Feature Type Spectrum Technique

Abstract

Sensors are used to generate sample set data representing objects in a sample set. A computer system analyzes the sample set data to determine the frequencies with which features in a feature set are observed in the objects in the sample set. An example of such output is a bar chart representing the frequency of observation of features in the feature set in a particular object. The feature output may be used to identify one or more obscure (i.e., low frequency) features in the particular object. Machine learning may be used to learn associations between sample set data and features in the feature set.

What is claimed is: 
     
         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 second 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.