MACHINE GENERATED CONTENT NAMING IN AN INFORMATION CENTRIC NETWORK

• US 20190327171A1
• Filed: 06/28/2019
• Published: 10/24/2019
• Est. Priority Date: 06/28/2019
• Status: Active Grant

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Nallampatti Ekambaram, Venkatesan, Jha, Satish Chandra, Smith, Ned M., Alam, S. M. Iftekharul, Ramirez Loaiza, Maria, Zhang, Yi, Arrobo Vidal, Gabriel
• Granted Patent: US 11,637,772 B2
• Time in Patent Office: Days
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/049 Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
• CPC Class Codes: G06N 3/063 using electronic means
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 3/088 Non-supervised learning, e.g. competitive learning
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: H04L 45/507 Label distribution
• CPC Class Codes: H04L 67/568 Storing data temporarily at an intermediate stage, e.g. caching
• CPC Class Codes: H04L 67/63 Routing a service request depending on the request content or context

Abstract

Systems and techniques for machine generation of content names in an information centric network (ICN) are described herein. For example, a node may obtain content. An inference engine may be invoked to produce a name for the content. Once the content is named, the node may respond to an interest packet that includes the name of the content. The response is a data packet that includes the content.

Claims

1. A device for machine generation of content names in an information centric network (ICN), the device comprising:

processing circuitry; and

a memory including instructions that, when executed by the processing circuitry, configure the processing circuitry to;

obtain content;

invoke an inference engine to produce a name for the content; and

respond to an interest packet that includes the name with a data packet that includes the content.

2. The device of claim 1, wherein the interest packet is received before the inference engine is invoked, and wherein an attribute of the interest packet was provided to the inference engine to produce the name.

3. The device of claim 2, wherein the attribute of the interest packet is at least one of an entity name that originated the interest packet or a location of an entity that originated the interest packet.

4. The device of claim 2, wherein the attribute is a content feature set.

5. The device of claim 1, wherein the inference engine is an artificial neural network.

6. The device of claim 5, wherein the artificial neural network is trained with a constraint that limits a form of the name.

7. The device of claim 1, wherein the inference engine does not employ an artificial neural network.

8. The device of claim 1, wherein the inference engine is used at each node in an ICN to name the content.

9. A method for machine generation of content names in an information centric network (ICN), the method comprising:

obtaining content;

invoking an inference engine to produce a name for the content; and

responding to an interest packet that includes the name with a data packet that includes the content.

10. The method of claim 9, wherein the interest packet is received before the inference engine is invoked, and wherein an attribute of the interest packet was provided to the inference engine to produce the name.

11. The method of claim 10, wherein the attribute of the interest packet is at least one of an entity name that originated the interest packet or a location of an entity that originated the interest packet.

12. The method of claim 10, wherein the attribute is a content feature set.

13. The method of claim 9, wherein the inference engine is an artificial neural network.

14. The method of claim 13, wherein the artificial neural network is trained with a constraint that limits a form of the name.

15. The method of claim 9, wherein the inference engine does not employ an artificial neural network.

16. The method of claim 9, wherein the inference engine is used at each node in an ICN to name the content.

17. At least one machine-readable medium including instructions for machine generation of content names in an information centric network (ICN), the instructions, when executed by processing circuitry, cause the processing circuitry to perform operations comprising:

obtaining content;

invoking an inference engine to produce a name for the content; and

responding to an interest packet that includes the name with a data packet that includes the content.

18. The at least one machine-readable medium of claim 17, wherein the interest packet is received before the inference engine is invoked, and wherein an attribute of the interest packet was provided to the inference engine to produce the name.

19. The at least one machine-readable medium of claim 18, wherein the attribute of the interest packet is at least one of an entity name that originated the interest packet or a location of an entity that originated the interest packet.

20. The at least one machine-readable medium of claim 18, wherein the attribute is a content feature set.

21. The at least one machine-readable medium of claim 17, wherein the inference engine is an artificial neural network.

22. The at least one machine-readable medium of claim 21, wherein the artificial neural network is trained with a constraint that limits a form of the name.

23. The at least one machine-readable medium of claim 17, wherein the inference engine does not employ an artificial neural network.

24. The at least one machine-readable medium of claim 17, wherein the inference engine is used at each node in an ICN to name the content.