Granted Patent B2

US 12,273,310 · App. 17/677,915 · Granted Apr 8, 2025

Inventors: Dennis Reil (Großheide, DE); Frank Steffen (Hamburg, DE); Martin Richter (Edewecht, DE)

Assignee: OPEN TEXT HOLDINGS, INC.

H04L51/212G06N20/00G06Q30/0281H04L51/234

Abstract

Claims (76)

1. An intelligent delivery system comprising:

an intelligent delivery server configured to execute a self-learning machine learning engine which is trained to identify one of a plurality of output connectors for delivery of a communication based on received context information, intended recipient information, and previously collected event information indicating a delivery of a previous communication, wherein the plurality of output connectors are coupled to the intelligent delivery server;

one or more input connectors coupled to the intelligent delivery server, wherein the one or more input connectors coupled to the intelligent delivery server receive input data for the communication, wherein the received input data comprises:

intended recipient information; and

context information;

a flow model configured to:

access stored information of the previously collected event information based on the received input data;

generate a communication flow for a communication using the received input data, wherein a type of communication is determined by the received input data;

wherein the flow model is further configured to:

receive, in real-time, user input modifying the flow model during execution of the flow model, and in response to the real-time user input, dynamically modify the communication flow during execution of the flow model;

wherein the intelligent delivery server selects a different one of the plurality of output connectors for delivery of the communication based on the type of communication, context information associated with the communication, and the real-time user input modifying the flow model; and

route, by the intelligent delivery server the communication to the selected different one of the plurality of output connector for delivery of the communication.

2. The system of claim 1 , further comprising:

an event collector coupled to the plurality of output connectors and configured to collect event information associated with delivery of the communication by the selected one of the output connectors;

an event database coupled to the event collector and configured to store event information collected by the event collector.

3. The system of claim 2 , further comprising a delivery tracker configured to receive event information from the event database, the delivery tracker configured to track delivery of the communication via the selected one of the output connectors, the delivery tracker further configured to provide delivery information to the machine learning engine.

4. The system of claim 3 , wherein the machine learning engine is configured to receive delivery information from the delivery tracker, the machine learning engine configured to self-learn based on the received delivery information and to thereby adapt the training of the machine learning engine to identify output connectors for delivery of communications.

5. The system of claim 1 , wherein the intelligent delivery system is implemented in a customer communications management system, the communications management system comprising:

an input queue configured to store data for generation of communications for delivery to corresponding recipients;

an orchestration server coupled to receive communications from the input queue; and

wherein the flow model is for processing of the communication, the flow model executed by the orchestration server;

wherein the intelligent delivery server machine is coupled to the orchestration server and configured to perform an intelligent delivery task of the flow model.

6. The system of claim 1 , wherein the communication comprises a common communication directed to two different recipients, wherein intelligent delivery server selects different ones of the output connectors for delivery of the common communication based on differences in recipient information associated with the different recipients.

7. The system of claim 1 , wherein the communication is one of two or more communications directed to a single recipient, wherein the intelligent delivery server selects different ones of the plurality of output connectors for delivery of the two or more communications based on differences in context information associated with the two or more communications.

8. A method for intelligent delivery of communications, the method comprising:

executing a self-learning machine learning engine on an intelligent delivery server, the machine learning engine trained to identify one of a plurality of output connectors for delivery of a communication based on received context information, intended recipient information, and previously collected event information indicating a delivery of a previous communication, wherein the plurality of output connectors are coupled to the intelligent delivery server;

receiving, by the intelligent delivery server via one or more input connectors coupled to the intelligent delivery server, wherein the one or more input connectors coupled to the intelligent delivery server receives input data for the communication flow, wherein the received input data comprising;

the context information; and

intended recipient information; and

accessing, by a flow model, stored information of the previously collected event information based on the received input data;

generating, by the flow model, the communication flow using the received input data, wherein a type of communication is determined by the received input data;

receiving, in real-time, user input modifying the flow model during execution of the flow model, and in response to the real-time user input, dynamically modifying the communication flow by the flow model during execution of the flow model;

selecting, by the intelligent delivery server, a different one of the plurality of output connectors for delivery of the communication based on the type of communication, context information associated with the communication, and the real-time user input modifying the flow model;

routing, by the intelligent delivery server, the communication to the selected different one of the plurality of output connector for delivery of the communication.

