Patent Document ID: 20160247091
Application ID: 15142099
Patent Status: 0

Claim One:
1. A method performed by at least one computer processor executing computer program instructions stored on a non-transitory computer-readable medium, the method comprising: (A) generating, for each feature F in a plurality of features, a plurality of frequencies of observation of feature F in an object O 1 , wherein the plurality of features includes at least one physical feature and at least one use-based feature, comprising: (A) (1) receiving first textual input from a first human; (A) (2) receiving second textual input from a second human, wherein the first textual input differs from the second textual input; (A) (3) mapping the first textual input and the second textual input to the same feature F 0 in the plurality of features; and (A) (4) determining that the first textual input and the second textual input indicate that the object O 1 has feature F 0 ; (B) generating output representing the plurality of frequencies of observation of each feature F in object O 1 ; (C) identifying, based on the plurality of frequencies of observation of each feature F in object O 1 , a first subset of the plurality of features having frequencies satisfying a low frequency criterion, comprising: (C) (1) generating, for each feature F in the plurality of features, a frequency count for feature F in object O 1 based on the plurality of frequencies of observation of feature F in object O 1 ; and (C) (2) determining, for each feature F in the plurality of features, whether the frequency count for feature F satisfies the low frequency criterion, comprising: determining that the feature count for a first one of the plurality of features satisfies the low frequency criterion; and determining that the feature count for a second one of the plurality of features does not satisfy the low frequency criterion; (D) automatically learning a first association between the first textual input and the feature F 0 , and storing first association data representing the first association; (E) automatically learning a second association between the first textual input and the feature F 0 , and storing second association data representing the second association; and wherein the frequency count for at least one feature F is equal to zero.