All The Claims

1. A food can coated at least in part on the interior with a composition comprising a polyester comprising the reaction product of
a) a polyol; and
b) a bis-epoxy reacted with a mono- andor di-phenolic carboxylic acid andor ester,

wherein the composition is substantially free of BPA and BPF and substantially free of BADGE and BFDGE.
2. The food can of claim 1, wherein the weight average molecular weight of the polyester is 14,000 to 15,000.
3. The food can of claim 1, wherein the wt % of polyester in the composition is 70 to 99 wt %, based on total solids weight.
4. The food can of claim 1, wherein the polyol is ethylene glycol.
5. The food can of claim 1, wherein the bis-epoxy comprises hydrogenated bisphenol A moieties.
6. The food can of claim 1, wherein the composition, when cured, is the last coating applied to the can.
7. The food can of claim 1, wherein the carboxylic acid comprises diphenolic acid.
8. The food can of claim 1, wherein the carboxylic ester comprises a paraben.
9. The food can of claim 1, wherein the paraben comprises methyl paraben.
10. The food can of claim 1, wherein the coating further comprises an acrylic grafted to the polyester.
11. The food can of claim 1, wherein the composition further comprises a solvent.
12. The food can of claim 1, wherein the coated portion of the food can comprises a can end.
13. A food can coated at least in part on the interior with a composition consisting essentially of a polyester comprising the reaction product of
a) a polyol; and
b) a bis-epoxy reacted with a mono- andor di-phenolic carboxylic acid andor ester.
14. The composition of claim 13, wherein the composition is substantially free of BPA and BPF and substantially free of BADGE and BFDGE.
15. A food can coated at least in part on the interior with a composition comprising a polyester that has a loss in molecular weight of less than 50% in an acidic medium.
16. The food can of claim 15, coated at least in part on the interior with a composition comprising a polyester that has a loss in molecular weight of less than 40% in an acidic medium.
17. The food can of claim 15, coated at least in part on the interior with a composition comprising a polyester that has a loss in molecular weight of less than 30% in an acidic medium.
18. The food can of claim 15, coated at least in part on the interior with a composition comprising a polyester that has a loss in molecular weight of less than 20% in an acidic medium.
19. A polyester comprising the reaction product of
a) a polyol; and
b) a bis-epoxy reacted with a mono-andor di-phenolic carboxylic acid andor ester.
20. The polyester of claim 19, wherein the polyol is ethylene glycol.
21. The polyester of claim 19, wherein the bis-epoxy comprises hydrogenated bisphenol A moieties.
22. The polyester of claim 19, wherein the carboxylic acid comprises diphenolic acid.
23. The polyester of claim 19, wherein the carboxylic ester comprises a paraben.
24. The polyester of claim 23, wherein the paraben comprises methyl paraben.

The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.

1. A method for outputting a dataset based upon anomaly detection, the method comprising:
receiving a training dataset having a plurality of n-grams that includes a first plurality of distinct training n-grams, wherein each of the first plurality of distinct training n-grams is a first size;
computing a first plurality of appearance frequencies, wherein each of the first plurality of appearance frequencies corresponds to one of the first plurality of distinct training n-grams;
receiving an input dataset including first input n-grams, wherein each of the first input n-grams is the first size;
defining a first window in the input dataset;
identifying first matching n-grams by determining whether the first input n-grams in the first window correspond to one of the first plurality of distinct training n-grams;
computing a first anomaly detection score for the input dataset using the first matching n-grams and the first plurality of appearance frequencies, wherein the first anomaly detection score is indicative of the presence of anomalous n-grams in the input dataset; and
outputting the input dataset based on the first anomaly detection score.
2. The method of claim 1, further comprising:
defining a second window in the input dataset;
identifying second matching n-grams by determining whether the first input n-grams in the second window correspond to one of the first plurality of distinct training n-grams; and
computing a second anomaly detection score for the input dataset using the second matching n-grams and first plurality of appearance frequencies.
3. The method of claim 2, further comprising determining which of the first anomaly detection score and the second anomaly detection score is higher.
4. The method of claim 2, wherein the plurality of n-grams in the training dataset also includes a second plurality of distinct training n-grams that are each a second size, and the input dataset also includes second input n-grams that are each the second size, and wherein the method further comprises:
computing a second plurality of appearance frequencies, wherein each of the first plurality of appearance frequencies corresponds to one of the second plurality of distinct training n-grams;
identifying third matching n-grams by determining whether the second input n-grams in the first window correspond to one of the second plurality of distinct training n-grams;
computing a third anomaly detection score for the input dataset using the third matching n-grams and the second plurality of appearance frequencies; and
determining based upon the third anomaly detection score whether the input dataset contains an anomaly.
5. The method of claim 4, wherein the first size and the second size are randomly or pseudo-randomly chosen.
6. The method of claim 1, wherein the first plurality of distinct training n-grams comprises grouped n-grams and the first matching n-grams comprise grouped n-grams.
7. The method of claim 1, further comprising excluding from the plurality of n-grams in the training dataset a portion of the training dataset that includes malicious code.
8. A method for outputting a dataset based upon anomaly detection, the method comprising:
receiving a training dataset having a plurality of n-grams that includes a first plurality of distinct training n-grams, wherein each of the first plurality of distinct training n-grams is a first size;
selecting the first plurality of distinct training n-grams on a random basis, pseudo-random basis, or secret basis;
receiving an input dataset including first input n-grams, wherein each of the first input n-grams is the first size;
determining a first matching count of the first input n-grams that correspond to one of the first plurality of distinct training n-grams;
determining a first total count of the first input n-grams;
determining a first anomaly detection score using the first matching count and the first total count, wherein the first anomaly detection score is indicative of the presence of anomalous n-grams in the input dataset; and
outputting the input dataset based on the first anomaly detection score.
9. The method of claim 8, further comprising storing the first plurality of distinct training n-grams in a Bloom filter.
10. The method of claim 9, wherein the Bloom filter uses at least two hash functions.
11. The method of claim 8, wherein the plurality of n-grams in the training dataset also includes a second plurality of distinct training n-grams that are each a second size, and the input dataset also includes second input n-grams that are each the second size, and wherein the method further comprises:
determining a second matching count of the second input n-grams that correspond to one of the second plurality of distinct training n-grams;
determining a second total count of the second input n-grams;
determining a second anomaly detection score using the second matching count and the second total count; and
determining based upon the second anomaly detection score whether the input dataset contains an anomaly.
12. A method for outputting a dataset based upon anomaly detection, the method comprising:
receiving a first training dataset having a plurality of n-grams that includes a first plurality of distinct training n-grams, wherein each of the first plurality of distinct training n-grams is a first size;
receiving a second training dataset having a plurality of n-grams that includes a second plurality of distinct training n-grams, wherein each of the second plurality of distinct training n-grams is the first size;
computing a first plurality of appearance frequencies, wherein each of the first plurality of appearance frequencies corresponds to one of the first plurality of distinct training n-grams;
computing a first plurality of uniformities of distribution, wherein each of the first plurality of uniformities of distribution corresponds to one of the first plurality of distinct training n-grams;
computing a second plurality of uniformities of distribution, wherein each of the second plurality of uniformities of distribution corresponds to one of the second plurality of distinct training n-grams;
determining a first plurality of most-heavily weighted n-grams from the first plurality of distinct training n-grams using at least one of: the first plurality of appearance frequencies; the first plurality of uniformities of distribution; and the second plurality of uniformities of distribution;
selecting a subset of the first plurality of most-heavily weighted n-grams, wherein the subset includes m n-grams and at least one of the n-grams in the subset is outside of the top m of the first plurality of most-heavily weighted n-grams;
receiving an input dataset including first input n-grams, wherein each of the plurality of first input n-grams is the first size;
obtaining a subset of a second plurality of most-heavily weighted n-grams from the first input n-grams that correspond to the subset of the first plurality of distinct training n-grams;
classifying the input dataset as containing an anomaly using the subset of the first plurality of most-heavily weighted n-grams and the subset of the second plurality of most-heavily weighted n-grams; and
outputting a dataset based upon the classifying of the input dataset.
