In an embodiment of the invention, a method of classifying a data object includes: scanning the data object; evaluating contents of data objects base on at least one selected rule; and generating a digital DNA sequence that classifies at least some contents in the data object.
Please help me write a proper abstract based on the patent claims. CLAIM: 1.-30. (canceled) 31. A computer-implemented method for generating a sequence that classifies a data object, comprising: associating each of a plurality of weight values with a respective rule and with a respective trait; applying each of the respective rules to the data object to select expressed traits from the respective traits; and generating a sequence comprising each of the weight values associated with the expressed traits, wherein each of the weight values corresponds to a likelihood of whether the data object belongs to a respective class of data objects. 32. The method of claim 31, further comprising calculating a sequence weight based on the weight values associated with each of the expressed traits, wherein calculating a sequence weight comprises: associating a decay rate with one or more of the weight values, wherein the decay rate diminishes successive instances of a repeated weight value with respect to the sequence weight. 33. The method of claim 31, wherein the weight values comprise positive and negative confidence values, the positive confidence values corresponding to a high likelihood that the data object belongs to a respective class of data objects, and the negative confidence values corresponding to a low likelihood that the data object belongs to a respective class of data objects. 34. The method of claim 31, wherein the rules comprise a rule value; and wherein the data object comprises a data value. 35. The method of claim 34, wherein the data value comprises a string. 36. The method of claim 34, wherein the data value comprises a byte pattern. 37. The method of claim 34, wherein the data value comprises a code. 38. The method of claim 34, wherein the data value comprises a name of a process. 39. The method of claim 34, wherein applying each of the respective rules to the data object comprises determining whether the rule value matches the data value. 40. The method of claim 34, further comprising: associating a control code with a first trait to inter-associate the first trait with a second trait; and wherein each of the inter-associated traits is selected as an expressed trait based on whether the rule value of the other inter-associated trait matches the data value. 41. The method of claim 31, wherein one or more of the rules comprises a rule restriction indicating whether the rule is case-sensitive. 42. The method of claim 31, wherein one or more of the rules comprises a rule restriction indicating a location of text to be searched. 43. The method of claim 34, wherein one or more of the rule values comprises wildcard characters. 44. A device for generating a sequence that classifies a data object, comprising: a memory; and a sequencing engine, wherein the sequencing engine is configured to be executed by one or more processors, the sequencing engine including instructions for: associating each of a plurality of weight values with a respective rule and with a respective trait; applying each of the respective rules to the data object to select expressed traits from the respective traits; and generating a sequence comprising each of the weight values associated with the expressed traits, wherein each of the weight values corresponds to a likelihood of whether the data object belongs to a respective class of data objects; and storing the generated sequence in a selected target memory. 45. The device of claim 44, further comprising calculating a sequence weight based on the weight values associated with each of the expressed traits, wherein calculating a sequence weight comprises: associating a decay rate with one or more of the weight values, wherein the decay rate diminishes successive instances of a repeated weight value with respect to the sequence weight. 46. The device of claim 44, wherein the weight values comprise positive and negative confidence values, the positive confidence values corresponding to a high likelihood that the data object belongs to a respective class of data objects, and the negative confidence values corresponding to a low likelihood that the data object belongs to a respective class of data objects. 47. The device of claim 44, wherein the rules comprise a rule value; and wherein the data object comprises a data value. 48. The device of claim 47, wherein the data value comprises a string. 49. The device of claim 47, wherein the data value comprises a byte pattern. 50. The device of claim 47, wherein the data value comprises a code. 51. The device of claim 47, wherein the data value comprises a name of a process. 52. The device of claim 47, wherein applying each of the respective rules to the data object comprises determining whether the rule value matches the data value. 53. The device of claim 47, further comprising: associating a control code with a first trait to inter-associate the first trait with a second trait; and wherein each of the inter-associated traits is selected as an expressed trait based on whether the rule value of the other inter-associated trait matches the data value. 54. The device of claim 44, wherein one or more of the rules comprises a rule restriction indicating whether the rule is case-sensitive. 55. The device of claim 44, wherein one or more of the rules comprises a rule restriction indicating a location of text to be searched. 56. The device of claim 47, wherein one or more of the rule values comprises wildcard characters. 57. The device of claim 44, wherein the data object is stored in the memory. 58. The device of claim 44, wherein the sequencing engine is executed when the data object is received by the device over a network. 59. A non-transitive computer-readable storage medium storing instructions for generating a sequence that classifies a data object, the instructions comprising instructions for: associating each of a plurality of weight values with a respective rule and with a respective trait; applying each of the respective rules to the data object to select expressed traits from the respective traits; and generating a sequence comprising each of the weight values associated with the expressed traits, wherein each of the weight values corresponds to a likelihood of whether the data object belongs to a respective class of data objects.