SYSTEMS AND METHODS FOR INTELLIGENT CUSTOMIZATION OF AN AUTOMATION CONTROL SERVICE

• US 20150309491A1
• Filed: 03/25/2015
• Published: 10/29/2015
• Est. Priority Date: 04/29/2014
• Status: Active Grant

• Current Assignee: Cox Communications Incorporated (Cox Enterprises Incorporated)
• Original Assignee: Cox Communications Incorporated (Cox Enterprises Incorporated)
• Inventors: Patel, Dipan, Finnerty, Nadine, Shrum, Edgar V. Jr., Goldberg, Andrew, Taylor, Haynes
• Granted Patent: US 10,168,676 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: H04L 12/2803 Home automation networks

Abstract

Aspects of the disclosure relate to intelligent automation control of an environment based at least on intelligence associated with behavior of an operator and/or equipment.

Claims

1. A method for automation control, comprising:

accessing, by a computing system comprising at least one memory device and at least one processor, one or more information streams from a group of agents associated with an operational environment;

determining, by the computing system and based on at least one of the one or more information streams, intelligence associated with interactivity of an agent of the group of agents with an automation control asset; and

supplying, by the computing system, a recommendation for an automation control service for the operational environment based at least on the determined intelligence.

2. The method of claim 1, wherein the supplying comprises supplying a second recommendation for an automation control asset, wherein the recommended automation control asset is configured to regulate of at least one agent of the group of agents.

3. The method of claim 2, wherein the second recommendation comprises at least one of a recommendation for a specific control mode of the automation control asset, the specific control mode comprising a configuration of tasks for the at least one agent.

4. The method of claim 1, wherein the supplying comprises supplying a second recommendation for adjusting operation of the at least one agent.

5. The method of claim 1, wherein the recommended automation control service complements an extant automation control service configured to regulate the operational environment.

6. The method of claim 1, wherein determining, by the computing system, the intelligence comprises generating, by the computing system, a model of the interactivity based at least on unsupervised learning, wherein the model comprises one or more of statistical Markov model, a neural network model, or a Gaussian mixture model.

7. The method of claim 1, wherein determining, by the computing system, the intelligence comprises generating, by the computing system, a model of the interactivity based at least on supervised learning, wherein the model comprises one or more of statistical Bayes model, a neural network model, or regression analysis.

8. The method of claim 1, wherein determining, by the computing system, the intelligence comprises determining a correlation between events associated with at least one second agent of the group of agents, the at least one second agent being in at least one of (i) a specific control state of a control sequence based on the automation control service or (ii) a specific agent state.

9. The method of claim 7, wherein the control state comprises a condition of the controlled operational environment, the condition being responsive to implementation of the control sequence, and

wherein an agent state comprises a condition of the at least one second agent responsive to one or more of activity within the controlled operational environment or the control state.

10. The method of claim 1, wherein the determining, by the computing system, the intelligence comprises determining a structure of events associated with the at least one agent,

the at least one agent being in a specific control state or a specific agent state, and wherein the structure comprises a predetermined sequence of control tasks or a pattern of activity of the at least one agent.

11. A system, comprising:

at least one memory device having instructions encoded thereon; and

at least one processor coupled to the at least memory device and configured to execute the instructions and, in response, further configured at least to;

access one or more information streams from a group of agents associated with an operational environment;

determine intelligence associated with interactivity of an agent of the group of agents with an automation control asset; and

supply a recommendation for an automation control service for the operational environment based at least on the determined intelligence.

12. The system of claim 11, wherein to supply further comprises to supply a second recommendation for an automation control asset, wherein the recommended automation control asset is configured to regulate of at least one agent of the group of agents.

13. The system of claim 12, wherein the second recommendation comprises at least one of a recommendation for a specific control mode of the automation control asset, the specific control mode comprising a configuration of tasks for the at least one agent.

14. The system of claim 11, wherein to supply further comprises to supply a second recommendation for adjusting operation of the at least one agent.

15. The system of claim 11, wherein the recommended automation control service complements an extant automation control service configured to regulate the operational environment.

16. The system of claim 11, wherein to determine the intelligence further comprises to generate a model of the interactivity based at least on unsupervised learning, wherein the model comprises one or more of statistical Markov model, a neural network model, or a Gaussian mixture model.

17. The system of claim 11, wherein to determine the intelligence further comprises to generate a model of the interactivity based at least on supervised learning, wherein the model comprises one or more of statistical Bayes model, a neural network model, or regression analysis.

18. The system of claim 11, wherein to determine the intelligence further comprises to determine a correlation between events associated with at least one second agent of the group of agents, the at least one second agent being in at least one of (i) a specific control state of a control sequence based on the automation control service or (ii) a specific agent state.

19. The system of claim 17, wherein the control state comprises a condition of the controlled operational environment, the condition being responsive to implementation of the control sequence, and wherein an agent state comprises a condition of the at least one second agent responsive to one or more of activity within the controlled operational environment or the control state.

20. The system of claim 11, wherein to determine the intelligence further comprises to determine a structure of events associated with the at least one agent, the at least one agent being in a specific control state or a specific agent state, and wherein the structure comprises a predetermined sequence of control tasks or a pattern of activity of the at least one agent.