13. The method of claim 12, wherein the plurality of n-grams in the first training dataset also includes a third plurality of distinct training n-grams that are each a second size, the plurality of n-grams in the second training dataset also includes a fourth plurality of distinct training n-grams that are each the second size, and the input dataset also includes second input n-grams that are each the second size, and wherein the method further comprises:
computing a second plurality of appearance frequencies, wherein each of the second plurality of appearance frequencies corresponds to one of the third plurality of distinct training n-grams;
computing a third plurality of uniformities of distribution, wherein each of the third plurality of uniformities of distribution corresponds to one of the third plurality of distinct training n-grams;
computing a fourth plurality of uniformities of distribution, wherein each of the fourth plurality of uniformities of distribution corresponds to one of the fourth plurality of distinct training n-grams;
determining a third plurality of most-heavily weighted n-grams from the third plurality of distinct training n-grams using at least one of: the second plurality of appearance frequencies; the third plurality of uniformities of distribution; and the fourth plurality of uniformities of distribution;
determining a fourth plurality of most-heavily weighted n-grams using the first plurality of most-heavily weighted n-grams and the third plurality of most-heavily weighted n-grams; and
classifying the input dataset as containing an anomaly using the fourth plurality of most-heavily weighted n-grams.
14. The method of claim 12, further comprising:
receiving a third training dataset having a plurality of n-grams that includes a third plurality of distinct training n-grams, wherein each of the third plurality of distinct training n-grams is the first size and contains malicious code;
computing a third plurality of uniformities of distribution, wherein each of the third plurality of uniformities of distribution corresponds to one of the third plurality of distinct training n-grams; and
determining a third plurality of most-heavily weighted n-grams from the third plurality of distinct training n-grams using at least one of: the first plurality of appearance frequencies; the first plurality of uniformities of distribution; and the third plurality of uniformities of distribution; and
classifying the input dataset as containing an anomaly using a subset of the third plurality of most-heavily weighted n-grams and the subset of the second plurality of most-heavily weighted n-grams.
15. The method of claim 12, wherein obtaining the subset of the second plurality of most-heavily weighted n-grams comprises:
identifying a plurality of matching n-grams from the first input n-grams that correspond to the first plurality of distinct training n-grams;
computing a second plurality of appearance frequencies, wherein each of the second plurality of appearance frequencies corresponds to one of the plurality of matching n-grams;
computing a third plurality of uniformities of distribution, wherein each of the third plurality of uniformities of distribution corresponds to one of the plurality of matching n-grams; and
determining the second plurality of most-heavily weighted n-grams from the plurality of matching n-grams using at least one of the second plurality of appearance frequencies; and the third plurality of uniformities of distribution; and
selecting the subset of the second plurality of most-heavily weighted n-grams corresponding to the subset of the first plurality of most-heavily weighted n-grams.
16. The method of claim 12, wherein the first training dataset comprises a dataset free of known instances of malicious code.
17. The method of claim 12, wherein the second training dataset comprises a dataset containing at least one known instance of malicious code.
18. The method of claim 12, wherein determining the first plurality of most-heavily weighted n-grams comprises:
computing a weight for each of the first plurality of distinct training n-grams; and
selecting as the first plurality of most-heavily weighted n-grams certain of the plurality of distinct training n-grams having the highest weights.
19. The method of claim 12, wherein the first size is randomly or pseudo-randomly determined.
20. The method of claim 19, wherein the first size is kept secret.
21. The method of claim 12, wherein the number of n-grams in the first plurality of most-heavily weighted n-grams is predetermined.
22. The method of claim 21, wherein the number of n-grams in the first plurality of most-heavily weighted n-grams can be adjusted.
23. A method for outputting a dataset based upon anomaly detection, the method comprising:
receiving a training dataset having a plurality of n-grams that includes a first plurality of distinct training n-grams, wherein each of the first plurality of distinct training n-grams is a first size;
computing a first plurality of appearance frequencies, wherein each of the first plurality of appearance frequencies corresponds to one of the first plurality of distinct training n-grams;
obtaining a first pseudo count associated with the first plurality of appearance frequencies;
computing a first total count of the number of n-grams of the plurality of n-grams in the training dataset that are the first size;
computing a first maximum possible count of distinct n-grams of the first size in the plurality of n-grams;
computing a second total count of the first plurality of distinct training n-grams;
computing a first smoothing factor;
computing a first probability that the first plurality of distinct training n-grams are found in the training dataset using at least one of: the first plurality of appearance frequencies,
the first pseudo count, the first total count, the second total count, and
the first smoothing factor;
computing a first consistency score of the plurality of n-grams in the training dataset that are the first size using the first maximum possible count and the first probability;
receiving an input dataset including first input n-grams, wherein each of the first input n-grams is the first size;
obtaining a second consistency score of the first input n-grams;
classifying the input dataset using the first consistency score and the second consistency score; and
outputting a dataset based upon the classifying of the input dataset.
24. The method of claim 23, wherein the plurality of n-grams in the training dataset also includes a second plurality of distinct training n-grams that are each a second size, and the method further comprises:
computing a second plurality of appearance frequencies, wherein each of the second plurality of appearance frequencies corresponds to one of the second plurality of distinct training n-grams;
obtaining a second pseudo count associated with the second plurality of appearance frequencies;
computing a third total count of the number of n-grams of the plurality of n-grams in the training dataset that are the second size;
computing a second maximum possible count of distinct n-grams of the second size in the plurality of n-grams;
computing, a fourth total count of the second plurality of distinct training n-grams;
computing a second smoothing factor;
computing a second probability that the second plurality of distinct training n-grams are found in the training dataset using at least one of: the second plurality of appearance frequencies, the second pseudo count, the third total count, the fourth total count, and the second smoothing factor;
computing a second consistency score of the plurality of n-grams in the training dataset that are the second size using the second maximum possible count and the second probability; and
classifying the input dataset using the second consistency score.
25. The method of claim 24, wherein the first size is greater than the second size.
26. The method of claim 24, further comprising computing a third probability that the second plurality of distinct training n-grams are found in the training dataset given a presence of the first plurality of distinct training n-grams.
27. The method of claim 23, wherein obtaining the second consistency score of the first input n-grams comprises:
identifying a plurality of matching n-grams from the first input n-grams that correspond to the first plurality of distinct training n-grams;
computing a second plurality of appearance frequencies, wherein each of the second plurality of appearance frequencies corresponds to one of the plurality of matching n-grams;
obtaining a second pseudo count associated with the second plurality of appearance frequencies;
computing a third total count of the first input n-grams;
computing a third maximum possible count of distinct n-grams of the first input n-grams;
computing a fourth total count of distinct n-grams of the first input n-grams;
computing a second smoothing factor;
computing a second probability that the distinct n-grams of the first input n-grams are found in the input dataset using at least one of: the second plurality of appearance frequencies, the second pseudo count, the third total count, the fourth total count, and the second smoothing factor; and
computing a third consistency score of the first input n-grams using the third maximum possible count and the second probability.
28. The method of claim 27, further comprising classifying the input dataset as containing an anomaly if the second consistency score is below a threshold value.
29. The method of claim 23, wherein the training dataset comprises a first count of the top-most-frequently occurring n-grams extracted from network data traffic.
