, the user's past behavior, and/or the user's engagement score from a predictive model to engage with a communication.
1 . A reinforcement learning model configured at a given point in time:
to receive digital data about a state of a user at the given point in time;
to receive digital data about an environment at the given point in time;
to receive digital data about a campaign at the given point in time;
to optimize total expected future number of positive rewards at the given point in time; and
to execute an action at the given point in time.
2 . The reinforcement learning model of claim 1 , wherein the state of the user at the given point in time is a number of communications the user has received in a particular time period.
3 . The reinforcement learning model of claim 1 , wherein the state of the user at the given point in time is a time since a last communication.
4 . The reinforcement learning model of claim 1 , wherein the state of the user at the given point in time is the user's past behavior.
5 . The reinforcement learning model of claim 1 , wherein the state of the user at the given point in time is the user's engagement score from a predictive model to engage with a communication.
6 . The reinforcement learning model of claim 1 , wherein the environment is a date and time.
7 . The reinforcement learning model of claim 1 , wherein the digital data about the campaign is a campaign type.
8 . The reinforcement learning model of claim 1 , wherein the digital data about the campaign is the user's past interaction with the campaign.
9 . The reinforcement learning model of claim 1 , wherein the digital data about the campaign is the user's past interaction with other campaigns.
10 . The reinforcement learning model of claim 1 , wherein the digital data about the campaign is a plurality of users' past interactions with the campaign.
11 . The reinforcement learning model of claim 1 , wherein the digital data about the campaign is a plurality of users' past interactions with other campaigns.
12 . The reinforcement learning model of claim 1 , wherein the action is transmitting a communication.
13 . The reinforcement learning model of claim 1 , wherein the action is refraining from transmitting a communication.
14 . The reinforcement learning model of claim 1 , further comprising the reinforcement learning model configured to receive digital data about a constraint.
15 . The reinforcement learning model of claim 14 , wherein the constraint is a maximum number of communications to send in a particular time period.
16 . The reinforcement learning model of claim 1 , further comprising the reinforcement learning model configured to aggregate data from multiple clients.
17 . The reinforcement learning model of claim 1 , wherein the reinforcement learning model is a neural network.
18 . The reinforcement learning model of claim 1 , further comprising the reinforcement learning model configured at the given point in time to perform a comparison of an output of the reinforcement learning model to an actual output generated from application of the output.
19 . The reinforcement learning model of claim 18 , further comprising the reinforcement learning model configured at the given point in time to update to the reinforcement learning model.
20 . The reinforcement learning model of claim 1 , wherein the action is prioritizing between communications.