SYSTEMS AND METHODS FOR INTELLIGENT AUTOMATION CONTROL SERVICES

• US 20150309490A1
• 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 10,656,607 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 behavior of an agent of the group of agents or in proximity to the operational environment; and

determining, by the computing system, a control operation based at least on the determined intelligence, the control operation being associated with an automation control asset configured to regulate of at least one agent of the group of agents.

2. The method of claim 1, wherein the group of agents comprises a group of devices, and wherein the accessing comprises monitoring, by the computing system, operational activity at one or more of the group of devices, wherein the group of devices comprises at least one of customer premises equipment, a transducer, a sensor, a medical device, a three-dimensional printer, a detector, a scanner, a code-reader device, a radiofrequency identification device (RFID) reader, a controller, a telecommunication receiver, a telecommunication transmitter, a telecommunication transceiver, a tethered computing device, a wireless computing device, a wearable device, or an appliance.

3. The method of claim 1, further comprising grouping, by the computing system, the determined control operation into an automation mode of the automation control asset, wherein the automation control asset comprises at least one automation mode, each automation mode comprising a configuration of tasks for the at least one agent.

4. The method of claim 3, further comprising directing at least one agent of the group of agents to operate based at least on the automation mode.

5. The method of claim 4, wherein the directing comprises one or more of selecting, by the computing system, the at least one agent, or

determining, by the computing system, an agent to be added to the group of agents.

6. The method of claim 3, wherein the grouping comprises updating, by the computing system, at least one configuration of tasks for the at least one agent.

7. The method of claim 3, wherein the grouping comprises generating, by the computing system, a new configuration of tasks associated with the at least one automation mode.

8. The method of claim 1, wherein the supplying comprises determining, by the computing system, a first configuration of tasks for at least one device of the group of devices based on a second configuration of tasks associated with a second automation control asset.

9. The method of claim 1, wherein determining, by the computing system, the intelligence comprises generating, by the computing system, a model of the behavior of the agent 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.

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

11. 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.

12. The method of claim 1, wherein the determining, by the computing system, the intelligence comprises determining a pattern of events associated with the agent, the pattern comprising one or more of (i) a pattern of sequences of events, (ii) a time-domain pattern in the at least one of the one or more information streams, or (iii) a space-domain pattern in the at least one of the one or more information streams;

or

determining correlations between patterns in the at least one of the one or more information streams.

13. 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 behavior of an agent of the group of agents or in proximity to the operational environment; and

determine a control operation based at least on the determined intelligence, the control operation being associated with an automation control asset configured to regulate of at least one agent of the group of agents.

14. The system of claim 13, wherein the group of agents comprises a group of devices, and wherein the accessing comprises monitoring, by the at least on processor, operational activity at one or more of the group of devices, wherein the group of devices comprises at least one of customer premises equipment, a transducer, a sensor, a medical device, a three-dimensional printer, a detector, a scanner, a code-reader device, a radiofrequency identification device (RFID) reader, a controller, a telecommunication receiver, a telecommunication transmitter, a telecommunication transceiver, a tethered computing device, a wireless computing device, a wearable device, or an appliance.

15. The system of claim 13, wherein the at least one processor is further configured, in response to execution of the instructions, to group the determined control operation into an automation mode of the automation control asset, wherein the automation control asset comprises at least one automation mode, each automation mode comprising a configuration of tasks for the at least one agent.

16. The system of claim 15, wherein the at least one processor is further configured, in response to execution of the instructions, to direct at least one agent of the group of agents to operate based at least on the automation mode.

17. The system of claim 16, wherein the at least one processor is configured, in response to execution of the instructions, to:

direct further comprises one or more of select the at least one agent, or

determine an agent to be added to the group of agents.

18. The system of claim 15, wherein the at least one processor is configured, in response to execution of the instructions, to group further comprises to update at least one configuration of tasks for the at least one agent.

19. The system of claim 15, wherein the at least one processor is configured, in response to execution of the instructions, to group further comprises to generate a new configuration of tasks associated with the at least one automation mode.

20. The system of claim 13, wherein to supply further comprises to determine a first configuration of tasks for at least one device of the group of devices based on a second configuration of tasks associated with a second automation control asset.

21. The system of claim 13, wherein to determine the intelligence further comprises to generate a model of the behavior of the agent 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.

22. The system of claim 13, wherein to determine the intelligence further comprises to generate a model of the behavior of the agent based at least on supervised learning, wherein the model comprises one or more of statistical Bayes model, a neural network model, or regression analysis.

23. The system of claim 13, 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.

24. The system of claim 13, wherein to determine the intelligence further comprise to:

determine a pattern of events associated with the agent, the pattern comprising one or more of (i) a pattern of sequences of events, (ii) a time-domain pattern in the at least one of the one or more information streams, or (iii) a space-domain pattern in the at least one of the one or more information streams;

or

determine correlations between patterns in the at least one of the one or more information streams.