30. The method of claim 29, further comprising adjusting the first maximum possible count.
31. The method of claim 30, wherein adjusting the first maximum possible count comprises:
determining a second count of discarded n-grams not chosen as part of the top-most-frequently occurring n-grams; and
adding the first count of the top-most-frequently occurring n-grams and the second count of the top-most-frequently occurring n-grams to provide the first maximum possible count.
32. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for outputting a dataset based upon anomaly detection, the method comprising:
receiving a training dataset having a plurality of n-grams that includes a first plurality of distinct training n-grams, wherein each of the first plurality of distinct training n-grams is a first size;
computing a first plurality of appearance frequencies, wherein each of the first plurality of appearance frequencies corresponds to one of the first plurality of distinct training n-grams;
receiving an input dataset including first input n-grams, wherein each of the first input n-grams is the first size;
defining a first window in the input dataset;
identifying first matching n-grams by determining whether the first input n-grams in the first window correspond to one of the first plurality of distinct training n-grams;
computing a first anomaly detection score for the input dataset using the first matching n-grams and the first plurality of appearance frequencies, wherein the first anomaly detection score is indicative of the presence of anomalous n-grams in the input dataset; and
outputting the input dataset based on the first anomaly detection score.
33. The medium of claim 32, the method further comprising:
defining a second window in the input dataset;
identifying second matching n-grams by determining whether the first input n-grams in the second window correspond to one of the first plurality of distinct training n-grams; and
computing a second anomaly detection score for the input dataset using the second matching n-grams and first plurality of appearance frequencies.
34. The medium of claim 33, the method further comprising determining which of the first anomaly detection score and the second anomaly detection score is higher.
35. The medium of claim 33, wherein the plurality of n-grams in the training dataset also includes a second plurality of distinct training n-grams that are each a second size, and the input dataset also includes second input n-grams that are each the second size, and wherein the method further comprises:
computing a second plurality of appearance frequencies, wherein each of the second plurality of appearance frequencies corresponds to one of the second plurality of distinct training n-grams;
identifying third matching n-grams by determining whether the second input n-grams in the first window correspond to one of the second plurality of distinct training n-grams;
computing a third anomaly detection score for the input dataset using the third matching n-grams and the second plurality of appearance frequencies; and
determining based upon the third anomaly detection score whether the input dataset contains an anomaly.
36. The medium of claim 35, wherein the first size and the second size are randomly or pseudo-randomly chosen.
37. The medium of claim 32, wherein the first plurality of distinct training n-grams comprises grouped n-grams and the first matching n-grams comprise grouped n-grams.
38. The medium of claim 32, the method further comprising excluding from the plurality of n-grams in the training dataset a portion of the training dataset that includes malicious code.
39. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for outputting a dataset based upon anomaly detection, the method comprising:
receiving a training dataset having a plurality of n-grams that includes a first plurality of distinct training n-grams, wherein each of the first plurality of distinct training n-grams is a first size;
selecting the first plurality of distinct training n-grams on a random basis, pseudo-random basis, or secret basis;
receiving an input dataset including first input n-grams, wherein each of the first input n-grams is the first size;
determining a first matching count of the first input n-grams that correspond to one of the first plurality of distinct training n-grams;
determining a first total count of the first input n-grams;
determining a first anomaly detection score using the first matching count and the first total count, wherein the first anomaly detection score is indicative of the presence of anomalous n-grams in the input dataset; and
outputting the input dataset based on the first anomaly detection score.
40. The medium of claim 39, the method further comprising storing the first plurality of distinct training n-grams in a Bloom filter.
41. The medium of claim 40, wherein the Bloom filter uses at least two hash functions.
42. The medium of claim 39, wherein the plurality of n-grams in the training dataset also includes a second plurality of distinct training n-grams that are each a second size, and the input dataset also includes second input n-grams that are each the second size, and wherein the method further comprises:
determining a second matching count of the second input n-grams that correspond to one of the second plurality of distinct training n-grams;
determining a second total count of the second input n-grams;
determining a second anomaly detection score using-the second matching count and the second total count; and
determining based upon the second anomaly detection score whether the input dataset contains an anomaly.
43. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for outputting a dataset based upon anomaly detection, the method comprising:
receiving a first training dataset having a plurality of n-grams that includes a first plurality of distinct training n-grams, wherein each of the first plurality of distinct training n-grams is a first size;
receiving a second training dataset having a plurality of n-grams that includes a second plurality of distinct training n-grams, wherein each of the second plurality of distinct training n-grams is the first size;
computing a first plurality of appearance frequencies, wherein each of the first plurality of appearance frequencies corresponds to one of the first plurality of distinct training n-grams;
computing a first plurality of uniformities of distribution, wherein each of the first plurality of uniformities of distribution corresponds to one of the first plurality of distinct training n-grams;
computing a second plurality of uniformities of distribution, wherein each of the second plurality of uniformities of distribution corresponds to one of the second plurality of distinct training n-grams;
determining a first plurality of most-heavily weighted n-grams from the first plurality of distinct training n-grams using at least one of: the first plurality of appearance frequencies; the first plurality of uniformities of distribution; and the second plurality of uniformities of distribution;
selecting a subset of the first plurality of most-heavily weighted n-grams, wherein the subset includes m n-grams and at least one of the n-grams in the subset is outside of the top m of the first plurality of most-heavily weighted n-grams;
receiving an input dataset including first input n-grams wherein each of the plurality of first input n-grams is the first size;
obtaining a subset of a second plurality of most-heavily weighted n-grams from the first input n-grams that correspond to the subset of the first plurality of distinct training n-grams;
classifying the input dataset as containing an anomaly using the subset of the first plurality of most-heavily weighted n-grams and the subset of the second plurality of most-heavily weighted n-grams; and
outputting a dataset based upon the classifying of the input dataset.
44. The medium of claim 43, wherein the plurality of n-grams in the first training dataset also includes a third plurality of distinct training n-grams that are each a second size, the plurality of n-grams in the second training dataset also includes a fourth plurality of distinct training n-grams that are each the second size, and the input dataset also includes second input n-grams that are each the second size, and wherein the method further comprises:
computing a second plurality of appearance frequencies, wherein each of the second plurality of appearance frequencies corresponds to one of the third plurality of distinct training n-grams;
computing a third plurality of uniformities of distribution, wherein each of the third plurality of uniformities of distribution corresponds to one of the third plurality of distinct training, n-grams;
computing a fourth plurality of uniformities of distribution, wherein each of the fourth p1urality of uniformities of distribution corresponds to one of the fourth plurality of distinct training n-grams;
determining a third plurality of most-heavily weighted n-grams from the third plurality of distinct training n-grams using at least one of: the second plurality of appearance frequencies; the third plurality of uniformities of distribution; and the fourth plurality of uniformities of distribution;
determining a fourth plurality of most-heavily weighted n-grams using the first plurality of most-heavily weighted n-grams and the third plurality of most-heavily weighted n-grams; and
classifying the input dataset as containing an anomaly using the fourth plurality of most-heavily weighted n-grams.
45. The medium of claim 43, the method further comprising:
receiving a third training dataset having a plurality of n-grams that includes a third plurality of distinct training n-grams, wherein each of the third plurality of distinct training n-grams is the first size and contains malicious code;
computing a third plurality of uniformities of distribution, wherein each of the third plurality of uniformities of distribution corresponds to one of the third plurality of distinct training n-grams; and
determining a third plurality of most-heavily weighted n-grams from the third plurality of distinct training n-grams using at least one of: the first plurality of appearance frequencies; the first plurality of uniformities of distribution; and the third plurality of uniformities of distribution; and
classifying the input dataset as containing an anomaly using a subset of the third plurality of most-heavily weighted n-grams and the subset of the second plurality of most-heavily weighted n-grams.
