Dynamic feature set management

• US 9,881,072 B2
• Filed: 08/14/2014
• Issued: 01/30/2018
• Est. Priority Date: 08/14/2014
• Status: Active Grant

• Current Assignee: McAfee, LLC
• Original Assignee: McAfee, LLC
• Inventors: Muttik, Igor, Whittington, Paul H.
• Primary Examiner(s): VO, TRUONG V
• Application Number:
  US14/459,933
• Publication Number:
  US 20160048573A1
• Time in Patent Office: 1,265 Days
• CPC Class Codes: G06F 16/23 Updating
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 18/253 of extracted features
• CPC Class Codes: H04W 4/70 Services for machine-to-machine communication [M2M] or machine type communication [MTC]

Abstract

In an example, a network is described with a plurality of data sources. Each data source may provide a feature, such as a data type that the data source collects or generates. A data aggregator may be connected to the network, and configured to collect, classify, and merge features as appropriate. The data aggregator includes a discriminator for classifying features, a merger, unmerger, converter, and evaluator. Features are provided to one or more expert systems configured to control one or more systems based on the features. Feedback to the data aggregator is used to evaluate the success of a merge. When a merge is found to be unhelpful, features may be unmerged.

Claims

1. An apparatus, comprising:

a feature database comprising data source features for an expert system to receive environmental inputs and apply them to a system; and

a discriminator operable to;

receive a new data source feature from a network-connected data source comprising an environmental input and a structurally-standardized metadata description of the feature; and

autonomously classify the new feature according to a feature taxonomy, wherein classifying comprises inspecting the feature metadata, comparing the feature to an existing feature in the feature database, and assigning the feature a class or species based on the comparing.

2. The apparatus of claim 1, further comprising a merger operable to merge the new data source feature with the existing feature to create a combined feature.

3. The apparatus of claim 2, further comprising a network interface, and wherein the apparatus is operable to send the combined feature over the network interface.

4. The apparatus of claim 3, further comprising an evaluator operable to receive feedback from the network interface, and to consume the combined feature from the merger.

5. The apparatus of claim 4, further comprising an unmerger operable to determine that the merging to create the combined feature was not substantially beneficial, and to unmerge the combined feature into the new data source feature and the existing feature.

6. The apparatus of claim 1, further comprising a converter operable to convert the new data source feature into a form compatible with a value of the existing feature.

7. The apparatus of claim 6, wherein converting the new data source feature comprises converting from one unit system to another unit system.

8. The apparatus of claim 1, wherein feature conversion is operable to use the feature taxonomy for merging the feature with other features.

9. The apparatus of claim 1, wherein the feature metadata is in a standards-compliant format.

10. The apparatus of claim 9, wherein the standards-compliant format is extensible markup language.

11. The apparatus of claim 1, wherein autonomously classifying further comprises inferring a classification based on a feature name in the feature metadata.

12. The apparatus of claim 1, wherein autonomously classifying further comprises inferring a classification based on a data type name in the feature metadata.

13. The apparatus of claim 12, wherein autonomously classifying comprises fuzzy matching.

14. One or more tangible, non-transitory computer-readable mediums having stored thereon executable instructions operable to instruct a processor to:

receive a new data source feature from a network-connected data source comprising an environmental input and a structurally-standardized metadata description of the feature; and

autonomously classify the new feature according to a feature taxonomy, wherein classifying comprises inspecting the feature metadata, comparing the feature to an existing feature in a feature database comprising data source features for an expert system to receive environmental inputs and apply them to a system, and assigning the feature a class or species based on the comparing.

15. The one or more mediums of claim 14, further operable to merge the new data source feature with the existing feature to create a combined feature.

16. The one or more mediums of claim 15, further operable to send the combined feature over a network interface.

17. The one or more mediums of claim 16, further operable to receive feedback from the network interface, and to determine whether the merging to create the combined feature was substantially beneficial.

18. The one or more mediums of claim 17, further operable to determine that the merging to create the combined feature was not substantially beneficial, and to unmerge the combined feature into the new data source feature and the existing feature.

19. The one or more mediums of claim 14, further operable to convert the new data source feature into a form compatible with the existing feature.

20. The one or more mediums of claim 19, wherein converting the new data source feature comprises converting from one unit system to another unit system.

21. The one or more mediums of claim 14, wherein the feature metadata is in a standards-compliant format.

22. The one or more mediums of claim 14, wherein autonomously classifying further comprises inferring a classification based on a feature name in the feature metadata.

23. The one or more mediums of claim 14, wherein autonomously classifying further comprises inferring a classification based on a data type name in the feature metadata.

24. A method comprising:

receiving a new data source feature from a network-connected data source comprising an environmental input and a structurally-standardized metadata description of the feature; and

autonomously classifying the new data source feature according to a feature taxonomy, wherein classifying comprises inspecting the feature metadata, comparing the feature to an existing feature in a feature database comprising data source features for an expert system to receive environmental inputs and apply them to a system, and assigning the feature a class or species based on the comparing.

25. The method of claim 24, further comprising:

merging the new data source feature with the existing feature to create a combined feature;

sending the combined feature over a network interface;

receiving feedback from the network interface to determine whether the merging to create the combined feature was substantially beneficial; and

determining that the merging to create the combined feature was not substantially beneficial, to unmerge the combined feature into the new data source feature and the existing feature.