Goal Oriented Intelligent Scheduling System

1 . A system, comprising:

an orchestration engine configured to receive communications from a client device of a client and a provider device of a provider, the orchestration engine configured to execute workflows across a plurality of artificial intelligence engines, the plurality of artificial intelligence engines comprising:

a first set of artificial intelligence engines configured to work in conjunction to execute a booking workflow for processing a booking request received from the client device or the provider device,

a second set of artificial intelligence engines configured to work in conjunction to execute a rebooking workflow for process a rebooking request received upon detecting a first trigger event, and

a third set of artificial intelligence engines configured to work in conjunction to execute an event filling workflow to optimize a schedule of the provider.

2 . The system of claim 1 , wherein the first set of artificial intelligence engines comprises:

a first artificial intelligence engine comprising a deep reinforcement learning algorithm trained to identify a best appointment time for the client based on historical booking information of the client and an existing schedule of the provider.

3 . The system of claim 2 , wherein the first set of artificial intelligence engines comprises:

a second artificial intelligence engine comprising a deep reinforcement learning network configured to identify a best appointment time for the provider by reducing travel time to a client location.

4 . The system of claim 2 , wherein the first set of artificial intelligence engines comprises:

a second artificial intelligence engine comprising a deep reinforcement learning network configured to identify the best appointment time by minimizing fragmentation in the existing schedule of the provider.

5 . The system of claim 2 , wherein the first set of artificial intelligence engines comprises:

a second artificial intelligence engine comprising a neural network trained to move an existing reservation by reconciling conflicts with the existing schedule of the provider and minimizing schedule fragmentation.

6 . The system of claim 1 , wherein the first set of artificial intelligence engines comprises:

a first artificial intelligence engine comprising a recurrent neural network trained to identify a list of clients for an open appointment slot based on constraints of a service being offered at the open appointment slot and historical booking information associated with the list of clients.

7 . The system of claim 1 , wherein the second set of artificial intelligence engines comprises:

a first artificial intelligence engine comprising a recurrent neural network trained to predict a number of days between a current appointment for the client and a future appointment for the client based on service type and time slot; and

a second artificial intelligence network comprising a deep reinforcement learning network trained to identify a set of best appointment times for the provider based on the predicted number of days.

8 . The system of claim 1 , wherein the third set of artificial intelligence engines comprises:

a first artificial intelligence engine comprising a recurrent neural network trained to generate a list of clients most likely to book an appointment at a given time slot;

a second artificial intelligence agent comprising a generative adversarial network coupled with a deep learning keyword extractor trained to generate a customized communication for each client in the list of clients;

a third artificial intelligence agent contextual bandit algorithms configured to determine, for each client in the list of clients, a frequency at which to send the customized communication; and

a fourth artificial intelligence engine comprising an online machine learning model trained to identify, for each client in the list of clients, a best time of day to deliver the customized communication to the client based on a context of the customized communication.

9 . A method, comprising:

receiving, by a computing system, a request to schedule an appointment for a client, wherein the appointment is for a service with a service provider;

generating, by a deep reinforcement learning network of the computing system, a set of recommend times for the service, wherein set of recommend times minimizes fragmentation in a schedule of the service provider;

causing, by the computing system, the set of recommended times to be displayed to the client; and

responsive to receiving a selection from the client, generating, by the computing system, an appointment event for the client.

10 . The method of claim 9 , further comprising:

detecting, by a computing system, a trigger event, wherein the trigger event indicates that the appointment event for the client is over; and

responsive to the detecting, executing, by the computing system, a rebooking workflow to book a future appointment event for the client by:

predicting, by a recurrent neural network, a number of days between the appointment event for the client and the future appointment event based on service type and historical booking information associated with the client, and

based on the predicted number of days, generating, by the deep reinforcement learning network, a set of best appointment times for the future appointment event, wherein the set of best appointment times minimizes fragmentation in a schedule of the service provider.

11 . The method of claim 10 , further comprising:

causing, by the computing system, the set of best appointment times to be displayed to the client; and

responsive to receiving a further selection from the client, generating, by the computing system, a new appointment event for the client.

12 . The method of claim 9 , further comprising:

receiving, by the computing system from the client, a further request to move the appointment event; and

responsive to the further request, identifying, by the computing system, a new proposed appointment event by reconciling conflicts with existing appointments in the schedule and further minimizing fragmentation of the schedule.

13 . The method of claim 9 , further comprising:

receiving, by the computing system, a second request to create a second appointment event from a provider computing system; and

based on the second request, identifying, a recurrent neural network, a list of clients for an open appointment slot based on constraints of a service being offered at the open appointment slot and historical booking information associated with the list of clients.

14 . The method of claim 13 , further comprising:

recommending, by a second deep reinforcement learning network, the open appointment slot be moved to a new appointment slot responsive to determining that the new appointment slot reduces provider travel time to a client location.

15 . A method, comprising:

detecting, by a computing system, a trigger event to execute a workflow to fill an open appointment with a provider;

responsive to detecting the trigger event, generating, by a recurrent neural network, a list of clients for the open appointment based on constraints of a service being offered at the open appointment and historical booking information associated with the list of clients;

for each client in the list of clients, generating, using a generative adversarial network, a customized communication;

determining, by the computing system using contextual bandwidth algorithms, a frequency at which to send each customized communication;

identifying, by an online machine learning model of the computing system, a best time of day to deliver the customized communication to each client in the list of clients based on a context of the customized communication; and

sending, by the computing system, each customized communication.

16 . The method of claim 15 , further comprising:

receiving, by the computing system, an indication that a first client in the list of clients has taken the open appointment; and

responsive to the indication, generating, by the computing system, an appointment event for the first client.

17 . The method of claim 16 , further comprising:

responsive to generating the appointment event, analyzing, by a deep reinforcement learning network of the computing system, a schedule of the provider to determine whether any existing appointments can be moved to minimize fragmentation in the schedule.

18 . The method of claim 16 , further comprising:

receiving, by the computing system from the first client, a further request to move the appointment event; and

responsive to the further request, identifying, by the computing system, a new proposed appointment event by reconciling conflicts with existing appointments in a schedule of the provider.

19 . The method of claim 16 , further comprising:

detecting, by the computing system, a second trigger event, wherein the second trigger event indicates that the appointment event for the first client is over; and

responsive to the detecting, executing, by the computing system, a rebooking workflow to book a future appointment event for the client by:

predicting, by a second recurrent neural network, a number of days between the appointment event for the client and the future appointment event based on service type and historical booking information associated with the client, and

based on the predicted number of days, generating, by a deep reinforcement learning network, a set of best appointment times for the future appointment event, wherein the set of best appointment times minimizes fragmentation in a schedule of the provider.

20 . The method of claim 19 , further comprising:

causing, by the computing system, the set of best appointment times to be displayed to the first client; and

responsive to receiving a further selection from the first client, generating, by the computing system, a new appointment event for the first client.