46. The medium of claim 43, wherein obtaining the subset of the second plurality of most-heavily weighted n-grams comprises:
identifying a plurality of matching n-grams from the first input n-grams that correspond to the first plurality of distinct training n-grams;
computing a second plurality of appearance frequencies, wherein each of the second plurality of appearance frequencies corresponds to one of the plurality of matching n-grams;
computing a third plurality of uniformities of distribution, wherein each of the third plurality of uniformities of distribution corresponds to one of the plurality of matching n-grams; and
determining the second plurality of most-heavily weighted n-grams from the plurality of matching n-grams using at least one of the second plurality of appearance frequencies; and the third plurality of uniformities of distribution; and
selecting the subset of the second plurality of most-heavily weighted n-grams corresponding to the subset of the first plurality of most-heavily weighted n-grams.
47. The medium of claim 43, wherein the first training dataset comprises a dataset free of known instances of malicious code.
48. The medium of claim 43, wherein the second training dataset comprises a dataset containing at least one known instance of malicious code.
49. The medium of claim 43, wherein determining the first plurality of most-heavily weighted n-grams comprises:
computing a weight for each of the first plurality of distinct training n-grams; and
selecting as the first plurality of most-heavily weighted n-grams certain of the plurality of distinct training n-grams having the highest weights.
50. The medium of claim 43, wherein the first size is randomly or pseudo-randomly determined.
51. The medium of claim 50, wherein the first size is kept secret.
52. The medium of claim 43, wherein the number of n-grams in the first plurality of most-heavily weighted n-grams is predetermined.
53. The medium of claim 52, wherein the number of n-grams in the first plurality of most-heavily weighted n-grams can be adjusted.
54. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for outputting a dataset based upon anomaly detection, the method comprising:
receiving a training dataset having a plurality of n-grams that includes a first plurality of distinct training n-grams, wherein each of the first plurality of distinct training n-grams is a first size;
computing a first plurality of appearance frequencies, wherein each of the first plurality of appearance frequencies corresponds to one of the first plurality of distinct training n-grams;
obtaining a first pseudo count associated with the first plurality of appearance frequencies;
computing a first total count of the number of n-grams of the plurality of n-grams in the training dataset that are the first size;
computing a first maximum possible count of distinct n-grams of the first size in the plurality of n-grams;
computing a second total count of the first plurality of distinct training n-grams;
computing a first smoothing factor;
computing a first probability that the first plurality of distinct training n-grams are found in the training dataset using at least one of: the first plurality of appearance frequencies,
the first pseudo count, the first total count, the second total count, and
the first smoothing factor;
computing a first consistency score of the plurality of n-grams in the training dataset that are the first size using the first maximum possible count and the first probability;
receiving an input dataset including first input n-grams, wherein each of the first input n-grams is the first size;
obtaining a second consistency score of the first input n-grams;
classifying the input dataset using the first consistency score and the second consistency score; and
outputting a dataset based upon the classifying of the input dataset.
55. The medium of claim 54, wherein the plurality of n-grams in the training dataset also includes a second plurality of distinct training n-grams that are each a second size, and the method further comprises:
computing a second plurality of appearance frequencies, wherein each of the second plurality of appearance frequencies corresponds to one of the second plurality of distinct training n-grams;
obtaining a second pseudo count associated with the second plurality of appearance frequencies;
computing a third total count of the number of n-grams of the plurality of n-grams in the training dataset that are the second size;
computing a second maximum possible count of distinct n-grams of the second size in the plurality of n-grams;
computing a fourth total count of the second plurality of distinct training n-grams;
computing a second smoothing factor;
computing a second probability that the second plurality of distinct training n-grams are found in the training dataset using at least one of: the second plurality of appearance frequencies, the second pseudo count, the third total count, the fourth total count, and the second smoothing factor;
computing a second consistency score of the plurality of n-grams in the training dataset that are the second size using the second maximum possible count and the second probability; and
classifying the input dataset using the second consistency score.
56. The medium of claim 55, wherein the first size is greater than the second size.
57. The medium of claim 55, the method further comprising computing a third probability that the second plurality of distinct training n-grams are found in the training dataset given a presence of the first plurality of distinct training n-grams.
58. The medium of claim 54, wherein obtaining the second consistency score of the first input n-grams comprises:
identifying, a plurality of matching n-grams from the first input n-grams that correspond to the first plurality of distinct training n-grams:
computing a second plurality of appearance frequencies, wherein each of the second plurality of appearance frequencies corresponds to one of the plurality of matching n-grams;
obtaining a second pseudo count associated with the second plurality of appearance frequencies;
computing a third total count of the first input n-grams;
computing a third maximum possible count of distinct n-grams of the first input n-grams;
computing a fourth total count of distinct n-grams of the first input n-grams;
computing a second smoothing factor;
computing a second probability that the distinct n-grams of the first input n-grams are found in the input dataset using at least one of: the second plurality of appearance frequencies, the second pseudo count, the third total count, the fourth total count, and the second smoothing factor; and
computing a third consistency score of the first input n-grams using the third maximum possible count and the second probability.
59. The medium of claim 58, the method further comprising classifying the input dataset as containing an anomaly if the second consistency score is below a threshold value.
60. The medium of claim 54, wherein the training dataset comprises a first count of the top-most-frequently occurring n-grams extracted from network data traffic.
61. The medium of claim 60, the method further comprising adjusting the first maximum possible count.
62. The medium of claim 61, wherein adjusting the first maximum possible count comprises:
determining a second count of discarded n-grams not chosen as part of the top-most-frequently occurring n-grams; and
adding the first count of the top-most-frequently occurring n-grams and the second count of the top-most-frequently occurring n-grams to provide the first maximum possible count.
63. A system for outputting a dataset based upon anomaly detection, the system comprising:
a digital processing device that:
receives a training dataset having a plurality of n-grams that includes a first plurality of distinct training n-grams, wherein each of the first plurality of distinct training n-grams is a first size;
computes a first plurality of appearance frequencies, wherein each of the first plurality of appearance frequencies corresponds to one of the first plurality of distinct training n-grams;
receives an input dataset including first input n-grams, wherein each of the first input n-grams is the first size;
defines a first window in the input dataset;
identifies first matching n-grams by determining whether the first input n-grams in the first window correspond to one of the first plurality of distinct training n-grams;
computes a first anomaly detection score for the input dataset using the first matching n-grams and the first plurality of appearance frequencies, wherein the first anomaly detection score is indicative of the presence of anomalous n-grams in the input dataset; and
outputs the input dataset based on the first anomaly detection score.
64. The system of claim 63, wherein the digital processing device also:
defines a second window in the input dataset;
identifies second matching n-grams by determining whether the first input n-grams in the second window correspond to one of the first plurality of distinct training n-grams; and
computes a second anomaly detection score for the input dataset using the second matching n-grams and first plurality of appearance frequencies.
65. The system of claim 64, wherein the digital processing device also determines which of the first anomaly detection score and the second anomaly detection score is higher.
66. The system of claim 64, wherein the plurality of n-grams in the training dataset also includes a second plurality of distinct training n-grams that are each a second size, and the input dataset also includes second input n-grams that are each the second size, and wherein the digital processing device also:
computes a second plurality of appearance frequencies, wherein each of the first plurality of appearance frequencies corresponds to one of the second plurality of distinct training n-grams;
identifies third matching n-grams by determining whether the second input n-grams in the first window correspond to one of the second plurality of distinct training n-grams;
computes a third anomaly detection score for the input dataset using the third matching n-grams and the second plurality of appearance frequencies; and
determines based upon the third anomaly detection score whether the input dataset contains an anomaly.