9. The method of claim 8 , further comprising:

collecting, by an event collector coupled to the plurality of output connectors, event information associated with delivery of the communication by the selected one of the output connectors;

storing, in an event database coupled to the event collector, the event information collected by the event collector;

receiving, by a delivery tracker, the event information from the event database;

tracking, by the delivery tracker, delivery of the communication via the selected one of the output connectors; and

providing, by the delivery tracker, delivery information to the machine learning engine;

receiving, by the machine learning engine, the delivery information from the delivery tracker; and

self-learning, by the machine learning engine, based on the received delivery information and thereby adapting the training of the machine learning engine to identify output connectors for delivery of communications.

10. The method of claim 8 , implementing the intelligent delivery system in a customer communications management system, including:

storing, by an input queue, communications for delivery to corresponding recipients;

receiving, by an orchestration server, communications from the input queue; and

executing, by the orchestration server, a flow model for processing of the communication;

performing, by the intelligent delivery server machine which is coupled to the orchestration server, an intelligent delivery task of the flow model.

11. The method of claim 8 , wherein the communication comprises a common communication directed to two different recipients, the method further comprising selecting, by the intelligent delivery server different ones of the output connectors for delivery of the common communication based on differences in recipient information associated with the different recipients.

12. The method of claim 8 , wherein the communication is one of two or more communications directed to a single recipient, the method further comprising selecting, by the intelligent delivery server, different ones of the plurality of output connectors for delivery of the two or more communications based on differences in context information associated with the two or more communications.

13. A computer programming product for communication management and delivery, comprising:

a non-transitory storage medium comprising computer instructions that when loaded into a memory of a processor, the processor performing:

executing a self-learning machine learning engine on an intelligent delivery server, the machine learning engine trained to identify one of a plurality of output connectors for delivery of a communication based on received context information, intended recipient information, and previously collected event information indicating a delivery of a previous communication, wherein the plurality of output connectors are coupled to the intelligent delivery server;

receiving, by the intelligent delivery server via one or more input connectors coupled to the intelligent delivery server, wherein the one or more input connectors coupled to the intelligent delivery server receives input data for the communication flow, wherein the received input data comprising;

the context information; and

intended recipient information; and

accessing, by a flow model, stored information of the previously collected event information based on the received input data;

generating, by the flow model, the communication flow using the received input data, wherein a type of communication is determined by the received input data;

receiving, in real-time, user input modifying the flow model during execution of the flow model, and in response to the real-time user input, dynamically modifying the communication flow by the flow model during execution of the flow model;

selecting, by the intelligent delivery server, a different one of the plurality of output connectors for delivery of the communication based on the type of communication, context information associated with the communication, and the real-time user input modifying the flow model;

routing, by the intelligent delivery server, the communication to the selected different one of the plurality of output connector for delivery of the communication.

14. The computer program product of claim 13 , the processor further performing:

collecting, by an event collector coupled to the plurality of output connectors, event information associated with delivery of the communication by the selected one of the output connectors;

storing, in an event database coupled to the event collector, the event information collected by the event collector;

receiving, by a delivery tracker, the event information from the event database,

tracking, by the delivery tracker, delivery of the communication via the selected one of the output connectors;

providing, by the delivery tracker, delivery information to the machine learning engine;

receiving, by the machine learning engine, the delivery information from the delivery tracker; and

self-learning, by the machine learning engine, based on the received delivery information and thereby adapting the training of the machine learning engine to identify output connectors for delivery of communications.

15. The computer program product of claim 13 , the processor further performing:

implementing the intelligent delivery system in a customer communications management system, including:

storing, by an input queue, communications for delivery to corresponding recipients;

receiving, by an orchestration server, communications from the input queue; and

executing, by the orchestration server, a flow model for processing of the communication; and

performing, by the intelligent delivery server machine which is coupled to the orchestration server, an intelligent delivery task of the flow model.

16. The computer program product of claim 13 , wherein the communication comprises a common communication directed to two different recipients, the processor further performing selecting, by the intelligent delivery server different ones of the output connectors for delivery of the common communication based on differences in recipient information associated with the different recipients.

17. The computer program product of claim 13 , wherein the communication is one of two or more communications directed to a single recipient, the processor further performing selecting, by the intelligent delivery server, different ones of the plurality of output connectors for delivery of the two or more communications based on differences in context information associated with the two or more communications.

Assignments (2)

Continuity (1)

References Cited (199)