67. The system of claim 66, wherein the first size and the second size are randomly or pseudo-randomly chosen.
68. The system of claim 63, wherein the first plurality of distinct training n-grams comprises grouped n-grams and the first matching n-grams comprise grouped n-grams.
69. The system of claim 63, wherein the digital processing device also excludes from the plurality of n-grams in the training dataset a portion of the training dataset that includes malicious code.
70. A system for outputting a dataset based upon anomaly detection, the system comprising:
a digital processing device that:
receives a training dataset having a plurality of n-grams that includes a first plurality of distinct training n-grams, wherein each of the first plurality of distinct training n-grams is a first size;
selects the first plurality of distinct training n-grams on a random basis, pseudo-random basis, or secret basis;
receives an input dataset including first input n-grams, wherein each of the first input n-grams is the first size;
determines a first matching count of the first input n-grams that correspond to one of the first plurality of distinct training n-grams:
determines a first total count of the first input n-grams;
determines a first anomaly detection score using the first matching count and the first total count, wherein the first anomaly detection score is indicative of the presence of anomalous n-grams in the input dataset; and
outputs the input dataset based on the first anomaly detection score.
71. The system of claim 70, wherein the digital processing device also stores the first plurality of distinct training n-grams in a Bloom filter.
72. The system of claim 71, wherein the Bloom filter uses at least two hash functions.
73. The system of claim 70, wherein the plurality of n-grams in the training dataset also includes a second plurality of distinct training n-grams that are each a second size; and the input dataset also includes second input n-grams that are each the second size, and wherein the digital processing device also:
determines a second matching count of the second input n-grams that correspond to one of the second plurality of distinct training n-grams;
determines a second total count of the second input n-grams;
determines a second anomaly detection score using the second matching count and the second total count; and
determines based upon the second anomaly detection score whether the input dataset contains an anomaly.
74. A system for outputting a dataset based upon anomaly detection, the system comprising:
a digital processing, device that:
receives a first training dataset having a plurality of n-grams that includes a first plurality of distinct training n-grams, wherein each of the first plurality of distinct training n-grams is a first size;
receives a second training dataset having a plurality of n-grams that includes a second plurality of distinct training n-grams, wherein each of the second plurality of distinct training n-grams is the first size;
computes a first plurality of appearance frequencies, wherein each of the first plurality of appearance frequencies corresponds to one of the first plurality of distinct training n-grams;
computes a first plurality of uniformities of distribution, wherein each of the first plurality of uniformities of distribution corresponds to one of the first plurality of distinct training n-grams;
computes a second plurality of uniformities of distribution, wherein each of the second plurality of uniformities of distribution corresponds to one of the second plurality of distinct training n-grams;
determines a first plurality of most-heavily weighted n-grams from the first plurality of distinct training n-grams using at least one of: the first-plurality of appearance frequencies; the first plurality of uniformities of distribution; and the second plurality of uniformities of distribution;
selects a subset of the first plurality of most-heavily weighted n-grams, wherein the subset includes in n-grams and at least one of the n-grams in the subset is outside of the top m of the first plurality of most-heavily weighted n-grams;
receives an input dataset including first input n-grams, wherein each of the plurality of first input n-grams is the first size;
obtains a subset of a second plurality of most-heavily weighted n-grams from the first input n-grams that correspond to the subset of the first plurality of distinct training n-grams;
classifies the input dataset as containing an anomaly using the subset of the first plurality of most-heavily weighted n-grams and the subset of the second plurality of most-heavily weighted n-grams; and
outputs a dataset based upon the classifying of the input dataset.
75. The system of claim 74, wherein the plurality of n-grams in the first training dataset also includes a third plurality of distinct training n-grams that are each a second size, the plurality of n-grams in the second training dataset also includes a fourth plurality of distinct training n-grams that are each the second size, and the input dataset also includes second input n-grams that are each the second size, wherein the digital processing device also:
computes a second plurality of appearance frequencies, wherein each of the second plurality of appearance frequencies corresponds to one of the third plurality of distinct training n-grams;
computes a third plurality of uniformities of distribution, wherein each of the third plurality of uniformities of distribution corresponds to one of the third plurality of distinct training n-grams;
computes a fourth plurality of uniformities of distribution, wherein each of the fourth plurality of uniformities of distribution corresponds to one of the fourth plurality of distinct training n-grams;
determines a third plurality of most-heavily weighted n-grams from the third plurality of distinct training n-grams using at least one of:
the second plurality of appearance frequencies;
the third plurality of uniformities of distribution; and
the fourth plurality of uniformities of distribution;
determines a fourth plurality of most-heavily weighted n-grams using the first plurality of most-heavily weighted n-grams and the third plurality of most-heavily weighted n-grams; and
classifies the input dataset as containing an anomaly using the fourth plurality of most-heavily weighted n-grams.
76. The system of claim 74, wherein-the digital processing device also:
receives a third training dataset having a plurality of n-grams that includes a third plurality of distinct training n-grams, wherein each of the third plurality of distinct training n-grams is the first size and contains malicious code;
computes a third plurality of uniformities of distribution, wherein each of the third plurality of uniformities of distribution corresponds to one of the third plurality of distinct training n-grams; and
determines a third plurality of most-heavily weighted n-grams from the third plurality of distinct training n-grams using at least one of:
the first plurality of appearance frequencies;
the first plurality of uniformities of distribution; and
the third plurality of uniformities of distribution; and
classifies the input dataset as containing an anomaly using a subset of the third plurality of most-heavily weighted n-grams and the subset of the second plurality of most-heavily weighted n-grams.
77. The system of claim 74, wherein the digital processing device in obtaining the subset of the second plurality of most-heavily weighted n-grams also:
identifies a plurality of matching n-grams from the first input n-grams that correspond to the first plurality of distinct training n-grams;
computes a second plurality of appearance frequencies, wherein each of the second plurality of appearance frequencies corresponds to one of the plurality of matching n-grams;
computes a third plurality of uniformities of distribution, wherein each of the third plurality of uniformities of distribution corresponds to one of the plurality of matching n-grams; and
determines the second plurality of most-heavily weighted n-grams from the plurality of matching n-grams using at least one of:
the second plurality of appearance frequencies; and
the third plurality of uniformities of distribution; and
selects the subset of the second plurality of most-heavily weighted n-grams corresponding to the subset of the first plurality of most-heavily weighted n-grams.
78. The system of claim 74, wherein the first training dataset comprises a dataset free of known instances of malicious code.
79. The system of claim 74, wherein the second training dataset comprises a dataset containing at least one known instance of malicious code.
80. The system of claim 74, wherein the digital processing device in determining the first plurality of most-heavily weighted n-grams also:
computes a weight for each of the first plurality of distinct training n-grams; and
selects as the first plurality of most-heavily weighted n-grams certain of the plurality of distinct training n-grams having the highest weights.
81. The system of claim 74, wherein the first size is randomly or pseudo-randomly determined.
82. The system of claim 81, wherein the first size is kept secret.
83. The system of claim 74, wherein the number of n-grams in the first plurality of most-heavily weighted n-grams is predetermined.
84. The system of claim 83, wherein the number of n-grams in the first plurality of most-heavily weighted n-grams can be adjusted.
85. A system for outputting a dataset based upon anomaly detection, the system comprising:
a digital processing device that:
receives a training dataset having a plurality of n-grams that includes a first plurality of distinct training n-grams, wherein each of the first plurality of distinct training n-grams is a first size;
computes a first plurality of appearance frequencies, wherein each of the first plurality of appearance frequencies corresponds to one of the first plurality of distinct training n-grams;
obtains a first pseudo count associated with the first plurality of appearance frequencies;
computes a first total count of the number of n-grams of the plurality of n-grams in the training dataset that are the first size;
computes a first maximum possible count of distinct n-grams of the first size in the plurality of n-grams;
computes a second total count of the first plurality of distinct training n-grams;
computes a first smoothing factor;
computes a first probability that the first plurality of distinct training n-grams are found in the training dataset using at least one of: the first plurality of appearance frequencies, the first pseudo count, the first total count, the second total count, and the first smoothing factor;
computes a first consistency score of the plurality of n-grams in the training dataset that are the first size using the first maximum possible count and the first probability;
receives an input dataset including first input n-grams, wherein each of the first input n-grams is the first size;
obtains a second consistency score of the first input n-grams;
classifies the input dataset using the first consistency score and the second consistency score; and
outputs a dataset based upon the classifying of the input dataset.
86. The system of claim 85, wherein the plurality of n-grams in the training dataset also includes a second plurality of distinct training n-grams that are each a second size, and wherein the digital processing device also:
computes a second plurality of appearance frequencies wherein each of the second plurality of appearance frequencies corresponds to one of the second plurality of distinct training n-grams;
obtains a second pseudo count associated with the second plurality of appearance frequencies;
computes a third total count of the number of n-grams of the plurality of n-grams in the training dataset that are the second size;
computes a second maximum possible count of distinct n-grams of the second size in the plurality of n-grams;
computes a fourth total count of the second plurality of distinct training n-grams;
computes a second smoothing factor;
computes a second probability that the second plurality of distinct training n-grams are found in the training dataset using at least one of: the second plurality of appearance frequencies, the second pseudo count, the third total count, the fourth total count, and the second smoothing factor;
computes a second consistency score of the plurality of n-grams in the training dataset that are the second size using the second maximum possible count and the second probability; and
classifies the input dataset using the second consistency score.
87. The system of claim 86, wherein the first size is greater than the second size.
88. The system of claim 86, wherein the digital processing device also computes a third probability that the second plurality of distinct training n-grams are found in the training dataset given a presence of the first plurality of distinct training n-grams.
89. The system of claim 85, wherein the digital processing device in obtaining, the second consistency score of the first input n-grams also:
identifies a plurality of matching n-grams from the first input n-grams that correspond to the first plurality of distinct training n-grams;
computes a second plurality of appearance frequencies, wherein each of the second plurality of appearance frequencies corresponds to one of the plurality of matching n-grams;
obtains a second pseudo count associated with the second plurality of appearance frequencies;
computes a third total count of the first input n-grams;
computes a third maximum possible count of distinct n-grams of the first input n-grams;
computes a fourth total count of distinct n-grams of the first input n-grams;
computes a second smoothing factor;
computes a second probability that the distinct n-grams of the first input n-grams are found in the input dataset using at least one of: the second plurality of appearance frequencies, the second pseudo count, the third total count, the fourth total count, and the second smoothing factor; and
computes a third consistency score of the first input n-grams using the third maximum possible count and the second probability.
90. The system of claim 89, wherein the digital processing device also classifies the input dataset as containing an anomaly if the second consistency score is below a threshold value.
91. The system of claim 85, wherein the training dataset comprises a first count of the top-most-frequently occurring n-grams extracted from network data traffic.
92. The system of claim 91, wherein the digital processing device also adjusts the first maximum possible count.
93. The system of claim 92, wherein the digital processing device in adjusting the first maximum possible count also:
determines a second count of discarded n-grams not chosen as part of the top-most-frequently occurring n-grams; and
adds the first count of the top-most-frequently occurring n-grams and the second count of the top-most-frequently occurring n-grams to provide the first maximum possible count.

1. A load control system for controlling the amount of power delivered from an AC power source to a lighting load provided in a space, the load control system comprising:
a load control device adapted to be coupled in series electrical connection between the AC power source and the lighting load for controlling the amount of power delivered to the lighting load to control the intensity of the lighting load, the load control device operable to receive wireless control signals and to control the intensity of the lighting load in response to the wireless control signals; and
two occupancy sensors, each operable to independently detect an occupancy condition in the space and to transmit an occupied wireless control signal to the load control device in response to detecting the occupancy condition, the occupancy sensors further operable to transmit a vacant wireless control signal to the load control device in response to detecting a vacancy condition in the space;
wherein the load control device adjusts the intensity of the lighting load to a first intensity in response to receiving the occupied wireless control signal from at least one of the occupancy sensors, and adjusts the intensity of the lighting load to a second intensity less than the first intensity in response to receiving vacant control signals from both of the occupancy sensors,
the load control device associating a unique identifier with each occupancy sensor and storing in respective memory locations an occupancyvacancy status of each occupancy sensor in accordance with its unique identifier, the load control device adjusting the intensity of the lighting load to a second intensity when the occupancyvacancy statuses of the memory locations for both of the occupancy sensors denote vacancy.
2. The load control system of claim 1, wherein each of the occupancy sensors are characterized by a unique serial number, the unique serial number comprising the unique identifier, the load control device comprising a memory for storage of the serial numbers of the two occupancy sensors, the load control device responsive to wireless control signals including the serial numbers of the two occupancy sensors.
3. The load control system of claim 2, wherein the load control device keeps track of the occupancy sensors from which the load control device received the occupied wireless control signals.
4. The load control system of claim 3, wherein the load control device adjusts the intensity of the lighting load to the second intensity in response to receiving vacant control signals from all of the occupancy sensors from which the load control device received the occupied wireless control signals.
5. The load control system of claim 1, wherein each of the occupancy sensors transmits a third wireless control signal to the load control device in response to detecting a continued occupancy condition in the space, the load control device adjusting the intensity of the lighting load to the second intensity in response to determining that no wireless control signals have been received from one of the occupancy sensors for the length of a predetermined timeout period and receiving a vacant control signal from the other occupancy sensor.
6. The load control system of claim 1, wherein the second intensity is 0%, such that the load control turns the lighting load off in response to receiving vacant control signals from both of the occupancy sensors.
7. The load control system of claim 1, wherein the second intensity is a non-off intensity.
8. A method of controlling the amount of power delivered from an AC power source to a lighting load provided in a space, the method comprising the steps of:
providing two occupancy sensors in the space;
detecting by one of the occupancy sensors an occupancy condition in the space;
transmitting by one of the occupancy sensors an occupied wireless control signal in response to the step of detecting the occupancy condition;
receiving at a load control device the occupied wireless control signal from at least one of the occupancy sensors;
adjusting the intensity of the lighting load to a first intensity using the load control device in response to the step of receiving the occupied wireless control signal from at least one of the occupancy sensors;
detecting by one of the occupancy sensors a vacancy condition in the space;
transmitting by one of the occupancy sensors a vacant wireless control signal in response to the step of detecting a vacancy condition in the space;
receiving at the load control device vacant control signals from both of the occupancy sensors;
associating a unique identifier with each occupancy sensor and storing in respective memory locations an occupancyvacancy status of each occupancy sensor in accordance with its unique identifier at the load control device, and
adjusting the intensity of the lighting load to a second intensity less than the first intensity using the load control device when the occupancyvacancy statuses of the memory locations for both of the occupancy sensors denote vacancy.
9. The method of claim 8, wherein the second intensity is 0%, such that the step of adjusting the intensity of the lighting load to a second intensity comprises turning the lighting load off in response to the step of receiving vacant control signals from both of the occupancy sensors.
10. The method of claim 8, wherein the second intensity is a non-off intensity.
11. A load control system for controlling the amount of power delivered from an AC power source to a lighting load provided in a space, the load control system comprising:
a load control device adapted to be coupled in series electrical connection between the AC power source and the lighting load for controlling the amount of power delivered to the lighting load to control the intensity of the lighting load, the load control device operable to receive wireless control signals and to control the intensity of the lighting load in response to the wireless control signals; and
an occupancy sensor operable to detect an occupancy condition in the space and to transmit a first wireless control signal to the load control device in response to detecting the occupancy condition, the load control device operable to adjust the intensity of the lighting load to a first intensity in response to the first wireless control signal, the occupancy sensor further operable to transmit a second wireless control signal to the load control device in response to detecting a continued occupancy condition in the space;
wherein the load control device adjusts the intensity of the lighting load to a second intensity less than the first intensity in response to determining that no wireless control signals have been received from the occupancy sensor for the length of a predetermined timeout period; and
the occupancy sensor further comprising an occupancy timer, on the expiration of the occupancy timer, the occupancy sensor transmitting a vacant wireless control signal to the load control device, the load control device storing an occupancyvacancy status of the occupancy sensor in a memory location of a memory of the load control device.
12. The load control system of claim 11, wherein the load control device initializes a failsafe timer with the predetermined timeout period and restarts the failsafe timer in response to receiving the second wireless control signals from the occupancy sensor, the load control device operable to adjust the intensity of the lighting load to the second intensity when the failsafe timer expires.
13. The load control system of claim 12, wherein the occupancy sensor is characterized by a unique serial number, the load control device comprising a memory for storage of the serial number of the occupancy sensor, the load control device responsive to wireless control signals including the serial number of the occupancy sensor.
14. The load control system of claim 13, wherein the load control device comprises a first actuator and the occupancy sensor comprises a second actuator, the load control device operable to store the serial number of the occupancy sensor in the memory in response to simultaneous actuations of the first and second actuators.
15. The load control system of claim 14, wherein the load control device adjusts the intensity of the lighting load to the first intensity in response to an actuation of the first actuator, and starts the failsafe timer when the intensity of the lighting load is at the first intensity and the serial number of the occupancy sensor is stored in the memory.
16. The load control system of claim 12, wherein the second wireless control signal comprises an occupied-no-action command, and the first wireless control signal comprises an occupied-take-action command.
17. The load control system of claim 12, wherein the load control device adjusts the intensity of the lighting load to the first intensity and starts the failsafe timer in response to receiving the first wireless control signal.
18. The load control system of claim 11, wherein the second intensity is 0%, such that the load control turns the lighting load off in response to receiving vacant control signals from both of the occupancy sensors.
19. The load control system of claim 11, wherein the second intensity is a non-off intensity.
20. A method of controlling the amount of power delivered from an AC power source to a lighting load provided in a space, the method comprising the steps of:
providing an occupancy sensor in the space;
detecting by the occupancy sensor an occupancy condition in the space;
transmitting by the occupancy sensor a first wireless control signal in response to the step of detecting the occupancy condition;
adjusting the intensity of the lighting load to a first intensity using a load control device in response to the first wireless control signal;
detecting by the occupancy sensor a continued occupancy condition in the space;
transmitting by the occupancy sensor a second wireless control signal in response to the step of detecting a continued occupancy condition in the space;
determining in the load control device that no wireless control signals have been received from the occupancy sensor for the length of a predetermined timeout period;
adjusting the intensity of the lighting load to a second intensity less than the first intensity using the load control device in response to the step of determining that no wireless control signals have been received;
providing the occupancy sensor with an occupancy timer;
transmitting by the occupancy sensor a vacant wireless control signal on the expiration of the occupancy times; and
storing an occupancyvacancy status of the occupancy sensor in a memory location of a memory of the load control device.
21. The method of claim 20, further comprising the steps of:
initializing a failsafe timer with the predetermined timeout period; and
restarting the failsafe timer in response to receiving the second wireless control signals from the occupancy sensor;
wherein the step of adjusting the intensity of the lighting load to a second intensity further comprises adjusting the intensity of the lighting load to the second intensity when the failsafe timer expires.
22. The method of claim 21, further comprising the step of:
starting the failsafe timer in response to receiving the first wireless control signal.
23. The method of claim 21, further comprising the steps of:
pressing a button;
adjusting the intensity of the lighting load to the first intensity in response to the step of pressing a button; and
starting the failsafe timer in response to the step of pressing a button.
24. The method of claim 20, wherein the second intensity is 0%, such that the step of adjusting the intensity of the lighting load to a second intensity comprises turning the lighting load off in response to the step of receiving vacant control signals from both of the occupancy sensors.
25. The method of claim 20, wherein the second intensity is a non-off intensity.
26. A load control device for controlling the power delivered from an AC power source to a lighting load provided in a space having at least two occupancy sensors, the load control device comprising:
a controllably conductive device adapted to be coupled in series electrical connection between the AC power source and the lighting load for controlling the amount of power delivered to the lighting load to control the intensity of the lighting load;
a wireless receiver for receiving wireless control signals from the occupancy sensors, the wireless control signals comprising an occupied wireless control signal indicating an occupancy condition in the space and a vacant wireless control signal indicating a vacancy condition in the space; and
a controller operatively coupled to the controllably conductive device and the wireless receiver for controlling the amount of power delivered to the lighting load in response to the wireless control signals;
wherein the controller adjusts the intensity of the lighting load to a first intensity in response to receiving the occupied wireless control signal from at least one of the occupancy sensors, and adjusts the intensity of the lighting load to a second intensity less than the first intensity in response to receiving vacant control signals from both of the occupancy sensors,
the controller associating a unique identifier with each occupancy sensor and storing in respective memory locations an occupancyvacancy status of each occupancy sensor in accordance with its unique identifier, the controller adjusting the intensity of the lighting load to the second intensity load when the occupancyvacancy statuses of the memory locations for both of the occupancy sensors denote vacancy.
27. The load control device of claim 26, further comprising:
a memory for storage of the unique identifiers, each unique identifier comprising a unique serial number of each of the two occupancy sensors;
wherein each of the wireless control signals from the occupancy sensors includes the unique serial number of the respective occupancy sensor, the controller responsive to wireless control signals including the serial numbers of the two occupancy sensors.
28. The load control device of claim 27, wherein the controller keeps track of the occupancy sensors from which the load control device received the occupied wireless control signals.
29. The load control device of claim 28, wherein the controller adjusts the intensity of the lighting load to the second intensity in response to receiving vacant control signals from all of the occupancy sensors from which the controller received the occupied wireless control signals.
30. The load control device of claim 26, wherein the second intensity is 0%, such that the load control device turns the lighting load off in response to receiving vacant control signals from both of the occupancy sensors.
31. The load control device of claim 26, wherein the second intensity is a non-off intensity.

The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.

We claim:

1. In a Home Network telephone system, a method for backing up user data, the method comprising:
establishing a gateway to serve at least one Home Network endpoint; and,
at the gateway, storing a copy of user data associated with each endpoint.
2. The method of claim 1 further comprising:
supplying the user data to the endpoints; and,
locally storing a copy of the user data at the endpoints.
3. The method of claim 2 wherein supplying the user data to the endpoints is an action selected from the group including:
supplying the endpoint with an initial start-up configuration of user data; and,
resupplying the endpoint in response to the endpoint losing the locally stored copy of the user data.
4. The method of claim 3 further comprising:
generating user data at an endpoint; and,
wherein storing a copy of user data associated with each endpoint at the gateway includes storing the user data generated at the endpoint.
5. The method of claim 4 wherein establishing a gateway includes:
transceiving calls on an external telephone line that communicate call information in a first format;
converting between the first format and a digital Home Network format;
establishing traffic channels to communicate information in the Home Network format; and,
establishing a control channel to manage storage and supply of the user data.
6. The method of claim 5 wherein storing a copy of user data associated with endpoint at the gateway includes storing user data selected from the group including telephone directories, calling line ID (CLID) lists, call-logs, and user preferences for organizing the stored data.
7. The method of claim 6 wherein storing user data associated with each endpoint at the gateway includes storing the user data in nonvolatile gateway memory.
8. The method of claim 6 wherein establishing a gateway to serve at least one Home Network endpoint includes establishing a unique identifier for each endpoint; and,
wherein storing user data associated with each endpoint at the gateway includes storing the user data cross-referenced to endpoint identifiers.
9. The method of claim 8 wherein establishing a control channel to manage storage and supply of the user data includes:
the endpoint inquiring if the gateway supports user data backup functions; and,
the endpoint receiving communications from the gateway that the backup function is supported.
10. The method of claim 9 wherein establishing a control channel to manage storage and supply of the user data includes:
at an endpoint, setting an archive flag to the on position;
communicating user data to the gateway for backing up;
receiving an acknowledgement from the gateway; and,
setting the archive flag to the off position.
11. The method of claim 10 further comprising:
initially powering on a first endpoint;
wherein establishing a control channel to manage storage and supply of the user data includes:
following the initial powering, inquiring if the gateway supports user data backup functions;
receiving communications from the gateway that the backup function is supported, but that no user data is stored for the first endpoint; and,
at the first endpoint, setting an archive flag to the on position;
wherein storing a copy of user data associated with each endpoint at the gateway includes communicating first endpoint user data to the gateway for backing up;
wherein establishing a control channel to manage storage and supply of the user data further includes:
receiving an acknowledgement from the gateway; and,
setting the archive flag to the off position.
12. The method of claim 10 further comprising:
powering on a first endpoint after its locally stored copy of user data has been lost;
wherein establishing a control channel to manage storage and supply of the user data includes:
following powering on, inquiring if the gateway supports user data backup functions;
receiving communications from the gateway that the backup function is supported, and that user data has been stored for the first endpoint; and,
wherein supplying the user data to the endpoints includes communicating the stored user data to the first endpoint from the gateway.
13. The method of claim 12 wherein storing user data associated with each endpoint at the gateway includes storing a generic user data configuration in the gateway memory; and,
wherein communicating the stored user data to the first endpoint from the gateway includes communicating the generic user data configuration.
14. The method of claim 10 wherein establishing a control channel to manage storage and supply of the user data includes:
receiving a copy of the user data at the gateway from an endpoint;
sending an acknowledgement from the gateway to the endpoint that the user data has been received; and,
wherein storing a copy of user data associated with each endpoint at the gateway includes storing the user data received from the endpoint.
15. The method of claim 10 wherein generating user data at an endpoint includes modifying user data at a first endpoint;
the method further comprising:
at the first endpoint, setting an archive flag to the on position;
wherein storing a copy of user data associated with each endpoint at the gateway includes sending a copy of the modified user data to the gateway from the first endpoint; and,
the method further comprising:
sending an acknowledgement from the gateway to the first
endpoint that the modified user data has been received.
16. The method of claim 10 wherein establishing a control channel to manage storage and supply of the user data includes:
an endpoint requesting user data from the gateway;
the gateway supplying a copy of the user data to the requesting endpoint; and
the endpoint sending an acknowledgement to the gateway that the user data has been received.
17. The method of claim 10 further comprising:
a first endpoint losing its locally stored copy of user data;
the first endpoint requesting user data from the gateway;
the gateway supplying a copy of the user data to the requesting first endpoint; and
the first endpoint sending an acknowledgement to the gateway that the user data has been received.
18. The method of claim 8 wherein storing user data associated with each endpoint at the gateway includes:
each endpoint periodically transmitting user data to the gateway for backing up; and,
the gateway updating stored user data in response to the periodic transmissions from each endpoint.
19. A Home Network telephone system for backing up user data, the system comprising:
at least one endpoint having a port to transceive telephone communications; and,
a gateway having a port to transceive telephone communication with the at least one endpoint, the gateway having a memory to store a copy of user data associated with each endpoint.
20. The system of claim 19 wherein the gateway supplies the user data to the endpoint; and,
wherein the endpoint receives the user data from the gateway and stores a copy of the user data in a local memory.
21. The system of claim 20 wherein the gateway resupplies the endpoint in response to the endpoint losing the copy of the user data stored in its local memory.
22. The system of claim 21 wherein the endpoint has a user interface to generate user data, the endpoint storing the generated user data in local memory; and,
wherein the gateway stores a back-up copy of the user data generated by the endpoint in the gateway memory.
23. The system of claim 22 wherein the gateway has a port for transceiving calls on an external telephone line that communicate call information in a first format, the gateway converting between the first format and a digital Home Network format, establishing traffic channels to communicate information in the Home Network format, and establishing a control channel to manage storage and supply of the user data.
24. The system of claim 23 wherein the gateway stores user data selected from the group including telephone directories, calling line ID (CLID) lists, call-logs, and user preferences for organizing the stored data.
25. The system of claim 24 wherein the gateway memory is a nonvolatile memory.
26. The system of claim 24 wherein the gateway establishes a unique identifier for each endpoint and stores the user data cross-referenced to endpoint identifiers.
27. The system of claim 26 wherein the endpoint uses the control channel to inquire if the gateway supports user data backup functions; and,
wherein the gateway uses the control channel to communicate to the endpoint that the backup function is supported.
28. The system of claim 27 wherein the endpoint uses the control channel to set an archive flag to the on position and communicates user data to the gateway for backup;
wherein the gateway uses the control channel to send an acknowledgement that the user data has been received; and,
wherein the endpoint sets the archive flag to the off position in response to receiving the acknowledgement.
29. The system of claim 28 wherein a first endpoint initially powers up and uses a control channel to inquire if the gateway supports user data backup functions;
wherein the gateway uses a control channel to send communications to the endpoint that the backup function is supported, but that no user data is stored for the first endpoint; and,
wherein the first endpoint sets an archive flag to the on position, communicates first endpoint user data to the gateway for backup, and sets the archive flag to the off position in response to receiving an acknowledgement from the gateway.
30. The system of claim 28 wherein a first endpoint powers up after its copy of user data stored in local memory has been lost and uses the control channel to inquire if the gateway supports user data backup functions;
wherein the gateway uses the control channel to send communications that the backup function is supported, and that user data has been stored for the first endpoint; and,
wherein the first endpoint uses the control channel to receive the stored user data from the gateway and to send an acknowledgement to the gateway.
31. The system of claim 30 wherein the gateway stores a generic user data configuration for the first endpoint in the gateway memory and uses the control channel to communicate the generic user data configuration to the first endpoint.
32. The system of claim 28 wherein the gateway uses the control channel to request a copy of the user data from an endpoint, receives a copy of the user data from the endpoint, sends an acknowledgement to the endpoint that the user data has been received, and stores the user data received from the endpoint.
33. The system of claim 28 wherein a first endpoint modifies the user data, uses a control channel to set an archive flag to the on position, and sends a copy of the modified user data to the gateway; and,
wherein the gateway uses the control channel to send an acknowledgement to the first endpoint that the modified user data has been received.
34. The system of claim 28 wherein the endpoint uses the control channel to request user data from the gateway, receives the copy of the user data from the gateway, and sends an acknowledgement to the gateway that the user data has been received.
35. The system of claim 28 wherein a first endpoint loses its copy of user data stored in local memory, uses a control channel to request user data from the gateway, and sends an acknowledgement to the gateway in response to receiving a copy of the user data from the gateway.
36. The system of claim 26 wherein the endpoint periodically transmits user data to the gateway for backing up; and,
wherein the gateway updates stored user data in response to the periodic transmissions from the endpoint.
37. The system of claim 19 wherein the endpoint is selected from the group of devices including hardwired devices, wireless devices, telephones, fax machines, video telephones, multifunction peripherals (MFCs), and computers.
38. The system of claim 19 wherein the gateway supplies the endpoint with an initial start-up configuration of user data.